The Nature and Antecedence of Client Loyalty in Professional Business Services by Academic Conferences and publishing International

The Nature and Antecedence of Client Loyalty in Professional Business Services

A doctoral dissertation by

Dr Bill Nichols

Submitted to the Henley Business School, University of Reading in partial fulfilment for the Degree of Doctor of Business Administration

ISBN: 978-1-909507-53-1 CopyrightÂŠ Bill Nichols Licence to publish granted to Academic Conferences and Publishing International Limited, 2013 For more information see www.academic-conferences.org

The Nature and Antecedence of Client Loyalty in Professional Business Services

A Thesis submitted in partial fulfilment for the Degree of Doctor of Business Administration.

By: Bill Nichols

Henley Business School University of Reading January 2009

Abstract This study focuses on the nature and antecedence of loyalty among clients in the Professional Business Services’ (PBS) industry. An understanding of both the nature of loyalty, and its principal direct and indirect determining factors, is called for urgently given the reported, progressive 25-year decline in consultancy incumbency duration. Currently, among practitioners, the phenomenon attracts conflicting and dichotomous explanations: principally relationships versus performance. Following metatriangulation principles, the study develops tests and seeks to validate an explanatory conceptual model. It identifies relevant influential constructs and seeks to corroborate, or validate extensions of, appropriate scales. Although an initial ‘Convergent’ model is unsupported empirically due to high construct nondiscrimination, a competing ‘Uni-Conative’ model, based on a conjecture of Oliver (1999), is subsequently validated. For both models, the theoretical framework is founded in three major literature streams: predominantly B2C post-purchase effects; relationship marketing/interorganisational performance; and client-agency relations. Incorporating insights from initial qualitative research, the study adopts a quantitative research strategy founded upon a positivist stance. Unusually, given the nature of PBS continuous service provision, observation is conducted intra- as opposed to post-consumption. The consequent empirical investigation applies Factor Analysis (both exploratory and confirmatory), Multiple Regression Analysis and Maximum Likelihood Estimation (structural equation modelling). A major function of the study is the establishment of a detailed understanding, under conditions of PBS intra-consumption, of a single superordinate conative construct of ‘Goodwill’ (loyalty-satisfaction). In addition significant contributions are made to the understanding in PBS of both attribute assessment (perceived

service quality) and key attitudinal explanatory constructs such as perceived value and trust. Using a principally online survey to sample UK PR Consultancy clients, the Convergent Model incorporates a total of 13 provisional hypotheses of which 12 are supported initially. In the competing Uni-Conative model, four out of five final hypotheses are supported. These relate perceived value, a functional dimension of perceived service quality and (indirectly) trust to the level of a client’s goodwill (loyalty-satisfaction).

A postulated direct link between trust and loyalty-

satisfaction is unsupported. The findings make three principal contributions to the marketing discipline by: (1) providing a theoretical framework that explains both the nature of loyalty in PBS and furthers our understanding of the factors motivating client loyalty behaviour; (2) supplying empirical evidence to validate the final ‘Uni-Conative’ model; and (3) facilitating the development and validation of scales for perceived service quality, perceived value and loyalty-satisfaction that will be beneficial to PBS marketing management. More broadly the analysis will interest all those seeking to: manage loyalty; determine an appropriate balance between acquisition and retention marketing expenditures; and, finally, assess the financial value of the ‘goodwill’ asset.

Acknowledgements Primary thanks are due to my first supervisor, Dr. Madge Lyman of Henley Business School for her wisdom, invaluable insights and above all her patience and kindness over a seven-year marathon. Likewise thanks to my second supervisor, Professor Albert Caruana of the University of Malta whose pithy comments were always helpful and thought-provoking. Also at Henley to Dr. Carola Hillenbrand whose statistical knowledge and thoughtfulness proved invaluable in the final stages and, not least, to the former head of doctoral studies, Dr. David Price, whose kindness and support to a part-time entrepreneur-academic was exceptional and exemplary. I am also grateful to my friends, Dr. Jonathan Ivy of Birmingham City University for his always helpful contributions and fellow doctoral students, John Cavill at Henley and Steve Constable at Kingston, who both shared the marathon and whose simple willingness to chat over endless curry-suppers restored sanity on many occasions. Likewise to my former fellow directors at The Whiteoaks Consultancy who supported the study in its early years and whose willingness to tolerate academic investigation is much appreciated. My final thanks go above all else to my family, especially to my wife, Lizzy and my sons, Elliot and Douglas, whose own university ‘journeys’ are now beginning. Their love, support and often bemused tolerance of this ‘strange passion’ made it all possible. Without them, this thesis simply would not have existed and it is dedicated to them.

Table of Contents Abstract………………………………………………………………………………………..

Acknowledgements………………………………………………………………………

Table of Contents…………………………………………………………………………

List of Tables………………………………………………………………………………..

xviii

List of Figures……………………………………………………………………………….

xxiv

Abbreviations……………………………………………………………………………….

xxviii

ONE – INTRODUCTION………………………………………………….

1.1

Research Focus…………………………………………………………………

1.1.1

The Ascent and Importance of Loyalty………………………….

1.1.2

The Value of Professional Business Services………………..

1.1.3

The Conundrum of Loyalty in PBS………………………………..

1.2

Origins of the Research……………………………………………………

1.3

Structure of Thesis…………………………………………………………..

1.3.1

Research Questions……………………………………………………..

1.3.2

Framework…………………………………………………………………..

1.3.3

Model Development……………………………………………………..

1.3.4

Orientation…………………………………………………………………..

1.3.5

Methodology………………………………………………………………..

1.3.6

Data Analysis………………………………………………………………..

1.4

Intended Contribution of the Thesis………………………………….

1.5

Chapter Summary……………………………………………………………..

TWO LITERATURE REVIEW – PART ONE………………………..

2.1

Overview and Introduction……………………………………………….

2.2.

Organisation…………….………………………………………………………

2.3

Construct 1: Loyalty-Commitment……………………………………

2.3.1

Overview……………………………………………………………………..

2.3.2

The Loyalty-Conundrum….……………………………………………

2.3.3

Conceptualisation: Behaviour vs. Intentions…………………

2.3.4

Domain and Operationalisation: The Role of Interpersonal Loyalty…………………………………………………..

Construct Label and Proxies: Loyalty-Commitment………

Constructs 2&3: Transactional and Cumulative Satisfaction

2.4.1

Overview………………..…………………………………………………..

2.4.2

Conceptualisation…………………………………………………………

2.4.3

Domains and Operationalisation…………………………………..

2.3.5

2.4

2.5

2.6

Competing Construct 4: Loyalty-Satisfaction…………………..

2.5.1

Overview……………………………………………………………………..

2.5.2

Loyalty-Commitment, Satisfaction and Firm Performance…………………………………………………………………

2.5.3

Conceptualisation: The Loyalty-Ladder…………………………

2.5.4

Operationalisation……………………………………………………….

Construct 5: Perceived Value………………………………………….

2.6.1

Introduction……………………………………………….……………….

2.6.2

Conceptualisation: Perceived Value, Perceived Equity And Perceived Justice…………………………………………………

Domain and Operationalisation………………………………….

Construct 6: Trust………………..…………………………………………

2.7.1

Overview…………………………………………………………………….

2.7.2

Conceptualisation………………………………………………….…….

2.7.3

Domain and Operationalisations…..……………………………..

Construct 8: Perceived Service Quality……………………………..

2.6.3

2.7

2.8

2.9

2.10

2.11

2.12

2.8.1

Overview………………………………………………………………………

2.8.2

Conceptualisation…………………………………………………………

2.8.3

Domain and Operationalisation: Technical Factors……….

2.8.4

Domain and Operationalisation: Functional Factors…….

2.8.5

Domain and Operationalisation: Reputational Factors…

2.8.6

Weighting of Dimensions………..………………..………………….

Construct 8: Disconfirmation…………………………………………..

2.9.1

Overview……………………………………………………………………..

2.9.2

Conceptualisation………………………………………………………..

2.9.3

Domain and Operationalisation……………………………………

Moderation and Moderators…………………………………………..

2.10.1 Overview……………………………………………………………………..

2.10.2 Services and The Resource-Based Paradigm…………………

2.10.3 Service Taxonomy and PBS Characteristics………………….

2.10.4 Moderators…………………………………………………………………

Theoretical Underpinnings and Model Domain Boundaries

2.11.1 Overview…………………………………………………………………….

2.11.2 Buyer Behaviour Theory………………………………………………

2.11.3 Human Relationship Theory………………………………………..

2.11.4 Attitude Theory…………………………………………………………...

Models 1: The B2C Stream……………………………………………….

2.12.1 Overview……………………………………………………………………..

2.12.2 B2C: The Emergence of Multivariate Models………………..

2.12.3 The European Customer Satisfaction Index (ECSI)

2.13

And Extensions……………………………………………………………..

Models 2: The B2B Stream………………………………………………..

2.13.1 Overview……………………………………………………………………….

2.13.2 The Relationship Marketing Paradigm……………………………

2.13.3 Trust-Commitment 1: The MZD Framework……………………..

2.13.4 Trust-Commitment 2: The KMV Model…………………………….

2.14

Models 3: A Convergence Model………………………………………..

2.15

Models 4: The Case for A Competing Model……………………….

2.15.1 Overview…………………………………………………………………………

2.15.2 Critique of Convergence: The Case for Anti-Thesis…………..

2.15.3 The P2B Stream: Client-Agency Relations………………………..

2.15.4 Tolerance Theory and A Competing ‘Goodwill’ Model……..

Chapter Summary……………………………………………………………….

THREE – INITIAL MODEL AND HYPOTHESES…………..………

3.1

Introduction and Overview…………………………………………………..

3.2

The Research Process and Model Building…………………………….

4.3.1

Step One: Units or Variables of Interest….………………………….

4.3.2

Step Two: The Laws of Interaction………..……………………………

4.3.3

Step Three: Boundaries……………………………………………………..

4.3.4

Step Four: System States…………………………………………………..

Hypothesis Formulation………………………………………………………..

3.3.1

Overview……………………………………………………………………………

3.3.2

H1-H3: Perceived Service Quality, Disconfirmation and

2.16

3.3

Attitudes……………………………………………………………………………

3.3.3

H4-H7: The Antecedence of Cumulative Satisfaction………….

3.3.4

H8-H10: The Antecedence of Loyalty-Commitment – Direct Paths……………………………………………………………………….

3.3.5

The Concept and Application of Mediation………………………..

3.3.6

H11: Perceived Value, Cumulative Satisfaction and Loyalty-Commitment………………………………………………………….

3.3.7

H12: Perceived Service Quality, Cumulative Satisfaction,

Perceived Value and Loyalty-Commitment…………………………..

3.4

A Competing Model: Goodwill……………………………………………….

3.5

Chapter Summary………………………………………………………………….

FOUR – METHODOLOGY……………………………………………………… 100 4.1

4.2

Introduction……………………………………………………………………………

100

4.1.1

Overview……………………………………………………………………………..

100

4.1.2

Purposiveness: Research Goals………………………………………………

101

Research Strategy 1: Design, Orientation and The Nature of The Investigation……………………………………………………………………….

101

4.2.1

Introduction and Overview…………………………………………………….. 102

4.2.2

Question 1: Epistemological Orientation………………………………… 102

4.2.3

Question 2: Morphology of Explanation and Logical Empiricism 103

4.2.4

Question 3: Conflicting Desiderata………………………………………..

104

4.2.5

Question 4: Alignment with Epistemological Tradition……………

104

4.2.6

Summary and Quantitative Methodology………………………………. 104

4.3

Research Strategy 2: Key Principles…………………………………………..

105

4.4

Research Conceptual Framework……………………………………………..

106

4.2.1

Generation and Replication…………………………………………………..

106

4.4.2

Time Horizon…………………………………………………………………………

108

4.5

Operationalisation and The Research Instrument……………………

109

4.5.1

Introduction and Overview…………………………………………………..

109

4.5.2

The Research Instrument and Key Principles…………………………

109

4.5.3

Construct 1: Loyalty-Commitment………………………………………..

111

4.5.4

Construct 2: Trust………………………………………………………………..

114

4.5.5

Construct 3: Transactional Satisfaction………………………………..

115

4.5.6

Construct 4: Cumulative Satisfaction……………………………………

115

4.5.7

Construct 5: Perceived Value……………………………………………….

116

4.6

4.5.8

Construct 6: Disconfirmation……………………………………………….

117

4.5.9

Construct 7: Perceived Service Quality………………………………..

118

4.5.10 Classificatory Variables……………………………………………………….

120

Psychometric Properties of the Research Instrument – Principles 121 4.6.1

Introduction and Overview……………………………………………………

4.6.2

Reliability……………………………………………………………………………….. 121

4.6.3

Content Validity……………………………………………………………………… 123

4.6.4

Construct Validity…………………………………………………………………..

123

4.6.5

The Limits of Construct Validity………………………………………………

124

4.7

Model Constructs – Psychometric Status…………………………………..

124

4.8

Considerations in the Amalgamation of Instruments…………………

128

4.9

Population, Sample and Sampling Process…………………………………

129

4.10

121

4.9.1

Introduction and Overview…………………………………………………….. 129

4.9.2

Choice of Population………………………………………………………………. 129

4.9.3

Size of Qualifying Population – Client Firms……………………………

131

4.9.4

Nature of Population: Key Informants vs. Multiple Contacts….

131

4.9.5

Sample Size Required…………………………………………………………….

132

4.9.6

Sampling Frame and Method – Part 1……………………………………

135

4.9.7

Sampling Frame and Method – Part 2……………………………………

135

4.9.8

Data Collection Process………………………………………………………..

136

4.9.9

Data Collection Results…………………………………………………………

139

Chapter Summary ……………………………………………………………………

140

FIVE – DATA ANALYSIS ONE: DESCRIPTIVES, SCALES AND FACTOR ANALYSIS.…………………………………………………….. 142 5.1

Introduction and Overview………………………………………………………..

142

5.2

Data Preparation: Screening of the Final Data Set……………………..

143

5.2.1

143

Overview……………………………………………………………………………….

5.3

5.4

5.5

5.2.2

Missing Data…………………………………………………………………………

144

5.2.3

Outliers ………………………………………………………………………………..

144

5.2.4

Non-Coverage Error: Responsibility Criterion…………………………. 145

5.2.5

Respondent and Non-Response Bias…………………………………..

146

5.2.6

Between-Groups Comparison (Stormark vs. Consultancy)…..

147

5.2.7

Tests for Normality……………………………………………………………..

149

5.2.8

Summary…………………………………………………………………………….

149

Descriptive Statistics……………………………………………………………………

149

5.3.1

Overview…………………………………………………………………………….

149

5.3.2

Industry Practice, Firm Size and Service Requirements……….

150

5.3.3

Respondent Status, Responsibilities and Relationship Duration 152

5.3.4

Summary…………………………………………………………………………….

154

Scales – Descriptives and Reliability……………………………………….

155

5.4.1

Introduction……………………………………………………………………….

155

5.4.2

Scale 1 - Loyalty-Commitment……………………………………………..

156

5.4.3

Scale 2- Trust………………………………………………………………………

158

5.4.4

Scale 3 – Transactional Satisfaction……………………………………..

160

5.4.5

Scale 4 – Cumulative Satisfaction………………………………………..

162

5.4.6

Scale 5 – Perceived Value……………………………………………………

162

5.4.7

Scale 6 – Disconfirmation…………………………………………………….

164

5.4.8. Scale 7 – Perceived Service Quality……………………………………..

165

5.4.9

Summary…………………………………………………………………………….

167

Exploratory Factor Analysis…………………………………………………….

168

5.5.1

Introduction and Principles………………………………………………….

168

5.5.2

EFA - Loyalty-Commitment – Base Version………………………….

169

5.5.3

EFA – Loyalty-Commitment – Alternative Solutions…………….

170

5.5.4

EFA - Perceived Service Quality…………………………………………..

172

5.5.5

EFA – Perceived Service Quality – Factor Interpretation……..

175

5.6

5.5.6

EFA – Perceived Service Quality – Factor Reliability……………..

177

5.5.7

Summary…………………………………………………………………………….

178

Confirmatory Factor Analysis and Construct Validity ……………..

178

5.6.1

Overview……………………………………………………………………………

178

5.6.2

Principles, Measures, Guidelines and Diagnostics………………

179

5.6.3

CFA Solution 1 – Perceived Service Quality…………………………

181

5.6.4

Validity and Reliability – Perceived Service Quality……………..

184

5.6.5

CFA Solution 2 – Attitudes………………………………………………….

186

5.6.6

Validity and Reliability – Attitude Constructs……………………..

189

5.6.7

A Note on Loyalty-Commitment………………………………………….

191

5.6.8

Summary……………………………………………………………………………

192

5.7

Hypothesis Revision…….………………………………………………………

193

5.8

Chapter Summary………………………………………………………………….

193

SIX – DATA ANALYSIS TWO – PRINCIPAL HYPOTHESES AND MODELS……………………………………………………………………

196

6.1

Introduction and Overview…………………………………………………….

196

6.2

Multiple Regression Analysis – Key Principles………………………..

197

6.3

Reporting Format…………………………………………………………………..

199

6.4

MRA1 – The Antecedence of Perceived Value………………………..

199

6.4.1

Hypothesis………………………………………………………………………….

199

6.4.2

Summary of Phases of Analysis……………………………………………

200

6.4.3

Phase One Results: PSQ and Perceived Value……………………..

200

6.4.4

Phase Two Results: PSQ Factors and Perceived Value………….

200

6.4.5

Phase Three Results: PSQ Factors, Disconfirmation and Perceived Value…………………………………………………………………….

201

6.4.6

Summary of Results……………………………………………………………..

202

6.4.7

Assumptions and Compliance………………………………………………..

204

6.5

6.6

MRA2 – The Antecedence of Trust…………………………………………..

207

6.5.1

Hypothesis…………………………………………………………………………….

207

6.5.2

Summary of Phases of Analysis………………………………………………

207

6.5.3

Phase One Results: PSQ and Trust…………………………………………

207

6.5.4

Phase Two Results: PSQ Factors and Trust…………………………….

208

6.5.5

Phase Three Results, PSQ Factors, Disconfirmation and Trust……………………………………………………………………………….

208

6.5.6

Summary of Results………………………………………………………………

209

6.5.7

Assumptions and Compliance………………………………………………..

211

MRA3 – The Antecedence of Satisfaction…………………………………

213

6.6.1

Hypotheses…………………………………………………………………………..

213

6.6.2

Summary of Phases of Analysis……………………………………………..

214

6.6.3

Phase One Results: PSQ and Satisfaction……………………………….

214

6.6.4

Phase Two Results: PSQ-Factors and Satisfaction………………….. 214

6.6.5

Phase Three Results: PSQ-Factors, Disconfirmation and Satisfaction…………………………………………………………………………….

215

6.6.6

Summary of Phase One-Three Results……………………………………

215

6.6.7

Phase Four Results: Antecedence of Satisfaction including

6.6.8

6.7

Perceived Value and Trust……………………………………………………..

217

Regression Assumptions and Compliance……………………………….

218

MRA4 – The Antecedence of Loyalty-Commitment…………………..

220

6.7.1

Hypotheses…………………………………………………………………………….

220

6.7.2

Summary of Phases of Analysis………………………………………………

220

6.7.3

Phase One Results…………………………………………………………………

221

6.7.4

Phase Two Results………………………………………………………………

222

6.7.5

Summary of Results……………………………………………………………

222

6.7.6

Regression Assumptions and Compliance……………………………

224

6.8

6.9

MRA5 – Mediation Analysis 1: Perceived Value and Satisfaction

226

6.8.1

Overview and Hypothesis……………………………………………………

226

6.8.2

Results and Compliance………………………………………………………

226

6.8.3

Analysis of Mediation…………………………………………………………

228

MRA6 – Mediation Analysis 2 – PSQ, Attitudes and Loyalty-Commitment………………………………………………………

229

6.9.1

Overview and Hypothesis…………………………………………………..

229

6.9.2

Summary of Phases of Analysis………………………………………….

230

6.9.3

Phase One Results: PSQ Factors………………………………………..

230

6.9.4

Phase Two Results: Mediation Analysis………………………………

231

6.10

Convergence Model: Hypothesis Summary (MRA Findings)…..

234

6.11

Convergence Model: Measurement Model CFA…………………….

235

6.11.1 Overview…………………………………………………………………………….

235

6.11.2 Final Convergent Model CFA………………………………………………

236

6.11.3 Convergent Measurement Model CFA: Validity…………………..

239

6.11.4 Summary……………………………………………………………………………

241

6.12

Convergence Model: Structural Model………………………………….

242

6.13

The Competing Model: Alternatives and Re-Specification……..

245

6.13.1 Overview and Context……………………………………………………….

245

6.13.2 Alternative Model Development Procedure……………………….

245

6.13.3 Uni-Conative Model: CFA Results………………………………………

246

6.13.4 Uni-Conative Model: Convergent and Discriminant Validity

247

6.13.5 Uni-Conative Model: Content and Nomological Validity…….

248

6.13.6 Uni-Conative Model: Structural Model Assessment…………..

253

6.13.7 Uni-Conative Structural Model: Summary………………………….

257

Moderation Hypotheses and Generalisability………………………

257

6.14.1 Overview………………………………………………………………………….

257

6.14

6.15

6.14.2 The Concept and Application of Moderation…………………….

258

6.14.3 Moderation Extension Hypotheses…………………………………..

259

6.14.4 Moderation Results………………………………………………………….

259

Chapter Summary………………………………………………………………..

260

SEVEN – CONCLUSIONS AND IMPLICATIONS FOR FUTURE RESEARCH………………………………………………………….

262

7.1

Overview……………………………………………………………………………..

262

7.2

Conclusions…………………………………………………………………………

262

7.2.1

Introduction and Overview……………………………………………….

262

7.2.2

Conclusions 1-3: The Domain, Conceptualisation and Superordination of Loyalty-Satisfaction………………………………….

7.3

7.4

264

7.2.3

Conclusions 4-6: The Direct Antecedence of Loyalty-Satisfaction 265

7.2.4

Conclusions 7-10: The Indirect Antecedence of Loyalty-Satisfaction………………………………………………………………

267

7.2.5

Conclusions 11: Constructs and Scales………………………………..

268

7.2.6

Conclusions 12: Extensions………………………………………………….

270

7.2.7

Conclusions 13: Out-takes – The Convergent Model……………..

271

Implications and Recommendations for Marketing Management 272 7.3.1

Overview……………………………………………………………………………….

272

7.3.2

Environment: Strategic Analysis and Choice…………………………

272

7.3.3

Expectations and Retention Management……………………………

274

7.3.4

Engagement: The Practical Side of Service Quality……………….

276

7.3.5

Equity: A Broader Orientation………………………………………………

277

7.3.6

Empowerment: A Limited Application of Trust…………………….

278

7.3.7

Goodwill: A New Metric for Professional Business Services..

279

Limitations of the Research Study………………………………………….

280

7.4.1

280

Overview……………………………………………………………………………

7.4.2

Model Scope………………………………………………………………………

280

7.4.3

Methodological Issues……………………………………………………….

281

7.4.4

Sample and Data Collection issues……………………………………..

282

7.4.5

Service Category and Generalisability…………………………………

283

7.5

Contributions of the Thesis……………………………………………………

284

7.6

Directions for Future Research………………………………………………

285

7.7

Epilogue: Learning and the Research Process………………………..

288

BIBLIOGRAPHY……………………………………………………………….

290

APPENDICES……………………………………………………………………….

346

One:

The Instrument…………………………………………………………………………

346

Two:

Instrument: Sources and Analysis…………………………………………….

347

Three: The PR Panel (2005)…………………………………………………………………

348

Four: Data Collection (Illustrative Letters)…………………………………………

349

Five:

Outliers……………………………………………………………………………………

351

Six:

Perceived Service Quality EFA: Sample Anti-Image Correlation Matrix……………………………………………………………………………………..

353

Seven: CFA: Partial Replication of Grayson and Ambler (1999)…………..

355

Eight: MRA1: Perceived Value-Illustrative Supporting Data ………………

357

Nine: MRA2: Trust-Illustrative Supporting Data……………………………….

359

Ten:

MRA3: Satisfaction-Illustrative Supporting Data…………………….

361

Eleven: MRA4: Loyalty-Commitment-Illustrative Supporting Data…….

363

Twelve: Moderation Background………………………………………………………

365

ABBREVIATIONS………………………………………………………………………………..

368

List of Tables Table 1.1:

Thesis Outline and Route-Map………………………………………...

Table 2.1:

Principal Perspectives on Loyalty Formation…………………….

Table 2.2A/B:

Organisation and Summary of Contributions by Model……

Table 2.3

Loyalty-Principal Contributions……………………………………….

Table 2.4

The Domain of Loyalty……………………………………………………

Table 2.5A/B

Definitions/Parameters of Satisfaction…………………………..

Table 2.6

Postulated Components of Loyalty-Satisfaction……………..

Table 2.7

Tri-Dimensional Service Attributes: Classifications and Underpinning………………………………………………………………….

Table 2.8

Indicators of Technical Quality………………………………………..

Table 2.9

Indicators of Functional Quality………………………………………

Table 2.10

Indicators of Reputational Quality…………………………………..

Table 2.11

Loyalty-Commitment and Relational Behaviours…………….

Table 4.1

Key Constructs………………………………………………………. ……….

Table 4.2

Classificatory Variables……………………………………………………

Table 4.3

Hypothesis Set: Principal Convergence Model and Goodwill Model Comparison…………………………………………..

Table 4.1a

Research Set – Superset/Principles………………………………….

107

Table 4.1b

Research Set – Subset/Applications………………………………..

108

Table 4.2

Illustrative Language Adaptations…………………………………..

111

Table 4.3a

Loyalty-Commitment: Scale Items………………………………….

112

Table 4.3b

Loyalty-Commitment: Scale Items………………………………….

112

Table 4.4

Trust: Scale Items…………………………………………………………..

114

Table 4.5

Transactional Satisfaction: Scale Items………………………….

115

Table 4.6

Cumulative Satisfaction: Scale Items…………………………….

116

Table 4.7

Perceived Value: Scale Items………………………………………..

117

Table 4.8

Disconfirmation: Scale Items…………………………………….. ..

117

Table 4.9a

Perceived Service Quality: Items #1-11…………………………..

118

Table 4.9b

Perceived Service Quality: Items #12-27………………………..

119

Table 4.10

General Classificatory Variables…………………………………….

120

Table 4.11

Classificatory Moderating Variables………………………………

121

Table 4.12

Status and Validation of Key Constructs: 1-5…………………

125

Table 4.13

Status and Validation of Key Constructs: 6-7…………………

127

Table 5.1

Summary of Response Rates………………………………………..

143

Table 5.2

Qualifying Respondent Criteria…………………………………….

146

Table 5.3

Consultancy vs. Stormark: Independent Samples Test Results………………………………………………………………..

148

Table 5.4

Principal Service Needs………………………………………………

151

Table 5.5

Management Categories and Responsibilities…………….

153

Table 5.6

Management Status and Firm/Personal Relationship Durations (Months)…………………………………………………….

153

Table 5.7

Loyalty-Commitment – Key Statistics…………………………

157

Table 5.8

Trust: Comparative Reliability……………………………………

158

Table 5.9

Trust: Inter-Item Statistics………………………………………..

159

Table 5.10

Transactional Satisfaction – Descriptives, Reliability and Item-Total Statistics…………………………………………………

Table 5.11

Cumulative Satisfaction – Descriptives, Reliability and Item-Total Statistics…………………………………………………

Table 5.12

161

162

Perceived Value – Descriptives, Reliability and Item-Total Statistics………………………………………………...

163

Table 5.13

Disconfirmation – Descriptives, Reliability and Item-Total Statistics………………………………………………...

Table 5.14

165

Perceived Service Quality – Descriptives, Reliability and Item-Total Statistics………………………………………………………. .

166

Table 5.15

Scale Reliability Overview and Summary………………………….

168

Table 5.16

Loyalty-Commitment EFA – Preliminaries/MSA……………….

170

Table 5.17

Loyalty-Commitment Alternative Factor Solutions Communalities……………………………………………………………….

Table 5.18

171

Perceived Service Quality – Total Variance Explained and Sums of Squared Loadings……………………………………………..

172

Table 5.19

PSQ-Communalities……………………………………………………….

172

Table 5.20

PSQ-Measures of Sampling Adequacy……………………………

173

Table 5.21

PSQ Factor Analysis – Factor Loadings………………………….

174

Table 5.22

PSQ Total Variance Explained and Allocated Proportionately…………………………………………………………….

Table 5.23

177

PSQ Confirmatory Factor Analysis – Summary of Iterations and Outcome……………………………………………………………….

182

Table 5.24

PSQ: Average Variance Extracted and Construct Reliabilities

185`

Table 5.25

PSQ: Discriminant Validity…………………………………………..

186

Table 5.26

Attitudes CFA – Confirmatory Factor Analysis – Summary of Principal Iterations and Outcomes………………………………

Table 5.27

188

Attitudes CFA – Average Variance Extracted and Construct Reliabilities…………………………………………………………………….

190

Table 5.28

Attitude CFA – Discriminant Validity…………………………………..

190

Table 5.29

Principal Convergence Model – Revised Hypothesis Set…….

193

Table 5.30

Chapter Six – Summary of Major Findings and Interventions

Table 6.1

Confirming the Integrity of the Multiple Regression Analysis Procedure…………………………………………………………….

Table 6.2

Perceived Value MRA1: Predictive Accuracy and Model Fit

Table 6.3

Perceived Value MRA1: Co-efficients…………………………………

Table 6.4

Perceived Value MRA1: Regressor-Predictor and InterPredictor Correlations……………………………………………………….

Table 6.5

194

198

203

205

Perceived Value MRA1: Collinearity Diagnostics and Durbin-Watson………………………………………………………………….

206

Table 6.6

Trust MRA2: Predictive Accuracy and Model Fit………………..

210

Table 6.7

Trust MRA2: Coefficients…………………………………………………..

211

Table 6.8

Trust MRA2: Regressor-Predictor and Inter-Predictor Correlations……………………………………………………………………….

212

Table 6.9

Trust MRA2: Collinearity and Durbin-Watson……………………..

213

Table 6.10

Satisfaction MRA3: Phases 1-3 Predictive Accuracy and Model Fit…………………………………………………………………………….

216

Table 6.11

Satisfaction MRA3: Phase 1-3 Coefficients……………………………

217

Table 6.12

Satisfaction MRA3: Phase 4 Coefficient Values and Percentage of Variance Explained……………………………………………………………….

Table 6.13

218

Satisfaction MRA3: Phases 1-3 Regressor-Predictor and InterPredictor Correlations…………………………………………………………..

219

Table 6.14

Satisfaction MRA3: Collinearity and Durbin-Watson……………….

220

Table 6.15

Loyalty-Commitment MRA4: Predictive Accuracy/Model Fit….

223

Table 6.16

Loyalty-Commitment MRA4: Co-efficients……………………………..

224

Table 6.17

Loyalty-Commitment MRA4: Collinearity and Durbin Watson..

225

Table 6.18

Loyalty-Commitment MRA4: Correlations………………………………

225

Table 6.19

Mediation MRA5: Value-Satisfaction Principal Results…………..

227

Table 6.20

Mediation MRA6: Model Summary and ANOVA……………………

230

Table 6.21

Mediation MRA6: Preliminary Regression, Coefficients and Collinearity Statistics…………………………………………………………….

231

Table 6.22

Mediation MRA6: Principal Results of Regression Paths………..

233

Table 6.23

Mediation MRA6 – Phase 2: Unstandardised Co-efficients and and Relationship Significance………………………………………………….

Table 6.24

233

Principal Convergence Model: Revised Hypothesis Set (Provisional Support)………………………………………………………………

235

Table 6.25

Final Convergent Model: Table of Variables……………………………

236

Table 6.26

Final Convergent Measurement Model: CFA Iterations………….

238

Table 6.27

Final Convergent Model: Convergent Validity Assessment…….

240

Table 6.28

Final Convergent Model: Discriminant Validity Assessment…..

241

Table 6.29

Final Convergent Structural Model: Goodness of Fit Assessment…………………………………………………………………………..

243

Table 6.30

Uni-Conative Model: CFA Goodness of Fit Assessment………….

246

Table 6.31

Uni-Conative Model: Convergent Validity Assessment………….

247

Table 6.32

Uni-Conative Model: Discriminant Validity Assessment…………

248

Table 6.33

Content Validity, The Loyalty-Ladder and the Uni-Conative Construct of Loyalty-Satisfaction…………………………………………….

Table 6.34

Uni-Conative Nomological Validity: Loyalty-Satisfaction as a Predictor of Loyalty Behaviours……………………………………………..

Table 6.35

249

250

Uni-Conative Nomological Relationships: Loyalty Behaviours and the Loyalty-Ladder…………………………………………………………..

251

Table 6.36

Partial Regression Co-efficient Analysis………………………………….

252

Table 6.37

Uni-Conative Model: Applicable Hypothesis Set………………………. 254

Table 6.38

Uni-Conative Model: Structural Goodness-of-Fit Assessment….. 255

Table 6.39

Uni-Conative Model: Standardised Effects………………………………. 256

Table 6.40

Uni-Conative Model: Hypothesis Report………………………………….

257

Table A5.1

Cases with Repeated Univariate Outliers…………………………………

351

Table A5.2

Cases with Repeated Univariate Outliers (2)……………………………

352

Table A5.3

Repeated Multivariate Outliers……………………………………………….

352

Table A7.1

Replication CFA: Model Fit………………………………………………………

355

Table A7.2

Replication CFA: AVEs and Construct Reliabilities……………………

356

Table A8.1

MRA1-Perceived Value: Casewise Diagnostics…………………………

358

Table A9.1

MRA2-Trust: Casewise Diagnostics………………………………………..

360

Table A10.1

MRA3-Satisfaction: Casewise Diagnostics……………………………..

362

Table A11.1

MRA4-Loyalty-Commitment: Casewise Diagnostics……………….

364

List of Figures Figure 1.1

Convergent Model of Loyalty-Commitment……………….

Figure 1.2

Competing ‘Uni-Conative’ Model……………………………….

Figure 2.1

Customer Loyalty Typology……………………………………….

Figure 2.2

Chronological Dimensions of Satisfaction Measurement……………………………………………………………

Figure 2.3

The Loyalty Ladder (Oliver 1999)………………………………..

Figure 2.4

The Domain of Perceived Value………………………………….

Figure 2.5

Trust as ‘Janus’: Reciprocal and Antecedent………………

Figure 2.6

PBS: Technical Factors Continuum……………………………….

Figure 2.7

Disconfirmation and Total Perceived Quality……………

Figure 2.8

The Hierarchy of Expectations…………………………………..

Figure 2.9

The Goods-to-Services Continuum………………………………

Figure 2.10

Taxonomy of Services………………………………………………….

Figure 2.11

An Integrated Framework for Buyer Behaviour………….

Figure 2.12

Simplified Model of Intentions Formation……………………

Figure 2.13

Theory of Planned Behaviour……………………………………..

Figure 2.14

MPAA Attitude Formation: Context and Mechanisms…

Figure 2.15

MPAA Attitude Recruitment, Retrieval and Assessment

Figure 2.16

Perceived Value, Cumulative Satisfaction and LoyaltyIntentions………………………………………………………………….

Figure 2.17

European Customer Satisfaction Index……………………..

Figure 2.18

ECSI Extended…………………………………………………………..

Figure 2.19

MZD Theoretical Model…………………………………………….

Figure 2.20

The KMV Model………………………………………………………..

Figure 2.21

A Postulated Convergence Model………………………………

Figure 2.22

Trust, Perceived Value and Tolerance Theory…………...

Figure 2.23

Competing ‘Goodwill’ or Uni-Conative Model……………

Figure 3.1

The Process of Theory Building………………………………..

Figure 3.2

Hypothesised Domains of Transactional and

Cumulative Satisfaction……………………………………………

Figure 3.3

The Domain of Perceived Value……………………………….

Figure 3.4

Hypothesised Dimensions and Factors of Perceived Service Quality………………………………………………………..

Figure 3.5

Convergent Research Model and HH1-12..……………..

Figure 3.6

Model Boundaries…………………………………………………..

Figure 3.7

The Antecedence of Loyalty-Commitment……………….

Figure 3.8

The Principles of Mediation……………………………………..

Figure 3.9

Full and Partial Mediation………………………………………..

Figure 3.10

Mediating Perceived Value………………………………………

Figure 3.11

Joint Partial Mediation of Perceived Service Quality..

Figure 3.12

Competing Uni-Conative Model………………………………

Figure 4.1

Research Design and Methodology: Process……………

100

Figure 4.2

The Research Space…………………………………………………

106

Figure 4.3

Construct Summary and Item Sources………………………

110

Figure 4.4

Mail/Email Procedure: Consultancies (A)………………..

137

Figure 4.5

Contact Procedure: Stormark (B)…………………………….

139

Figure 5.1

Industry Practice Areas……………………………………………

150

Figure 5.2

Duration Statistics…………………………………………………..

154

Figure 5.3

Trust: Semantic Analysis…………………………………………

159

Figure 5.4

Transactional Satisfaction: Semantic Analysis………..

161

Figure 5.5

Perceived Service Quality Updated: EFA Factors……

176

Figure 5.6

18-item Perceived Service Quality CFA………………….

181

Figure 5.7

Final 13-item Perceived Service Quality Post-CFA….

183

Figure 5.8

Initial 16-item Attitudes CFA…………………………………

187

Figure 5.9

Final Eight-Item Attitude Scales Post-CFA………………

189

Figure 5.10

Loyalty-Commitment Semantic Analysis……………….

192

Figure 6.1

Mediation MRA6: Joint Mediation…………………………

232

Figure 6.2

Final Convergent Measurement Model…………………

237

Figure 6.3

Final Convergent Model: Structural View………………

243

Figure 6.4

Final Convergent Model: Final Structural Model……

244

Figure 6.5

Uni-Conative Path Model……………………………………..

254

Figure 6.6

Uni-Conative Final Structural Model……………………..

256

Figure 7.1

The Indicators of Loyalty-Satisfaction (Goodwill) and Loyalty Behaviours……………………………………………….

270

Figure A8.1

MRA1-Perceived Value: Linearity and Homoscedasticity

357

Figure A8.2

MRA2-Perceived Value: Normality of Error Distribution

358

Figure A9.1

MRA2-Trust: Linearity and Homoscedasticity…………..

359

Figure A9.2

MRA2-Trust: Normality of Error Distribution……………

359

Figure A10.1

MRA3-Satisfaction: Linearity and Homoscedasticity…

361

Figure A10.2

MRA3-Satisfaction: Normality of Error Distribution….

362

Figure A11.1

MRA4-Loyalty-Commitment: Linearity and Homoscedasticity…………………………………………………….

Figure A11.2

363

MRA4-Loyalty-Commitment: Normality of Error Distribution………………………………………………………………

364

Abbreviations ACSI

American Customer Satisfaction Index

ADM

Additive Difference Model

AVE

Average Variance Extracted

B2B

Business-to-Business

B2C

Business-to-Consumer

BTWT ‘Better Than, Worse Than’ CBB

Consumer Buying Behaviour

CEBR

Centre for Economic and Business Research (UK)

CFA

Confirmatory Factor Analysis

CIPR

Chartered Institute of Public Relations (UK)

Construct Reliability

DMU

Decision-Making Unit

EFA

Exploratory Factor Analysis

ECSI

European Customer Satisfaction Index

GLS

Generalised Least Squares

ITC

Item-Total Correlation

KMV

Key Mediated Variable

MCA

Management Consultancies Association (UK)

MLE

Maximum Likelihood Estimation

MPAA Multiple Pathway Anchoring and Adjustment (Model) MRA

Multiple Regression Analysis

MRSS Minimum Returned Sample Size NPV

Net Present Value

OBB

Organisational Buying Behaviour

OBB/P Organisational Buying Behaviour for Professional Business Services (PBS)

P&L

Profit and Loss Statement (Income Statement)

P2B

Professional-to-Business

PBC

Perceived Behavioural Control

PBS

Professional Business Services

PPE

Post-Purchase Effects

Public Relations

PRCA

Public Relations Consultants Association (UK)

RATER Responsiveness, Assurance, Tangibles, Empathy and Reliability (acronym for final five-factor SERVQUAL scale RM

Relationship Marketing

SEM

Structural Equation Modelling

SET

Social Exchange Theory

SLT

Social Learning Theory

SIC

Squared Inter-Correlation

SMC

Squared Multiple Correlation

SNB

Social Normative Belief

TpB

Theory of Planned Behaviour

TRA

Theory of Reasoned Action

UCSI

United States Census of Service Industries

Bill Nichols – I. Introduction……………………………Page |1

CHAPTER ONE – INTRODUCTION 1.1

Research Focus

1.1.1 The Ascent and Importance of Loyalty When US brand, Betty Crocker, introduced its first reward coupons in the late 1920s, it became unknowingly the progenitor of loyalty marketing. Eighty years later and loyalty has climbed the marketing mountain. Today it is claimed that “loyal customers (are) the lifeblood of an organization, regardless of its scale and business scope” (Chen and Quester 2006, p.188) and that ‘loyalty’ is the essence of marketing (Lam, Shankar, Erramilli and Murthy 2004; Sharma and Patterson 1999). ‘Loyalty’ – or as commonly labelled in the balance of this thesis, LoyaltyCommitment (*1) – measures: The level of a client’s willingness – as manifested in both behavioural and attitudinal terms - to maintain a preferred service relationship consistently now and in the future despite situational influences and marketing efforts having the potential to cause switching behaviour (based on Oliver 1999).

Loyalty-Commitment is also the commonly designated intentional antecedent of three principal loyalty behaviours: retention; recommendation; and share-ofwallet (Keiningham, Cooil, Aksov, Andrassen and Weiner 2007; Reichheld and Sasser 1990; Zeithaml 2000). Reasons for the construct’s exalted status today are widely advanced. A loyal clientele: delivers increased revenue (Berry 1995); reduces customer acquisition 1.

Key constructs (e.g. Loyalty, Trust) are italicised throughout. This is partly for easy identification and reference. But principally to acknowledge a critical limitation. Thus, if we validate a construct such as Trust, then we corroborate a proposition to the effect that a construct (which we choose to label Trust) has meaning and demonstrable, replicable relationships with other neighbouring constructs. We do not, however, validate a conceptualisation of trust which has some agreed universal meaning (=df) or truth value (Blois 1999; Scruton 1994).

Bill Nichols – I. Introduction……………………………Page |2

costs (Reichheld 1996); facilitates cross-selling (Dwyer, Schurr and Oh 1987); lowers the cost of serving repeat purchasers (Reichheld 1993; Reichheld and Sasser 1990); and improves overall profitability (Allen 2006; Heskett, Jones, Loveman, Sasser and Schlesinger 1994).

In aggregate, according to 14-industry Bain &

Company research data, a 5% customer retention increase delivers a 25-95% net present value (NPV) profit increase (Reichheld 1996).

[Note: a table of

abbreviations appears at the end of the abstract and contents section.] Indicatively a 16-point retention improvement at the author’s former firm contributed to a 190% increase in net profit during 2004-06. Additionally, the loyalty-behaviour of advocacy attracts up to 50% of new service customers (Keaveney 1995) and may accelerate growth rates (Reichheld 2006). Unsurprisingly, as a result, this focal construct is the subject of its own industry. Aspects include: loyalty schemes such as loyalty cards in most supermarkets (e.g. Sainsburys’ Nectar in the UK); frequent flyer loyalty air miles, beginning with American Airlines in 1981; magazines (e.g. The Loyalty Magazine in the US); practitioner guides (e.g. the now third edition of Wise Research’s 900-page The Loyalty Guide); specialist marketing agencies (such as London’s near 20-year old ICLP); and even dedicated market research sites such as Ipsos-MORI. Meanwhile, at the corporate level, Loyalty-Commitment is for some a critical measure of vendor organisations’ health (Lovelock 1983; Crosby, Evans and Cowles 1990). It is also, in terms of firm performance outcomes, a key indicator of customer lifetime value, NPV and net organisational worth (Williams and Visser 2002). 1.1.2 The Value of Professional Business Services (PBS) PBS are “performances of assignments or service agreements which apply some form of expert or tacit knowledge by professionally accredited and affiliated service providers whose relationships with buyers…. is fiduciary” (Conchar 1998, p.256).

They include sectors such as law, accountancy, advertising and public

relations consultancy (Maister 1993).

Bill Nichols – I. Introduction……………………………Page |3

Collectively PBS constitutes a major global industry.

As annual fee revenue

examples: global management consultancy = ~US$100 billion (UK Management Consultancies Association, MCA, 2004), 10% UK-based; US PR consultancies US$7.4 billion (US Census of Service Industries, UCSI, 2005); UK PR consultancies £1.2 billion (Centre for Economic and Business Research/Chartered Institute of Public Relations, [CEBR/CIPR 2005]); and UK advertising agencies = >US$3.3 billion (UK Advertising Association 2003). In aggregate US data suggests that US professional, technical and scientific services account for approximately 18% of total service industry income - (~US$864 billion, UCSI 2005). PBS also offers a critical secondary value: an ideal opportunity in which to study the nature and antecedence of Loyalty-Commitment intra-consumption under conditions of: (1) close client-vendor engagement (Lu 2002), a key driver of firm performance (Christopher, Payne and Ballantyne 1991); and (2) extended continuous service provision (Bolton and Lemon 1999). 1.1.3 The Conundrum of Loyalty in PBS Since (1) Loyalty-Commitment “can make the difference between financial success and disaster” (MacStravic 1994, p. 53) and (2) insufficient allocation of firm resources to retention has a far greater impact on profitability than to acquisition (Reinartz, Krafft and Hoyer 2004), it follows (3) that Loyalty-Commitment is – or ought to be: A strategic enterprise focus (Ball, Coelho and Machas 2004; Ewing, Pinto and Soutar 2001); and One of today’s highest strategic marketing priorities (Evanschitzky and Wunderlich 2006; Oliver 1999; Taylor, Hunter and Longfellow 2006). Yet, for all the hype, retention - at least in creative PBS services - is deteriorating progressively. Average US advertising agency tenure declined from 7.2 years (1984) to 5.3 by the mid-1990s (Ewing, Pinto and Soutar 2001). More recent evidence reports that two-thirds of clients defected within five years (Kulkarni, © Bill Nichols (2009)

Bill Nichols – I. Introduction……………………………Page |4

Vora and Brown 2003) – accelerating by 2006, according to this study, to less than four. Such disloyalty, or relationship dissolution, generates significant time and financial costs for both parties (Waller 2004). Developing new client-consultancy relationships often requires up to two years (Michell and Sanders 1995). By extension negative advocacy reduces profitability: US computer firm Dell, for example, calculates that by halving negative advocacy it would increase annual profit by $160 million (Reichheld 2006). Meanwhile, there are few answers for PBS marketers wishing to reverse negative trends. Customer retention research is overall “largely fragmented and in need of an empirically verified general theory” (Hellier, Geursen, Carr and Rickard 2003, p.1762) and minimal in PBS (Edgett and Parkinson 1993; Patterson, Johnson and Spreng 1997; Pritchard, Havitz and Howard 1999). No consensus exists regarding either the domain and operationalisation of ‘loyalty’ (Evanschitzsky and Wunderlich 2006; Oliver 1999; Rauyruen, Miller and Barrett 2007) or its relationship with postulated antecedents such as Trust, Perceived Value or Satisfaction (Agustin and Singh 2005). Worse, given its uncertain nomological relationships, ‘Loyalty’ is sometimes derided as an anaemic measure (Hess and Story 2005).

In summary much remains to be learned about ‘loyalty’, its

antecedents and consequences (Zeithaml 2000).

1.2

Origins of the Research

For the author, the ‘loyalty conundrum’ first emerged in the early 1980s at the London office of Edelman, then and still (2008) one of the world’s top five independent PR consultancies. The young author-account manager was puzzled by clients’ divergent reaction to, and evaluations of, near-identical (by formal internal measures) service quality. One client was complimentary, easy to work with and intending contract renewal; another full of concerns and complaints yet loyal; and a third unresponsive, uncommunicative and unexpectedly one morning, no reasons given, terminating its account.

Bill Nichols – I. Introduction……………………………Page |5

This confusing drama played itself out frequently over the author’s near 30-year PR career, the majority consultancy-side and the last 14 as joint-head of his own firm until exit by sale to an MBO team in December 2007. It was also replicated during marketing assignments in other PBS disciplines e.g. law, accountancy and management consultancy. Over time, the author encountered two broad explanations: both with practitioner and academic support. In summary: A ‘Relationship School’ is typified by Gummesson (1981, p81): “Social contacts between the individual professional and the client are important: mutual understanding and trust which are crucial for the long-term development of the firm are fostered by interaction at work and social relationships.” It is also summarised succinctly by an unnamed McKinsey partner: “everyone realises that the client relationship is paramount, not the specific project we happen to be working on at the moment.” (Maister 1993, p308); A ‘Performance/Value School’ is (1) summarised by Webster (1986) who notes early that satisfaction with service quality is the antecedent of referrals and (2) enriched by a past president of the UK Management Consultancies Association, Brian Small:

“delivering projects on time, to

budget even with clear proof of goals achieved, that’s important hygiene satisfaction stuff. But what motivates a client is the way you add value – a project bonus, industry knowledge, tip-off about a deal… could be almost anything” (in conversation, 2002).

1.3.

Structure of Thesis

These two schools are the genesis for the investigation which culminates in this thesis. To assist the reader in navigation, a route-map is supplied at Table 1.1 overleaf and the principal components are summarised in this section.

Bill Nichols – I. Introduction……………………………Page |6

Table 1.1: Thesis Outline and Route-Map

Chapter 1 – Introduction →

Chapter 2 – Literature Review Sets the context. Outlines key constructs. Explores principal perspectives on the nature and antecedence of loyalty as the basis of two models: Convergent and competing Uniconative or ‘Goodwill’.

LR…………………………………….

and Initial Hypotheses→

Chapter 4 - Methodology→

Chapter 5 – Data Analysis I

Conceptual development and theoretical underpinning for proposed Convergent Model together with principal hypothesis set.

Describes research strategy and overall conceptual framework, formulation of instrument, psychometric issues and sampling framework and procedure.

Data preparation and descriptives. Presents initial EFA and CFA procedures for testing scale reliability and validity.

Scope of research. Describes the conundrum of loyalty in PBS. Sets out Research Questions and intended contribution of thesis.

Provides theoretical underpinning including discussion of theories of buying behaviour, attitude formation and service.

Chapter 3 – Preliminary Research, Model Development

Chapter 7 – Implications and Chapter 6 – Data Analysis II→

Conclusions→

Annexes

Presents results from the testing of both the Convergent and competing Goodwill Model via MRA and SEM using maximum likelihood estimation.

Conclusions regarding the study’s findings are presented and discussed including relevant limitations and practitioner recommendations. Overall contribution is described together with proposals for future research.

References, Appendices, Glossary of Abbreviations

1.3.1 Research Questions There are many calls to: (1) develop the conceptualisation of loyalty, explore its dimensionality and extend the generalisability of measurement into new markets and territories (e.g. Ball, Coelho and Machas 2004); and (2) help managers allocate marketing resource versus soft and hard attribute areas of service quality (e.g. Auh 2005). Based on Oliver’s (1999) research agenda, accordingly, this study’s goals are to help PBS marketers (1) “identify loyal segments through means other than repeat purchase patterns” (Oliver 1999, p.45) and (2) “cultivate loyalty” (p.43). The thesis addresses three principal questions: © Bill Nichols (2009)

Bill Nichols – I. Introduction……………………………Page |7

1. What is the nature of ‘loyalty’ in PBS? 2. What is the antecedence of ‘loyalty’ in PBS both direct and indirect? and 3. How should PBS practitioner-managers best cultivate client ‘loyalty’?

1.3.2 Framework The thesis’s underlying rationale is that truly sustainable long-term loyalty requires the consultancy both to maintain an excellent relationship (‘engagement’) and to deliver high perceived value (positive equity) to the client (*2) in that relationship. 1.3.3 Model Development Responding to the research question, the literature review (Chapter Two) first introduces (1) seven principal constructs. These include Loyalty-Commitment itself and the attribute platform of Perceived Service Quality founded on Grönroos’s (1984) tripartite service quality taxonomy: technical/competence, functional/social and reputational.

Germane attitudinal constructs include: Trust (Moorman,

Zaltman and Deshpande 1992); Perceived Value (Bolton and Lemon 1999; Woodall 2003) and both transactional and cumulative Satisfaction (Garbarino and Johnson 1999; Giese and Cote 2003; Oliver 1997). It also discusses (2) moderation - as evidenced by the environment of services in general and professional services in particular – and postulated key moderators. A key premise of the literature search was to explore potentially complementary perspectives on the question of loyalty-formation. A multidisciplinary approach embraces concepts and models from the literatures of social psychology, marketing (especially post-purchase effects), interorganisational performance, relationship marketing and buyer-seller relationships (especially client-agency relations). These support discussions of (3) principal perspectives and extant

For consistency throughout, unless specifically debarred by context, a customer of a PBS firm is always referred to as a ‘client’ and the firm as a ‘consultancy’.

Bill Nichols – I. Introduction……………………………Page |8

models of the nature and antecedence of ‘loyalty’ formation and (4) relevant principal theoretical underpinning. Following metatriangulation theory, (5) the principal perspectives are formalised in (1) an initial ‘Convergent’ model (Figure 1.1, Chapter Three) and hypothesis set and (2) a competing uniconative or ‘Goodwill’ model (Figure 1.2).

Figure 1.1 – A Convergent Model of Loyalty-Commitment

Moderators Perceived Value

Perceived Service Quality

Transactional Satisfaction

Moderators

Cumulative Satisfaction LoyaltyCommitment

Trust

Attributes

Attitudes

Intentions

Source: Author.

Chapter Three adds supporting empirical references for key linkages in the model. No known previous work employing this integrated model has been identified in the literature.

Bill Nichols – I. Introduction……………………………Page |9

Figure 1.2 – Competing Uni-Conative Model Trust

Perceived Value

Loyalty – Satisfaction)

Perceived Service Quality

Source: Author.

1.3.4 Orientation Chapter Four discusses methodology beginning with orientation. “The purpose of theory is to increase scientific understanding through a systematized structure capable of explaining and predicting phenomena” (Hunt 1991, p.21). nomothetic social research this refers to human behaviour (Hayes 2000).

In Thus,

although the study acknowledges the phenomenological perspective - in order to understand individual behaviour, one must explore self-perceptions (Hayes 2000) – it pursues a positivist research paradigm. One may aggregate perceptions to create knowledge independent of anybody’s claim to know (Popper 1972). Specifically, the study seeks to explain the phenomenon of PBS client LoyaltyCommitment in terms of the positive ‘what is’ - as opposed to normative ‘should be’ (Hunt 1991) - and ‘for what reasons’ (Thietart et al 2001). Since contextually both the explanatory perspective and knowledge applied are strictly limited, the model is inductive-statistical and not strictly falsifiable: i.e. the explanans only confers a certain likelihood that the phenomenon studied will occur in a given context (Hunt 1991). One can corroborate not confirm (Popper 1972).

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This approach accords with much marketing/social science practice. It is consistent with the predominantly positivist tradition in all three major perspectives reviewed and it facilitates both (2) rapid efficient data collection and (3) desirable goals such as replicability and extension (Berthon, Ewing, Pitt and Berthon 2003; Wright and Kearns 1998, further Chapter Four). Limitations arising are reviewed in the closing Chapter Seven. 1.3.5

Methodology

The balance of Chapter Four summarises the overall research design and related methodological considerations.

The target population of UK PR Consultancy

clients is estimated in the range 9200-10750 (5.9.3). An approximate convenience sample of 1237 clients was obtained from a custom database (‘Stormark’) and a group of participating consultancies. It was intended that a minimum sample of 140-150 should be obtained during the collection period (November 2006-March 2007). The 77-item measuring instrument deployed was principally (67%) an amalgamation of the prior instruments of Patterson and Spreng (1997) and Grayson and Ambler (1999). Both have prior empiric validation in relevant PBS including management consultancy, market research and advertising. This follows the principle that “replications are an important component of research in that they convert tentative belief into reliable knowledge” (Berthon et al 2003, p. 511). A number of construct extensions (e.g. the case of Perceived Value) were developed for the study. 1.3.6 Data Analysis Following initial data preparation, data obtained was subjected, inter alia, to exploratory and confirmatory factor analysis (EFA/CFA), multiple regression analysis (MRA), and structural equation modelling (SEM) using maximum likelihood estimation (MLE). Principal resources were SPSSTM 12.0/15.0 and AMOSTM 16.0. This enabled investigation and verification of the relationships hypothesised and their statistical significance (Chapters Five/Six).

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1.4

Intended Contribution of the Thesis

Penultimately in this chapter, it is relevant to identify the intended contributions of the thesis to the body of knowledge related to client loyalty and professional business services (PBS). They were to: Develop a conceptual model that explains the nature and antecedence of ‘loyalty’ in the specific context of PBS: Test and validate the model in terms of its explanatory power and predictive capability; Identify constructs that influence clients’ formation of ‘loyalty’ intentions; and Corroborate, and/or validate extensions of, existing scales as part of the wider validation of an appropriate PBS ‘loyalty’ instrument. These contributions should also offer significant utility to PBS marketers who seek to reverse the phenomenon of declining incumbency. More broadly, enhanced understanding of Loyalty will assist general marketing practitioners by: Complementing the emerging stream which seeks to optimise the apportionment of marketing expenditure between acquisition and retention (Neckermann 2004); Enriching brand valuation models whose common formulation - discounted value of future earnings attributable (Doyle 1994) - in effect estimates future patronage; and Creating a critical building block to support emerging models whose presumptions are that: o Marketing represents an investment as opposed to an expense (Almquist and Wyner 2001); and that

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o Its conversion of tangible and intangible inputs into outputs such as sales, profitability and brand equity may be fully observed and measured (Ratnatunga and Ewing 2005).

1.5

Chapter Summary

This chapter describes the origins of the research (1.2) and sets the scene for the loyalty-conundrum in general and for its moderation in the PBS environment in particular (1.1). It also summarises the structure of the thesis and provides a ‘route-map’ (1.3). This acts as a preliminary to an initial outline of the intended contribution both to the body of knowledge and to marketer practice (1.4).

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CHAPTER TWO – LITERATURE REVIEW 2.1

Overview and Limitations

Literature reviews facilitate theory development. They “close areas where a plethora of research exists and uncover areas where research is needed” (Webster and Watson 2002, p. xiii).

In this case, the principal task is to develop a theoretical conjecture regarding the

domain, dimensionality and antecedents of the construct of loyalty – subsequently ‘LoyaltyCommitment’. The investigation has two main dimensions: the client’s perspective of his consultancy and the environmental context (PBS – professional business services) in which it operates. These boundaries exclude potentially complimentary perspectives e.g. the effects on attitude formation of either (1) personality factors and/or (2) the client-consultant dyadic interaction. To address this challenge, this chapter accordingly reports, discusses and synthesises relevant theory and empirical research.

Key sources include the social psychology,

marketing (especially post-purchase effects), interorganisational performance, relationship marketing and buyer-seller relationships (particularly client-agency relations) literatures.

2.2

Organisation

Conceptual meaning is identified and allocated by: (1) attribution (statement of characteristics); (2) structure (organisational/hierarchical representation); and/or (3) disposition, e.g. associations and relationships (Bagozzi 1984). Following this approach, the literature review is organised in five principal parts beginning with construct attribution. It presents: First (2.3-2.9), the principal ‘actors’ (i.e. key constructs identified) including key sources, conceptualisation, domain and operationalisation. Seven attract wide support: LoyaltyCommitment, Transactional- and Cumulative Satisfaction, Perceived Value, Trust, Disconfirmation and Perceived Service Quality. These appear in the reverse (right-to-left) implied structural model order: first the principal target endogenous variable, LoyaltyCommitment, followed by attitudes and finally attributes. The eighth, Loyalty-Satisfaction, is

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a postulated superordinate construct based on a conjecture of Oliver (1999). It takes the final endogenous role in the Competing Model. Second (2.10), the principal situational influences (moderators) under which the constructs operate. This includes discussions of (1) the general characteristics of services, (2) the specific parameters of professional business services (PBS) and (3) a series of derived moderating constructs e.g. Client-Buyer Experience and Firm Relationship Duration (2.10). Third (2.11), turning to disposition, the review describes the governing ‘rules’ (i.e. principal theoretical underpinnings) which suggest the principal relationships and associations of both actor-constructs and situational influence/moderators. This includes key contributions from relevant relationship, buyer behaviour and attitude modelling theories. Fourth (2.12-2.14), within this overall theoretical framework, the review explores the commonalities of two major streams of literature. Each originates in different marketing ‘traditions’ – B2C post-purchase effects and B2B ‘trust-commitment’ interorganisational performance – and is founded on heterogeneous disciplines, theories and marketing schools (Lagrosen and Svensson 2006, Table 2.1,further below on P2B):

Table 2.1 – Principal Perspectives on Loyalty Formation Marketing Tradition

Consumer (B2C)

Business-to-Business

Professional to Business (P2B)

Principal Literature Stream

Marketing Post-Purchase Effects (PPE)

Interorganisational Performance – Relationship Marketing

Client-Agency Relations

Principal Theory

Equity/Adaptation Level/Social Exchange

Social Exchange

Social Exchange/Equity (Tolerance)

Marketing School

Buyer Behaviour

Social Exchange (noneconomic transactional)

Services Marketing (noneconomic relational)

Key Model(s)

Service Profit Chain/European Customer Satisfaction Index

Trust-Commitment

‘Trust-Value’ or Tolerance

Principal Contributions

Patterson and Spreng (1997)

Morgan and Hunt (1994)

Davies and Palihawadana (2006)

Oliver (1999)

Moorman, Zaltman and Deshpande (1992);

Davies and Prince (2005)

Parasuraman, Zeithaml and Berry (1988)

Grayson and Ambler (1999)

Beard (1999); Henke (1995).

Source: Author.

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These reviews form the basis for an application of metatriangulation theory (Lewis and Grimes 1999) and the postulation of a contingent ‘Convergence Model’ of loyalty formation and antecedence. Fifth and finally, the review identifies potential ‘inadequacies’ (Scruton 1994) in the Convergence Model and argues the case for a Competing or ‘Goodwill’ Model. It is (1) founded in part on Oliver’s conjecture (1999) regarding Loyalty-Satisfaction and the complimentary presentation of his loyalty-ladder (1999) and (2) supported by findings from a third principal P2B modelling stream (Table 2.1 above). This more parsimonious model, and its theoretical justification, is presented in conclusion (2.15). The contributions of the five parts and their relationships to both ‘Convergence’ and ‘Competing Goodwill’ Models are summarised in two parts at Tables 2.2A/B:

Table 2.2 (A) Organisation and Summary of Contributions by Model SS

Title

2.1

Overview and Limitations

2.2

Organisation

Key Source

Part 1 - Key Constructs ('Actors') 2.3

Loyalty-Commitment

Oliver 1999; Jones and Taylor 2007

2.4

Cumulative and Transactional Satisfaction

Giese and Cote 2003; Oliver 1997; Garbarino and Johnson 1999

2.5

Loyalty-Satisfaction

Oliver 1999

2.6

Perceived Value

Zeithaml 1988; Woodall 2003

2.7

Trust

Moorman et al 1992; Morgan and Hunt 1994

2.8

Perceived Service Quality

Gronroos 1982; 2000; Patterson and Spreng 1997; Grayson and Ambler 1999

2.8.3

PSQ - Technical Factors

Patterso and Spreng 1997; Moorman et al 1992; Mayer et al 1995

2.8.4

PSQ - Functional Factors

Parasuraman et al 1988; Patterson and Spreng 1997; Moorman et al 1992

2.8.5

PSQ - Reputational Factors

Morgan and Hunt 1994; Doney and Cannon 1997

2.9

Disconfirmation

Spreng and Page 2003; Santos and Boote 2003

Part 2 - Moderators (Situational Influences)

2.10.3

Service Theory and the Resource-Based Paradigm Service Taxonomy and PBS Characteristics

2.10.4

Moderators

2.10.2

Grove et al 2003; Vargo and Lusch 2004 Conner and Prahalad 1996; Lu 2002

Palmatier et al 2006; Grayson and Ambler 1999; Davies and Palihawadana 2006

CONV

COMP

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Table 2.2 (B) Organisation and Summary of Contributions by Model SS

Title

Key Source

Converg

Compete

Part 3 - Theoretical Underpinnings (Rules of Disposition) 2.11.2

Organisational Buying Behaviour (OBB)

Chofray and Lilien 1980; Sheth 1973

2.11.2

Consumer Buying Behaviour (CBB)

Skinner 1938; Howard and Sheth 1969

2.11.2

BB Contingency Theory and PBS

Wilson 2000

2.11.3

Equity

Adams 1963; Leventhal 1977

2.11.3

Social Exchange

Homans 1958; Kelley and Thibaut 1978

2.11.3

Expectancy-Disconfirmation

Helson 1964; Spreng and Page 2003

2.11.4

Theory of Reasoned Action

Fishbein 1963; Fishbein and Ajzen 1975

2.11.4

Theory of Planned Behaviour

Ajzen 1988; 1991.

2.11.4

Multiple Pathway Anchoring and Adjustment Model

Cohen and Reed 2006

Part 4 - Principal Streams and The Convergence Model 2.12

B2C Stream - Multivariate Models and ECSI

2.13

B2B Stream - Relationship Marketing and Trust-Commitment Theory

2.14

A Convergence Model

Patterson and Spreng 1997; Martenson et al 2000 Fruchter and Sigue 2004; Knemeyer and Murphy 2005; Moorman et al 1992; Morgan and Hunt 1994 Hennig-Thurau and Klee 1997; Lin and Ding 2005

Part 5 - Case For Anti-Thesis and A Competing Model 2.15.2

The Case for 'Anti-Thesis'

Nunnally 1978; Hunt 1991

2.15.3

The P2B 'Trust-Value' Stream

Davies and Palihawadana 2006

2.15.4

A Competing 'Goodwill' Model

2.16 Chapter Summary Note: coded green = related to Convergence Model; coded red = related to Competing Model.

2.3

Construct 1: Loyalty-Commitment

2.3.1 Overview This section introduces the research’s principal target construct: ‘loyalty’ or as designated here, Loyalty-Commitment. It is: The level of a client’s willingness – as evidenced by both relative attitude and behaviours - to maintain a preferred service relationship consistently now and in the future despite situational influences and marketing efforts having the potential to cause switching behaviour (based on Oliver 1999).

Loyalty-Commitment is for many a conundrum: at once ‘much lauded’ but ‘little understood’ (Oliver 1999, p.43, 2.3.2). This section addresses the lack of consensus (Uncles, Dowling and Hammond 2003) regarding conceptualisation (2.3.3), domain and operationalisation (2.3.4) and domain label (2.3.5).

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2.3.2 The Loyalty Conundrum To help explain the loyalty conundrum, meet John – a regular weekly patron of ‘Bernie’s Brasserie’, a restaurant in Metro-City. We encounter him one Wednesday. He is still very dissatisfied with last Saturday evening’s visit. “It was awful,” John tells us. “Waited over an hour, main course cold and, to cap it all, the waiter was rude.” We press him. “True,” he concedes, “over some 25 visits, the value is outstanding and Bernie is a great friend.” Will John be there next Saturday? “Almost certainly.” This fictional scene typifies commonly observed ambivalent phenomena – e.g. dissatisfied Loyalty or satisfied defection (Chiou and Droge 2006) – which characterise the loyalty debate. Here are oppositely-valenced attitudes: positive Perceived Value and short-term Dissatisfaction. They are co-existent antecedents of loyalty. Analysed by reference to the multiple pathway anchoring and adjustment process (‘MPAA’, Cohen and Reed 2006, 2.11.4), we observe the representational sufficiency of current dis-satisfaction cede precedence to the underlying functional sufficiency of value and personal relationship. In John’s case, ‘loyalty’ is maintained. 2.3.3 Conceptualisation: Behaviour vs. Intentions The loyalty literature contains three principal conceptual strands: behavioural, attitudinal and integrated attitudinal-behavioural. The last includes two major recent contributions which are addressed individually (the second in the later discussion of Loyalty-Satisfaction, 2.5). They are summarised at Table 2.3. The first strand postulates that client loyalty manifests principally in behaviours e.g.: (1) Loyalty-Retention, the client’s formal/contractual maintenance, even if temporarily dormant, of a consultancy relationship (Keiningham et al 2007); (2) Loyalty-Advocacy, his likelihood or willingness to recommend the consultancy (Danaher and Rust 1996; East et al 2000; Lam et al 2004). It is manifested by apostles (Heskett et al 1994). They create a ripple effect (Gremler and Brown 1999) in either positive or negative forms (Athanassopoulos et al 2001; Fullerton 2003; Harrison-Walker 2001; Reichheld 1996; Reichheld and Sasser 1990);

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(3) Loyalty-Patronage, the level of sales committed (Keiningham et al 2007; Reichheld and Sasser 1990; Zeithaml 2000).

It is commonly assessed by share-of-wallet

expenditure (East, Gendall, Hammond and Lomax 2005; Neal 1999). Under conditions of intra-consumption observation (per this research), it is a constant.

Table 2.3 Loyalty – Principal Contributions Narrative

Summary

Major References

Strand 1. Purchasing Behavioural (2.7.4)

Current purchasing behaviour and volumes predicts future activity.

Jacoby and Chestnut (1978) Hess and Story (2005)

Strand 2. Purchasing Inclination

Strength of predisposition to repurchase/continue purchasing.

Bennett and Rundle-Thiele (2004)

Strand 3. Integrated inclinationbehavioural

Segments loyalty by integrating both observed behaviour and stated inclinations.

Day (1969) Dick and Basu (1994) Pritchard, Havitz and Howard (1999)

Contribution Integrates both behaviours which 1: Interindicate maintenance and relative personallyattitude based Loyalty

Rusbult et al (1999) Lawler and Yoon (1993) Lawler, Thye and Yoon (2000) Jones and Taylor (2007)

Contribution 2: The Loyalty Ladder

Ajzen and Fishbein (1975) Gremler and Brown (1996) Oliver (1999)

Loyalty is hierarchical and evolves/deepens over time in alignment with the mediated impact model.

Source: Author.

Further, consistent with early consumer buying behaviour (CBB) stream (2.11.2), measurement of past/current behaviours enables prediction of future behaviours (Hess and Story 2005; Jacoby and Chestnut 1978; Skinner 1938, 2.11.2). In this context, return briefly to John, our restaurant patron and habitué of several Metro-City restaurants.

Any

restaurateur capturing his share-of-wallet dining-out data will possess a modestly reliable predictor of future patronage (Keiningham et al 2007; Perkins-Munn, Aksoy, Keiningham and Estrin 2005). However, this first strand has only limited reliability. ‘Spurious’ - as opposed to ‘true’ loyalty may arise through opportunity, convenience or habit (Dick and Basu 1994; East et al 2005, p. 13). Yesterday’s satisfied restaurant patron will be neither necessarily committed today nor returning next weekend (Hellier et al 2003). Similarly, considering a second

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behaviour, Loyalty-Advocacy, Reichheld’s (2003) claim that we require only clients’ ‘net promoter scores’ to predict long-term sales performance is unsupported empirically (Keiningham et al 2007; Morgan and Rego 2006). Addressing these limitations, a second strand focuses on a client’s underlying intention: i.e. the relative strength of his predisposition to re-purchase. It is commonly anchored by reference to: either (1) competitive alternatives (Anderson and Sullivan 1993; Patterson and Spreng 1997); and/or (2) relative brand preference (Bennett and Rundle-Thiele 2004); and/or (3) an established client attitude. The last includes three principal constructs (each the subject of following sections, 2.4, 2.6-2.7): Transactional or Cumulative Satisfaction with services (Oliver 1999); Perceived Value, the evaluation of what is paid versus given up (Bolton and Drew 1991); and Trust, the client’s willingness to rely on, and have confidence in, the consultant (Moorman, Zaltman and Deshpande 1992).

This strand equally

encounters limitations since its efficacy is subject to moderation and the client’s anchoring and adjustment process (Cohen and Reed 2006, 2.11.4). Back at ‘Bernie’s Brasserie’, for example, to predict an outcome based on John’s palpable dissatisfaction alone would lead us to erroneous conclusions. Accordingly, to provide a solution, the third and now mainstream strand advances an integrated intentional-behavioural perspective (Day 1969; Pritchard, Havitz and Howard 1999). It is typified by the ‘Customer Loyalty Typology’ (‘CLT’, Dick and Basu 1994, Figure 2.1). However, although compelling in principle, such early integrated models generate mixed findings. For example, in the CLT case, the correlation is (1) unsupported in a supermarket context (East, Sinclair and Gendall 2000) but (2) finds “some validity in subscription type markets” such as retail banking (Garland and Gendall 2004, p.81). The difference in these examples may be attributable either to an extraneous factor (e.g. switching barriers or, in the banking case, the presence of a low-level relationship) or inadequate conceptualisation and operationalisation. This is addressed next.

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Figure 2.1: Customer Loyalty Typology High

Latent

Relative Attitude LoyaltyCommitment

True Loyalty

None

Low Low

Spurious Repeat – Loyalty Patronage

High

Source: Dick and Basu (1994).

2.3.4 Domain and Operationalisation: The Role of ‘Interpersonal Loyalty’ A recent framework for intentional-behavioural integration is provided by applying, as a proxy for the client-consultancy relationship, the interpersonal loyalty literature’s operationalisation of a couple’s loyalty to each other (Jones and Taylor 2007). The principle of borrowing from personal relationship theory in social psychology is well supported: examples include interpersonal bonds (Crosby, Evans and Cowles 1990) and ultimate relationship commitment (Gwinner, Gremler and Bitner 1998; Strandvik and Liljander 1994). Contextually a couple describes any form of interdependent relationship in which emotional closeness is sufficient for partners to consider themselves a couple (Levinger 1997, author’s ital). Consistent with social exchange theory (2.11.3), it postulates an overall nine-indicator domain and two dimensions for the business domain: relationship maintenance behaviours and relative attitude (Rusbult, Wieselquist, Foster and Witcher 1999). “Regardless of the target (friend, spouse, service provider), loyalty captures in essence what Oliver (1999) refers to as ‘what the person does’ (behavioral loyalty) and the psychological meaning of the relationship (attitudinal/cognitive loyalty)” (Jones and Taylor 2007, p. 45).

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In detail, this conceptualisation first incorporates three relationship maintenance, or ‘stay’, behaviours (Lawler and Yoon 1993). Each represents patterns of exchange deliberately chosen by actors over available alternatives (Molm, Takahashi and Peterson 2000). In personal terms, they include injunctions to avoid divorce, remain in a relationship and uphold sexual fidelity (Rusbult 1980).

These align with three common behavioural

indicators identified in the marketing literature: (1) (non-) switching, (2) repurchasing and (3) exclusive repurchasing (Bansal, Irving and Taylor 2004; Oliver 1999; Zeithaml et al 1996). This alignment is consistent with a key inter-organisational definition of ‘commitment’ as “warranting maximum efforts at maintaining it” (Morgan and Hunt 1994, p.23). Collectively, relative attitude captures the emotional bonds engendered by repeated experiences of successful exchanges between partners (Lawler, Thye and Yoon 2000). Some contributions treat its cognitive and affective clusters as independent dimensions (e.g. Bansal et al 2004) but this view is unsupported empirically (Jones and Taylor 2007). Second, individuals in affective terms: perceive their partner superior to available alternatives; are willing to publicise his virtues; and finally operate altruistically (Jones and Taylor 2007). These indicators are consistent in the marketing literature respectively with: (4) strength of preference (Dick and Basu 1994); (5) intention to advocacy (Reichheld 2003); and (6) altruism (Price, Feick and Guskey 1995), or non-opportunistic behaviour (John 1984, Williamson 1975). Third and finally, cognitive interpersonal indicators include: emotional fidelity, interdependence (or collective representation of self and partner) and willingness to make sacrifices (Jones and Taylor 2007). In the marketing literature, they are consistent globally with the inter-organisational stream’s definition of commitment as an “enduring desire to maintain a valued relationship” (Moorman et al 1992, p.316).

Specifically they align

respectively with: (7) exclusive consideration (Gremler and Brown 1996); (8) identification with service provider/consultancy (Butcher, Sparkes and O’Callaghan 2001) or brand beliefs (Bloemer, De Ruyter and Wetzels 1999); and finally (9) willingness to tolerate price increases (De Ruyter, Bloemer and Peeters 1997; Grisaafe 2001). Each cognitive indicator reflects a client-subject’s conscious decision-making and tests for motivation (Caruana 2002; Gremler and Brown 1999, 1996). For example the power of price/sacrifice is validated (Fullerton

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2003) and further corroborated in two studies: one a laboratory experiment, the other real usage over time (Homburg, Koschate and Hoyer 2005). Collectively representing the putative domain of ‘loyalty’, these nine-indicators and their principal definitions and sources, are summarised at Table 2.4:

TABLE 2.4

THE DOMAIN OF ‘LOYALTY’

Dimension

Item

Definition

Source

Repurchase Intentions

Intend to maintain relationship and make next purchase with supplier

Zeithaml et al 1996

Behavioural Intentions/Conative

Switching Intentions/Dependence

Exclusive Intentions Attitudinal Affective Intentions

Attitudinal Cognitive Intentions

Relative attitude, strength of preference

Able to switch to another supplier in same category, level of dependence Client's intention to dedicate all purchases in category to a particular service provider

Bansal et al 2004; Fullerton 2003

Oliver 1999

Altruism (negative opportunism)

Considering supplier first choice among alternatives Accommodating partner with non-opportunistic intentions

Willingness to recommend (personally)

Willingness to act as (personal) advocate on behalf of the supplier

Butcher et al 2001

Sacrifices

Willingness to tolerate price increases

De Ruyter et al 1998; Grisaafe 2001.

Will consider only one supplier for this type of service Thinking of the supplier as an extension of buyer/buyer's team

Gremler and Brown 1996; Dwyer et al 1987

Exclusive consideration

Identification

Zeithaml et al 1996; Dick and Basu 1994 Dick and Basu 1994; John 1984

Butcher et al 2001

Source: Author.

In summary, within the established conceptualisations of both psychology and social psychology literatures, this approach offers a current best practice definition of the putative domain of ‘loyalty’. To expand upon the previous definition, it measures: the level of a client’s willingness – as manifested in both behavioural and attitudinal terms to maintain a preferred service relationship consistently now and in the future despite

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2.3.5 Construct Label and Proxies: ‘Loyalty-Commitment’ The construct domain described above attracts several labels.

It is: ‘loyalty’ in the

predominantly B2C marketing post-purchase effects stream (Oliver 1999); ‘commitment’ from interorganisational relationship performance (Morgan and Hunt 1994); and, occasionally, in borrowings from psychology, ‘relationship satisfaction’ (Kumar 2002; Hocutt 1998; Rusbult 1983). ‘Commitment’s’ role as a proxy for intentional ‘loyalty’ is indicated by its widely hypothesised (Bansal et al 2004; Fullerton 2003; Gundlach, Achrol and Mentzer 1995; Hadjikhani and Thilenius 2005; Hocutt 1998) and corroborated role as a relational mediator of service attribute effects on ‘loyalty’ behaviours (Gundlach et al 1995; Harrison-Walker 2001; Palmatier et al 2006; Pura 2005). Sector corroborations include hotels (Mattila 2006), retail brands (Fullerton 2005), industrial B2B (Hewett et al 2002), airline services (Pritchard, Havitz and Howard 1999) and retail services (Bloemer and de Ruyter 1998). Overall its use as a proxy for, or dimension of, ‘loyalty’ is increasingly supported (Delgado-Ballester and Munuera-Aleman 2001; Oliver 1999; Taylor, Hunter and Longfellow 2006). Accordingly, the designation of Loyalty-Commitment is employed in the balance of this thesis.

2.4

Constructs 2 & 3: Transactional and Cumulative Satisfaction

2.4.1 Overview As noted above, a widely-postulated principal antecedent attitudinal anchor of LoyaltyCommitment is ‘satisfaction’ (2.3.2). It measures the level of a client’s contentment with the pleasure/benefits obtained and investment required during a specified period of service consumption and fulfilment experience (based on Giese and Cote 2003; Hellier et al 2003; Oliver 1997). This section addresses in sequence: (1) conceptualisation in terms of the key components of this definition - properties, period of observation (which generates twin constructs of

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Transactional and Cumulative Satisfaction), timing of observation and intensity (2.4.2); and (2) the domains and their operationalisation (2.4.3). 2.4.2 Conceptualisation Regarding first the properties of ‘client contentment’, the satisfaction literature offers three competing perspectives: (1) cognitive, (2) affective and (3) global: The first frames ‘satisfaction’ as a cognitive-only state or rational assessment (Bolton and Drew 1991; Howard and Sheth 1969); The second conceptualises ‘satisfaction’ as an indicator of the emotion which follows an initial rationalisation (Russell, Weiss and Mendelsohn 1989).

It is the most

widely-supported in the literature (e.g. Cadotte, Woodruff and Jenkins 1987; Halstead et al 1994; Oliver 1997; Spreng, MacKenzie and Olshavsky 1996) as evidenced also by an exhaustive grounded-theory review (Giese and Cote 2003); The third is founded on early contributions which depict the construct as a single, wide-ranging global attitude (Cardoso 1965). It argues that ‘satisfaction’ captures a client’s entire ‘contentment’ response to a service - both emotional and cognitive (Ivens 2005; Patterson and Spreng 1997). At a level, in the absence of a formal descriptive scientific language (Teas and Palan 1997), the debate is sterile. However, based on recent advances in neuroscience and psychology (Boyatzis and McKee 2005), the third global view is generally adopted here. The second conceptual dimension addresses the focal point and duration, or chronicity, of the client’s observation. Reverting to John at Bernie’s Brasserie, his reported ‘satisfaction’ may vary if his focal point is one recent bad experience only (transactional) or his generally happy ~25 visits over the last two years (cumulative). Specifically: Transactional Satisfaction refers to a client’s evaluation of a specific (aspect of) short-term consumption experience (Garbarino and Johnson 1999; Oliver 1997); and Cumulative Satisfaction represents the client’s learned predisposition (Eagly and Chaiken 1993; Roest and Pieters 1997) towards an aggregation of consumption experience (Hellier et al 2003; Homburg, Koschate and Hoyer 2005; Mittal and Kamakura 2001; Olsen and Johnson 2003).

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Other variables being equal, in a competitive scenario (e.g. Bernie’s) the cumulative version is supported as a more accurate predictor of consequent intentions and behaviours (Oliver 1997; Olsen and Johnson 2003). This focal point issue may be moderated by, third, the timing of observation (pre/anticipated, intra- or post).

In this study the PBS ontological focus is intra-

consumption: i.e. the ‘what is’ of the client’s satisfaction with a PR consultancy as opposed to ‘what might be’ or ‘what was’. The interaction of the two chronological parameters (timing and duration) is reconciled illustratively at Figure 2.2:

Figure 2.2: Chronological Dimensions of Satisfaction Measurement PostConsumption

Post Transactional Post Cumulative Intra Transactional

Timing of Observation

Satisfaction

PreConsumption One Hour Cumulative

Period of Observation

Many Years

Transactional

Source: Author.

Fourth and finally, client perceptions may vary in intensity. The typical Likert scale anchors strong emotions: customer delight (amalgamating joy and surprise) and customer disgust (Oliver, Rust and Varki 1997; Rust and Oliver 2000; Santos and Boote 2003). Both extremes represent profound positive/negative states (Kumar, Olshavsky and King 2001; Reichheld 1993; Rust and Oliver 2000). They may also possess greater predictive powers (McNeilly and Barr 2006): Xerox reports that ‘totally satisfied’ customers are six times more likely than their ‘merely satisfied’ counterparts to repurchase within 18 months (Jones and Sasser 1995).

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Given these properties and consistent with Multiple Pathway Anchoring and Adjustment theory (Cohen and Reed 2006, 2.11.3), any PBS client may, like John at Bernie’s retrieve sharply different emotions (short-term anger/unhappiness, long-term contentment) (Boyatzis and McKee 2005). These will in turn lead to significant disassociation (Wilson and Hodges 1992) and potentially oppositely valenced attitudes (Cohen and Reed 2006).

Key

contributions to the satisfaction literature by properties are summarised at Table 2.5a/b:

Table 2.5a: Definitions/Parameters of Satisfaction Source

Conceptual Definition

Properties

Focus – Frame

Time

Period

Olsen and Johnson

Stored overall evaluation of customer’s overall purchase and consumption experience

Affective

Performance

Post

CumLong

Hellier et al 2003

The degree of overall pleasure/contentment felt resulting from service’s ability to fulfil desires, expectations and needs

Affective

Performance vs. Benchmark

Post

Cum Long

Garbarino and Johnson 1999

An overall evaluation based on the total purchase and consumption experience with a good or service over time (Anderson, Fornell and Lehmann 1994)

CognitiveAffective

Performance

Post

CumLong

Oliver 1997

Judgement that service or attribute provides a pleasurable level of consumption-related fulfilment including under/over.

Fulfilment affective

Performance vs. Benchmark

Intra

Cum

Halstead, Hartmann and Schmidt 1994

Customer comparison of performance vs. prepurchase standard

Affective

Performance vs. Benchmark

Intra or post

Trans

Table 2.5b: Definitions/Parameters of Satisfaction Source

Conceptual Definition

Properties

Focus – Frame

Time

Period

Westbrook and Oliver 1991

Evaluative judgement concerning a specific purchase selection

Cognitiveevaluative

Selection

Postchoice (i.e. pre consumption)

Trans Short

Oliver and Swan 1989

A function of fairness, preference and disconfirmation

Primarily cognitive

The Salesperson

Intra

Trans Short

Cadotte, Woodruff and Jenkins 1987

A feeling developed from use experience

Affective

Product use and experience

Intra

Cum Long

Churchill and Surprenant 1982

Outcome of purchase and use based on comparison of costs, rewards and consequences

Cognitive

Extended comparison

Postconsumption

CumLong

Oliver 1981

Evaluation of surprise inherent in a product acquisition or consumption experience. Therefore an emotion.

Affective

Disconfirmati on of performance and feelings

Intra

Short – Trans.

Source: Author.

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2.4.3 Domains and Operationalisation As a founding father of satisfaction studies warned, measurement “poses a complex problem” (Cardoso 1965, p.244). Any object may attract multiple satisfaction attitudes of varying intensity (Bitner and Hubert 1994; Giese and Cote 2003). Each individual attitude will be contextual (Marsh and Yeung 1999; 2.4.2). Accordingly any model of LoyaltyCommitment formation should accommodate the effects and inter-relationship of multiple types (Chiou and Droge 2006). Specifically, domain scope for: The transactional case should be (1) fully rounded (Cardoso 1965, p.249; Giese and Cote 2003) and (2) encompass dimensions of quality of service, task quality, advice/consulting and proactivity/relationships (Oliver 1999; below 4.5.5); and The cumulative case, consistent with an operationalisation already validated in the PBS context of management consultancy (Patterson and Spreng 1997), should incorporate both hedonic and utilitarian components well as an overall decisional/wisdom of appointment item (below 4.5.6).

2.5

Competing Construct 4: ‘Loyalty-Satisfaction’

2.5.1 Overview Based on a conjecture of Oliver (1999), this section presents a competing view of ‘loyalty’ and ‘satisfaction’ which, it argues, may be dimensions or components of a single superordinate construct of Loyalty-Satisfaction or ‘goodwill’. This conjecture is: (1) Founded on empirical findings regarding the three-way relationships between various operationalisations of ‘satisfaction’, ‘loyalty’ and as a proxy of for ultimate behaviours, firm performance outcomes such as sales or shareholder value (2.5.2); (2) Encapsulated in a conceptual review of Oliver’s (1999) widely discussed ‘loyaltyladder’ (2.5.3); and (3) Iterated in an initial discussion of domain and operationalisation (2.5.4). 2.5.2 Loyalty-Commitment, Satisfaction and Firm Performance The inter-relationships of constructs of ‘satisfaction’, ‘loyalty’ and key dependent performance variables are widely studied.

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First Satisfaction is widely postulated as a critical antecedent of objective ‘firm performance outcomes’ (Anderson, Fornell and Lehmann 1994; Heskett et al 1994; Jones and Sasser 1995; Kaplan and Norton 1992; Reichheld 1993). Empiric corroborations include: (1) sales growth and (2) return on assets (Smith and Wright 2004); (3) shareholder value (Anderson et al 2004); and (4) increased cashflows and (5) reduced variability (Gruca and Rego 2005). Second, a similar relationship is also widely postulated for Loyalty-Commitment (HennigThurau, Gwinner and Gremler 2002; Reichheld and Shefter 2000; Woodruff 1997; Yang and Peterson 2004). Although empiric corroborations are fewer in number (Keiningham et al 2007; Morgan and Rego 2006), at least partly validated outcomes include: (1) increased revenue (Berry 1995); (2) reduced customer acquisition costs (Reichheld 1996); (3) cross selling facilitation (Dwyer, Schurr and Oh 1987); (4) lowered costs for serving repeat purchasers (Reichheld 1993; Reichheld and Sasser 1990); and (5) improved overall profitability (Allen 2006; Heskett et al 1994). More broadly, according to McKinsey & Co, a firm that focuses on both retention and share-of-wallet adds ten times greater value than one that studies retention alone (Coyles and Gokey 2002). Third, however, the postulated relationship of Satisfaction as an antecedent of LoyaltyCommitment attracts only limited support (Bloemer and De Ruyter 1998; McDougall and Levesque 2000; Olsen, Wilcox and Olsson 2005) as summarised in a meta-analysis (Bennett and Rundle-Thiele 2004). Generally, Satisfaction is an ‘unreliable precursor’ to Loyalty (Oliver 1999, p34): of satisfied/very satisfied customers, for example, 65-85% will defect according to Bain & Company analysis (Reichheld 1996). It follows that: (1) Either Loyalty-Commitment is an inefficient prism for the transmission of attitudinal effects to firm performance outcome and an ‘anaemic customer measure’ (Hess and Storey 2005, p320); or (2) Satisfaction and Loyalty-Commitment may be acting as either joint (M1, M2) or integrated mediators (M) of attribute perceptions on ultimate behaviours. Back at Bernie’s Brasserie, this latter view would accommodate the case of John’s oppositely valenced attitudes as dissatisfied loyalist.

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2.5.3 Conceptualisation: The Loyalty Ladder Given this hypothesis, Oliver (1999), in the second major contribution to integrated attitudebehaviour theory about loyalty (Table 2.3), conjectures that ‘cumulative satisfaction’ and ‘loyalty’ may be interdependent or fused components of a superordinate construct. Labelled here Loyalty-Satisfaction, it would incorporate co-existent cognitive, affective and conative dimensions. A client “displaying this state has logical, personal and communal loyalty sustainers” (1999, p.42). Such a construct would be consistent with Oliver’s (1999) multi-layered re-conceptualisation of a loyalty-ladder (Raphael and Raphael 1995). Founded on the theory of reasoned action (2.11.4), it evolves over time (Gremler and Brown 1996) and offers a counterpoint to the conventional presumption of multiple constructs (Figure 2.3):

Figure 2.3: The Loyalty Ladder

Readiness to Act

Deeply-Held Commitment Positive Attitude Cognitive Assessment

Cognitive

Affective

Conative

Behavioural

Source: Oliver (1999).

Consistent with complementary conceptualisations in relationship marketing (Christopher, Payne and Ballantyne 1991; White and Schneider 2000), the ladder has won recent acceptance as “a viable candidate for commensurable agreement” (Evanschitzsky and Wunderlich 2006; Taylor, Hunter and Longfellow 2006, p.18).

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Theoretical underpinning for the ladder of Loyalty-Satisfaction is provided by the mediated impact model (Ajzen and Fishbein 1975).

Thus the ladder’s first cognitive ‘rung’ is

knowledge-based. It is “of a shallow state… no deeper than mere performance” (Oliver 1999, p.35). Further, consistent with social exchange theory (2.11.3), it is grounded on a client’s general belief that maintenance yields higher net benefits than termination (Geyskens, Steenkamp, Scheer and Nirmalya 1996; Kelley and Thibaut 1978). Such a belief may be either (1) voluntary (Hadjikhani and Thilenius 2005; Hocutt 1998) or (2) reflect compulsion. Examples of the latter include e.g. high switching costs (Bansal et al 2004), perceived dependence (Fullerton 2003) or personal or organisational resistance to change (Fullerton 2003; Yanamandram and White 2006). One unpublished study, for example, reports that nearly 50% of mid-size UK legal firms’ private clients exhibit ‘inertia loyalty’ (Nichols 1997, p.73). At the second affective rung, liking or positive attitude develops “on the basis of cumulatively satisfying usage occasions” (Oliver 1999, p.35). Akin therefore to Cumulative Satisfaction (Hellier et al 2003), rung two generates initial emotional bonds (Moorman et al 1992). Nonetheless, the client remains vulnerable to switching (Oliver 1999). Further ascent is a function of social normative belief about, and motivation towards, the loyalty behaviour (Fishbein and Ajzen 1975). Thus the third conative rung is triggered by repeated “episodes of positive affect” (Oliver 1999, p.35). Here the client manifests both a “deeply held commitment to buy” and “fortitude” to resist competitive overtures (Oliver 1999, pp.35, 37). Next the fourth rung evidences a ‘readiness to act’ - even a ‘willingness to overcome obstacles’ (p.37) - in order to demonstrate ‘loyalty’ (p.35).

Finally,

complementary extensions to Oliver (1999) conceptualise a hierarchy of behaviours in which private ‘word-of-mouth’ advocates (Raphael and Raphael 1995) and public evangelists (Banks and Daus 2002) appear respectively on fifth and sixth rungs. This conceptualisation of a Loyalty-Satisfaction ladder up which an ascent occurs over time is consistent with social penetration, or ‘onion’ theory: “relationships change, normally becoming deeper, as people gradually reveal themselves to one another over time” (Baack, Fagliasso and Harris 2000, p.39). Re-stated in business relationship terms, and borrowing from the financial literature, the integrated construct describes the engendering of

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‘goodwill’. It captures a favourably disposed attitude towards someone or something which entails expectations of economic benefits (Egginton 1990) and is expected to generate future value (Tollington 1999). Further in terms of MPAA (multiple pathway anchoring and adjustment) theory, 2.11.4), the lower two rungs equate to representational sufficiency: the third – repeated episodes – to a test of functional sufficiency (Cohen and Reed 2006); and the fourth to the consequent behaviour. 2.5.4 Operationalisation One recent operationalisation of the ‘loyalty-ladder’ (Taylor, Hunter and Longfellow 2006) reports acceptable R2s for the dependent variables in the structural model (.21-.62, RMSEA=.045; CFI=.980; SRMR=.074). On this basis, one would expect Loyalty-Satisfaction to evidence the following components (Table 2.6):

Table 2.6: Postulated Components of Loyalty-Satisfaction Variables (Taylor et al 2006)

Predicted Components of Loyalty-Satisfaction

Hedonic Attitude

Utilitarian Attitude

Hedonic Cumulative Satisfaction (Patterson and Spreng 1997) Utilitarian Cumulative Satisfaction (Patterson and Spreng 1997)

Word-of-Mouth Behavioural Loyalty

Private Loyalty Advocacy (Butcher et al 2001)

Loyalty-Intentions 1 (affective overtones and commitment)

Relationship Commitment - first choice preference (Zeithaml et al 1996)

Loyalty-Intentions 2 (cognitive intention to repurchase)

Brand Commitment (Zeithaml et al 1996)

2.6.

Construct 5: Perceived Value

2.6.1 Introduction This section reverts to those constructs identified as potential antecedents of LoyaltyCommitment. Next Perceived Value captures the client’s cognitive assessment of the value of a consultancy’s service performance accounting for all factors of cost/monetary and

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time/non-monetary investment (Patterson and Spreng 1997; Woodall 2003) – both hedonic and utilitarian (Oliver 1997; Woodall 2003). More succinctly, it indicates the maximum amount a client will pay (Bolton and Lemon 1999). Widely popularised by the customer value delivery movement (Gale 1994), Perceived Value briefly achieved high popularity as a turn-of-the-century marketing fad (Sinha and DeSarbo 1998). Interest is evidenced in an estimated 18 aliases e.g. ‘customer value’, ‘customer valued quality’ and ‘value consciousness’ (Woodall 2003).

This section addresses

conceptualisation (2.6.2) and domain and operationalisation (2.6.3). 2.6.2 Conceptualisation: Perceived Value, Perceived Equity and Perceived Justice Conceptually, Perceived Value is founded on key tenets of equity theory (2.11.3). Equity postulates that human beings believe that rewards/punishments should be distributed equally to the level or value of contributions (Adams 1963).

It integrates

distributive/output, interactive and procedural/contextual dimensions (Leventhal 1977; Smith and Bolton 2002).

These three are in turn based philosophically on the

conceptualisation of perceived justice i.e. respectively a property of actions, people and affairs (Scruton 1994). Thus, Perceived Value, according to one recent grounded theory conceptualisation, assesses investment as a whole including: (1) attributes (e.g. client acknowledges service benefits, ‘actions’); (2) consequences (client enjoys interaction, ‘people’); and (3) end-state (client is comfortable with process, ‘affairs’) (Bhattacharya and Singh 2008). 2.6.3 Domain and Operationalisation Accordingly Perceived Value should measure the client’s overall utility ratio - what is received and given (Zeithaml 1988) or get-to-give (Heskett et al 1994) – across a potentially very rich domain (Woodall 2003, Figure 2.4). Further, consistent with the multiple conceptualisation of Satisfaction, Perceived Value may also capture either a short-term transactionally-based evaluation (current value-for-money) or long-term cumulative attitude (‘taking all factors into account over the lifetime of the relationship’).

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Figure 2.4: The Domain of Perceived Value

Source: Woodall (2003).

However, in practice, consistent with one strand in the literature which regards the construct as only a narrowly based complement - ‘economic satisfaction’ (Ivens 2005) - to Satisfaction, the domain is commonly operationalised as a single-item only: ‘value-formoney’ e.g. Patterson and Spreng 1997). Based on this discussion and compliant with calls to enrich this operationalisation (Patterson and Spreng 1997; Woodall 2003), it should (at the least) also incorporate: (1) Non-monetary components (Woodall 2003); (2) Equitable comparison of alternative offerings (Bolton and Lemon 1999); and (3) The setting of a germane price context for fee acceptability overall (Voss, Parasuraman and Grewal 1998, 5.5.7).

2.7.

Construct 6: Trust

2.7.1 Overview The third principal postulated attitudinal antecedent of Loyalty-Commitment, Trust measures the degree to which a client-trustor is willing to (continue to) rely on, and have confidence in, a consultant-trustee (Moorman, Zaltman and Deshpande 1992, p.315).

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Rooted in social exchange theory (Auh 2005; Blois 1999, 2.11.3), it is a well-supported building block for the relationship marketing paradigm (Wilson 1995). This section discusses its conceptualisation (2.7.2) and domain and operationalisation (2.7.3) in the context of the social psychology stream. This requires careful differentiation from alternative conceptualisations e.g.: Sociology: a collective or institutional phenomenon arising in group relations (Lewis and Weigert 1985; Tan and Sutherland 2004); and Psychology: predominantly a personality trait - therefore an inherent disposition or propensity to trust others (Mayer et al 1995). 2.7.2 Conceptualisation Both Satisfaction and Perceived Value capture assessments of past attribute performance in order to form a current attitude. Trust, however, is more complex: like Janus, it faces in two chronological directions and is a potentially rich mediator. Philosophically, Trust is forward-looking. To continue to trust implies a personal risk assumed or an investment committed (Tan and Sutherland 2004).

In effect one’s

expectations of altruism/benevolent intentions (an indicator of Loyalty-Commitment, Table 2.4 above). It acknowledges vulnerability to another’s possible (but not expected) ill-will and reflects one’s ability to tolerate uncertainty (Blois 1999). Conversely, to trust in the rear view is make an assessment of trustworthiness (Doney and Cannon 1997; Schurr and Ozanne 1985). It is to assert that “the trustworthy party is reliable… consistent, competent, honest, fair, responsible… benevolent” (Zinedin and Johnson 2000, p.248). As a result of its ‘Janus’ status, Trust’s role may be complex. For example, Trust may both provide a pre-existent framework for a client’s service assessment and serve as a mediator of assessments to consequent intentional constructs such as Loyalty-Commitment (Davies and Palihawadana 2006, Figure 2.5). Further, the effects of Trust may vary over time. It may, for example and consistent with the ‘birds of a feather’ similarity-attraction paradigm (Baskett 1973), intensify in response to social behaviours (Auh 2005) or reduce as a function of familiarity (Grayson and Ambler 1999).

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Figure 2.5: Trust as Janus: Reciprocal and Antecedent

Trust Intention (e.g. LoyaltyCommitment) Attitude (e.g. Cumulative Satisfaction)

Service Attributes Source: Author.

2.7.3 Domain and Operationalisation Given Trust’s potentially dynamic and evolutionary relationships (Johnson, Hermann and Huber 2006; Mittal, Kumar and Tsiros 1999), a researcher should specify carefully the object, context and chronological point of observation (Doney and Cannon 1997; Sirdeshmukh et al 2002).

For example, in a PBS context, client Trust does not vest

automatically in both individual consultant and firm and/or transfer between them (Czerniawka 2006). Both must be specified. To meet this need, Crosby and Stephen (1987) provide a tripartite conceptual framework for Trust including: (1) key contact person (2) core service and (3) organisation. It is captured in Moorman et al’s (1992) replicated (Grayson and Ambler 1999) five-item scale (further 4.4.4). This scale also reflects principal attributes identified in the literature e.g.: dependability (Sirdeshmukh, Singh and Sabol 2002);

ability/competence

(Ratnasingham

and

Pavlou

2003);

benevolence

intention/altruism (Doney and Cannon 1997; Sirdeshmukh et al 2002) and the closelyrelated integrity (Doney and Cannon 1997; Mayer, Davis and Schoorman 1995).

2.8

Construct 7: Perceived Service Quality

2.8.1 Overview Preceding sections introduced putative direct attitudinal antecedents of LoyaltyCommitment (i.e. Cumulative Satisfaction, Perceived Value, and Trust). In this context,

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attitudes are the product of a client’s beliefs about service attributes. In a compensatory model they are summed over all attributes (Fishbein and Ajzen 1975). Accordingly, next, the identification, selection and modelling of service attributes - Perceived Service Quality (PSQ), a client’s “overall assessment of the standard” of a service (Hellier et al 2003, p.1766) - is a critical foundation of any intentions model. The formulation of PSQ is “very complex” because quality is “whatever the customer perceives it to be” (Gronroos 2000, pp.61, 63). Consistent with the theory of typologyspecificity (Lapierre 1996) and as discussed further below in terms of PBS moderation (2.10), any PSQ scale often requires extensive customisation (Buttle 1996). For example, in the SERVQUAL stream (Parasuraman, Zeithaml and Berry 1985, 1988), industry-specific versions include: electronic commerce (Alzola and Robaina 2005); career services (Engelland, Workman and Singh 2000); banking (Hussey 1999; Lewis, Orledge and Mitchell 1994); ocean freight (Mehta and Duravasula 1998); and DINESERV/restaurants (Stevens 1995). To address ‘complexity’, this section explores conceptualisation (2.8.2) and the domain and operationalisation of PSQ. Its principal framework is Grönroos’s (1984) tri-dimensional technical, functional and reputational - formulation of quality (2.8.3-2.8.5). 2.8.2 Conceptualisation The traditional view of service attributes and service quality (aggregate attributes) reflects an internal, or engineer’s, perspective.

It focuses on zero defects and conformance to

requirements (Crosby 1979). By contrast, the now dominant external orientation is founded on the client’s overall experience: “the total composite product and service characteristics of marketing, engineering, manufacturing and maintenance through which the product and service in use will meet the expectations of the customer” (Feigenbaum 1991, p.8). To capture this overall view, the ‘Nordic School’ PSQ stream offers a tripartite formulation (e.g. Gronroos 1984; Lehtinen and Lehtinen 1982; Oliver 1997; Table 2.7).

Equally

supported by more recent service attribute analyses (e.g. Liang and Wang 2004), it is also consistent with other germane tri-dimensional theoretical conceptualisations e.g.: (1) perceived justice (Scruton 1994, 2.6.2); (2) perceived equity (Smith and Bolton 2002; 2.6.2);

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and (3) buyer experience of service delivery (Howard and Sheth 1969). Key dimensions include: ‘Technical’ (competence/’knows that’) denotes core service delivery e.g. a lawyer’s expertise (Gronroos 1984); ‘Functional’ (social/’knows how’) connotes the manner, or performance, of service provision (Hausman 2003); ‘Reputational’ (ethics/’knows why’) judges perceived credibility or professionalism (Thakor and Kumar 2000), or, alternatively, acceptance measured by relationship proximity (Goodwin and Gremler 1996).

TABLE 2.7

Tri-Dimensional Service Attributes – Classifications and Underpinning Vendor Input

Client-Vendor Interaction

Vendor Service Output

Buyer Modes of Judgement - Perceived Justice (Scruton 1994)

Affairs

People

Actions

Buyer Service Assessment Perceived Equity (Smith and Bolton 2002)

Context - procedural

Interactive

Distributive Output-based

Buyer Experience of Service Delivery (Howard and Sheth 1969)

Significative symbolic (e.g. quality, reputation)

Process/Interaction

Deliverables, Actions

Service Attributes (Liang and Wang 2004)

Symbolic

Experiential

Functional

(Oliver 1997)

Environmental-Where

Functional –How

Technical – What

(Lehtinen and Lehtinen 1984)

Corporate

Interactive

Physical

(Gronroos 1982)

Reputational

Functional/Social

Technical – Competence

Ontology of Performance (Scruton 1994)

Aesthetic Value

Knows How

Knows That

Vendor Input

C-V Interaction

Vendor Output

Source: Author.

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By comparison, the widely-deployed alternative SERVQUAL conceptualisation (Buttle 1996; Parasuraman, Zeithaml and Berry 1985, 1988, 1991) is limited since it principally captures only the ‘functional’ dimension.

Illustratively, explanation of variance is increased by

introducing: Technical (or outcome) quality items in a restaurant context (45.0% to 71.5%, ρ=<.01, Richard and Allaway 1993); and Technical and reputational items in a PBS/’Investing’ accountancy context, 79.4% and 63% respectively (Weekes, Scott and Tidwell 1996; Freeman and Dart 1993) versus 51% with ‘vanilla’ SERVQUAL (Hong and Goo 2004, ρ=<.01 all cases). 2.8.3 Domain and Operationalisation - Technical Factors Technical quality, or core service delivery, aggregates five putative factors in PBS. Collectively they represent a process-to-outcome continuum (Figure 2.6):

Figure 2.6: PBS Technical Factors Continuum

Results Outcomes Involvement Wider Network Capability

Specific Methodologies

Problem Identification

Source: Author.

The first four’s postulated effects are predicted to increase under PBS moderation in an environment characterised by: (1) ambiguity (Halinen 1997); (2) lack of client technical competence (Ojasolo 2001); and (3) conditions of low visibility and long-time to benefits (Lu 2002, further below 2.10). Accordingly, a productive meeting, idea or creative process may substitute for a formal outcome or final deliverable.

Evidentially, in a source PBS

management consultancy instrument explaining a high 80.0% of Satisfaction variance, each © Bill Nichols 2009

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dimension - except ‘involvement’ – reports acceptable individual construct reliabilities (CR=0.73–0.86; Patterson and Spreng 1997). In detail, the first three describe “skills, competencies and characteristics that enable a party to have influence within some specific domain” (Mayer, Davis and Schoorman 1995, p.717). Collectively, they operationalise ‘strategic counselling’, a well-supported attribute in the P2B stream (Davies and Palihawadana 2006, further 2.15.3). The fourth factor extends Mayer et al’s (1995) influence to active ‘consultant involvement’. It is defined by scope of activities undertaken or interim outcomes achieved (Moorman, Zaltman and Deshpande 1992). For example in PR consultancy these include copywriting, media out-reach and project management. The closer consultant involvement so: (1) the greater the client’s (willing) dependence (Moorman et al 1992); and (2) his reduced propensity to switch (Hocutt 1998; Morgan and Hunt 1994; Patterson and Smith 2003; Westbrook 1997). Technical factor indicators are summarised at Table 2.8:

Table 2.8 – Indicators of Technical Quality Service Attribute

Scale References

Other Key Sources

Results/Outcomes

Patterson and Spreng 1997

Bolton and Lemon 1999; Zeithaml 1981

Proactive Approach

SERVQUAL-10 (PZB 1988)

Satisfaction conceptualisation (Oliver 1997)

Problem Identification

Patterson and Spreng 1997

Up-to-Date Methodologies

Patterson and Spreng 1997

Competence

SERVQUAL-10 (PZB 1988)

Ratnasingham and Pavlou 2003; Mayer et al 1995

Consulting Advice

Satisfaction conceptualisation (Oliver 1997)

PZB (1988)

Consultant Expertise

SERVQUAL-10 (PZB 1988)

Palmatier et al (2006)

Perceived Dependence/Level of Consultant Involvement

Moorman Zaltman and Deshpande (1992)

Bansal, Irving and Taylor 2004; Fullerton 2003; Hocutt 1998; Westbrook 1997

Global network capability/credibility

Patterson and Spreng (1997)

SERVQUAL-10 (PZB 1988)

Source: Author.

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2.8.4 Domain and Operationalisation – Functional Factors Functional quality connotes the manner, or performance, of service provision (Hausman 2003). Its hypothesised indicators associate with three putative factors (Table 2.9):

Table 2.9: Indicators of Functional Quality Service Attribute

Scale References

Other Key Sources

Quality of core service – responsiveness, reliability, dependability

SERVQUAL-10 (PZB 1988) ; Patterson and Spreng (1997)

Sirdeshmukh et al 2002; Morgan and Hunt 1994 Oliver 1997.

Quality of Interaction

Moorman et al (1992); Grayson and Ambler (1999)

Courtesy, communications, access, responsiveness, customer awareness (PZB 1988)

Quality of relationship (emotional/social bonds)

Patterson and Spreng (1997)

Kumar 2002; Hocutt 1998; Rusbult 1983

Source: Author.

First, ‘service’ is founded on Patterson and Spreng’s (1997) management consultancy conceptualisation. It effectively aggregates two SERVQUAL RATER factors: responsiveness and reliability. Second, ‘interaction quality’ (effectively the aggregate of five of Parasuraman et al’s (1988) original ten dimensions) assesses overall relationship productivity and benefits (HennigThurau, Gwinner and Gremler 2002; Moorman et al 1992; Morgan and Hunt 1994). Interaction also implies: Service dramaturgy (Brown, Fisk and Bitner 1994; Grove and Fisk 1983) whose effects via front-line employees/consultants - on business performance are wellsupported (Chen and Quester 2006; Reynolds and Beatty 1999); and Both practical and social components (Auh 2005; Hausman 2003; Lovelock 1996; Price, Arnould and Teirney 1995) e.g. bonding created by shared knowledge (Liljander and Strandvik 1995).

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Third and finally, relationship quality captures the degree of rapport engendered (Patterson and Spreng 1997). It may embrace both social and emotional bonds (Hocutt 1998; Kumar 2002; Rusbult 1983; Turnbull and Wilson 1989). 2.8.5 Domain and Operationalisation – Reputational Factors Reputational quality captures the client’s evaluation of the consultant’s values, brand and reputation (Gronroos 2000, Table 2.10):

Table 2.10: Indicators of Reputational Quality Service Attribute

Scale References

Other Key Sources

Opportunism/altruism – integrity

Morgan and Hunt 1994; John 1984;

Sirdeshmukh et al 2002; Doney and Cannon 1997;; Doney and Cannon 1997; Mayer et al 1995

Servicescape - Presentation

Parasuraman et al 1988; Bitner 1992; Reimar and Kuehn 2005

Source: Author.

Among putative factors, the first, altruism (negatively, opportunism) aggregates two streams. The first, the rational cheater model, is ubiquitous in economics (Williamson 1975). It posits that employees or suppliers behave opportunistically “when the perceived marginal benefit of so doing exceeds the marginal cost” (Nagin, Rebitzer, Sanders and Taylor 2002, p.850). The second, the psychology impulse control model, postulates that a reward’s attractiveness is inversely proportional to the delay in receiving it (Nagin et al, p853). To obviate short-term issues, for example, a consultant might be economical with the truth and/or load an invoice with an extra margin. A second reputational attribute, the dramaturgical context of service – or servicescape (Bitner 1992) - signals, or symbolises, the nature of the consultant’s influence (Howard and Sheth 1969). Following cue utilisation theory, it may provide a surrogate indicator of quality

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(Dabholkar, Thorpe and Rentz 1996; Babakus and Boller 1992). The equivalent of overall service packaging (Ward, Bitner and Barnes 1992), it includes (1) “exterior and interior design (and) ambient conditions such as temperature, noise, odour” (Reimer and Kuehn 2005, p.786) and (2) tangibles such as collateral and presentation materials (Parasuraman, Zeithaml and Berry 1991). There is, however, little supporting evidence for this second attribute. Few studies consider multiple cues (Baker, Grewal and Parasuraman 1994). Only one demonstrates effects on both experienced, as well as expected, quality and in both utilitarian and hedonic contexts (Reimar and Kuehn 2005).

A comprehensive

operationalisation and measurement tool is still required (Reimar and Kuehn 2005, p. 801). 2.8.6 Weighting of Dimensions Limited evidence suggests that the weighting of a client’s attribute perceptions is contextual (typology-specific). Where technical quality is a given, functional attributes (1) typically will dominate (Palmatier, Dant, Grewal and Evans 2006; Baker and Lamb 1993; Higgins and Ferguson 1991) and (2) may secure retention even if the former is less than optimal (Gwinner, Gremler and Bitner 1998).

Conversely PBS evidence suggests that, under

‘Investing’ conditions of low service visibility and long-deferred benefits, technical quality is stronger (Patterson and Spreng 1997). More generally, weighting may vary even within a similar product portfolio e.g. financial service (Liang and Wang 2004). This area offers a fruitful field for future research.

2.9

Construct 9: Disconfirmation

2.9.1 Overview The final principal construct in the set identified from the literature is Disconfirmation: “the cognitive comparison between the consumers’s prepurchase standard and what he or she actually received” (Spreng and Page, 2003 p.32).

Disconfirmation is positive (if the

prepurchase standard or Expectations are surpassed), neutral (if met) and, if unfulfilled, negative (Howard and Sheth 1969; Oliver 1997; Oliver 1980). By adding Disconfirmation to any given model we enrich understanding of a client’s attribte perceptions by contrasting expected quality to quality actually experienced (Perceived Service Quality, 2.8). © Bill Nichols 2009

The outcome of their interaction - total perceived quality - is

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commonly modelled as an antecedent of client attitudes (e.g. Satisfaction/Trust, 2.4, 2.7). This section explores Disconfirmation’s conceptualisation (2.9.2) and domain and operationalisation (2.9.3). 2.9.2 Conceptualisation Founded on adaptation-level theory (Helson 1964), conceptually Disconfirmation acts, in MPAA terms, as a functional sufficiency test of the representational Perceived Service Quality (PSQ) judgement. The client’s experienced quality is confirmed/disconfirmed by comparison with his expectation. In simple terms, it measures the gap between the two (Figure 2.7):

Figure 2.7: Disconfirmation and Total Perceived Quality Expected Quality

Disconfirmation

Total Perceived Quality/ Satisfaction

Experienced Quality

Source: Gronroos 2000.

Disconfirmation, accordingly, is commonly measured by a difference, or gap, score methodology. This form of indirect assessment is both useful to diagnose service shortfalls (Hellier et al 2003) and parsimonious since it requires two measures only: performance and expectations.

The latter, however, typically encounters six operational issues. These

support its elimination in favour of a specific Disconfirmation construct (Dabholkar, Shepherd and Thorpe 2000; Peter, Churchill and Brown 1993; Spreng and Page 2003; Yi 1990):

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(1) Inherent operational difficulties in double administration of a common instrument for both expectations and perceived performance (Buttle 1996); (2) Availability of at least 56 often incompatible reference standards (Santos and Boote 2003, Figure2.8) e.g.: realistic evaluation (Spreng et al 1996); experience-based norms (Woodruff, Cadotte and Jenkins 1983); minimum tolerable emotional state (Zeithaml, Berry and Parasuraman 1993); and ‘acceptable’ and ‘desirable’ bracketing a zone of tolerance (Zeithaml and Bitner 1996);

Figure 2.8: The Hierarchy of Expectations Simple

ideal Normative (should) Desired (want) Predicted (will)

Confirmation Positive disconfirmation Zone of tolerance Deserved

Minimum tolerable (adequate)

Negative disconfirmation

Intolerable

Worst imaginable

Source: Santos and Boote (2003).

(3) Consequent instability of the difference score methodology (Athanassopoulous, Gounaris and Stathakopoulos 2001; Llosa, Chandon and Orsingher 1998; McLeary and Swan 1996); (4) Assimilation theory postulates that an independent Expectations construct reduces overall explicative power since client perceptions migrate to the higher tangibility of an ‘experienced’ construct (Churchill and Surprenant 1982; Oliver 1997; Patterson 1993; Patterson and Spreng 1997). Evidentially, deployment of an experience-only Perceived Service Quality construct generally improves explanation of variance (Hellier et al 2003; Johnson, Nader and Fornell 1996; Spreng and Page 2001); (5) Prompting Expectations may introduce distortion (Yu 2005); and finally

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(6) Expectations often exhibit instability over time (Cadotte, Woodruff and Jenkins 1987). In a PBS continuous service context, they are expected to rise during consumption (Davies and Palihawadana 2006; Grayson and Ambler 1999). Given these limitations, recent findings support the employment of an independent Disconfirmation construct. Specifically the Additive Difference Model (ADM) offers the most effective solution (Spreng and Page 2003; Spreng, MacKenzie and Olshavsky 1996; Tversky 1969). ADM has two components: (1) the client’s belief (about the service object); and (2) his evaluation in terms of goodness/badness (Spreng and Page 2003). 2.9.3 Domain and Operationalisation The most empirically reliable execution of the ADM conceptualisation is provided by the ‘better than, worse than’ (BTWT) formulation (Spreng and Page 2003). Accordingly: The belief component is anchored by terms such as “exactly as I expected/extremely different from what I expected”; and The evaluative by ‘goodness/badness (further 4.5.8).

2.10 Moderation and Moderators 2.10.1 Overview Given the preceding description of the key actors (constructs) defined by the literature, this section next places them in the context of germane professional business services (PBS) situational influences (moderators) which affect their relationships.

In sequence, it

discusses: The nature of services (as opposed to goods) in general highlighting the emerging resource-based paradigm and its knowledge-based implications for the study of relevant service attributes (2.10.2); The differentiating characteristics of PBS as a function of service taxonomy (2.10.3); and

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The implications of both in terms of contextual moderation and the postulated influence of individual moderators such as Client-Buyer Experience and Personal/Firm Relationship Duration (2.10.4). 2.10.2 Services and the Resource-Based Paradigm Services are now a major force in developed economies (1.1.2). Service-led growth is a variable of the positive relationship between market orientation and firm performance (Jaworski and Kohli 1993; Rust and Chung 2006). Service principles extend far beyond traditional service sectors (e.g. retail and leisure). They now embrace goods companies and public-sector organisations (Quinn, Doorley and Pacquette 1990). Service is therefore a broad term. According to the current UK government definition, it embraces all aspects of distribution, transport and communications, financial/business services and government services (Office of National Statistics 2008). The study of service began in the 1960s. Pioneers include Judd (1964), Levitt (1972), Shostack (1977), Lovelock (1983), Gronroos (1984, 1982) and Parasuraman, Zeithaml and Berry (1985). Early progress is chronicled by Brown, Fisk and Bitner (1996). Subsequent maturity is evidenced by dedicated university courses, faculty and peer-reviewed journals (Grove, Fisk and John 2003). By the mid-1980s, four distinct streams had emerged: service management, service customisation, financial impact and - the principal focus here customer satisfaction/relationships (Rust and Chung 2006). Today a paradigm transformation is underway.

The formerly dominant ‘four myths’

framework (Vargo and Lusch 2004) differentiated services (deeds, acts or performances) from physical-only goods (Berry 1980) by four attributed negatives: (1) heterogeneity (i.e. the propensity for the same service to be performed differently by different staff in multiple locations), (2) intangibility, (3) perishability and (4) simultaneous client-provider productionconsumption (Edgett and Parkinson 1993; Lovelock 1991, 1981; Parasuraman, Zeithaml and Berry 1985; Shostack 1977). But evidentially many services possess one or more opposite characteristic and the paradigm lacks rigour or research confirmation in any research project (Lovelock and Gummesson 2004, p32; Vargo and Lusch 2004). By contrast, the emerging ‘resource-based’ paradigm: (1) postulates resource-based models of exchange and competition (Hunt 2002); (2) theorises that “economic exchange is fundamentally about

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service provision” (Vargo and Lusch 2004, p.326); and (3) seeks to embody Levitt’s (1972) early claim that everybody is in service. Thus, it is postulated, goods are appliances or components used in service provision and service itself is the superordinate category (Rust 1998; Vargo and Lusch 2004, p.326). The potential implications for this study are twofold. First, if validated, resource-based theory indicates knowledge-based provision (Conner and Prahalad 1996).

Service outcomes, it suggests, are a function of

processes by which (1) knowledge is blended and used and (2) learning and/or development occurs through interaction (Conner and Prahalad 1996). Accordingly, it indicates heightened effects for service attributes such as technical expertise (2.8.3) and functional interaction quality (2.8.4); Second, in the strategy literature, the counterpoint to knowledge as “a basic source of advantage in competition” (Conner and Prahalad 1996, p.478) is the opportunistic view of the firm. Where opportunism accompanies investment, it may act as a negative driver of performance outcomes (Williamson 1975).

Accordingly, it

underpins the incorporation of perceived opportunism as a component of reputation quality (2.8.5). 2.10.3 Service Taxonomy and PBS Characteristics The preceding points apply to services in general. However, the theory of typology specificity postulates that any industry, or industry cluster, will also exhibit shared differentiating characteristics (Lapierre 1996; Olorunniwo, Hsu and Udo 2006). In the PBS case, by general consent the cluster includes inter alia e.g. law, accountancy and management consultancy, medical, investment banking and marketing creative services such as public relations/advertising (Maister 1993).

However, the study of service

classification, and its implications, remains limited. Among four illustrative contributions, first the non-empiric ‘service continuum’ highlights the pure, intellectual or knowledge-based basis of the PBS cluster (Lovelock 1983, Shostack 1977, Figure 2.9). PBS are, in one memorable phrase, ‘products of the mind’ (Sinclair 1982).

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Figure 2.9: The Goods-to-Service Continuum

Capital Goods

Retail

Consumer Goods

Professional Services

Service

Source: based on Lovelock (1983).

The second aligns service classification with the knowledge-based view (Conner and Prahalad 1996) by proposing key parameters measuring the: (1) level of consultant labour required; and (2) degree of client-consultant interaction generated (Schmenner 1986). Necessarily this would be ‘high-high’ in the PBS case. Third, a more recent, empiric and multi-cultural client perceptual taxonomy (Lu 2002) subordinates hypothesised factors to two principal parameters which align with the technical and functional dimensions of Perceived Service Quality (2.8.3-2.8.4): (1) Based on the sociological concept of social time, the functional relative time elapsed until a client achieves core service benefits (Lewis and Weigert 1981); and (2) The client’s ‘level of perceptibility of the core (technical) service’, including involvement generated (Lu 2002, p.44, Figure 2.10). Deploying these parameters generates four major service clusters. For example ‘Sparkling’ services (e.g. a restaurant) are characterised by high service visibility and a short duration to benefits. Conversely ‘Investing’, which includes PBS, possesses low service perceptibility and a long duration to benefit achievement. Additionally within the PBS cluster, to differentiate individual firms, a third reputational parameter is also required e.g.: (1) expertise (Lovelock 1983); (2) problem-solving capability (Ojasolo 2007); or (3), more broadly, perceived professionalism (Thakor and Kumar 2000). © Bill Nichols 2009

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Figure 2.10: Taxonomy of Services

Sparkling

(Restaurants, Theatres, Telecoms)

High

Core Service Perceptibility

Decisional (Education, Insurance))

Trusting ((Hairdressing, Travel))

Investing (PBS, Consulting, Advertising)

Low Immediate

Extended

Time to Realisation of Cost Service Benefits Source: Lu (2002).

Fourth and finally, this reputational dimension of ‘perceived professionalism’ is fundamental to a formal, empiric PBS domain definition. It captures performance, knowledge and expertise/professionalism within explicit boundaries as: “Performances of assignments or service agreements which apply some form of expert or tacit knowledge by professionally accredited and affiliated service providers, whose relationship with buyers… is fiduciary. The service provider should have a distinct economic identity…” (Conchar, 1998, p.256, ital added).

This observed dimensionality aligns the classification of PBS with the overall tri-dimensional framework discussed above (2.8.2, Table 2.7). Moderating implications for a model of ‘loyalty’ antecedence are discussed next. 2.10.4 Moderators A moderator (M) serves to mitigate/enhance the effects of a predictor variable (X) on a criterion (Y) (MacKinnon 2008) e.g.: Client-Buyer Experience (M) versus Trust (X) on LoyaltyCommitment (Y). By exploring moderation we test both for: (1) an individual respondent’s © Bill Nichols 2009

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personal functional sufficiency (Cohen and Reed 2006); and (2) a model’s overall generalisability (MacKinnon 2008).

Given the just-discussed PBS extended duration

characteristic (2.10.3), PBS respondent observation occurs typically intra-consumption as opposed to retails’ more normative pre- or post-consumption. (Back at Bernie’s Brasserie it is unlikely, say, that we would interview John during his meal.) This context highlights a number of potential moderators: First the effects of both client-consultant (Personal-) and client-consultancy (Firm-) Relationship Duration (PRD/FRD) may vary/differ over time (Doney and Cannon 1997; Grayson and Ambler 1999).

Duration may be a ‘’seed” of relationship

“destruction” (Grayson and Ambler 1999, p. 139). For example, extended PRD/FRD may reverse the generally positive direction of a Trust-to-Loyalty-Commitment relationship. Second Interaction Frequency (IF) - as measured per unit of time i.e. X face-to-face meetings per week (Hocutt 2000, 1998; Palmatier et al 2006) – may increase service visibility but “breed boredom and a desire for new ideas” (Moorman et al 1992, p. 323). Increased IF may, for example, invert a positive Trust-to-Perceived Value relationship; Third the level of Client-Buyer Experience - in procuring services from multiple consultant-vendors over time (Davies and Palihawadana 2006; Patterson and Spreng 1997) – may mitigate previous moderators as it increases; and Finally, consistent with its complex Janus-like status (2.6), Trust may also acts as a moderator and metamorphosise over a relationship’s life-time – potentially from early relationship enabler to mature-stage negative inhibitor of positively perceived service attributes (Hocutt 2000, 1998). In summary, it is “both theoretically and methodologically important to ascertain the stage of a relationship before performing a causal analysis” (Grayson and Ambler 1999, p. 139). [For discussion of operationalisation, further below 4.5.10].

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2.11 Theoretical Underpinnings and Model Domain Boundaries 2.11.1 Overview Beyond the actors (key constructs) and immediate situational influences (moderators) discussed above (2.3-2.10), any final ‘play’ (model of construct/moderator relationships) requires the researcher to: Make initial choices/assumptions regarding the underlying rules (boundary domains and their principal theoretical contributions); and Identify their likely implications for the postulation/interpretation of key model relationships. Accordingly this section summarises three major theoretical streams and their relevant contributions: Consumer- and organisational buying behaviour (CBB/OBB):

identifying a

contingent PBS model (OBB/P) and its implications for this study’s governing clientperceptual framework (2.11.2); Relationships: generating insights regarding the central client-consultant (vendorbuyer) engagement from equity, social exchange and adaptation level theories (2.11.3); Attitudinal modelling: (1) focusing on the critical attitudinal link between attribute assessment and intentions/behaviour and (2) indicating potential refinements to the mainstream tradition (theories of reasoned action and planned behaviour) that underpins much extant literature (2.11.4). 2.11.2 Buyer Behaviour Theory Established theory discriminates consumer- and organisational buying behaviour (CBB/OBB) (Kotler 1994; Lilien, Kotler and Moorthy 1992) but neither profiles accurately the professional case (OBB/P).

For example, typical OBB/considered purchase theory

presupposes a large manufacturing business (Wilson 2000). Consequent buying complexity (Chofray and Lilien 1980; Sheth 1973; Webster and Wind 1972) is characterised by: (1) multi-person decision-making units (DMUs); (2) derived demand (end-client to supply

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chain); (3) extended (months/years) contractual negotiation; and (4) ‘satisfaction of a total need’ (Lilien et al 1992, p.137).

However, only the last applies to the typical individual

OBB/P client who is rarely inclined to protracted negotiation and whose custom-unique demand is usually direct (Maister 1993). Conversely, in CBB, the individual buyer is the focal point. Founded on the theory of behavioural learning (Skinner 1938), its most influential model (Howard and Sheth 1969), it organises consumer response by reference to: (1) outputs/actions (know that); (2) process/interaction (know how); and finally (3) significative, symbolic or social inputs such as quality, distinctiveness and reference groups (know why).

These dimensions are

consistent with the prior-discussed tripartite modes of client experience and attribute assessment (2.8.2). This CBB model is, however, limited by: (1) a predominantly ‘impulse’ view of consumer behaviour; and (2) mixed empirical findings (Lehmann, O’Brien, Farley and Howard 1974, p.51). To accommodate the individual but considered OBB/P buyer, a more recent contingency formulation identifies inter-tradition social and commercial commonalities (Doyle 1994; Wilson 2000) and adapts an alternative CBB task model (Robinson, Faris and Wind 1967, Figure 2.11):

Figure 2.11: An Integrated Framework for Buyer Behaviour Corporate Golf

Exceptional

Professional Services

Treats Company Cars

Exceptions/Considered Retail Therapy

Housing, Education

CBB/OBB

Purchasing Significance

Corporate Travel Gadgets, Magazines

Impulse

Chores Groceries, Petrol

Stationery Magazines

Routine

Leisurely Traditional OBB Traditional CBB

Source: based on Wilson (2000).

Purchasing Mode/Style

Professional

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It concludes that: For items of exceptional significance (ranging from a child’s education or real estate in both cases to PR consultancy), both individual consumer and business buyers may act in a professional considered mode; and Accordingly, in the PBS context, it is acceptable to apply the insights of both OBB and CBB streams. 2.11.3 Human Relationship Theory The second major theoretical underpinning, relationship theory, offers three principal contributions: (1) equity (Adams 1963); (2) social exchange (Homans 1958); and (3) adaptation-level/ expectancy disconfirmation (Helson 1964). The first, equity, postulates that human beings believe that rewards/punishments should be distributed proportionately to the level/value of contributions (Adams 1963).

Although

limited by a propensity to exaggerate fairness in social relationships (Lawler 2001), its effects are likely to increase in a PBS-type environment characterised by ‘fuzzy’ outcomes (Ojasolo 2001). It: (1) influences generally both the business-to-consumer (B2C) and clientagency P2B streams (2.12, 2.15.3); (2) provides a primary direct source for some early empiric B2C studies (e.g. Oliver and Swan 1989) as summarised by Szymansky and Henard (2001); and (3) supplies principal underpinning for the construct of Perceived Value (2.6.2). The second, social exchange theory (‘SET’), presupposes more formal, if ultimately subjective, human cost-benefit analysis (Homans 1958; Kelley and Thibaut 1978). It postulates that perceived relationship outcomes are a function of associated rewards and costs (Gottman 1998) including social assets/liabilities and positive/negative cognitions and behaviours (Rusbult 1983). Extensions also incorporate affect or emotion (Allen, Machleit and Kleine 1992) and, as part of overall relational development, the concept of sociallyorientated relationships (Murstein, Cerreto and MacDonald 1977).

It provides: (1)

comprehensive underpinning for both the B2B and P2B streams (2.13; 2.15.3) and specifically (2) the construct of Trust (2.7).

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Third and finally an important SET variant, interdependence theory, shares theoretical foundations in adaptation-level theory (Helson 1964) with the expectancy-disconfirmation paradigm (2.9). It incorporates partners’ expectations (Kelley and Thibaut, 1978) and postulates that where actual relationship outcomes exceed expected outcomes, individuals are more likely to experience ‘relationship satisfaction’. This predicates a significant role for Disconfirmation in the determination of all principal attitudinal constructs.

Relevant

attitude theory is the subject of the next section. 2.11.4 Attitude Theory Attitude modelling theory has had the “most profound impact” on buying behaviour theory (Cohen and Reed 2006; Lilien et al 1992, p22). Attitudes are unified and enduring states of readiness to respond (Eagly and Chaiken 1993). They reveal learned predispositions to behave consistently with respect to given objects (Schiffman and Kanuk 1996).

In a

compensatory model, by summing cognitive assessments of all qualifying attributes (Perceived Service Quality, 2.8; Lilien et al 1992), attitudes bring meaning, or shape, to otherwise chaotic and unorganised experience (Katz 1960). Adopting this approach for high-involvement PBS type considered services (Wilson 2000, 2.11.2) is well supported (Bettman 1979). Among three key contributions in this stream, the first, the formerly dominant Theory of Reasoned Action, ‘TRA’, (Fishbein and Ajzen 1975) provides often unacknowledged theoretical underpinning to perhaps the majority of research studies in both B2B and B2C streams. It postulates that: Intentions formation is a function of the aggregate effects of attitude, social normative beliefs (SNBs) and individual motivation (Figure 2.12); Initially cognitive attributes are mediated by affective ones to generate conative outcomes (Eggert and Ulaga 2002) – the complementary ‘mediated impact model’ (MIM). TRA’s generalisability is corroborated by an 86-study meta-analysis reporting an adequate average .54 correlation between (selected) intentions and actions (Sheppard, Hartwick and Warshaw 1988),

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Figure 2.12: Simplified Model of Intentions Formation Motivation Su Subjective Normal Beliefs (2.4.5)

Intentions

Attributes Attitudes

Attributes

Attitudes

Intentions

Source: based on Fishbein and Ajzen (1975).

TRA’s application provides theoretical explanations for key issues arising from the prior discussions of key constructs.

For example, if attitude and SNB conflict, dissonance

reduction theory predicts that a client will align the most flexible driver with the least (Festinger 1957).

Thus a client’s dissatisfaction (attitude) will not necessarily trigger

consultancy termination if he must also respect other colleagues’ extended relationship duration (Nadeem 2007).

Conversely, the moderation of high motivation (MC) – in the

shape say of his extremely negative Trust - may enforce termination, concerns notwithstanding (Sheppard, Hartwick and Warshaw 1988, p.325). Since TRA presupposes voluntary client behaviour, a second and later contribution, the Theory of Planned Behaviour (TpB, Ajzen 1991, 1988, Figure 2.13), acknowledges involuntary cases (e.g. budgetary constraint) by incorporating the concept of perceived behavioural control (PBC). In the PBS case, this supports the inclusion of a compulsion indicator as part of the operationalisation of Loyalty-Commitment (2.3).

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Figure 2.13 – Theory of Planned Behaviour

Attitude to Behaviour

Knowledge

Subjective Norms

Behavioural Intention

Behaviours

Perceived Behavioural Control

Source: based on Ajzen (1991, 1988).

A third and final contribution partly challenges this orderly TRA/TpB structure and offers: (1) both a partial re-interpretation of the basis for the proposed Convergent Model; and (2) theoretical underpinning for the Competing Model. Its premises are that: (1) Recent neuroscientific and psychological evidence demonstrates that emotion precedes cognition (Boyatzis and McKee 2005). This may amend traditionallyinferred TRA/TpB relationship directions: e.g. retrieved, primarily affective Trust may precede, or influence reciprocally, a primarily cognitive assessment (e.g. Perceived Value); (2) Multiple stimulation mechanisms - e.g. personal experience, transmitted information and inferential/analogical reasoning - may generate multiple attitudes towards the same object (Cohen and Reed 2006; Lord, Paulson, Sia, Thomas and Lepper 2004; Figure 2.6). “Even oppositely valenced attitudes toward the same object may coexist” (Cohen and Reed 2006, p3): e.g. negative Transactional Satisfaction and positive Cumulative Satisfaction (2.4). Once competing attitudes are embedded, retrieval is contingent e.g. a function of environment, relationship longevity or strength (Tulving and Thomson 1973);

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(3) Instability is often latent – particularly under intra-consumption observation. It may occur through e.g. cognitive re-organisation, changes in belief structure and/or disassociation (Wilson and Hodges 1992). For example, progressive disassociation under the moderation of extended Personal Relationship Duration (2.10.3) may trigger decline in a client’s advocacy (East, Gendall, Hammond and Lomax 2005; Grayson and Ambler 1999). This may apply to a full range of moderators (Johnson, Herrman and Huber 2006; Mittal, Kumar and Tsiros 1999). These developments are accommodated by the MPAA (multiple pathway anchoring and adjustment) model (Cohen and Reed 2006, Figure 2.14).

Figure 2.14: MPPA Attitude Formation Context and Mechanisms Object/Issue Aspects Salient

Outside-In Attitude Formation Mechanisms

Exposure to Concept in Context

Attitude Formation and Storage

Personal Factors Salient

Inside-Out Attitude Formation Mechanisms

Source: Cohen and Reed (2006).

Emerging MPAA evidence (Cohen, Reed and Belyavsky 2005) suggests that a final intention is the product of two tests: 1. Representational sufficiency: is the response “well-formed and coherent… as opposed to some hazy, vague thought” (Cohen and Reed 2006, p.11)? And,

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2. Given the first, functional sufficiency or readiness to act: is the personal attitude coherent and is the underlying value closely aligned with the behaviour? (Figure 2.15).

Figure 2.15: MPAA Attitude Recruitment/Retrieval and Assessment Attitude Recruitment Successful Retrieval – Accessible Attitudes

Unsuccessful Retrieval – Inaccessible

Attitude Guides to Behaviour

CONSTRUCT ATTITUDE from available information ATTITUDE Reconstruction

Yes

Retrieve Relevant Evaluative Information

Conflict Identified?

Attitude Adjustment – Conflict Resolution

Representational Sufficiency

Functional Sufficiency

Yes

Source: Cohen and Reed (2006).

On this basis, to re-state the earlier example, before acting on affective representational dissatisfaction with the consultancy (and possibly terminating its contract), a client will test the attitude versus his overall level of functional commitment. Thus final behavioural intentions may be captured by an integrated representational-functional construct (e.g. Loyalty-Satisfaction).

2.12 Models 1: The B2C Stream 2.12.1 Overview This and the following section (2.13/’B2B Stream’) explore illustrative extant explicatory models of loyalty and its antecedence. Rather like ‘plays’, they offer interpretations of relationships between key actors (constructs) and immediate situational influences (moderators).

Each stream also adopts its own domain boundary rules (theoretical

underpinnings (2.11, Table 2.1).

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This section addresses in sequence (1) the emergence of B2C multivariate models (2.12.2) and (2) the currently most comprehensive formulation, the European Customer Satisfaction Index (ECSI, Martenson, Gronholdt and Kristenson 2000, 2.12.3). The latter highlights potential for cross-stream integration or convergence. 2.12.2 B2C: The Emergence of Multivariate Models The 40-year old B2C post-purchase effects stream is founded on both equity and social exchange relationship theory (2.11.2) and makes extensive use of the expectancydisconfirmation paradigm (2.9, 2.11.2).

Its focal construct is Satisfaction. A widely-

deployed academic/practitioner metric (Gupta and Zeithaml 2006), it is the subject of (1) over 40 different scales (Danaher and Haddrell 1996) and (2) more than 500 relevant correlations (Szymansky and Henard 2001). By contrast, just 5.6% of these correlations incorporate consequences such as ‘loyalty’. The majority of contributions focus on key bivariate relationships e.g.: (1) Satisfaction→Firm Performance Outcomes; (2) Satisfaction→Loyalty (see discussions 2.5.2; Keiningham et al 2007; Morgan and Rego 2006); and (3), most commonly, Perceived Service Quality→Satisfaction (Garbarino and Johnson 1999; Szymansky and Henard 2001). Collectively, these studies are sometimes loosely aggregated as part of a service-profit chain (Allen 2006).

Empiric findings are mixed.

For example predictive antecedents (e.g.

Perceived Service Quality, Satisfaction) correlate inconsistently with postulated criterion variables such as profitability (Gale 1994; Westbrook 1997) or ‘loyalty’ (Dick and Basu 1994). Generalisability is often also limited (Anderson, Fornell and Mazvancheryl 2004; Smith and Wright 2004). Seeking improved explication, increasing sophistication is reflected in the growing number of B2C multivariate empiric studies (e.g. Bolton and Drew 1991; Boulding, Kalra, Staelin and Zeithaml 1993; Hellier et al 2003; Hocutt 1998; Price, Arnould and Tierney 1995; Storbacka, Strandvik and Gronroos 1994). A germane case conducted in PBS management consultancy adds Perceived Value to Cumulative Satisfaction to achieve a resulting increase in R2, (.75 versus .43, Figure 2.16):

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Figure 2.16: Perceived Value, Cumulative Satisfaction and Loyalty-Intentions

Outcomes

34 (

Methodology

21(3.85)

(7. 63

4.9 4)

) 91 0) .4 (5

16 (2 .5 0)

Service

57(8.44)

) 37 (2. 11 16 (2 .5 0)

) 49 5. (1 88

Satisfaction

Intentions

34(4.4 1)

Value

11 (2.

17 )

Relationship

8) 3.5 24(

16( 2.7 40 5) (5 .4 9)

Global

Problem identification

Completely standardised co-efficients, t-values in parentheses. Chi-square = 35.30, 43 df

Source: Patterson and Spreng (1997).

2.12.3 The European Customer Satisfaction (ECSI) and Extensions The B2C stream’s most comprehensive solution to date is the European Customer Satisfaction Index (ECSI). Based on prior American Customer Satisfaction Index research (ACSI, Fornell et al 1996), it was developed by a pan-European team (Martenson, Gronholdt and Kristenson 2000, Figure 2.17).

The underpinning econometric model ties latent

variables to a customer satisfaction index (ECSI).

Consistent with this study’s prior

discussion of key constructs and theoretical underpinning, it incorporates seven major constructs. Satisfaction is the dependent of: Expectations (Disconfirmation) which is jointly mediated by: o Perceived Value; and o Three discriminated dimensions of Perceived Service Quality including:



Image (or reputational quality);



Perceived Quality of ‘hardware’ (technical quality); and



Perceived Quality of ‘software’ (functional quality).

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It is also the antecedent of: Loyalty (and) Complaints (or negative Loyalty-Advocacy).

Figure 2.17: European Customer Satisfaction Index

Source: Ball, Coelho and Machas 2004

Pan-European ECSI sector corroborations include retail banking, cable TV, fixed and mobile ‘phones, supermarkets and public transport (Ball, Coelho and Machas 2004). For example, in one 11-bank study, explanation of Satisfaction variance (R2) achieves a very acceptable 61.1-70.7% per bank range (Ball et al 2004). The ECSI model is less successful in explaining ‘loyalty’. Comparable banking study variance explanation (R2) ranges 48.1-52.9% (Ball et al 2004). This may be attributable in part to: (1) Path models by which ‘loyalty’ is a dependent only of Satisfaction, brand/firm image (or reputation quality) and complaints; and (2) The exclusion of one major construct, Trust (more commonly found in the B2B stream), from the earlier identified set. Trust’s introduction, in a suggested extension, results in modest Loyalty R2 improvements (+1.5-5.3%) in the banking case (Ball et al 2004; Vilares and Coelho 2003; Figure 2.18) and highlights the potential for cross-stream convergence.

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Figure 2.18: ECSI Extended

Source: Ball, Coelho and Machas 2004

2.13 The B2B Stream 2.13.1 Overview Following preceding discussion of the B2C stream (2.12), a second major modelling contribution is provided by the predominantly B2B interorganisational performance stream. Principal theoretical underpinning is provided by social exchange theory (‘SET’, 2.11) as executed in the relationship marketing paradigm. This section discusses relationship marketing and initial multivariate B2B models of loyaltyintentions (2.13.2) and two key contributions from the ‘trust-commitment’ literature: a. The MZD model (Moorman, Zaltman and Deshpande 1992, 2.13.3); and b. The KMV model (Morgan and Hunt 1994, 2.13.4). Further, building on the ECSI model (2.12.3), it demonstrates inter-stream communalities and concludes with a putative synthesis in terms of the components of the domain of Loyalty-Commitment. This lays the basis, in turn, for the following ‘case for convergence’ (2.14).

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2.13.2 The Relationship Marketing Paradigm The relationship marketing (RM) paradigm was first popularised in the early 1990s (Fruchter and Sigue 2004; Knemeyer and Murphy 2005). The focus of prolific study, it has inspired such initiatives as Customer Relationship Management or CRM (Palmatier et al 2006; Rao and Chad 2002). Its principal hypothesis is that by cultivating B2B relationships over time, firms engender intrinsic value (Blau 1964). Key relationship stages include e.g. awareness, engagement, maturity etc. (Crosby, Evans and Cowles 1990; Morgan and Hunt 1994). The paradigm aggregates all "activities directed at establishing, developing, and maintaining successful relational exchanges" (Morgan and Hunt 1994, p.22). Its scope includes: all focal relationships (Gummesson 2004; Morgan and Hunt 1994); relational marketing (Dwyer, Schurr and Oh 1987); relational contracting (MacNeil 1980); and collaborative partnerships and strategic alliances (Day 1990). A key research topic, and this study’s focus, is the nature of client-vendor/consultant exchanges generated by evolving relationships (Christopher, Payne and Ballantyne 1991; Dwyer, Schurr and Oh 1987). One meta-analysis summarises 111 independent samples and some 637 empirically-tested construct relationships (Palmatier et al 2006). Mixed initial research findings (Fournier, Dobscha and Mick 1998) generated scepticism about RM investment (Colgate and Danaher 2000). However relationship exchange is complex.

It extends, for example, across a broad transactional→relational spectrum

(Anderson and Narus 1991) and may also be moderated by individuals’ own levels of personal relational orientation (Dwyer et al 1987). One productive response developed Trust-based multivariate models. Consistent with the theory of reasoned action (2.11.3), it demonstrates that Trust is a critical antecedent of both (1) intentional (Loyalty)-Commitment (Achrol 1991; Morgan and Hunt 1994; Zinedin and Jonsson 2000) and (2) desirable firm-based relational outcomes e.g. inter-firm co-operation and reduced uncertainty (Gundlach, Achrol and Mentzer 1995; MacNeil 1980). contributions are reviewed in following sections.

Key

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2.13.3 Trust-Commitment 1: The MZD Framework The first key contribution in the B2B stream is - unusually - the subject of both initial validation and formal replication. Both also occur under conditions of PBS moderation (2.10): respectively advertising (Grayson and Ambler 1999) and market research (Moorman, Zaltman and Deshpande 1992). Five key findings emerge: (1) Both confirm that Trust is (at least in one role) an antecedent to Commitment; (2) Both indicate the complex nature of Trust by reporting it antecedent to attribute constructs of (a) ‘consultant involvement’ and (b) ‘interaction quality’; (3) Both support a direct attribute-intentions path for ‘interaction quality’ (b) to (Loyalty)-Commitment; (4) The replication alone supports a direct attribute-intentions path from ‘consultant involvement’ to Loyalty-Commitment (Grayson and Ambler 1999; Figure 2.19). This may indicate an intra-consumption effect given advertising’s continuous service provision (2.10) versus the common assignment-by-assignment basis of market research; and

Figure 2.19: The MZD Model of Trust/Commitment

Source: Moorman, Zaltman and Deshpande (1992).

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(5) Finally the replication also finds that so-called ‘dark-side’ attributes - such as (a) perceived loss of objectivity, (b) perceived opportunism and (c) rising expectations - may act as partial negative mediators of Trust. This outcome is consistent with opportunistic firm theory and the earlier role postulated for ‘perceived opportunism’ as a component of reputational quality (Williamson 1975, 2.10.2). These findings enrich understanding of loyalty-formation.

A major role for Trust is

confirmed in line with calls for its inclusion in the B2C ECSI model (2.12.3). Similarly, potential instability in the traditional linear attribute-attitude-intentions sequence (Akerlund 2005) is supported consistent with the MPAA model (2.11.3). 2.13.4 Trust-Commitment 2: The KMV Model The principles of the MZD model (2.13.3) are extended by a second major contribution to B2B/Trust-Commitment Theory, the widely-cited Key Mediated Variable [KMV] model (Morgan and Hunt 1994). It has three well-supported main postulates: (1) Trust and Commitment are intentional future-directional constructs (willing to commit, willing to trust); (2) Both are joint mediators of five precursor relational attributes and coantecedents of five behavioural outcomes; and finally (3) Commitment is also a part-mediator of Trust’s effects on those outcomes (Morgan and Hunt 1994, Figure 2.20). Overall KMV nomological validity is supported in an original retail tyre study (Morgan and Hunt 1994) and corroborated in a series of replications/part-replications including: retail (Wong and Sohal 2002); retail banking (Adamson, Chan and Handford 2003); not for profit (Macmillan, Money, Money and Downing 2005) and logistics (Morris and Carter 2005). Only one known study reports some limitations (Auh 2005). Further one KMV extension also validates a positive association between relational behaviours and performance outcomes (Morris and Carter 2005) – thus addressing a deficiency in the prior MZD model studies (2.13.2).

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Figure 2.20: Trust-Commitment Theory - The KMV Model

Termination Costs

Relationship Benefits

Commitment

Outcomes

Shared Values

Communications Effectiveness (Interaction Quality)

Trust

Money (Opportunistic Behaviour)

Source: Morgan and Hunt (1994).

These findings have three implications: 1. Given that increased Trust associates strongly with both co-operation enhancement and the mitigation of both conflict and decision-making uncertainty, it is reasonable to conjecture that conditions of PBS continuous service provision (as in the two MZD-model studies) will enhance its effects; 2. They support postulated relational enrichment (e.g. opportunism/altruism) of the service attribute platform in any integrated study (2.8): a. Both ‘communications effectiveness’ and ‘relationship benefits’, the latter specified to include transactional Satisfaction, are in principle components of Moorman et al’s (1992) interaction quality; b. ‘Relationship termination costs’ is a proxy for perceived dependence or switching costs in the B2C/PPE literature (e.g. Patterson and Smith 2001a). 3. Finally, given that the five KMV relational behavioural outcomes are consistent with key components of the postulated domain of Loyalty-Commitment (Table 2.11), a

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putative

synthesis

between

the

B2C

post-purchase

effects

and

B2B

interorganisational performance streams is supported.

Table 2.11: Loyalty-Commitment and Relational Behaviours

Behavioural Intentions

Forward Looking Indicator

Definition

Source(s)

Consequent Relational Behaviour Addressed (Morgan and Hunt 1994)

Repurchase Intentions

Intend to maintain relationship and make next purchase with supplier

Zeithaml et al 1996

Propensity to leave' perceived likelihood of termination

Bansal et al 2004; Fullerton 2003

Acquiescence - degree to which a partner accepts or adheres to another's requests.

Switching Intentions/Dependence

Exclusive Intentions

Attitudinal Intentions

Cognitive Intentions

Able to switch to another supplier in same category, level of dependence Client's intention to dedicate all category purchases to a particular SP; enduring to maintain a valued relationship

Oliver 1999; Moorman et al 1992

Relative attitude, strength of preference

Considering supplier first choice among alternatives

Zeithaml et al 1996; Dick and Basu 1994

Altruism (negative opportunism)

Accommodating partner with non-opportunistic intentions

Dick and Basu 1994; John 1984

Willingness to recommend (personally)

Willingness to act as (personal) advocate on behalf of the supplier

Butcher et al 2001

Willingness to recommend (publicly)

Readiness to act, even overcome obstacles

Oliver 1999

Feelings of attachment

Enduring desire to maintain a valued relationship

Exclusive consideration

Will consider only one supplier for this type of service

Moorman et al 1992 Gremler and Brown 1996; Dwyer et al 1987

Identification

Thinking of the supplier as an extension of buyer/buyer's team

Butcher et al 2001

Source: Author.

Co-operation - working together to achieve mutual goals.

Functional Conflict the positive outcome of conflict managed

Reducing Uncertainty via mutual trust and confidence.

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2.14 Models 3: A Convergence Model The preceding sections devoted to theoretical underpinnings and the two principal B2B and B2C streams (2.11-2.13) exhibit significant commonalities e.g.: (1) social exchange and equity foundations; (2) implied operationalisation of the key dependent variable of LoyaltyCommitment; and (3) complementary contributions to a common PBS attributes platform. This suggests commensurate agreement sufficient to apply metatriangulation theory (Lewis and Grimes 1999). The goal is to generate new convergent theory by (1) integrating multiple paradigms and (2) resolving inter-stream differences among both construct-sets and their relationship configuration. Such a move responds to calls to open the loyalty ‘black box’ (Lin and Ding 2005, p.60). it is also consistent with much emerging research practice including: (1) Calls to incorporate both Trust and ‘communications’ in the established ECSI model (Ball, Coelho and Machas 2004); (2) KMV’s modelling of Satisfaction as an antecedent of Trust (Morgan and Hunt 1994); (3) Co-modelling of key attitudinal constructs cross-stream in a range of studies e.g.: a. Satisfaction and Trust (Lin and Ding 2005); b. Satisfaction, Trust and (Loyalty-)Commitment (Beloucif, Donaldson and Kazanci 2004); and c. Cumulative Satisfaction and (Loyalty-)Commitment (Gustafsson, Johnson and Roos 2005; Verhoef 2003). Further a number of studies highlight potential convergence by postulating a single superordinate attitudinal construct of ‘relationship quality’ (e.g. Crosby, Evans and Cowles 1990; Dorsch, Swanson and Kelly 1998; Hennig-Thurau and Klee 1997). The last also incorporates Perceived Service Quality on the basis that “exchange is a fundamental precondition of a relationship” (Hennig-Thurau and Klee 1997, p.751). No known study yet incorporates Perceived Value. Based on these principles and developments and the detailed prior discussions of relevant constructs (including the transactional/cumulative Satisfaction discrimination), a postulated Convergence Model is presented at Figure 2.21:

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Figure 2.21: A Postulated Convergence Model Moderators Disconfirmation (Expected Quality)

Perceived Service Quality

Perceived Value

Moderators

Cumulative Satisfaction

Transactional Satisfaction

LoyaltyCommitment

Trust

Attributes

Attitudes Representational Sufficiency

Intentions Functional Sufficiency © Bill Nichols – Henley BS - 2009

Source: Author.

2.15 Models 4: The Case for a Competing Model 2.15.1 Overview Thus far this review adopts a positivist bias, applies the tenets of logical empiricism (Carnap 1936; Hunt 1991, pp.279ff, below Chapter Four) and identifies the commonalities of two major traditions (2.13). Further, consistent with metatriangulation theory, it integrates these multiple paradigms to postulate a new ‘Convergence Model’ of PBS LoyaltyCommitment antecedence. However, this outcome carries significant attendant risks or, philosophically, ‘inadequacies’ (Scruton 1994, p.151). Accordingly this section: First presents a critique of the Convergent Model and a case for anti-thesis (2.14.2); Second, provides a theoretical part-framework for a Competing Model based on a third and smaller P2B client-agency relations stream (e.g. Beard 1999; Henke 1995), a branch of the wider buyer-seller group (Dwyer, Schurr and Oh 1987; Wilson 1995); and

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Third, presents a Competing or ‘Goodwill’ Model. 2.14.2 Critique of Convergence: The Case for Anti-Thesis The prior case for the Convergence Model exhibits (at least) four potential inadequacies: the nature of observation, complexity, instability and, finally, the application of natural language systems. Collectively they suggest a competing point of departure. First, empirically, the convergent metatriangulation thesis is founded almost exclusively on post-consumption observation data. Conversely PBS intra-consumption observation is the subject of rare prior investigation (e.g. Davies and Palihawadana 2006).

Accordingly the

nature and/or configuration of constructs may vary. Second, the seven-construct Convergence Model is complex and far from parsimony. Most predecessor studies configure far fewer constructs. As a principle, such complexity often generates disorder – in this case potentially overlapping domains - and, ultimately, analogous to the second law of thermodynamics, entropy (Delavigne and Robertson 1994). Restated: in structural model language, in excessive degrees of freedom lies potential confusion. Third, to borrow from structuralism, while prior theory may support ‘surface’ order in the model, the deep structure may be inherently unstable and militate against a successful outcome (Althusser 1968). In the world of social scientific investigation: “instead of the domain of observables for any construct being tightly defined initially, more likely the nature of the domain will be suggested by numerous attempts to develop particular measures” (Nunnally 1978, p.99).

Amid such instability, to extend complexity by

reconceptualising one key construct is challenging. To accomplish it simultaneously with several may be supported empirically but only partly achievable. Fourth and finally, even implied surface order may be an illusion fostered by natural language labels. Such labels lack the “precision necessary for developing theoretically meaningful scientific concepts” (Hunt 1991; Teas and Palan 1997, p.52). Even limited validity is subject to erosion as indicators are amended, removed and replaced. “To speak a language is to commit ourselves to the double indeterminacy due to our reliance both on its formalism and on our own continued reconsideration of this formalism in its bearing on our

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experience” (Polanyi 1958, p.95). For example, to continue to label as Trust the two items remaining in Grayson and Ambler’s (1999) replication of the five-item original scale (Moorman et al 1992) is doubly misleading: (1) both to the items themselves which, semantically, describe attributes of empowerment; and (2) to a model which relies partly on a concept of ‘trust’ for its meaning. In summary, following the Hegelian principle, these four ‘inadequacies’ serve to ‘determine’ a more parsimonious outcome – particularly in the delineation of client attitude and intentions. Accordingly, while it is likely that the data will support many Convergent Model paths, a simpler outcome may provide better fit. 2.15.3 The P2B Stream: Client-Agency Relations Empirically the basis of a Competing Model is provided by the P2B stream. P2B theoretical underpinning is strongly founded on equity theory (Adams 1963; 2.11.3). Its principal goal is to explicate a complex operational climate in which client switching behaviour is endemic (Kulkarni, Vora and Brown 2003). Key findings are summarised in a series of useful reviews (Davies and Prince 2005; Halinen 1997; Palihawadana and Barnes 2005; Waller 2004). Principal constructs are Trust, Perceived Value and a relational or ‘functional’ component of Perceived Service Quality. Conceptually the stream is premised on the PBS moderating principle of outcome ambiguity (Lu 2002; 2.10.3). Clients often lack relevant technical competency (Ojasolo 2001; Swan, Trawick, Rink and Roberts 1988), find it difficult to assess outcomes objectively (Halinen 1997) and, as a result, encounter performance ambiguity (Halinen 1997). Such ambiguity, in turn, highlights any tangible critical service incident. Take, say, a failed corporate event. It will attract a client global emotion e.g. negative Trust. If: (Scenario 1) it now competes with oppositely valenced positive Perceived Value founded on complex (ambiguous) technical advice, it will prove more potent. The consequent critical incident dissatisfaction (final ‘functional sufficiency’) is the most commonly cited cause of relationship dissolution (Beard 1999; Davies and Prince 2005; Henke 1995; Mitchell, Cataquet and Hague 1996; Waller 2004);

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(Scenario 2): conversely, if Perceived Value is strongly and unambiguously anchored it should mitigate the risk of service incident ‘scapegoating’. Recent empiric evidence identifies three critical functional quality anchors (Davies and Palihadawana 2006): Strategic counsel: overall functional process including Patterson and Spreng’s (1997) PBS problem identification component; Constant account status information: functional aggregation of responsiveness, reliability and communication; and Consistent work processes: functional dependability component but also, following cue utilisation theory, manifesting interim outcome quality as an offset to ambiguity (Babakus and Boller 1992; Dabholkar, Thorpe and Rentz 1996).

The last is the strongest predictor of the consultant’s ability to both (1) manage client expectations and (2) achieve retention if supported by positive moderating effects e.g. Client-Buyer Experience (Davies and Palihawadana 2006, 2.10). In summary, in the P2B stream Trust plays a rich role.

It is at once (a) the

antecedent/initiator and (b) the moderator/sustainer of relationships (Hocutt 1998) as well as (c) the mediator/’binding ingredient’ (Swan et al 1988, p2) of Perceived Value. The latter, in turn, is the repository/immediate dependent of positive attribute assessment and mitigator of critical service incidents (Davies and Palihawadana 2006). Finally the aggregate dependent of both Perceived Value and Trust is conceptualised as a reservoir of ‘tolerance’ (Davies and Palihawadana 2006). A simplified version of the derived model is provided at Figure 2.22:

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Figure 2.22: Trust, Perceived Value and Tolerance Theory Trust – Relationship Experience

Critical SQ Incidents

Perceived Value

Client Tolerance

Service Attributes

Equity Norms

Source: based on Davies and Palihawadana (2006).

2.15.4 Tolerance Theory and A Competing ‘Goodwill’ Model ‘Tolerance’, accordingly, mitigates uncertainty/ambiguity. “Clients that display tolerance to positive (or negative) incidents reflect a generosity (or forgiveness) that signals strongly valued relationships” (Davies and Palihawadana 2006, p383).

Its conceptualisation is

consistent with both Cumulative Satisfaction and key postulated Loyalty-Commitment items e.g.: (1) exclusive relationship intentions (Moorman et al 1992); (2) attitudinal altruism (Dick and Basu 1994); and (3) mutual relationship identification (Butcher, Sparkes and O’Callaghan 2001). Richer than traditional ‘loyalty’, its conceptualisation is accordingly akin to the postulated single superordinate construct of Loyalty-Satisfaction. However, the chosen label suggests potential construct proliferation (Churchill 1979). ‘Tolerance’ is employed by only one other known interorganisational relationship retention study (Gassenheimer, Houston and Davis 1998).

Its interpersonal relations stream

analogue, social tolerance (Miller and Sears 1986), suggests that it is a variable of early purchasing experience and commercial socialisation. A more robust, and strongly founded, term is provided by ‘goodwill’ which offers an extensive domain heritage in accounting and finance. Its definition aligns closely with the nature of a merged intentional construct. It:

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describes a favourably disposed attitude towards someone or something; entails expectations of economic benefits (Egginton 1990); and combines factors which are expected to generate future value (Tollington 1999). It is the value of a business entity not directly attributable to its tangible assets and liabilities - an intangible asset, such as a brand, reputation or high employee morale, which provides a competitive advantage.

deduction, at least in PBS, this reputation is equivalent to the client’s combined perceptions of ‘satisfaction’ and ‘loyalty’. An adjusted Competing ‘Goodwill’ or uni-conative model is presented at Figure 2.23. It: (1) Specifies a final endogenous variable of Loyalty-Satisfaction (Goodwill); (2) Incorporates a primary mediating role of Perceived Value; and (3) Reflects the postulated complexity of Trust as at once direct antecedent of both Loyalty-Satisfaction and Perceived Value and a reciprocal moderator of Perceived Service Quality.

Figure 2.23: Competing ‘Goodwill’ or Uni-Conative Model Trust (Empowerment)

Perceived Value (Equity)

Perceived Service Quality – functional quality

Source: Author.

Loyalty – Satisfaction (Goodwill)

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2.16 Chapter Summary This chapter provides a review of major contributions to the literature on the nature and antecedence of ‘loyalty’ in Professional Business Services (PBS). In five sections, it serves to introduce: The ‘Actors’: the seven key established constructs (Loyalty-Commitment, Transactional- and Cumulative Satisfaction, Perceived Value, Trust, Disconfirmation and Perceived Service Quality) plus an eighth, founded on a conjecture of Oliver’s (1999), a postulated competing superordinate construct of Loyalty-Satisfaction (2.32.9); The Situational Influences: postulated moderators derived from the overall service context, its emerging resource-based paradigm and, following a review of service taxonomy, the specific environment of PBS (2.10); The ‘Rules’ of the Theatre: key domain boundaries which provide theoretical underpinning for the major historic contributions and the two Convergent and Competing Models which are postulated in this study.

These include buyer

behaviour, relationship and attitude modelling theory (2.11); The ‘Plays’: major contributions in the principal B2B/B2C ‘loyalty streams, their communalities and, finally, the application of metatriangulation theory to generate a Convergent Model of Loyalty-Commitment and its antecedence (2.12-2.14); Counterpoint: an alternative perspective founded on a review of ‘inadequacies’ in the principal thesis supported empirically by the contribution of a third, P2B clientagency relations stream leading to a postulated and more parsimonious Competing Model (2.15). In addition to the model outcomes, in terms of both operationalisation and measurement, the chapter enriches contextual PBS understanding of key constructs – inter alia, Perceived Value and transactional and cumulative Satisfaction. This is intended to improve (1) the overall reliability of the ultimate process of analysis and, if validated, (2) subsequent research.

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CHAPTER FOUR – RESEARCH METHODOLOGY 4.1

Introduction

4.1.1 Overview

Based on the prior literature review (Chapter Two) and initial model and hypothesis set (Chapter Three), this chapter describes the research design and methodological approach adopted.

It addresses four principal topics: (1) research strategy and overall conceptual framework (4.2-4.4); (2) formulation and operationalisation of the research instrument (4.5); (3) psychometric properties of the instrument and considerations in scale amalgamation (4.6-4.8); and finally (4) target population, sampling framework and process to the point of completed data collection (4.9/Figure 4.1):

Figure 4.1: Research Design and Methodology Process Research Strategy Instrument Operationalisation Psychometrics and Amalgamation

Population, Sampling and Data Collection

Data Analysis (Chapters 5-7)

Source: Author.

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Key chapter themes are summarised in the nine “hallmarks of scientific research” (Sekaran 2000, p.1): rigour, testability, replicability, precision, confidence, objectivity, generalisability, parsimony and, not least, ‘purposiveness’. This last is the first topic next (4.1.2).

4.1.2 ‘Purposiveness’: Research Goals

“The major purpose of science is to develop laws and theories to explain, predict, understand and control phenomena… The purpose of theory is to increase scientific understanding through a systematized structure (which is) capable of explaining and predicting phenomena” (Hunt 1991, p20). By application: (1) nomothetic social research is concerned with the elucidation of laws and theories about human behaviour (Hayes 2000); and (2) business research is a specific “truth-seeking function that gathers, analyses, interprets, and reports information so that business decision makers become more effective” (Hair et al 2003, p.5).

This study’s overarching aim is to develop a representative PBS Loyalty-Commitment model by identifying: (1) the nature and (2) antecedence of client LoyaltyCommitment in Professional Business Services (PBS); (3) the relationships, and their direction, of each determinant; and finally (4) relevant moderating factors that facilitate client base segmentation. Analysis is in two parts:

1. Testing (including both Exploratory and Confirmatory Factor Analysis [EFA/CFA]) of newly generated, or extended, scales for the constructs of Perceived Service Quality, Loyalty-Commitment, Transactional Satisfaction and Perceived Value (Chapter Five); and 2. Exploration, using Multiple Regression Analysis (MRA), of dependent and mediated construct relationships and final testing, using Structural Equation Modelling (SEM), of both the Convergent and competing Goodwill models (Chapter Six).

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The required framework is created by the research design which inter-locks the literature review, research questions, results and analysis (Thietart et al 2001). The choice of design is the ‘most important issue’ facing a social scientist (Remenyi, Williams, Money and Swartz 1998, p.43) and the principal subject of this chapter.

4.2

Research Strategy 1: Design, Orientation and The Nature of the Investigation

4.2.1 Overview

There are no precise rules which govern research strategy. Research problems often require compromise designs (Easterby-Smith, Thorpe and Lowe 2002).

Both

quantitative and qualitative methodologies may address the same question (Barthunek, Bobko and Venkatraman 1992).

A final choice is a variable of:

epistemological orientation (4.2.2); morphology of explanation (4.2.3); management of conflicting desiderata or research goals (4.2.4); and finally relationship to the field’s established epistemological tradition (4.2.5).

4.2.2 Question 1: Epistemological Orientation

The ontological hypothesis of positivism proposes that any knowledge object has its own independent essence (Hunt 1991).

Existence follows necessarily, not

contingently, from an accurate verification of an object’s properties (Scruton 1994). Consistent with Comte’s (1798-1857) principles, this proposition should also embrace social and social psychological phenomena e.g. constructs such as Trust or Perceived Value (Merton 1973).

The phenomenological hypothesis argues the contrary: the study of appearances is dependent on the subject and upon the subject individual’s consciousness (Scruton 1994). Consistent with Husserl (1859-1938), capturing such multiple essences may guide deeper understanding (Flew 1971).

However, such essences (1) either may

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not be attained (interpretativism) or, more radically, (2) do not exist independently of the mind (constructivism) (Thietart et al 2001).

Consequently phenomenology

“has never shown how a study of what is ‘given’ to consciousness can lead us to the essence of anything at all” (Scruton 1994, p.11).

Given the limitations of the phenomenological perspective, the researcher contends that reality - however distorted - is observer-independent (Thietart et al 2001). Further that it is ultimately possible to classify social psychological phenomena in terms of precise, universally-accepted units that are comparable to, say, centigrade, volts or litres. However, since “social reality is often extraordinarily complex” (Hunt 1991, p.437), this study limits this aspiration and adopts one perspective only: the client-perceptual.

Potentially complementary perspectives (e.g. client-provider

dyadic, personality factors and environmental context) are excluded.

4.2.3 Question 2: Morphology of Explanation and Logical Empiricism

Consistent with this positivist epistemological presumption (4.2.2), it is common practice to utilise an explanatory model which is hypothetico-deductive or ‘deductive-nomological’ (Hunt 1991): i.e. ‘to deduce from the laws’ (Hempel 1965, p.335). It denotes that if a) the explanans is true then b) “the explanandum must be true because the laws are of strictly universal form” (Hunt 1991, p.52).

However,

marketing science explanations typically contain at least one law in probabilistic form. For example, P (L, (T+S)) = r states that: the probability of positive Loyalty (L), subject to a combination of both positive Trust (T) and positive Satisfaction (S), is r. Such models are inductive-statistical i.e. the explanans only confers a certain likelihood that the phenomenon will occur (Hunt 1991). They are therefore not strictly falsifiable.

As a result, and consistent with logical empiricism, the

researcher’s morphological position: (1) accepts a weak falsifiability criterion and (2) seeks to gradually increase confirmation (Carnap 1936) by subjecting theory to increasingly severe tests of corroboration (Popper 1959).

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4.2.4 Question 3 - Conflicting Desiderata

The principles of logical empiricism also apply to the third question. Typically a researcher must balance three potentially conflicting desiderata: (1) generalisability of findings, (2) measurement precision, and (3) the level of existential realism created for participants (Runkel and McGrath 1972). For example, consciously to optimise measurement precision one might adopt a laboratory experiment but at the expense of both other goals.

Preceding positions highlight maximising

generalisability as the desirable goal and suggest a quantitative methodology (Runkel and McGrath 1972).

4.2.5 Question 4: Alignment with Epistemological Tradition

Finally, irrespective of the preceding three answers (4.2.2-4.2.4), a researcher must also define his position in relation to the established epistemological tradition of the field. Tradition creates a structure within which (1) replication and extension can occur efficiently and (2) new knowledge be generated (Wright and Kearns 1998). Most major empiric studies in the principal streams identified are similar epistemological hybrids. They both adopt a phenomenological perspective - the study of perceptions and appearances (Scruton 1994) – but pursue a positivist research paradigm. This viewpoint is adopted here.

4.2.6 Summary and Quantitative Methodology

Collectively these four answers support a quantitative approach and, to optimise generalisability, the adoption of a sampling survey methodology. This strategy also facilitates (1) rapid efficient data collection (Easterby-Smith et al 2002) and (2) desirable logical empiricist goals such as replicability and extension (Berthon, Ewing, Pitt and Berthon 2003; Wright and Kearns 1998). These latter are discussed next (4.3).

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4.3

Research Strategy 2: Key Principles

Progress in marketing science research depends on a subject matter which is: (1) distinct and (2) possesses underlying uniformities and regularities; and (3) whose “truth content” is “intersubjectively certifiable” (Hunt 1991, p165).

Empiric

corroboration, accordingly, requires that “different… investigators with different attitudes, opinions and beliefs, will be able to make observations and conduct experiments to ascertain the truth content” (Hunt 1991, p.165).

Replication and extension facilitate generalising representation across observations in different contexts (Berthon, Pitt, Ewing and Carr 2002). Although these principles are widely propounded by key cited authors (e.g. Patterson and Spreng 1997; Morgan and Hunt 1994; Moorman et al 1992), academic and publishing practices differ significantly. For example, during:

1970-1991, replication in five major fields averaged just 6.2% - with marketing least at 2.6% (Hubbard and Vetter 1996); 1980-2002, author-designated replications and extensions in three relevant PBS journals (Journal of Advertising, Journal of Advertising Research and International Journal of Advertising), numbered just 42 (Berthon, Ewing, Pitt and Berthon 2003).

Arguably 97% of marketing studies are “academic clutter” i.e. lacking independent corroboration (November 2004, p.47). A researcher must accordingly: (1) maximise falsifiability and theoretical competition; (2) overcome uncertainty through replication; and (3) use extension(s) to develop generalisations and identify boundary conditions (Wright and Kearns 1998).

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4.4

Research Conceptual Framework

4.4.1 Generation and Replication

An integrating research strategy framework suggests that any study occupies “a conceptual space bounded by generation and replication” (Berthon et al 2003, p.516, Figure 4.2).

Figure 4.2: The Research Space

Source: Berthon et al (2002)

Epistemologically, this space has four dimensions: problem/phenomenon (P), theory (T), method (M) and context (C). Further:

Research space (R) = def (P x T x M x C).

Within this space, a replication-to-generation continuum captures three broad categories:

(1) Pure replication, an exact duplication in which no parameter (other than the irreducible minimum of time and sample composition) changes;

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(2) Extension, a duplication in which one or more significant parameters are altered; and (3) Pure generation, a focal study in which all key parameters alter with respect to the target study (Berthon et al 2002).

To fulfil the strategy and principles outlined above, this study positions between ‘extension’ and ‘generation’. On the one hand, it seeks to replicate prior problem, theory, method and context. Its two principal target studies are founded in PBS contexts: (1) advertising from the B2B interorganisational performance stream (Grayson and Ambler 1999); and (2) management consultancy from B2C post purchase effects (Patterson and Spreng 1997). Together these sources account for two-thirds of final instrument items (below 4.5).

On the other hand, it aims simultaneously to generate “novelty” (Berthon et al 2002, p. 420). Parameter amendments in each research space dimension are captured at two levels: superset or principles (Table 4.1a); and subset or specific applications (Table 4.1b, Berthon et al 2002, pp.421-422). Major extensions, or generative changes, versus the target studies are highlighted in orange.

Table 4.1a: Research Space – Superset/Principles Dimension Label

Description and Status

(Sources)

All three principal literature perspectives: B2C, B2B, P2B (Chapter 3: 3.2-3.4).

Problem or Phenomenon

General: identifying an appropriate model of attitude formation which will predict Loyalty-Commitment.

Theory

Philosophical Underpinning: positivism. One may aggregate individual perceptions to create knowledge which is independent of anybody’s claim to know (Popper 1972).

Methodology

Principally quantitative - data production via sampling methodology.

Context

Remains within PBS domain but shifts to clients of UK PR Consultancies as opposed to UK advertising agencies (Grayson and Ambler 1999) or Australian management consultancies (Patterson and Spreng 1997).

Source: Author (based on Berthon et al 2002).

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Significant model developments include: (1) the introduction of Transactional Satisfaction to complement Cumulative Satisfaction; and (2) the postulated enrichment of Perceived Service Quality, Perceived Value and Loyalty-Commitment. Collectively, the differences are expected both to influence the data in a different manner (Lindsay and Ehrenberg 1993) and to support theory generalisability.

Table 4.1b: Research Space – Subset/Applications Dimension Label

Description and Status

(Sources)

Principally from marketing post purchase effects (Patterson and Spreng 1997) and interorganisational performance (Grayson and Ambler 1999)

Problem or Phenomenon

Both extension from Patterson and Spreng (1997) and generative - in the form of new hypotheses regarding relevant antecedent, mediating and moderating constructs and relationships.

Theory

Based on foundations of TRA/TpB but revised commensurate with extended perspective (2.4.4-2.4.5) – notably anchoring and adjustment process (Cohen and Reed 2006)

Methodology

Data Analysis: quantitative techniques including EFA, CFA and MRA as well as SEM.

Context

Interpretive: positivist, Anglo-Saxon, predominantly within the Anglo-American streams (or tradition) of marketing and social psychology literatures.

Source: Author (based on Berthon et al 2002).

Operationalisation and scale integration are discussed next (4.5) following a brief review of the temporal context (4.4.2).

4.4.2 Time Horizon

The study’s temporal status – longitudinal or cross-sectional – is “closely related to the research strategy issue” (Remenyi et al 1998, p.47). Longitudinal analyses focus on “the study of phenomena over the course of time (Thietart et al 2001, p.332). The approach is relevant when the researcher seeks to capture pre- and post behavioural change – e.g. as a result of brand development. Cross-sectional studies provide “a snapshot of the variables of interest at a single point in time (and are) typically selected to be representative of some known universe” (Churchill and

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Iacobucci 2005, p.115). They also: (1) place a high priority on the sampling process (Easterby-Smith, Thorpe and Lowe 2002); and (2) partly limit the researcher’s ability to explain relationships between observed phenomena (Easterby-Smith et al 2002).

This study’s cross-sectional choice reflects its key conditions: (1) the PBS environment of continuous service provision and (2) the constant status of an ultimate dependent variable, continuing Loyalty-Patronage. It enables in-depth exploration of the nature and determinants of Loyalty-Commitment at a single point across an extended range of client-consultancy relationship durations.

4.5

The Research Instrument

4.5.1 Introduction and Overview

Based on the research strategy, this section describes the development of the research instrument. It discusses: key principles adopted (4.5.2); the seven major constructs (4.5.3-4.5.9); and the supporting classificatory variables (4.5.10-4.5.11). Each individual construct section includes a reference tabulation of scale items featuring: (1) item number; (2) instrument text, (3) language adaptation status and (4) source. In addition:

(5) Newly generated items are highlighted in orange; and (6) A footnote accompanying each table reports both the nature of the scale (e.g. five-point bipolar) and references to both prior theoretical underpinning (Chapter Two) and the prior construct summary (Chapter Three).

4.5.2 The Research Instrument and Key Principles

The final instrument contains 77 items (Figure 4.3). These operationalise seven major constructs and 13 classificatory variables

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Key sources [Appendix One, ‘Instrument’ and Appendix Two, ‘Instrument: Background Analysis’] are:

(1) Patterson and Spreng (1997, P&S this section only) -

35 items

(2) Grayson and Ambler (1999, ‘G&A’, based on their replication/extension of Moorman, Zaltman and Deshpande, 1992, ‘MZD’); -

17 items

(3) Other (of which 15 generated for this study and 10 derived from other sources)

25 items.

Figure 4.3: Construct Summary and Item Sources Key Sources

• Key Constructs: Perceived Value, Perceived Service Quality, Trust, T/Satisfaction, C/Satisfaction, and Disconfirmation; • Principal classificatory variables: – – – –

Firm Relationship Duration Personal Relationship Duration Client Buyer Experience; and Interaction Frequency.

P&S G&A/MZD OthAdapted Generated

Source: Author.

In each case, the researcher aims to: capture discrete items and components (McMullan 2005); illuminate their relationships (Soderlund 2006); and create a reliable multi-item scale (Grisaafe 2001; Wong and Sohal 2003). To optimise validity and tap its given domain effectively, each construct scale also seeks to accommodate three key principles (Bearden and Netemeyer 1999):

(1) Adequacy acknowledges that “multiple responses reflect the ‘true’ response more accurately than does a single response” (Hair et al 1998, p.10). To

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improve representation and consistent with the literature, some scales include additional postulated items.

Where this occurs, to conform to

replication principles, the foundation of any appropriate reflective scale should be previously-validated and its chosen measures contextually germane (Berthon et al 2003). (2) Parsimony argues that, although there is no agreed optimum scale length and past suggestions range up to 33 items (Bearden and Netemeyer 1999; Prichard, Havitz and Howard 1999: (a) increased item-numbers may deliver higher and potentially misleading reliability measures (Bearden and Netemeyer 1999); and (b) the longer a scale the greater the risk of fatigueinduced respondent error (McMullan 2005); (3) Content validity requires that expert judges assess items newly adapted or generated Bearden and Netemeyer 1999) – a role played by the PR Panel (2005, Appendix Three) as demonstrated in sample language adaptation (Table 5.2):

Table 4.2: Illustrative Language Adaptations Moorman, Zaltman and Deshpande 1992 – Market Research Firms

Grayson and Ambler 1999 – Advertising Agencies

Research Instrument – PR Consultancy - 2006

Researcher

Account Executive

Account Manager

Marketing Research Information

Advertising Agency Input

PR Consultancy Advice

4.5.3 Construct 1 – Loyalty-Commitment

As noted, no current consensus exists regarding either (1) the conceptualisation (Jacoby and Chestnut 1978; Lam et al 2004) or (2) operationalisation of LoyaltyCommitment (Knox and Walker 2001; Yang and Peterson 2004). Required scale © Bill Nichols (2009).

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development is consistent with the two dimensions postulated in the interpersonal loyalty stream: relationship maintenance behaviours and relative attitude (Jones and Taylor 2007; Rusbult et al 1999). These dimensions embrace nine components which also align with postulated indicators of Oliver’s (1999) four-level ‘loyalty ladder’. Tables 4.3a/4.3b summarise the resulting scale:

Table 4.3a: Loyalty-Commitment Scale-Items #

Item

Source

Dimension

Expecting to consider only this PR Consultancy to handle any additional project requirements in the next 3-6 months.

P&S

Behavioural – Repurchasing Intentions

Of the view that the firm’s influence on your decision-making [is] significant and beneficial

N/ New – A suggested by G&A

Attitudinal Altruism (negative opportunism)

Feeling warmer/more positive about…. than other PR consultancies

P&S

Attitudinal – Relative Attitude

Caring about the future of your working relationship with your account manager

G&A

Attitudinal – Relative Attitude

Committed to your relationship with your PR account manager and team

G&A

Behavioural – Exclusive Consideration

Table 4.3b: Loyalty-Commitment Scale-Items #

Item

Source

Dimension

Treating your account manager and team as a part of your department

G&A

Attitudinal Identification

Committed to continuing to work with firm for foreseeable future

P&S

Behavioural Exclusive Intentions

Locked into a relationship over which you have little control

N/ A

New – PR Panel 2005

Behavioural – Switching Intentions

Willing to recommend the firm to a colleague in a non-competitive company with similar needs

P&S

Attitudinal – Private Advocacy

Willing for you/your firm to appear in a published case-study or conference presentation to recommend the PR firm

N/ A

New – PR Panel 2005

Behavioural – Public Advocacy

Footnote: (1) 10-item, seven-point bipolar scale anchored ‘strongly agree/strongly disagree’; (2) Theoretical underpinning: Chapters 2.3 (especially Table 2.4) and 3.2; (3) G&A = Grayson and Ambler (1999); P&S = Patterson and Spreng (1997); LA = Language Adaptation; (4) Source: Author.

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The scale’s foundation is an established bi-polar adjectival scale (Oliver and Swan 1989) that includes three intentional items: (1) cognitive (‘expect to consider only’); (2) affective (‘feeling warmer/more positive about’) and (3) conative (‘committed to relationship with’) (Oliver 1999).

In its immediate adapted source in PBS

management consulting (Patterson and Spreng 1997), it achieves high construct reliability (0.97) versus threshold acceptability of 0.60-0.70 (Hair et al 1998). This base is expanded to ten items by incorporating:

1. Grayson and Ambler’s (1999) three-item ‘commitment’ scale which provides required identification, relative attitude and exclusive consideration indicators. Contextually relevant (advertising/market research), it also achieves acceptable construct reliability in two studies (0.73/0.78). 2. Adapted items that assess: o Altruistic/opportunism or client perceptions of the consultancy’s level of beneficial intentions (formulated from a contextually-validated suggestion by Grayson and Ambler (1999)); o Private advocacy using the commonly-accepted formula, ‘willingness to recommend’ (Lam et al 2004; Fullerton 2003; Hennig Thurau et al 2002) and contextually supported (Patterson and Spreng 1997); 3. Newly-generated items to capture: o Behavioural compulsion generated by e.g. switching costs (Dwyer, Schurr and Oh 1987; Sharma and Patterson 2000) and/or perceived dependence (Fullerton 2003).

Operationally, experience suggests

that clients may “feel locked into a relationship over which (they) have little control” (PR Panel 2005, Appendix Three). This formulation also introduces a reverse-worded item to minimise acquiescence bias (Bearden and Netemeyer 1999); and o A new ‘public advocacy’ item (willingness to endorse publicly), an introduction recommended by the PR Panel (2005). It responds to Oliver’s (1999) loyalty criterion of fortitude and is consistent with the conceptualisation of an evangelist on the highest rung of the loyalty ladder (Banks and Daus 2002).

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Finally, one highlighted component - willingness to tolerate price increases (De Ruyter et al 1998; Grisaafe 2001) - is excluded on grounds of contextual complexity. As noted, PBS buyers often operate like individual consumers: whereas sophisticated procurement groups apply competitive standards following range-frequency theory (Niedrich, Shama and Wedell 2001), amateur buyers usually form unique internal reference prices (Kalynaram and Winer 1995). In an industry (PR Consultancy) characterised by complex fee and disbursement charges, such references are often complex or confused (PR Panel 2005/Appendix Three).

4.5.4 Construct 2 – Trust

Consistent with replication principles (Berthon et al 2003), Trust is measured by a five-item scale originally developed by Moorman Zaltman and Deshpande (1992, CR = 0.84, Table 4.4). It also includes a reverse-worded item to reduce acquiescence bias (Bearden and Netemeyer 1999).

Table 4.4: Trust Scale-Items #

Item

Source

If I or someone in my department could not be reached by our account manager, I would be willing to let him/her take important PR decisions without my involvement

G&A

I trust the account team to get the job done right without having to monitor their progress continuously

G&A

I trust my account manager to perform tasks on my behalf which I haven’t got the time or expertise to carry out personally

G&A

I am confident that my account manager and team can take on jobs on behalf of my staff if they lack the time or expertise to carry them out themselves.

G&A

I generally do not trust our account manager.

G&A

Notes

Footnote: (1) Five-item, seven-point bipolar scale anchored ‘strongly agree/strongly disagree’; (2) Theoretical underpinning: Chapters Two (2.7) and Three (3.2.1); (3) G&A = Grayson and Ambler (1999); LA = Language Adaptation; (4) Source: Author based on Grayson and Ambler (1999).

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4.5.5 Construct 3 – Transactional Satisfaction

In default of any appropriate measure in the principal target studies, scale development follows theoretical guidelines for service-based transactional Satisfaction (Oliver 1997). This includes four principal dimensions: service, advice, proactivity and task. Operationalisation is based on guidance from the PR Panel (2005).

Table 4.5: Transactional Satisfaction Scale-Items # Item: Thinking about your experience in recent months with your PR consultancy how satisfied today are you with:

Guideline

Source

1 The service delivered by your team

N/A

Oliver 1997

Panel 2005

2 The quality of advice

N/A

Oliver 1997

Panel 2005

3 The degree of proactivity

N/A

Oliver 1997

Panel 2005

4 The quality of completed tasks (e.g. copywriting, event management).

N/A

Oliver 1997

Panel 2005

Footnote: (1) Four-item, seven-point bipolar scale anchored ‘very satisfied/very dissatisfied’; (2) Theoretical underpinning: Chapters Two (2.6) and Three (3.2); (3) LA = Language Adaptation; (4) Source: Author based on Oliver (1997).

4.5.6 Construct 4 – Cumulative Satisfaction

Following replication principles (Berthon et al 2003), the study adopts Patterson and Spreng’s (1997) three item operationalisation of ‘all things into account’ Satisfaction (CR = 0.85). This includes decisional (wisdom of appointment), hedonic and general affective satisfaction components (Table 4.6).

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Table 4.6: Cumulative Satisfaction Scale-Items #

Item: based on your overall knowledge, experience of and feelings towards, your PR Consultancy and taking everything into account are you:

Source

Satisfied with your firm’s decision to appoint/re -appoint the PR consultancy

Yes

P&S

Feeling pleased with what you have received

Yes

P&S

Feeling satisfied with what you have received

Yes

P&S

Notes

Footnote: (1) Three-item, seven-point bipolar scale anchored ‘definitely yes/definitely not’; (2) Theoretical underpinning: Chapters Two (2.5) and Three (3.2); (3) LA = Language Adaptation; P&S = Patterson and Spreng (1997); (4) Source: Author based on Patterson and Spreng (1997).

4.5.7 Construct 5 – Perceived Value

There are numerous calls to extend the traditional single-item ‘value for money’ operationalisation of Perceived Value (Patterson and Spreng 1997; Sirdeshmukh et al 2002; Yang and Peterson 2004). The literature highlights three relevant extension items:

(1) Equity in non-financial benefits/sacrifices (Olsen and Johnson 2003); (2) Perceptions regarding fees-acceptability (Hellier et al 2003); and (3) Fair value versus competitive benchmarks (Yang and Peterson 2004).

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Table 4.7: Perceived Value Scale-Items #

Item: Reflecting now on your overall opinions of your PR consultancy… to what extent do you agree/disagree with the following statements:

Source

Considering the fees we are paying and the results we are obtaining, I believe we are receiving value for money

No P&S

What we are getting back justifies what we are putting in

No Olsen and Johnson 2003

The consultancy’s fees are acceptable

No Hellier et al 2003

Compared to what I know of competitive alternatives, we are obtaining fair value.

No Yang and Petersen 2004

Notes

Footnote: (1) Four-item, seven-point bipolar scale anchored ‘strongly agree/strongly disagree’; (2) Theoretical underpinning: Chapters Two (2.6) and Three (3.2); (3) LA = Language Adaptation; P&S = Patterson and Spreng (1997); (4) Source: Various as indicated.

4.5.8 Construct 6 – Disconfirmation

In default of an option in the primary instrument sources, the adopted two-item Disconfirmation scale is based on its empirically validated delivery of greatest effectiveness among five options (Spreng and Page 2002).

Table 4.8: Disconfirmation Scale-Items #

Item: thinking about the consultancy’s work in the last few months:

Source

How closely did its overall performance meet the expectations you held at the time you last reviewed the firm?

Yes

Spreng and Page 2003

How do you feel about the firm’s performance over the period since your last review

Yes

Spreng and Page 2003

Notes

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Footnote: (1) Two-item, seven-point bipolar scale anchored respectively ‘‘extremely different to what I expected/exactly what I expected’ AND ‘very bad/very negative vs. ‘very good/very positive’;; (2) Theoretical underpinning: Chapters Two (2.9) and Three (3.2); (3) LA = Language Adaptation; (4) Source: Author based on Spreng and Page (2003).

The scale replicates the Additive Difference Model (ADM) formulation: 1) closeness of performance to expectations (‘extremely different, exactly as expected’) and 2) feeling about performance (‘very bad, very good’).

4.5.9 Construct 7 – Perceived Service Quality

Following replication principles (Berthon et al 2003), the 27-item Perceived Service Quality (PSQ) scale derives largely from the two primary source instruments (Table 4.9a/4.9b).

Table 4.9a Perceived Service Quality - Scale Items (#1-#11) #

Item

Source

Consistently makes sure they understand our aims and goals

P&S

Technical/Problem Identification

Ensures it thorougly understands the issue or problem before commencing work on any campaign/project

P&S

Technical/Problem Identification

Comes up with innovative ideas and solutions Uses up-to-date methodologies

P&S P&S

Technical - Methodology Technical - Methodology

Makes good use of its contacts

P&S

Technical/Networking

Defining campaign objectives

G&A

Technical/Involvement

Researching, developing, recommending strategy

G&A

Designing/recommending creative solutions

G&A PR Panel

Yes

Technical/Involvement

Yes

Technical/Involvement

G&A PR Panel

Yes

Technical/Involvement

Yes

Technical/Involvement

3 4 5 6 7 8

9 Identifying/researching/creating relevant content 10 Pitching stories to media and other relevant audiences 11 Project management of the campaign

Dimension/Component

Technical/Involvement

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This includes: 14 Patterson and Spreng (1997) items comprising six reported dimensions (CR = 0.73-0.96); eight G&A items representing perceived interaction quality and perceived consultant involvement (CR= 0.74/0.76); two newly generated extension items for ‘consultant involvement’ designed to tap the full scope of PR consultancy operations - respectively ‘media outreach’ and ‘content creation’ (PR Panel 2005; PSQ #9-10); and three items (PSQ #25-27) which, consistent with Grayson and Ambler’s (1999) ‘dark side’ relationship hypotheses, capture a reputational dimension of opportunism/altruism (2.6.5). [These items are based in part on a two-item operationalisation of John’s (1984) original definition: selfinterest seeking with guile (Morgan and Hunt 1994, ά not reported). The third item‘lack of transparency regarding billing’ – was proposed by the PR Panel (2005) as central to the PR Consultancy context.]

Table 4.9b Perceived Service Quality - Scale Items (#12 - #27) 12 Consistently produces results which we are able to exploit 13

Produces results which will enable us to increase our organisation's marketing effectiveness Has established and maintains a good rapport with our staff

P&S

Technical/Results

P&S

15 Has developed a close working relationship with our staff 16 Meetings produce creative insights and new ideas

P&S G&A

Technical /Results Functional – Relationships Functional – Relationships Functional/Interaction

17 Account manager diplays a sound strategic understanding

G&A

Functional/ Interaction

18 19 20 21 22 23 24

Account manager is very client-orientated Interactions with our account manager are productive

G&A G&A

Functional/ Interaction Functional/ Interaction

Responds promptly

P&S

Functional/Service

Is thoroughly professional in all it does

P&S

Functional/Service

Shows some creativity in solving our problems

P&S

Functional/Service

Is reliable in meeting deadlines

P&S

Functional/Service

Is dependable Sometimes concerned that account team alters the facts slightly to meet their own needs Sometimes concerned that your account team promises to do things without actually doing them later Concerned that your account team responds to complaints slowly and ineffectively

P&S M&H 1994 M&H 1994 Ball et al 2004

Functional/Service

25 26 27

P&S

Yes

Reputational

Yes

Reputational

Yes

Reputational

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Footnote: (1) 27-item, seven-point bipolar scale anchored respectively ‘‘strongly agree/strongly disagree” (X18); “not important at all/very important” (X6); and ‘definitely not/definitely yes’ (X3); (2) Theoretical underpinning: Chapters Two (2.8) and Three (3.2); (3) LA = Language Adaptation; P&S = Patterson and Spreng (1997); G&A = Grayson and Ambler (1999); M&H = Morgan and Hunt (1994); (4) Source: Author based on various.

4.5.10 Classificatory Variables

Finally the instrument also incorporates two groups of classificatory variables: (1) those designed principally for data classification (Table 4.10); and (2) those deployed additionally to represent postulated moderating variables (Table 4.11).

Table 4.10: General Classificatory Variables #

Variable

Operationalisation

OC1

Principal Service Need

By nine principal PR service categories;

OC2

Firm Size

By employee numbers

OC3

Industry Classification

By seven categories commonly used in PR industry for practice areas.

OC4

Buyer Responsibility 1:

Hierarchical by four management layers

OC5

Buyer Responsibility 2:

% responsibility for ensuring that the PR programme is carried out

OC6

Buyer Responsibility 3:

Level of responsibility for the PR Consultancy’s most recent appointment/re-appointment

OC7

Buyer Responsibility 4:

Degree of representativeness of the respondent for the client-buying or decision-making group.

OC8

Buyer ExperienceAccount Managers

Account managers worked with at incumbent PR firm

OC9

Frequency of Review

Frequency of client’s review of the PR Consultancy’s appointment as retained, or

In this first group, for example, the buyer responsibility variables are intended primarily for screening purposes (below Chapter Five).

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Table 4.11: Classificatory Moderating Variables

4.6

Variable

CM1

Client Buyer Experience (by Firm) - CBE The number of PR firms with which the client-buyer has worked over his career

CM2

Personal Relationship Duration (PRD)

The length of the personal relationship between client buyer and account manager

CM3

Firm Relationship Duration (FRD)

The length of the firm-2-firm relationship between client firm and consultancy

CM4

Interaction Frequency (IF)

Aggregate measure based on frequency of meetings, conference calls and emails.

Notes

Psychometric Properties of Proposed Research Instrument Principles

4.6.1 Introduction and Overview

Research creates value only if it attains a high degree of both validity and reliability (Thietart et al 2001). While validity confirms reliability, the reverse is not the case. A mis-blown beer glass, for example, whose volume is 1.1 pints, is reliable: i.e. it allows different observers to measure the same object with the same instrument and arrive at identical conclusions. But it will be invalid versus a criterion ‘pint’ standard for volume measurement.

Since such criterion procedures are rarely available in the

social sciences (Carmines and Zeller 1990), the psychometric literature offers an alternative approach Bearden and Netemeyer 1993; Churchill and Iacobucci 2005; Nunnally 1978). This addresses: (1) reliability (4.6.2), (2) content validity (4.6.3) and (3) construct validity – convergent, discriminant and nomological (4.6.4/4.6.5).

4.6.2 Reliability

Reliability is the degree to which measures are error-free and yield consistent results (Peter 1979). Necessarily some error may occur in any measurement - physical or psychometric (Nunnally 1978).

The mis-blown 1.1 pint glass (4.6.1) introduces

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systematic bias versus the criterion standard. The same glass, filled randomly to say 95-99% of capacity compounds systematic error with random error. Scale reliability is therefore an “index” of consistency (Churchill and Iacobucci 2005, p.295). Options include:

Test-retest requires administration of the same instrument at different times to identical respondents. The procedure is limited by the observation that “actors often remember their first response and repeat it despite apparent changes” (Thietart et al 2001, p.202); Split-halves demands that the instrument be divided equally and presented to two similar groups of respondents, a procedure rendered difficult by the requirement to capture the relevant phenomena fully in both halves. The corrected correlation may be thought of as an estimate of coefficient alpha (Nunnally 1978); Alternate forms, similarly, requires the development of two parallel tests in which relevant phenomena are formulated accurately but differently - again a difficult procedure when working with limited and/or established scales.

Given these issues, coefficient ά, or Cronbach’s ά (1951), is most commonly employed (Churchill 1979; Churchill and Iacobucci 2005; Hair et al 1998; Peter 1979). There is general agreement that 0.70 is an acceptable lower limit (Hair et al 1998) – or rarely 0.60 (Nunnally 1978) – but no absolute standard applies. These levels need careful interpretation since the quantum of the coefficient is a variable of both (1) average correlation among items (internal consistency) and (2) aggregate itemnumbers (Nunnally 1978).

Where scale items exceed six, it is recommended that Exploratory Factor Analysis (EFA) test for dimensionality (Thietart et al 2001). This guidance applies only to the 10-item Loyalty-Commitment and 27-item Perceived Service Quality scales.

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4.6.3 Content Validity

Ideally content or face validity “focuses on the adequacy with which the domain of the characteristic is captured by the measure” (Churchill and Iacobucci 2005, p.293). Items “should appear consistent with the theoretical domain” (Bearden and Netemeyer 1999, p.4).

However, the principle is difficult to apply and

fundamentally subjective (Carmines and Zeller 1990).

Many constructs remain

subject to both denotative and connotative debate.

Assessment is restricted,

accordingly, to the boundaries of the tradition in which prior research occurs (Churchill 1979). It is reasonable to assert empiric content validity if – as applies in the majority of cases in this study - previously validated constructs are adopted without substantive adaptation from prior instruments. Where extension and/or new generation occur, assertion depends on procedures adopted (such as expert consultation) and is provisional. In either case, although statistical analysis may facilitate the process, “content validity principally rests upon an appeal to the propriety of content and the way it is presented” (Nunnally 1978, p.94).

5.6.4 Construct Validity

Whereas reliability addresses a scale’s internal consistency and content validity its preliminary acceptability, construct validity assesses whether it measures what it purports to measure (Churchill and Iacobucci 2005).

Affirmation occurs where

variables measuring (1) the same phenomenon correlate strongly (‘convergent validity’) and (2) different phenomena only weakly (‘discriminant validity’). It is also supported (3) where predictions based on a concept which an instrument purports to measure are confirmed (‘nomological validity’ Peter and Churchill 1986).

To establish either convergent and/or discriminant validity a researcher may use either (1) a multitrait-multimethod matrix (Campbell and Fiske 1959); or (2) more commonly Confirmatory Factor Analysis, or CFA (Carmines and Zeller 1990). In the convergent case, for example, loading between observed variable and latent variable should exceed 50% (Steenkamp and van Trijp 1991). Finally, nomological validity is

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supported empirically where: (1) relevant theory and/or prior research confirm a relationship between two constructs (Hair et al 1998); and (2) their respective measures correlate positively. In practice, it may also be tested by replication and CFA.

5.6.5 The Limits of Construct Validity

Finally the discussion of construct validity requires a caveat - often omitted from published findings (Blois 1999). To validate a construct of Trust is to corroborate a proposition that a construct labelled ‘Trust’ has meaning and replicable nomological relationships. It does not, however, validate a conceptualisation of ‘trust’ that possesses agreed universal meaning (=df) or truth value (Scruton 1994). Selfevidently in trust’s case no such universal agreement presently exists (Blois 1999). Italicised construct labels (e.g. Trust) highlight this limitation deliberately.

The

constraint also applies to interpretation and managerial implications. Morgan and Hunt (1994b), for example, confuse their Trust with universal trust when they conclude: ‘if managers hope to build co-operative relationships with their business partners, they first must work to establish commitment and trust” (p. 24). More accurately, it is only possible to say that working on certain aspects of trust and commitment (as defined in the constructs under review) will facilitate etc…

4.7

Model Constructs: Psychometric Status

This section applies the preceding framework (4.6). Tables 4.12/4.13 summarise (as available) the prior psychometric status of key measures. Principal findings are contributed by the two target studies. First, Patterson and Spreng (1997, p.424) performed a CFA on indicators for four key latent constructs: (1) Loyalty-Intentions (three original items replicated here as part of an expanded 10-item scale); (2) Cumulative Satisfaction (three original items replicated without extension); (3) Perceived Value (one item replicated here in an extended four item scale); and (4) a original 14-item Perceived Service Quality scale.

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Table 4.12: Status and Validation of Key Constructs Employed (1-5)

Construct

1A. LoyaltyIntentions

IB. LoyaltyCommitment

Notes

part-scale

2 – Trust

3- Satisfaction (Transactional)

4 - Cumulative Satisfaction

5 - Perceived Value original = single item

New scale based on Oliver (1997)

Patterson and Spreng (1997) Survey 207 128 62%

Client Firms of Australian Management Consulting Firms

Client Firms of Australian Management Consulting Firms Questioned by Authors N/A

Measure originally developed by: Data Collection Sample Size Response Response Rate

Patterson and Spreng (1997) Survey 207 128 62%

part-scale MZD (1992) refined by Grayson and Ambler (1999) Survey 1719/728 779/200 45.3%/27%

Target

Client Firms of Australian Management Consultancies

MZD (1992)/ Grayson and Ambler (1999) Survey 1719/728 779/200 45.3%/27% US Top 200 Advertiser marketing research managers /UK marketing or advertising managers

Content Validity Reliability

……… Yes 0.97

0.78/0.60

Yes 0.84/0.72

t-value=9.02+ AVE=0.93

Yes

Not Reported 3

Yes Confirmed by G&A 3

Yes Confirmed by G&A 5

t-value 9.02+ AVE 0.85 Confirmed by P&S Partly Confirmed 3

N/A

0 5

4 4

0 3

Convergent Validity Discriminant Validity Nomological Validity Original Items New Items (in extension) Total Items Source: Author.

……….Yes

Yes 0.95

From their CFA Patterson and Spreng (1997) report: ‘Adequate’ model fit overall (GFI = .86; CFI = .97; χ2 = 242.24, df = 155); Convergent validity evidenced by both significant factor loadings (t-values ranging between 9.02 and 15.94) and AVEs ranging from 0.60 to 0.93 – confirming that average variance extracted is greater than that due to measurement error (Fornell and Larcker 1981);

N/A N/A Partly Confirmed 1 3 4

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Discriminant validity confirmed by AVEs per construct exceeding the squared correlation between each construct and any other (Fornell and Larcker 1981); and High construct reliabilities (0.82-0.97). Additionally, nomologically, all hypothesised relationships are supported except for the direct effects of Perceived Value on Loyalty-Intentions which are subject to full mediation by Cumulative Satisfaction.

Secondly, Grayson and Ambler (1999) likewise submit all four principal measures to CFA. These include (Loyalty)-Commitment, Trust, and what are postulated here as the interaction quality and consultant involvement dimensions of Perceived Service Quality. Although not publishing full corresponding data to Patterson and Spreng (1997), following elimination of construct indicators with high intercorrelations (Gerbing and Anderson 1988), they achieve a good measurement fit (χ2 = 37.96 [df=34; ρ = .29], RMSEA = .024, CFI = .99, GFI = .97 and AGFI = .94.). Nomological validity is supported by loading of items expected to do so (Carman 1990) except in the case of a direct path from ‘consultant involvement’ to (Loyalty)-Commitment. Finally, concerning measures derived independently of the two principal target studies:

For the postulated opportunism/altruism dimension of Perceived Service Quality, Morgan and Hunt (1994) report that all measures in their model comply with required standards for reliability and validity (Anderson and Gerbing 1988; Jöreskog and Sörbom 1989); For Disconfirmation, Spreng and Page (2003) test five different operationalisations of Disconfirmation in two distinct contexts. For the additive difference model (ADM) employed in this study, they report acceptable model fit (p.44 ff) and conclude: “the ADM method… performs well in terms of consistent relationships with purported antecedents and the theoretical consequences of disconfirmation (p. 57)”; and

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For Transactional Satisfaction, content and nomological validity rest on the theoretical support provided by the literature (Oliver 1999).

Table 4.13 Status and Validation of Key Constructs Employed (6-7) 6 Disconfirmation

Construct

7a- Perceived Service Quality General Scale

7b Interaction Quality

7c Consultant Involvement

7d Perceived Opportunism

Morgan & Hunt (1994) based on John (1984) (*1)

Measure developed by:

Spreng & Page (2003)

Patterson & Spreng (1997)

Grayson and Ambler 1999

Data Collection Sample Size Response Response Rate

Survey (X2) N/A 207/273 N/A

Survey 207 128 62%

Survey 1719/728 779/200 45.3%/27%

Survey 1000 129 12.9%

Target Content Validity Reliability

Church members, students Yes (*3)

US Marketing/UK Marketing/ Advertising Yes 0.86/0.75

Survey 1719/728 779/200 45.3%/27% US Marketing/ UK Marketing / Advertising Yes 0.79/0.78

Yes

Confirmed Confirmed Not specifically tested 2 1 3

Convergent Validity Discriminant Validity Nomological Validity Original Items New Items extension) Total Items

N/A

Australian MC Yes (*2) t-value 9.02+ AVE 0.600.90

N/A

Yes

N/A 2

Not tested 14

Confirmed by G&A 4

Yes Not supported by G&A 4

0 2

0 14

0 4

2 6

Managers of US Tyre Dealers Yes Confirmed

(in

Footnote: 1. Because of high inter-correlations, Grayson and Ambler (1999) combined their initial scales for perceived opportunism and loss of objectivity. The resulting alpha is below the conventional 0.70 threshold. These issues encouraged the use of Morgan & Hunt's alternative here. 2. Dimensions

Outcomes

0.86

Method

0.82

Service

0.90

Relationship

0.95

Global (1) Problem Identification

N/A 0.73

3. Of five options in study explains most variation in Satisfaction in a service setting (32%).

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4.8

Considerations in the Amalgamation of the Instruments

Further to their psychometric status (4.7), the validity of “pre-existent scales is strongly linked” to both the native technical format and “context in which they are used” (Thietart et al 2001, p.174). Subsequent scale amalgamation, accordingly, raises six further principal contextual and technical issues:

(1) Two thirds of items derive directly from germane Professional Business Services’ (PBS) contexts: respectively market-research/advertising (Grayson and Ambler 1999; Moorman et al 1992) and management consulting (Patterson and Spreng 1997); (2) Adapted English language denotative and connotative validity is supported by initial pre-testing (PR Panel 2005, Appendix Three); (3) Cross-border scale export (respectively (A) US→UK and (B) Australia→UK) is supported on the basis that “just because national boundaries are easy to identify does not make them an appropriate variable for segmenting behaviour… international differences are not systematically larger than differences due to market environments studied or to technical characteristics” (Farley and Lehmann 1994, pp.120-121); (4) Technically, all scale authors employ Likert, or Likert type, scales. Such homogeneity facilitates amalgamation and integration of additional measures (Gill and Johnson 1991). Such scales are also easily administered; lend themselves to postal, telephone and electronic surveys; and support response levels; (5) All adopted scales deploy the seven-point standard at which reliability tends to level off (Nunnally 1978, p.595). New material is incorporated on the same basis. (6) Sixth and finally, although the majority of amalgamated scales are ordinal it is an accepted principle in psychology and the behavioural sciences to treat such scales as intervallic (Nunnally 1978; Remenyi et al 1998).

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In the last case, the more categories a scale possesses – say seven as in this case versus three or five – the more likely it exhibits the properties of ‘true’ interval scales (Bryman and Cramer 1992). This ‘intervallic’ classification is critical since “interval scales and ratio scales (both metric) provide the highest level of measurement precision, permitting nearly all mathematical operations to be performed” (Hair et al 1998, p8).

4.9

Choice of Population: Clients of Public Relations Consulting Firms

4.9.1 Introduction and Overview

Given appropriate strategy (4.2-4.3), framework (4.4) and instrument (4.5-4.8), it remains to discuss sampling and data collection.

This penultimate section,

accordingly, reports and supports:

The choice of UK PR Consultancy clients as target population (4.9.2); Quantifies population size by (1) client firms (4.9.3) and (2) key contacts (4.9.4) – supporting the latter as chosen benchmark; Establishes 140-150 as requisite minimum sample size both to represent the population and enable required statistical procedures (4.9.5); Describes the identification and initial testing of appropriate sample sources (4.9.6-4.9.7); and finally Reviews and summarises the data collection process (4.9.8-4.9.9).

4.9.2 Choice of Population

This study’s target population are clients of UK-based Public Relations (PR) Consultancies. In theory any organisation – commercial and not-for-profit - may employ a PR Consultancy since it has an observable and measurable condition of public relations. These relations, or reputation, are the “sum of experiences of employees and external groups” (Smythe, Dorward and Reback 1992, p.175).

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The practice of Public Relations, accordingly, is “planned persuasive communication designed to influence significant publics” (Marston 1963, p.3). These publics, in principle, include all stakeholders (MacMillan et al 2004). A PR Consultancy, by extension: (1) advises clients on how best to achieve organisational objectives by engaging in public discourse to educate and persuade key audiences; and (2) implements (predominantly media-based) campaigns of supporting educational and promotional activity (derived from US Council of PR Firms, CPRF, 2005). Such activity includes, inter alia, media relations, copywriting, event management and public affairs. Typical consulting practice, however, is narrower and concerned principally with media and investor relations (Marston 1963).

For the purposes of the study, a PR Consultancy is an organisation with two or more staff (i.e. not an individual freelance who typically provides only one or two services e.g. copywriting, event management).

Commonly, in the UK, this means an

organisation which is a member of the industry’s governing professional body, the Public Relations Consultancies Association (PRCA).

In 2006 the PRCA had 125

member-firms, each with an average of 30 clients and accounting in aggregate by its own estimate for 70% of full-service UK market fee income.

PR Consultancy is selected for four principal reasons:

1. Although a significant global niche industry generating annual fees in excess of $1/ $7 billion (respectively UK-PRCA 2004; US-USCSI 2003), its client relationships attract little prior empiric interest from the management sciences; 2. Replications and extensions in PR consultancy may extend both the context (Berthon et al 2000) and, potentially, generalisability of previous PBS findings e.g. market research (Moorman et al 1992), advertising (Grayson and Ambler 1999) and management consulting (Patterson and Spreng 1997); 3. Specifically, supporting the goal of generalisability, PR consultancy is a natural PBS hybrid. It combines the consulting process of management consultancy with the technical/creative content of advertising/market research; and

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4. If offers good accessibility for the author.

4.9.3 Size of Qualifying Population - Client Firms

Since the target population specifies clients of full-service UK PR consultancies, the qualifying client-firm population is estimated at ~5357 (i.e. 125 firms x an average 30 clients x 100/70 to account for non-PRCA members). The population of individual clients is approximately x2 (= ~10714) as demonstrated next.

[An alternative

benchmark supplied by the UK Council for Economic and Business Research (2005) suggests a similar ~9250].

4.9.4 Nature of Population: Key Informants vs. Multiple Contacts

The tradition of prior PBS research studies (4.9.2) defines the unit of analysis as a single ‘key individual’ per client firm. Individual views serve as proxy for wider clientorganisational perceptions (e.g. Patterson and Spreng 1997). Although justified by the principles of replication/extension (4.2-4.4), PR Consultancy requires a modified approach on two counts:

(1) Many larger client-firms award multiple parallel and long-term (~12-month) contracts controlled by different individuals (lead contacts). In late 2007, for example, one such client of the author’s former firm held seven discrete contracts (five UK divisions, Northern European coordination exercise and Pan-European management).

Each was autonomous and managed by

different contacts. Each principal contact’s view is, in this context, equally valid; (2) All but the smallest accounts commonly possess two distinct lead contacts: one for day-to-day management (typically ‘PR Manager’ or ‘Marketing Manager’) and one for strategic direction (Director/VP marketing). Practitioner experience, and limited empiric evidence, suggests that both contribute to the formation of views about the firm-consultancy relationship (Rossomme 2003).

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Both counts place a high priority on ensuring respondent appropriateness, the principles for which are supplied by Patterson and Spreng’s (1997) six-stage process (further below Chapter Five).

4.9.5 Sample Size Required

Given (1) a population of ~10714 individual clients or lead contacts and (2) the statistical procedures envisaged, the minimum required sample (N) is 140-150 as demonstrated in this section. It addresses, in turn, (1) minimum returned sample size (MRSS =~130) and compliance with the requirements of (2) confirmatory factor analysis (CFA) (N = 135-270), (3) SEM maximum likelihood estimation (MLE = 100200) and (4) multiple regression analysis (MRA = >140).

First, the calculation of baseline MRSS begins with Cochran’s (1977) formula for continuous (metric) data. This requires three key inputs: (A) the risk, or margin of error, acceptable; (B) the alpha (α) level, i.e. the risk acceptable that the true margin of error exceeds the acceptable margin of error (Type I error); and (C) an estimate of population variance.

In the case of (A), although a 5% margin of error is generally held acceptable for categorical data, a more conservative 3% is advised for continuous (metric) data (Krejcie and Morgan 1970). A 3% level states the researcher’s confidence that “the true mean of a seven-point scale (is) within + .21 (.03 x 7 scale points) of the mean calculated from the research sample” (Bartlett, Kotrlik and Higgins (2001). For input (B), an α of either <.01 or (as adopted) <.05 is generally advised for doctoral studies (Ary, Jacobs and Razavieh 1996). The α is incorporated by utilising the t-value (Cochran 1977). Finally for input (C), no known prior studies or pilot study results are available from which to obtain a reliable estimate of population mean (σ). Two principal solutions (C1 and C2) are available.

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C1 requires us to “determine the inclusive range of the scale and then divide by the number of standard deviations that would include all possible values in the range, and then square this number” (Bartlett et al 2001, p. 45). For a normally distributed variable, the range is “equal approximately to + three standard deviations” (Churchill and Iacobucci 2005, p.364).

In seven-point scales, accordingly, six standard

deviations (three either side of the mean) capture 98% of responses. The calculation of variance, accordingly, is 7/6 (i.e. 1.167) and the resulting formula (t)2 x (s)2/(d)2 where: t = value for selected alpha level of 0.25 in each tail = 1.96 (ά = 0.5); s = estimate of standard deviation in the population = 1.167; d = acceptable margin of error for mean being estimated = .21.

The C1 result, confirmed by Bartlett et al’s tables (2001, p. 48), is MRSS = 119 for a population of ~10700. Since this total does not exceed 5% (= 535) of the estimated population, Cochran’s (1977) correction formula is not required.

Alternatively C2 estimates population variance “from the conditions surrounding the approach to the problem” (Churchill and Iacobucci 2005, p.364). The nature of the mean-variance relationship depends both on number of scale points and shape of the response distribution (e.g. normal, symmetrically clustered or skewed).

Since

(1) a typical seven-point scale response range is 2.5-4.0, (2) conservative adoption of the high-end is recommended (Churchill and Iacobucci 2005, p.365), then (3) the C2 formula increases MRSS to ~130. As a final adjustment, given the researcher’s prior experience of a homogenous population in a narrow market segment, effect sizes are expected to be small-to-medium (say 0.35). Following Cohen’s (1977) advice to use α = <0.05 and achieve 80% power, this confirms MRSS = ~130 (Hair et al 1998, p.13).

Second, beyond baseline MRSS, obtained sample size must also support advanced statistical procedures e.g. confirmatory factor analysis (CFA). CFA tests the ability of a predefined factor model to fit an observed set of data (DeCoster 1998, further

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Chapter Six).

To estimate a required CFA sample (N), a minimum recommended

ratio of observations to considered variables or scales is 5:1 with closer to 10:1 preferred (Cattell 1978; Hair et al 1998, p.99).

Based on anticipated 27-item

attribute and 26-item attitude CFAs, this sets a minimum target of N = 135 (5 x 27) with more (up to 270) preferred.

Third, to support anticipated maximum likelihood estimation (MLE) - the most common model fitting procedure in structural equation modelling (SEM) - sample size range from 100-200. Established heuristics, for example, estimate that optimal MLE requires 200 “with increases occurring if misspecification is suspected, the model is overly large or complex (and/or) the data exhibit non-normal characteristics” (Hair et al 1998, p. 605; DeCoster 1998). Conversely recent studies report identify major limitations in these ‘rules-of-thumb’: minimum N is rather a variable of aspects of design (MacCallum, Widaman, Zhang and Hong 1999). Population factors, for example, may be recovered accurately even when N is relatively small (60-100) “as long as communalities are high and factors are adequately overdetermined” (MacCallum, Widaman, Preacher and Hong 2001, p.634).

Fourth and finally, to detect a significant R2 (co-efficient of determination), multiple regression analysis (MRA) requires a complex interplay between (A) sample size, (B) significance level (α) selected and (C) the number of independent variables or regressors (Bartlett et al 2001; Hair et al 1998). Based on anticipated applications, this generates the following formula:

+ Power =80% (probability of detecting as statistically significant a specific level of R2 or a regression coefficient at a specified significance level for a specific sample size; + Significance level (α) of <.05; + Up to 10 regressors; = Detection of R2 values only in excess of ~18% (Hair et al 1998).

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An achieved MRA sample size of >140 will increase the researcher’s ability to detect weaker relationships.

Accordingly, although specific requirements will be a variable of procedure, the minimum N for this study is estimated at 140-150 (the latter the mid-point in SEM advice). How this sample was obtained is described next.

4.9.6 Sampling Frame and Method – Part 1 - Pilot

Since the ideal sampling source, the UK PRCA, does not offer access to memberfirms’ clients, Haymarket Direct (HD) offers the next most representative population proxy. HD publishes all principal UK marketing/communications publications (including, inter alia, PR Week, Marketing and Campaign).

Consistent with the

target population estimate of ~10,700 (4.9.3), it reports 10,717 qualifying respondents with full PR Consultancy service purchasing responsibilities (2006).

To optimise generalisability, the ideal approach is probability sampling: “based on the premise that each element of a… population has a known but not necessarily equal, probability of being selected”. (Hair et al 2003, p.211). This ideal was constrained initially by (1) high acquisition cost and (2) lack of comparable case data from which to predict response rate.

Accordingly, a 1000-strong pilot (500

mailed/500 emailed) was undertaken using a systematic sampling approach. Restricted commercial single-use access provisions disallowed any form of response optimisation

procedure

(e.g.

preliminary

notification,

follow-up).

Strong

confidentiality provisions notwithstanding, a very poor response rate (0.4%) - two cases in each medium - were achieved at an unacceptable net cost of over £300 each. These were set aside for test purposes and the approach abandoned.

5.9.7 Sampling Frame and Method – Part 2

To provide an alternative data collection strategy, two complementary options were adopted. First a number of leading UK direct marketing firms possess proprietary

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alternative lists of marketing/communications decision-makers. Arguably the most representative, Stormark holds a regularly-validated PR-only list of just over 1000 decision-makers (~8% of population). The firm is also co-headed by a Cranfield DBA and sensitive to research requirements.

Second, the author’s former firm,

Whiteoaks, possesses strong UK industry connections. (During 2002-2005, for example, it was represented on the PRCA’s Southern Area governing committee). Initial PRCA-based contacts identified the potential for client access via five-to-eight member consultancies. Preliminary checks confirmed that these groups provide a plausible representation of medium-large size client firms:

For the ‘Stormark’ group an agreed review by a retained independent researcher, George Illingworth, reported a high proportion of publicly-quoted companies including both FT-SE100 and FT-SE250 (2006); and For the ‘Consultancy’ group (themselves ranging in annual fee size from £1.2 £20.0 million), a similar process confirmed a preponderance of listed or £100 million+ turnover firms.

4.9.8 Data Collection Process

Operationally, the ‘Consultancy’ and ‘Stormark’ groups required different data capture strategies. This is because “… no single survey design will be most efficient in every situation. The researcher must use knowledge of the particular population in deciding whether a response stimulating technique should be used and, if so, which…” (Walker and Burdick 1977, p.382). Response rate, although key, is but one factor among others including time, cost and access.

Figure 4.4 summarises the ‘Consultancy’ strategy and is linked by alphabetic reference to the following discussion:

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Figure 4.4: Mail/Email Procedure (Consultancies) (A) • Preliminary Notification (B) incorporating: – Academic sponsorship (C); – Charitable incentive (D); – Guaranteed anonymity (E); and – Respondent candour requested (F).

• PLUS 7 days: – Second electronic mailing (G); – Electronic survey format link (H); and – Alternative options (I).

• PLUS 21 days: – First electronic reminder (J).

• PLUS 35 days: – Second and final electronic reminder

Source: Author.

(A) To optimise response, an integrated mail/email data collection procedure (Appendix Four) is now well-accepted and supported (Cook, Heath and Thompson 2000; Dillman 2000). (B) A positive relationship between preliminary notification and subsequent response is well established for both mail (Fox, Crask and Kim 1988; Kanuk and Berenson 1975; Yu and Cooper 1983) and more recently email (Kaplowitz, Hadlock and Levine 2004).

Response enhancement is supported in this case by both: (C) sponsorship of a credible academic institution, in this case ‘Henley Management College’ (Kanuk and Berenson 1975; Roth and Be Vier 1998; Yu and Cooper 1983); and (D) a monetary incentive in the form of a £10 per respondent own-choice charity donation. Although higher monetary value correlates with higher response rates (Church 1993; Kanuk and Berenson 1975; Rose, Sidle and Griffith 2007; Yu and Cooper 1983), evidence regarding the charity motivator is conflicting (Furse and Stewart 1982).

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(E) Full anonymity and confidentiality were also incorporated as a necessary reassurance particularly to participating consultancy management teams. There is little evidence that either encourages response per se (Kanuk and Berenson 1975; Yu and Cooper 1983), although (F) guaranteed anonymity usually facilitates respondent candour (Kohli 1989).

Following advance notification and seven days for opt-out purposes, the independent researcher (a retired research manager with a leading consulting firm) issued electronically (G) the principal communication including (H) embedded survey link (Appendix Four).

This also offered both (1) alternative hard-copy or telephone interview options and (2) access to further information. The option of a (I) telephone interview is supported as a response facilitator (Yu and Cooper 1983). When accessed the survey link utilised an easy-to-use Teleform online format deliverable via the Henley Business School website.

The small minority of hard-copy submissions were

reviewed and submitted online by the independent consultant.

Data were initially

stored in an electronic Excel file.

Finally, the independent researcher issued (J/K) two electronic reminders to nonrespondents after 14 and 28 days respectively (i.e. 21 and 35 days after preliminary notification). Reminders are widely evidenced as effective in increasing response rates (Fox, Crask and Kim 1988; Kanuk and Berenson 1975; Roose, Lievens and Waege 2007; Yu and Cooper 1983).

Separately, Figure 4.5 summarises the ‘Stormark’ group strategy. Following an identical preliminary procedure (L) a telephone follow up offered either (M) an immediate or subsequent booked telephone interview. Subsequent reminders (J/K) were also via telephone.

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(Because of time constraints, the telephone process was conducted by Stormark itself whose team also filed completed data electronically to Henley under supervision of the senior ex-Cranfield director).

Figure 4.5: Contact Procedure (Stormark) • Preliminary Notification (B) incorporating: – Academic sponsorship (C); – Charitable incentive (D); – Guaranteed anonymity (E); and – Respondent candour requested (F).

• PLUS 7 days: – Telephone contact (L) leading to… – Interview (M).

• PLUS 21 days: – First telephone reminder (J).

• PLUS 35 days: – Second and final telephone reminder (K).

Source: Author.

4.9.9 Data Collection Results

First for the ‘Consultancy’ group, advance notifications were issued to 217 clients at five participating consultancies during September-November 2006 of which:

12 declined in writing to participate usually on ‘grounds of policy’; 104 remained unobtainable after the follow-up process summarised above; and 101 accepted representing an overall response of 46.5%: o By mail = 44.5%; and o By web = 55%.

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The rate achieved is very high in comparison to overall academic response rates which manifest a declining trajectory over 30 years (Baruch 1999). In addition to the nature of a convenience sample, qualitative contributions highlight two additional influential factors: (1) the supporting direct sponsorship of each client’s own consultancy; and (2) respondent’s perception of the survey’s high level of personal salience. The power of the latter is evidenced elsewhere (Roose, Lievens and Waege 2007).

Second, for the ‘Stormark’ group, a systematic sampling approach generated an initial 500-strong target list from a finally available 1020. Advance notification occurred in on a staggered basis during February 2007 with calls commencing one week later. Of the 500:

257 declined principally for reasons of ‘policy’ or ‘convenience’; A further 190 remained unobtainable after two follow-up calls; and 53 consented to participate representing an overall response of just fewer than 11%.

The ‘Stormark’ level is consistent with comparable pre-notified UK B2B studies. It also compares well with the reported 12% level found in original 1982 benchmark research by the Council of American Survey Research Organisations (cited by Churchill and Iacobucci 2005, p.382).

4.10

Summary and Conclusions

This chapter describes key decisions adopted during the study’s development stages addressing:

(1) research strategy; (2) conceptual framework; (3) instrument

formulation; (4) instrument psychometrics; (5) target population sampling frame; and (6) data collection. As noted, the necessary abandonment of the original sampling frame constitutes a partial limitation on ultimate generalisability of findings.

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In summary, the study adopts a positivist perspective and quantitative multivariate analysis techniques as appropriate to data obtained from an intra-consumption professional client-buyer survey. The following Chapter Five presents the first stage of analysis: data preparation, descriptive statistics and scale validation.

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CHAPTER FIVE – DATA ANALYSIS PART ONE 5.1

Introduction and Overview

For the UK Public Relations Consultancy industry, the annual cost of disloyalty is estimated to be £342 million (28.6% of annual fee income), according to analysis presented in this chapter.

To help understand this phenomenon and its

antecedents, previous chapters described: current literature and theory (Chapter Two); development of a model and initial hypotheses (Chapter Three); and the process of research design and methodological decision-making (Chapter Four).

This chapter begins the analysis phase. Its opening sections review: (1) decisions regarding, inter alia, missing data, outliers, non-coverage error, non-response bias and tests for normality (5.2); and (2) summaries of the respondents’ industry, firm size and service requirements; management status and their responsibilities and relationships (5.3).

Next, major construct scales are summarised and their internal reliability validated (5.4). Two extended examples - Loyalty-Commitment and Perceived Service Quality - are also necessarily subject to Exploratory Factor Analyses (‘EFA’, 5.5).

Penultimately, to support validity, reliability and, by extension, generalisability of findings, all principal scales are submitted to confirmatory factor analyses (CFA) (5.6). This multiple CFA approach is necessitated in part by the limitations of sample size availability versus observations required by a structural equation modelling (SEM) technique such as CFA (5.6.2). A closing summary incorporates a complete tabulation of procedures followed and outcomes adopted (5.7).

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5.2

Data Preparation: Screening of the Final Data Set

5.2.1 Introduction and Overview

As noted (4.9.8), data was collected online either directly or, including supervisory check, with a telephone interviewer’s facilitation. This method eliminates manual transfer requirements and minimises concomitant human data transfer or editing error. Responses obtained by source group, either ‘Consultancy’ or ‘Stormark’ (4.9), are aggregated at Table 5.1:

Table 5.1 - Summary of Response Rates Scores

Scores

% Rate

Consultancy

Stormark

All

Consult

Stor/k

All

Yes - Accepted and Participated

101

154

46.5%

10.6%

21.5%

Unobtainable - 'Not-At-Home'

104

190

294

47.9%

38.0%

41.0%

No - Declined to Participate

257

269

5.5%

51.4%

37.5%

Totals

217

500

717

100.0%

Response Rate = No. of completed interviews with responding units/number of eligible responding units in the sample (Council of American Survey Research Organisations). Source: Author.

Data was (1) captured initially in an Excel spreadsheet and then (2) exported electronically for review in SPSSTM V12 and later, for final analysis, (3) in SPSS V15. Following generally agreed principles (Churchill and Iacobucci 2005, p.377; Hair et al 2005 p.44), subsequent screening includes analysis of:

Missing data – casewise deletion and mean substitution (5.2.2); Outliers (5.2.3); Non-coverage error and responsibility criterion (5.2.4); Respondent and non-response bias (5.2.5); Between-group comparison – ‘Stormark’ vs. ‘Consultancy’ clients (5.2.6); and © Bill Nichols 2009

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Tests for normality (5.2.7).

5.2.2 Missing Data: Case-Wise Deletion and Mean Substitution

Of 154 individually-coded responses, initial data tabulation and visual inspection identified three cases each characterised by >10% missing data incidence. Their case-wise deletion: (1) removes approximately two thirds of total missing data (173 items) but less than 1.5% of total cases; and (2) mitigates consequential issues with statistical techniques (Hair et al 2005, p.55).

The remaining 151 cases contain only 94 missing data items (0.007%), an indication of quality (Churchill and Iacobucci, 2005, p.413). Nonetheless any missing data diminishes both sample size and power (Roth 1994). In such instances (where case-specific omission is <10%), mean substitution is preferred to listwise or pairwise deletion options (Donner 1982; Hair et al 2005; Roth 1994). Its adoption here is simple and provides values for all cases (Hair et al 2005, p.61) although it (1) may understate variance estimates and, to a limited extent, (2) distort distribution of values.

5.2.3 Outliers

Outliers are “observations with a unique combination of characteristics identifiable as distinctly different from other observations… typically…an unusually high or low value on a variable or a unique combination of values across several variables” (Hair et al 2005, p.73).

They require careful assessment since they can “greatly

distort analysis results” (Thietart et al 2001, p.297). Principal classes are founded respectively on: (1) procedural errors, (2) extraordinary events, (3) extraordinary observations and (4) unique combinations of values.

The most common

explanation, and justification for elimination, is response error. It may arise from e.g. misunderstanding, misreading or fatigue (Churchill and Iacobucci 2005).

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Potential univariate outliers are identified by visual inspection complemented by examination via SPSS boxplot analysis of outliers. The latter represents data distribution pictorially and indicates extreme values by observation number (Hair et al 2005). Potential multivariate outliers - which may have more adverse effects on solutions (Bollen 1987) - are identified in a sample of ~150 by Mahalanobis D2/df values that exceed 2.5 (Hair et al 2005, p.75).

In total, among the remaining 151 cases, nine are characterised by repeated outliers: two by univariate outliers only – 48 and 80; six by multivariate outliers only - 54, 107, 108, 109, 144 and 149; and one, 113, by both (Appendix Five, Tables A5.1-A5.3).

Considering the nine collectively, the researcher’s task is to “explain the particular positioning” of outliers (Thietart et al 2001, p.298). In this case, considered response and, therefore, retention is supported on two grounds. First the level of multivariate variance is consistent with qualitative ‘Stormark’ feedback that issues raised are salient and controversial. Second the principal issue with the ‘univariate three’ is extreme negative responses on key items (e.g. LOY7 – exclusive intentions and TS1 – satisfaction with service). Since, (1) mean firm relationship duration is just under 43 months (below 5.3.3) and (2), deductively, one would expect approximately 3.5 clients on point of termination, such responses are integral. Integrity is therefore the overarching concern (Hair et al 2005). The cases are, nonetheless, highlighted for reference in subsequent analysis.

5.2.4 Non-Coverage Error: Responsibility Criterion

The third data preparation issue is non-coverage. It occurs to the degree that a sample obtained represents a target population inadequately: specifically here in terms of respondent responsibility. To mitigate this risk, a four-stage screening procedure was adapted from a source study (Patterson and Spreng 1997, Table 5.2):

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Table 5.2 - Qualifying Respondent Criteria Criterion

Classificatory Variable

Score

EITHER (1) complete accountability for execution of current programme (Q17 - 0-100%)

OC5 - Buyer Responsibility 2 (Table 4.10)

100.00%

OR (2): complete responsibility for appointment or most recent re-appointment of PR Consultancy (Q20 - bipolar 1-7)

OC6 - Buyer Responsibility 3

Seven

OR (3): strongly representative of views of management colleagues (Q18 - 0-100%)

OC7 - Buyer Responsibility 4

>50%

OR IF BELOW 50% REPRESENTATIVENESS (4): hierarchical management level (Q3 - 1-4) OC4 - Buyer Responsibility 1 Source: Author adapted from Patterson and Spreng (1997).

Above Four

Of 151 remaining available cases, 19 fail either criterion (1) or (2). Of these, three are also non-compliant with the self-report representativeness criterion (3) but just one (Lavender 74) reported Level 4 management hierarchy. This last alone, accordingly, failed pre-qualification. Collectively, these remaining 150 cases meet the previously-defined minimum 140-150 sample size criterion (4.9.5).

5.2.5 Respondent and Non-Response Bias

These 150 cases were next reviewed for random non-sampling respondent error (RN-SRE) and non-response (N-R) bias. RN-SRE affects the degree to which any sample statistic varies from a true population value. Since it may influence a sample statistic both towards, and away, from a true value, it is generally less problematic than the latter (Churchill and Iacobucci 2005).

N-R bias is more significant. It concerns the unknown nature of non-respondent answers (Churchill and Iacobucci 2005). Principal N-R investigative options include: either (1) making contact with an N-R sample; or (2) performing ex-post stratification since it is generally accepted that actual post-second reminder responses provide a fair proxy (Armstrong and Overton 1977; Thietart et al 2001). © Bill Nichols 2009

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In either case, a t-test assesses the significance of between-group statistical differences. In this instance client engagement rules precluded further Option (1) action while only 12 replies fell into the ‘post-second’ Option (2) category. Although no significant differences are discernible, potential N-R bias is an acknowledged limitation.

5.2.6 Between-Groups Comparison - ‘Consultancy’ vs. ‘Stormark’

Next a major source of potential inter-group differences lies in the data collected from the distinct client-groups:

‘Consultancy’ and ‘Stormark’.

These were

obtained respectively from five participating consultancies and an industry proprietary list held by the eponymous firm (4.9.6-4.9.8). To assess potential between-group statistical significance, the majority of variables were submitted to independent t-tests and analysis of variance. (An appropriate non-parametric test, using Mann-Whitney U, applied to the remainder.)

In the former instance, using the Levene test, the null hypothesis is that there are no between-group differences.

Rejection of this null (i.e. heteroscedasticity

demonstrated and differences confirmed) is indicated if, at the 95% significance level, F-values of >3.84 apply (Hair et al 2005, p.392). [Specifically Fisher’s F measures variance between groups/variance within groups: it indicates the level of actual treatment variance versus that engendered by inevitable sampling error (Churchill and Iacobucci, p.499).] Rejection is corroborated if, in terms of equality of means, recommended 1.96 t-critical values are exceeded at 95% significance (Hair et al 2005, p.390).

Following this procedure, Levene null rejection is provisionally indicated for eight construct scale variables (95% significance) and, marginally, two additional variables at 90%. For two of the eight (PSQ10 and CS1, highlighted amber Table 6.3), rejection is also nearly corroborated in terms of significance of equality of means – both marginally exceeding ρ = .05. subsequent analysis. © Bill Nichols 2009

Both variables are revisited in

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Table 5.3: Consultancy vs. Stormark Independent Samples Test – Results Levene's Test F

Sig.

t-test equality of means Sig. (2t Df tailed)

4.579 5.839 7.454 9.382 9.896 8.114 3.298 3.431

0.034 0.017 0.007 0.003 0.002 0.005 0.071 0.066

-0.121 0.683 -1.897 -1.027 1.757 1.524 0.614 0.467

148 148 148 148 148 148 148 147

0.904 0.495 0.060 0.306 0.081 0.130 0.540 0.641

4.215 0.042 8.088 0.005 20.562 0.000 3.812 0.053 0.496 0.482 Mann Whitney

0.872 -2.227 -3.848 1.784 -0.614

148 148 148 148 148

0.385 0.027 0.000 0.076 0.540

Key Construct Variables PSQ3 - ideas and solutions PSQ6 - defining campaign objectives PSQ10 -media pitching PSQ26 - opportunism/promises CS1 - Satisfied with appointment L7 - Committed to working with PSQ4 - Up-to-date methodologies (90%) L8 - Locked into a relationship (90%)

Classificatory Variables CM1 - Client Buyer Experience (Firm) CM2 - Personal Relationship Duration CM4C - Contact/Telephone Calls CM4B - Contact/Conferences CM4D - Contact/Emails

OC9 - Frequency of Review 1741.000 0.001 Source: SPSS Output. Red = exclusion. Amber highlights potential issues.

The issues identified may be a consequence of Stormark vs. Consultancy respondent profile differences. These are indicated by corroborated rejections of the null (highlighted red, Table 5.3) for three classificatory variables: (1) CM2 – personal relationship duration; (2) CM4C contact by ‘phone and (3), using MannWhitney, OC9 - Frequency of Review.

Specifically Stormark clients: (1) review

consultancies more frequently (every nine months vs. less than six (mean 2.55 vs. 1.88); (2) sustain longer personal relationships with account managers (σ = 45.23 vs. 30.99 months); but (3) make less frequent ‘phone contact (x 4.00 weekly vs. 9.5). These three items (and, by extension, other ‘contact’ interaction variables) are excluded from subsequent analysis. The two principal groups are otherwise treated as without significant differences. © Bill Nichols 2009

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5.2.7 Tests for Normality

Finally, turning to normality, the Kolmogorov-Smirnov and Shapiro-Wilks tests assess the degree to which observed and expected variable frequencies accord. Values greater than +/- 2.58 (at 0.01 probability level) or +/- 1.96 (at 0.05) indicate potential violations (Hair et al 2005, p.82).

Although none are identified,

complementary graphical plot inspections highlight three leptokurtic, or unusually peaked, distributions: PSQ10 (already the subject of concern, 5.2.6) = 7.052; PSQ20 = 4.818; and TR5 = 6.790. They do not, however, exhibit unusual skewness. Since skewness is generally considered a more meaningful indicator of normality than kurtosis, all three items are retained but subject to later analysis.

5.2.8 Summary

Electronic data handling and transfer mitigates the overall risk of error commission. Within the remaining scope of error, three cases only evidence substantial missing data and are deleted case-wise from the initial 154 responses. Mean substitution addresses the remaining, and very low, total of 94 (0.007%) missing items.

Additionally inspection of outliers identifies nine strongly

influenced univariate and multivariate cases whose contextual retention is however justifiable: conversely a risk of unconfirmed non-response bias is acknowledged.

Finally, although no significant violations of normality are

identified, a small number of between-group differences are noted resulting in the exclusion of three classificatory variables.

5.3

Descriptive Statistics

5.3.1 Overview

This section describes respondent profiles, discusses their alignment with expectations and explores their implications. It covers: first client firms’ industry,

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classification by size and primary PR service requirements (5.3.2); and second respondents’ status, responsibilities and the duration of their relationships with consultancies (5.3.3).

5.3.2 Industry-Practice, Firm Size and Service Requirements

A benchmark for this study’s population distribution is provided by a UK industrywide survey carried out (15 months prior to this study’s fieldwork) by the Centre for Economics and Business Research on behalf of the Chartered Institute of Public Relations (CEBR/CIPR 2005).

Figure 5.1 provides a comparison first of practice area findings versus those reported for the sample (Variable OC3). The principal differences in this study are its (1) strong technology representation (35% vs. 8%) and (2) converse small publicand third-sector group (2% vs. 36%).

Figure 5.1: Industry Practice Areas CIPR 05

Author 06 Tech

8 11

16 11

Manuf

Tech

FMCG

Manuf

Retail Prof/Bus Serv Financial

42 4

FMCG

Retail 15

12 11

Pub/3rd Sector Other

P&BS Financial

Pub/3rd Other

Source: CEBR/CIPR-2005/Author 2006

To investigate potential technology-sector bias in the current data-set, an independent samples t-test procedure was again adopted (5.2.6).

It finds that

only one classificatory variable (OC8-Buyer Experience of Account Managers) is

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close to rejecting the null (i.e. confirming a consequent difference). Based on the greater reported experience of current firm account managers among technology clients (σ = 2.57 vs.1.98), it registers F = 6.044 (ρ = .015) and t=1.877 (ρ = .061).

Second, the same CEBR/CIPR benchmark also reports that 50% of UK client firms employ over 500 staff (2005). This is broadly consistent with the sample’s 38% representation in the same group (σ employee numbers = 2623, St. Dev: 8011.22, Variable OC2). The sample also aligns with the UK’s tripartite small/medium/large (= 0-49; 50-249; >250) commercial classification (Department for Business, Enterprise and Regulatory Affairs 2008). The latter’s benchmark by-revenue split (47-12-41%) is also broadly consistent with the sample’s 37-15-48%.

Third, turning to services, sample respondents’ principal requirements for general media relations support and communications strategy (Variable OC1) are again consistent with the benchmark’s ranking order of consultancy activity (CEBR/CIPR 2005, Table 5.4).

Table 5.4: Principal Service Needs Client Respondents

Valid %

Consultancy Functions - Ranking Order - CIPR 2005

General Media Relations

68.67

Media Relations

Communication Strategy/Planning

14.00

Strategic Planning

Marketing/Marketing support

6.00

Event Planning

Other

4.67

Branding and Marketing

Public Affairs

4.00

Public Affairs

Event/Exhibition Management

1.33

Investor/financial relations

0.67

Market Research

0.67

Investor Relations

100.00 Source: CEBR/CIPR-2005

In summary, findings on all three points (practice area, firm size and service requirements) provide confidence that the sample represents the population.

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5.3.3 Respondent Status, Responsibilities and Relationship Duration

This

section

reports

respondents’

management

status,

their

responsibilities and both the firm- and personal relationship duration established between respondents and their consultancies.

First, client-respondents self-assign management status hierarchically to one of four bands: (1) Director/CEO, (2) Senior and (3) Middle Manager and (4) Department Member. The three highest bands total 88% (of which 74% select the two intermediate levels). This total exceeds the 74% predicted by the CEBR/CIPR’s salary-based categorisation of in-house staff (2005).

The possible 14% over-

statement may indicate a self-report bias limitation (Arnold and Feldman 1981). [It is, however, less than a median 27% bias reported in one healthcare meta-study (Adams, Soumerai, Lomas and Ross-Degnan 1999)].

Second, respondents’ perceptions of their management responsibilities are closely aligned within- and between-groups. Marginally, as expected, senior managers believe themselves: (1) more accountable for programmes than others (σ = 83.68 vs. sample σ = 79.31); (2) more representative of views (σ = 83.98 vs. σ = 83.51 although below Department Members); and (3) most responsible for the last consultancy appointment decision (σ = 5.72 vs. sample σ = 5.39). The overall average review period (eight months) is more frequent than the 12 assumed by some practitioners (PR Panel 2005, Table 5.5).

More significant between-groups variation (in terms of both means and standard deviations) is evidenced in terms of relationship duration: both (1) personal between individual client and consultancy (Variable CM2); (2) and firm-to-firm (between client firm and consultancy, Variable CM3).

These show that as

expected: (1) firm-to-firm relationships are usually longer than personal ones and (2) especially so among middle managers where responsibility is passed on from time to time (Table 5.6).

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Table 5.5: Management Categories and Responsibilities Programme Accountability

Representative of Views

Review Decision

Frequency of Review

OC7

OC6

0C9

(%)

(1-7)

Months

Mean

78.86

82.81

5.57

6.00

St.Dev

24.43

22.96

1.54

1.26

Mean

83.68

83.98

5.72

8.00

St.Dev

26.72

17.02

1.44

1.17

Mean

81.02

82.10

5.46

8.00

St.Dev

25.19

21.64

1.74

0.86

Mean

60.83

87.06

3.94

11.00

St.Dev

33.97

12.67

2.07

1.54

Mean

79.31

83.51

5.39

8.00

150

St.Dev

27.48

19.16

1.72

1.14

Variable OC5 Director/CEO

Senior Management

Middle Management

Department Member

Total

Source: SPSS Output.

Table 5.6: Management Status and Firm/Personal Durations (Months) N

Personal Mean

Personal St.Dev

Firm Firm Mean St.Dev

Director/CEO

14.00%

29.14

21.42

31.90

22.86

Senior Management

38.00%

32.44

38.09

35.18

32.64

Middle Management

36.00%

41.02

42.39

53.92

54.74

Department Member

12.00%

39.61

37.12

46.04

33.98

150

100.00%

35.93

37.76

42.77

41.87

Total Source: SPSS Output.

Of most importance, the data (1) confirms anecdotal evidence of continuing decline in incumbency duration and (2) quantifies this study’s ‘loyalty conundrum’. It may be recalled (Chapter 1) that average US advertising agency tenure declined from 7.2 years (1984) to 5.3 by the mid 1990s (Ewing, Pinto and Soutar 2001). More recent evidence indicated that two-thirds defected in less than five years

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(Kulkarni, Vora and Brown 2003). Although today a few incumbencies still extend to 20 years (Figure 5.2), this data confirms further reduction and a mean UK firmto-firm relationship of just over 3.5 years (42.77 months, Table 5.6).

Figure 5.2: Duration Statistics Personal RD (Months)

Firm RD (Months)

Source: SPSS Output.

By extrapolating further from the CIPR/CEBR benchmark (5.3.2), this finding also ‘sets a price’ (£342 million annually) on the ‘loyalty’ conundrum.

5.3.4 Summary

This section identifies an excess sample weighting in favour of technology industry clients but no significant profile bias arising. It also finds normative firm size distribution and service requirements. Similarly, versus expected norms, it reports marginally greater seniority of respondents and consistent responsibility levels for programme management and consultancy appointment.

Finally it also

corroborates reports of continuing reduction in consultancy incumbency.

suggests that for the UK PR Consultancy sector, the annual ‘disloyalty’ charge is worth £342 million.

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5.4

Principal Scales: Descriptives and Reliability

5.4.1 Introduction

This next section reports on the seven principal construct scales. Each is reviewed by considering (1) characteristics of individual items, (2) those of the overall scale and (3) the relationship between individual items and the entire scale.

Each

discussion integrates descriptive statistics, initial scale refinement and scale reliability issues.

Commonly-referenced diagnostic measures are the:

1. Corrected item-total correlation (ITC), i.e. the Pearson correlation coefficient.

Measuring internal consistency and indicating relationship

strength between an individual item and other scores, an ITC score of >0.50 complies with minimum rule-of-thumb acceptability (Nunnally 1978); 2. Squared multiple correlation (SMC), or factor-loading.

Reporting the

degree to which observed item-variance is explicable by other scale-items the minimum recommended guideline score (N = 150) is 0.45 (Hair et al 2005, pp.128, 796); 3. Cronbach’s Alpha (α): offers the most widely-accepted test of the ‘internal homogeneity’ of items or overall scale reliability (Carmines and Zeller 1990; Churchill and Iacobucci 2005, p.296; Cronbach 1951). For most purposes “0.70 or higher will suffice” (Nunnally 1978, p.245); and 4. Standardised α scores which adjust the basic α scores to accommodate variation in item numbers (Thietart et al 2001).

Extended refinement and validity issues are addressed via EFA/CFA in later sections.

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5.4.2 Scale 1 - Loyalty-Commitment

The Loyalty-Commitment scale is designed to capture a client’s willingness to maintain a preferred service relationship now and consistently in the future despite situational influences and marketing efforts having the potential to cause switching behaviour (Oliver 1999). The initial scale comprises ten items, eight derived principally from two reference studies (Grayson and Ambler 1999; Patterson and Spreng 1997).

The average whole scale score is 53.25 with a standard deviation of 11.21. Most item means are high (range 5.01-5.82) and standard deviations consistent (1.171.59). There are two exceptions in each instance:

Among means the highest (L8 - ‘locked into a relationship’ inverse) and lowest (L10 - ‘willing to recommend publicly) at respectively 5.95 and 4.26; and Among standard deviations L10 again (2.07) and L1 (‘expect to consider only’/exclusive intentions) at 1.84.

By generating considerable respondent variation and, in one case, an extreme score, both L1 and L10 appear to have tapped effectively the degree of client motivation (Caruana 2002; Gremler and Brown 1999; 1996). Separately L8’s high score indicates low sample compulsion (or inertia).

[All scale items are

summarised and, for convenience, ranked by item mean at Table 5.7]

Further assessment suggests that both L8 and L10 present concerns. First L8 (= 0.280) is significantly below 0.50 rule-of-thumb item-total correlation (ITC) acceptability (Nunnally 1978). Second both L8 (=0.129) and L10 (=0.330) are well below 0.45 squared multiple correlation (SMC) guidelines (Hair et al 2005, pp.128, 796). A decision to remove them is predicated additionally on (1) their common status as newly-tested and previously unvalidated items (PR Panel 2005) and (2), in L8’s case only, by a small increase in Cronbach’s α facilitated by omission. © Bill Nichols 2009

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In addition, four items (L3, L4, L6 and L7) achieve only adequate compliance in terms of both ITC and SMC – a marginal status underlined by the minimal impact of their elimination on Cronbach’s α. This point is revisited in detail in subsequent analysis (5.6.7).

Table 5.7: Loyalty-Commitment – Key Statistics α 10 items = 0.890 (St. 0.897); α 8 items = 0.904 (St. 0.908).

Item Mean Ranked

Item St.Dev

Corrected Item-Total Correlation

Squared Multiple Correlation

Cronbach' s α (*)

L8 - Locked/Switching (14M)

5.95

1.57

0.280

0.129

0.902

L9 - Private Advocacy (14A)

5.82

1.48

0.734

0.630

0.872

L5 - Committed/Attitude (14B)

5.62

1.43

0.831

0.738

0.866

L4 - Caring/Attitude (14H) L7 - Committed to Working/Exclusive (14J) L6 - Part of Team/Identification (14E) L1 - Expect to Consider/Intentions (14C)

5.57

1.17

0.648

0.522

0.880

5.42

1.52

0.751

0.654

0.871

5.36

1.59

0.622

0.496

0.879

5.19

1.84

0.623

0.490

0.880

L3 - Feeling Warmer/Attitude (14N)

5.04

1.52

0.615

0.489

0.880

L2 - Influence/Altruism (14O)

5.01

1.47

0.768

0.669

0.870

L10 - Public Advocacy (14P)

4.26

2.07

0.559

0.330

0.888

Mean

Min

Max

Range

Variance

5.32

4.26

5.95

1.68

0.235

Mean

Variance

St.Dev

53.25

125.62

11.21

Item Means Scale Statistics (*) if deleted: Source: SPSS Output. Red = item removal; Amber highlights item issues.

Finally, regards scale reliability, both initial 10- and revised eight-item LoyaltyCommitment scales are well above the 0.70 guideline (0.904 - 8-item; 0.890 -10item). Compliance is also supported by: (1) the standardised α scores (0.908/0.897 respectively); and (2) comparative prior scores obtained for the embedded threeitem scales: 0.97 (Patterson and Spreng 1997) and 0.73/0.78 (Grayson and Ambler 1999; Moorman et al 1992).

Nonetheless, the α coefficient constitutes a ‘true

indicator of reliability’ only when six or less items are used and/or the concept is formally unidimensional. To confirm the latter an exploratory factor analysis is recommended (Thietart et al 2001, p.203). This follows at 5.5. © Bill Nichols 2009

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5.4.3 Scale 2 - Trust

Among postulated Loyalty-Commitment antecedents, the five-item Trust scale captures a client-buyer’s “willingness to rely on an exchange partner in whom one has confidence” (Moorman, Zaltman and Deshpande 1992, p.315). The average whole scale score is 26.88 (σ = 5.37).

Initial scale reliability is comfortably above the 0.70 ‘rule-of-thumb’ (α = 0.795; std. α = 0.814). It is also consistent with its original deployment (0.84, Moorman et al 1992); well above later replications (four items = 0.63; two post-CFA items = 0.74; Grayson and Ambler 1999); and aligns with overall prior evidence (Table 5.8):

Table 5.8: Trust Comparative Reliability Author (5 items)

G&A (4 )

MZD – 5

Cronbach's Alpha

0.80

0.63

0.84

Scale Mean

5.38

5.44

5.32

Standard Deviation

1.04

0.98

1.11

Sample Size

150

200

799

Professional Service

Advertising

M/Res

Geography UK UK US Sources: SPSS Output; G&A - Grayson and Ambler (1999); MZD - Moorman, Zaltman and Deshpande (1992).

Conversely both the individual item means (range: 4.11 – 6.46) and standard deviations (1.14-1.86) exhibit considerable variation and suggest potential complexity in the scale. There are two candidate-items for elimination (T1 and T5, Table 6.9). Both fall short of ITC and SMC recommended minimums and were removed by Grayson and Ambler (‘G&A’ 1999). Additionally, T5 was already noted for its leptokurtic profile (5.2.7) while T1’s elimination improves overall α reliability significantly to 0.826 (Table 5.9). By eliminating both scale reliability is improved (α = 0.795; std. α = 0.814).

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Table 5.9: Trust – Inter-Item Statistics

T1 - Decisions (10A)

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Squared Multiple Correlation (SMC)

Cronbach's Alpha if Item Deleted

22.7781

16.848

0.447

0.228

0.826

T2 - Get it Right (10C)

21.6103

17.267

0.689

0.489

0.719

T3 - AM to Perform (10E)

21.2855

17.899

0.722

0.600

0.715

T4- Team to Perform (10G)

21.4626

18.243

0.665

0.547

0.731

20.4321

20.974

0.465

0.275

0.788

T5 - Principle (10I)

Source: SPSS Output: Red highlight issue/removal of item. Amber = Issue

Additionally item T2, at 0.489 (SMC), is only marginally above the 0.45 SMC norm. It was also eliminated by Grayson and Ambler (1999) during confirmatory factor analysis (CFA, further 6.6.7) and retained only provisionally. Overall, the outcome supports

Grayson

and

Ambler’s

(1999)

call

re-conceptualise

the

operationalisation of Trust. On fresh review, Moorman et al’s (1992) original passive definition - ‘rely on the exchange partner’ – contrasts with the scale’s active encapsulation of progressive ‘empowerment’ as visualised at Figure 5.3. (For convenience of representation this diagram multiplies by 10 the SMC statistic).

Figure 5.3: Trust - Semantic Analysis 7 6

5 4 3

Mean

SMC*10

1 0 Principle (A)

Manager Tasks (B)

Team Tasks ©

No Monitoring (D)

Decisions (E)

Source: Author.

Applying semantic analysis, the client: begins at (A) with the principle of trust; next permits the account manager to carry out tasks on first his own (B) and then more

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generally his team’s behalf (C); extends at (D) to allowing performance without monitoring; and finally at (E) provides complete decision-making empowerment. This framework is matched by a concomitant and anticipated left-to-right reduction in mean scores (i.e. the higher the level of empowerment, the lower the rating). Finally the factor loading (SMC x10) peaks at B thereby revealing B and C as the construct core.

5.4.4 Scale 3 – Transactional Satisfaction

Next, among postulated Loyalty-Commitment antecedents, a new contextuallyadapted four-item scale for Transactional Satisfaction (TS) is based on specific conceptual guidelines (Oliver 1999). It measures a client’s contentment with the pleasure/benefits obtained, and investment required, during a specified period of service consumption and fulfilment experience (Giese and Cote 2003; Hellier et al 2003).

Average whole scale score is 21.39 (St. Dev = 4.39, σ = 5.35). Individual itemmeans are generally high (5.01-5.55) and standard deviations consistent (1.131.34). All ITCs and SMCs and the high α (0.909) for overall scale reliability are all comfortably above respective 0.50/0.45/0.70 benchmarks (Table 5.10).

Semantic analysis of the scale, meanwhile, runs (left-to-right) from first principles to task execution. This indicates general service (C) as the construct core and offers guidelines for (if required) further refinement (Figure 5.4).

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Table 5.10: Transactional Satisfaction - Descriptives, Reliability and Item Total Statistics Cronbach's Alpha

0.909

Alpha Based on Standardised Items

0.911

Mean, St.Dev and Correlations N

Mean

St.Dev

TS1

TS2

TS3

TS4

TS1 - Service (11A)

150

5.48

1.24

1.000

0.774

0.747

0.783

TS2 - Advice (11B)

150

5.34

1.13

0.774

1.000

0.708

0.682

TS3 - Proactivity (11C)

150

5.01

1.34

0.747

0.708

1.000

0.618

TS4 - Quality of Tasks (11D)

150

5.55

1.23

0.783

0.682

0.618

1.000

Item Mean

150

5.35

Item-Total Statistics

Scale Mean

Scale Variance

Corrected ITC

SMC

Alpha

St.Dev

Entire Scale

21.39

19.23

TS1 - Service (11A)

15.90

10.66

0.869

0.759

0.855

TS2 - Advice (11B)

16.04

11.76

0.802

0.649

0.882

TS3 - Proactivity (11C)

16.37

10.77

0.757

0.600

0.898

15.84

11.40

0.761

0.628

0.894

TS4 - Quality of Tasks (11D) Source: SPSS Output.

4.39

Figure 5.4: Transactional Satisfaction - Semantic Analysis 8 7 6 5 4

Mean

SMC*10

2 1

0 Proactivity Principle (A)

Advice (B)

Source: Author (based on SPSS Output).

Overall Service (C )

Task Quality (D)

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5.4.5 Scale 4 – Cumulative Satisfaction

In contrast to its transactional ‘cousin’ (5.4.4), the three-item Cumulative Satisfaction measures a client’s learned predisposition towards an aggregation of consumption experience (Hellier et al 2003; Olsen and Johnson 2003). It replicates a prior deployment in management consulting (Patterson and Spreng 1997). Whole scale mean is 16.34 (St. Dev = 3.65). Item means are within a high but narrow range (5.31-5.61) and standard deviations are consistent (1.20-1.46). ITCs, SMCs and α co-efficient for scale reliability are all comfortably above respective 0.50/0.45/0.70 threshold levels. The last (@0.921) compares favourably with an original CFA construct reliability of 0.95 (Patterson and Spreng 1997, Table 5.11):

Table 5.11: Cumulative Satisfaction Descriptives, Reliability and Item Total Statistics Cronbach's Alpha Pre and Final Alpha (Standardised) Pre and Final

0.921 0.928

0.951 0.951

N 150 150 150 150

Mean 5.61 5.31 5.42 5.46

Mean, St.Dev and Correlations CS1 Decision (14e) CS2 Hedonic (14g) CS3 Utility (14i) Item Mean

Item-Total Statistics

Scale Mean

Scale Variance

Entire Scale

16.34 13.39

St.Dev 1.46 1.20 1.26 Corrected ItemTotal Corr-tion

CS1 1.000 0.755 0.769

CS2 0.755 1.000 0.908

SMC

C'bach's Alpha if eliminated

CS3 0.769 0.908 1.000

St.Dev

P&S 1997 SMCs

3.65

(if item deleted) CS1 Decision (14e) CS2 Hedonic (14g)

10.72 5.76 11.03 6.57 10.92 6.23

0.780 0.878 0.887

0.610 0.833 0.841

0.951 0.864 0.850

CS3 Utility (14i) Source: SPSS Output/Patterson and Spreng (1997). P&S = Patterson and Spreng (1997).

However, item CS1 (satisfaction with decision to appoint/re-appoint) presents issues. Its correlations are weaker and its SMC significantly lower than for other items. A decision to eliminate – supported by a substantial increase in Cronbach’s α (0.951) - is also consistent with a prior concern identified in the preliminary © Bill Nichols 2009

0.902 0.756 0.902

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between-groups analysis (6.2.6; F = 9.896, ρ = 0.002; t = 1.757, ρ = 0.081). The item’s behaviour may be attributable to contextual semantic adaptation. Where previously (Patterson and Spreng 1997), it referred to a short-term management consulting assignment, it now addresses long-term appointment incumbency. In this context, ‘happiness with appointment’ may more accurately reflect a loyaltyindicator (a point revisited in Chapter Six).

5.4.6 Scale 5 – Perceived Value

The next Loyalty-Commitment antecedent, Perceived Value, assesses consultancy service performance taking into account all cost/monetary and time/nonmonetary investment factors (Oliver 1997; Woodall 2003). Commonly rendered as a single-item ‘value-for-money’ scale, the current four-item iteration responds to calls for enrichment (Patterson and Spreng 1997; Yang and Peterson 2004, Table 5.12).

Table 5.12: Perceived Value Descriptives, Reliability and Item Total Statistics Cronbach's Alpha

0.931

Alpha (Standardised)

0.931

Mean, St.Dev and Correlations N

Mean

St.Dev

PV1

PV2

PV3

PV4

PV1 - Value for Money (13A)

150

5.11

1.46

1.000

0.859

0.793

0.762

PV2- Justifies In and Out (13B)

150

5.18

1.40

0.859

1.000

0.724

0.686

PV3 - Acceptable Fees (13C)

150

5.15

1.31

0.793

0.724

1.000

0.810

PV4- Fair Value (13D)

150

5.39

1.25

0.762

0.686

0.810

1.000

Item Mean

150

5.21

SMC

C'bach's Alpha if elim’ed

St.Dev

Corrected ItemTotal Corr-tion

Item-Total Statistics

Scale Mean

Scale Variance

Entire Scale

20.83

24.33

PV1 - Value for Money (13A)

15.72

12.91

0.888

0.811

0.893

PV2- Justifies In and Out (13B)

15.65

13.82

0.821

0.743

0.915

PV3 - Acceptable Fees (13C)

15.67

14.29

0.841

0.733

0.909

PV4- Fair Value (13D)

15.44

14.99

0.809

0.695

0.920

4.93

(if item deleted)

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Whole scale mean total is 20.83 (St.Dev, 4.93). ITCs, SMCs and the high α coefficient (0.931) are all again significantly above threshold levels.

Significantly, the average item mean (@5.21) is lower than for comparable constructs (e.g. Trust, Transactional Satisfaction) and indicates a differentiated response. It is, even so, supported by a high 5.39 rating for the questionable PV4/Fair Value which, the data suggests, may be the ‘outlier’ of the four variables: weakest inter-correlations, lowest SMC and least impact on α if eliminated. This finding is carried down for further analysis.

5.4.7 Scale 6 – Disconfirmation

Disconfirmation is ‘the cognitive comparison between the *client’s+ prepurchase standard and what he or she actually received” (Spreng and Page 2003, p.32). The previously validated two-item scale is constructed using the Additive Difference Model (Spreng and Page 2003; Tversky 1969).

Scale mean is 10.77 (St.Dev, 2.08). Average item mean (5.38), in turn, is above Perceived Value and in line with other attitudinal scales while standard deviation is very low at 1.09. ITCs, SMCs and α co-efficient (0.889) for scale reliability again all comfortably exceed threshold levels (Table 5.13).

These positives notwithstanding, there is evidence of multicollinearity – based on identical skewness and kurtosis statistics for both items. This may explain the negative average covariance. Alternatively “with small sample sizes and small numbers of items… the true population covariances among items are positive (but) sampling error has produced a negative average covariance in a given sample of cases” (Nichols 1999).

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Table 5.13: Disconfirmation Descriptives, Reliability and Item Total Statistics Cronbach's Alpha

0.889

Alpha (Standardised)

0.881

Mean, St.Dev and Correlations N

Mean

St.Dev

D1 - Meeting Expectations (9A)

150

5.35

1.09

1.000

0.800

D2 - Feeling About Performance (9B)

150

5.41

1.10

0.800

1.000

Item Mean

150

5.38

Item-Total Statistics

Scale Mean

Scale Varianc e

Corrected Item-Total Corr-tion

SMC

C'bach' s Alpha

Entire Scale

10.77

4.310

D1 - Meeting Expectations (9A)

5.41

1.22

0.800

0.640

D2 - Feeling About Performance (9B) ** Value due to negative average covariance.

5.35

1.18

0.800

0.640

St.Dev 2.08

(if item deleted)

5.4.8 Scale 7 – Perceived Service Quality

Finally the seventh construct scale, Perceived Service Quality, is designed to capture a client’s “overall assessment of the standard of a service” (Hellier et al 2003, p.1766). The initial 27-item format for Professional Business Services (PBS) is sourced principally from two prior scales (Grayson and Ambler 1999; Patterson and Spreng 1997). Average scale score is 147.20 with a standard deviation of 22.09. Indicating considered response, the range of mean assessments (illustrated, for convenience, in ranking order, Table 5.14) is generally greater (4.38-6.31) than evidenced by the attitudinal constructs.

Broadly items with a service or ‘functional’ orientation are rated higher than ‘technical’ or results variables (Gronroos 2000). The highest of the latter (PSQ12 – ‘results benefiting marketing effectiveness’) ranks only 12th. Standard deviations are generally consistent (1.043-1.391) with six significant exceptions (1.615-1.944; PSQ05, 07, 08, 11, 25, 26 - highlighted amber, Table 5.14).

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Table 5.14: Perceived Service Quality Descriptives, Reliability and Item Total Statistics Cronbach's Alpha

(Pre) 0.933

Alpha (Standardised)

0.944

Entire Scale (PSQ)

Final 0.942

Items: 27/(22)

N=150

0.947

Scale Mean

Scale Variance

St.Dev

147.1 20

488.130

22.094

Mean

St.Dev

Scale Mean

Scale Variance

(If Item Del)

Correct ed ITC

SMC

α (If Item Del)

10 Media Pitching (7e)

6.31

1.342

140.79

465.80

0.356

0.432

0.933

20 Responsiveness (8c)

6.07

1.043

141.04

457.79

0.653

0.669

0.930

24 Dependability (8m)

5.97

1.064

141.14

451.37

0.792

0.778

0.928

14 Rapport

5.94

1.051

141.17

454.94

0.715

0.780

0.929

21 Professionalism (8j)

5.86

1.141

141.25

451.30

0.737

0.929

19 AM Interaction (10h)

5.83

1.006

141.28

455.00

0.745

0.784

0.929

18 Client Orientation(10f)

5.82

1.147

141.30

450.11

0.753

0.731

0.928

09 Content(7d)

5.74

1.378

141.37

464.63

0.362

0.395

0.933

23 Reliability (8f)

5.72

1.325

141.38

447.74

0.707

0.710

0.929

27 Slow on Complaints (inv)

5.72

1.391

141.39

454.47

0.536

0.526

0.931

15 Relationships (8k)

5.71

1.211

141.37

453.33

0.667

0.734

0.929

13 Effective Use of Res (8n)

5.50

1.168

141.61

453.04

0.678

0.677

0.929

05 Networking(8d)

5.48

1.278

141.62

451.13

0.651

0.615

0.929

01- Goal Understanding (8h) 17StrategicUnderstanding (10d) 02 Problem Understanding (8l)

5.48

1.202

141.60

449.78

0.762

0.783

0.928

5.47

1.283

141.64

451.56

0.639

0.671

0.929

5.41

1.187

141.68

453.14

0.681

0.760

0.929

25 Alters Facts (Inv)

5.23

1.762

141.86

460.91

0.324

0.319

0.935

22 Creativity (8e)

5.21

1.200

141.91

449.92

0.717

0.726

0.929

08 Solutions(7c)

5.15

1.615

141.98

458.05

0.396

0.499

0.933

12 Results (8a)

5.15

1.276

141.96

448.95

0.693

0.686

0.929

26 Keeps Promises (Inv)

5.11

1.775

142.02

452.44

0.430

0.476

0.933

03 Ideas(8i)

5.08

1.190

142.04

450.97

0.704

0.718

0.929

11 Project/M (7f) 16 Productive Meetings (10b)

5.01

1.776

142.12

459.67

0.331

0.474

0.935

4.99

1.141

142.12

455.47

0.641

0.605

0.930

07 Strategy(7b)

4.79

1.800

142.31

446.81

0.501

0.638

0.932

04 Methodologies(8b)

4.73

1.379

142.36

455.64

0.524

0.487

0.931

06 Campaign Plan (7a)

4.38

1.944

142.76

448.98

0.429

0.581

0.934

Scale Mean

5.45

Source:

SPSS

Output

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Five of these exceptions are, in turn, among seven variables which are also below the acceptable 0.50 ITC ‘rule of thumb’ (6.4.1) of which three (PSQ09, 10, 25) are also below the 0.45 SMC guideline for a 150-strong sample (Hair et al 2005). *PSQ10, notably, also fails Levene’s and the t-test (6.2.6) and manifests high kurtosis (6.2.7): in explanation ‘media outreach’ (PSQ10) is commonly the most controversial aspect of a PR Consultancy’s work.+ Elimination of these three (highlighted red, Table 5.14) is consistent with a neutral, or positive, impact on the α co-efficient.

Elimination is also applied to two further variables (PSQ06 and PSQ11, highlighted red) which likewise share high standard deviations and report below the 0.50 ITC guideline whose removal increases scale reliability.

Finally two similar items

(PSQ08 and PSQ26), whose scale reliability impact is neutral, are retained provisionally in the interests of scale content validity.

The revised 22-item scale improves an already high α coefficient to 0.942 (vs. 0.933 for 27-items). Limited prior reliability corroboration is provided by final CFA construct reliabilities (0.73-0.95) for the six extracted factors in Patterson and Spreng’s (1997) incorporated 12-item scale.

Nonetheless, as with Loyalty-

Commitment (6.4.2), since the α coefficient constitutes a ‘true indicator of reliability’ only when six or less items are used, an exploratory factor analysis (EFA) is also recommended (Thietart et al 2001, p.203).

This is addressed in the next

main section.

5.4.9 Summary

This section has described, refined and reported the reliabilities of the seven principal construct scales incorporated in the proposed model. A summary of actions adopted and final reliabilities is provided at Table 5.15. Notwithstanding the excellent results obtained, it is important to remember that scale reliability is an indication only of measurement consistency and not of ultimate validity – as per the mis-blown beer glass illustration (4.6.1). © Bill Nichols 2009

Consistent with the Anti-

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thesis/Competing Model perspective, what is measured in each case may either overlap with other constructs and/or aggregate dimensionality. Testing for construct validity is fundamental to the following sections of this chapter.

Table 5.15: Scale Reliability: Overview and Summary Final α

Scale

1. Loyalty-Commitment

0.904

Final Std. α

Variables removed

Initial items

Final Items

0.908

L8 and L10. Four others highlighted as weak.

5 4 3

4 2 27

2. Trust

0.838

0.840

T1 and T5 (and T2ighted as potential domain outlier)

3. Transactional Satisfaction

0.909

0.911

None

4. Cumulative Satisfaction

0.951

CS1

5. Perceived Value

0.931

None (but PV4 highlighted as potential domain outlier)

6. Disconfirmation

0.889

0.881

None

7. Perceived Service Quality

0.942

0.935

PSQ06, 09,10, 11, 25

5.5.

4 2

Exploratory Factor Analysis

5.5.1 Introduction and Principles

Extending the initial scale reliability analysis (5.4), this section reports exploratory factor analyses (EFA) for the two greater-than-six item scales: (1) LoyaltyCommitment (eight-items reduced from ten following initial scale refinement) and (2) Perceived Service Quality (22 from 27).

Factor analysis is an interdependence technique which explores the structure of interrelationships among a large number of variables (Hair et al 2005, p.104; Churchill and Iacobucci 2005, p.568). An EFA: determines “the most likely factor structure” (Cramer 2003, p.28); provides “a parsimonious description of a complex multi-faceted intangible” (Remenyi et al 1998, p.222); and enables a researcher to identify underlying theme(s). © Bill Nichols 2009

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Both EFAs principally employ the orthogonal principal components analysis (PCA) method. PCA transforms “interrelated variables into a set of uncorrelated linear combinations” (Churchill and Iacobucci 2005, p.574). “Each principal component factor explains more variance than would the loadings obtained from any other method of factoring” (Nunnally 1978, p.357). PCA’s adoption also replicates the principal literature tradition: over 50% of 1700 studies in a recent two-year period deployed PCA with Varimax rotation and Kaizer normalisation (Costello and Osborne 2005). Varimax is most successful in providing optimum separation of factors (Grimm and Yarnold 1995, p.105; Hair et al 2005, p.126; Nunnally 1978, p.385).

However, PCA fails to discriminate unique and shared variance. In contexts of high inter-item correlation, oblique methodologies offer a compelling alternative (Costello and Osborne 2005).

(If factors are truly uncorrelated, orthogonal and

oblique rotation should produce nearly identical results). The oblique option applies particularly to the Loyalty-Commitment case.

To assess PCA factor validity, the following are key:

(1) The latent root criterion: only factors with eigenvalues greater than one – i.e. accounting for at least the variance of a single variable if retained – are considered (Thietart et al 2001, p.305); and (2) The % of variance criterion: in the social sciences, a minimum solution of 60% is held ‘satisfactory’ (Hair et al 2005, p.120).

5.5.2 EFA 1: Loyalty-Commitment – Baseline Version

The

initial

PCA-based

eight-item

Loyalty-Commitment

EFA:

(1)

reports

communalities exceeding recommended 0.5 guidelines (Hair et al 2005, pp.130131); and (2) explains adequate variance above the 60% guideline - 61.18% versus 53.70% for the original 10-item scale. It is also compliant on all key parameters:

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1. Sample size (150) exceeds rule-of-thumb multiplication of item-numbers by five observations per item, i.e. in this case 8 x 5 = 40 (Hair et al 2005); 2. Visual inspection confirms all inter-correlations in the 8 x 8 matrix are above the 0.30 guideline (Churchill and Iacobucci 2005); 3. The anti-image correlation matrix (the negative of the partial correlation matrix) reveals only two co-efficients greater than 0.40 and a further four above 0.30 – providing further reassurance (Hair et al 2005); 4. Individual measures of sampling adequacy (MSA) are clustered in the range 0.843-0.906 - all above the 0.80 guideline (Hair et al 2005); 5. Penultimately, the Kaiser-Meyer-Olkin (KMO) index measure of sampling adequacy quantifies the degree of intercorrelation among variables and the appropriateness of factor analysis. It is close to the 0.90 border between ‘meritorious’ and ‘marvellous’ (Churchill and Iacobucci 2005; Hair et al 2005, p.99). This applies to both final 8-item and, comparatively, original 10-item iterations (Table 5.16); 6. Finally, the complementary Bartlett test of sphericity confirms the statistical probability that the matrix has significant correlations among at least some of its variables, ρ =< 0.001 (Hair et al 2005).

Table 5.16: Loyalty-Commitment EFA - Preliminaries/MSA

Kaiser-Meyer-Olkin Sampling Adequacy.

Measure

8-item

10-item

0.883

0.899

710.928 28 0.000

784.661 45 0.000

Bartlett's Test of Sphericity

~Chi-Square

Source: SPSS Output.

Df Sig.

5.5.3 EFA – Loyalty-Commitment – Alternative Solutions

Since the first unidimensional solution (5.5.2) is contrary to interpersonal loyalty theory which postulates two underlying factors - relationship maintenance

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behaviours and relative attitude (Rusbult et al 1999) – further tests were conducted. The oblique PCA methodology (with Oblimin rotation) confirms unidimensionality. Finally constrained two-factor Varimax and Oblimin procedures both report potential separation between predominantly behavioural (Factor 1) and attitudinal (Factor 2) variables but substantial mitigation by cross-loaders (Table 5.17).

They are, however, non-significant as indicated by second factor

eigenvalues (in both cases = .765, Hair et al 2005, p.120).

To report these

findings, Table 5.17 includes only factor loadings of 0.45 or higher as appropriate to a 150-strong sample (Hair et al 2005, p.128).

Table 5.17: Loyalty Commitment Alternative Factor Solutions – Communalities Varimax (*A)

Oblimin (B)

(*C)

1 Behaviour

2 Attitude

………………………………………………Factors

1 Behavioural

2 Attitude

L9 - Private Behaviour

0.671

0.474

0.621

0.638

0.618

0.531

Advocacy

(14A)/Limited

L5 - Committed/Attitude (14B) L4 - Caring/Attitude (14H) L7 - Committed to Working/Exclusive (14J)

0.491

-0.468

0.866

-0.932

0.683

-0.601

0.755

-0.772

L6 - Part of Team/Identification (14E) L1 - Expect to Consider Only/ Behavioural Intention (14C) L3 - Feeling Warmer/Switching Resistance (14N)

0.726

0.752

0.856

0.960

L2 - Influence/Altruism (14O)

0.688

0.478

0.640

(*A) Extraction Method: PCA. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 3 iterations. (*B) Extraction Method: PCAs. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 10 iterations. (*C) Absolute loading values less than 0.45 suppressed. Source: SPSS Output.

In summary the Loyalty-Commitment EFA corroborates the unidimensionality and reliability (α = 0.904) of the refined eight-item scale – well above the 0.70 guideline. Additionally, constrained factor solutions confirm the (non-significant) presence of an attitudinal-behavioural factor structure as predicted by recent interpersonal loyalty theory. © Bill Nichols 2009

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5.5.4 EFA 2: Perceived Service Quality

The second PCA/Varimax Perceived Service Quality (PSQ) EFA complies with both principal criteria: (1) ‘greater-than-one’ latent root (Thietart et al 2001, p.305) and (2) at 67.35%, the social science 60% percentage of variance (Hair et al 2005, p.120, Table 5.18). It is also supported by a near-identical Oblimin outcome and reports a four-factor solution.

Table 5.18: PSQ - Total Variance Explained Sums of Squared Loadings Rotation

Extraction

Factor 1 Factor 2 Factor 3 Factor 4

Total 10.987 1.590 1.177 1.063

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

49.941 7.228 5.35 4.833

49.941 57.169 62.518 67.351

6.337 4.260 2.297 1.924

28.804 19.363 10.440 8.744

28.804 48.166 58.607 67.351

Extraction: Principal Component Analysis Source: SPSS Output

All communalities also exceed 0.50 (Table 6.19) although one variable (PSQ04Methodologies: .541, highlighted amber) is marginal and discussed further below.

Table 5.19: Perceived Service Quality EFA – Communalities 22-items EFA 01- Goal Understanding (8h) 02 Problem Understanding (8l) 03 Ideas(8i) 04 Methodologies(8b) 05 Networking(8d)

0.670 0.667 0.706 0.541 0.631

15 Relationships (8k) 16 Productive Meetings (10b) 17StrategicUnderstanding (10d) 18 Client Orientation(10f) 19 AM Interaction (10h) 20 Responsiveness (8c)

07 Strategy(7b) 08 Solutions(7c)

0.756 21 Professionalism (8j) 0.802 22 Creativity (8e) 23 Reliability (8f)

12 Results (8a) 13 Effective Use of Res (8n)

0.689 24 Dependability (8m) 0.621 26 Keeps Promises (Inv) 0.692 27 Slow on Complaints (inv)

14 Rapport Source: SPSS Output. Amber highlights marginal variable.

0.677 0.573 0.606 0.640 0.710 0.670 0.689 0.734 0.710 0.764 0.660 0.611

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The solution also complies with key parameters:

1. Sample size (150) exceeds the ‘rule’ of a minimum five observations per item – i.e. 22 X 5 = 110 (Hair et al 2005); 2. Visual inspection confirms a substantial majority (86.6%) of intercorrelations are above the 0.30 guideline (Churchill and Iacobucci 2005); 3. The anti-image correlation matrix reveals only three co-efficients greater than 0.30 (Appendix Six). This provides reassurance (Hair et al 2005). 4. Individual measures of sampling adequacy (MSA) exceed 0.80 with one exception (0.778) and are all significantly over the 0.50 threshold (Appendix Five). 5. The Kaiser-Meyer-Olkin (KMO) MSA index is, at 0.912 (Table 5.20), again classified as ‘marvellous’ (Hair et al 2005, p.99); and 6. Finally, the Bartlett test of sphericity provides the statistical probability that the matrix has significant correlations among at least some of its variables, significance = 0.000 (Hair et al 2005, Table 5.20).

Table 5.20 – PSQ – Measures of Sampling Adequacy Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .912 Bartlett's Sphericity

Test

Approx. Chi-Square Df Sig.

2328.476 231 .000

Source: SPSS Output.

Table 5.21 reports the four-factor solution, each labelled for convenience in accordance with subsequent interpretation (5.5.5). It displays all 22 items and loadings above 0.45. It reveals three significant cross-loadings (highlighted amber), each explicable semantically:

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(1) Reliability (PSQ23), like potentially professionalism (PSQ21), connotes behaviour/ethics as well as service; (2) Networking (PSQ05) is for some an outcome per se and for others a natural manifestation of rapport/relationship-building; and (3) Use of results to increase marketing effectiveness (PSQ13) may indicate functional teaming as much as the technical result itself.

Table 5.21 PSQ Factor Analysis – Factor Loadings 1-Rapport

2Outcomes

PSQ03 Ideas(8i)

0.742

PSQ04 Methodologies(8b)

0.630

3-Ethics

4Expertise

PSQ07 Strategy(7b)

0.821

PSQ08 Solutions(7c)

0.880

PSQ12 Exploit Results(8a)

0.695

PSQ16 Productive Meetings(10b)

0.633

PSQ22 Creativity(8e)

0.737

PSQ026 Promises

0.775

PSQ027 Slow on Complaints

0.683

PSQ15 Relationship(8k)

0.782

PSQ14 Rapport(8g)

0.745

PSQ24 Dependability(8m)

0.736

PSQ19 AM Interaction(10h)

0.726

PSQ20 Responsiveness(8c)

0.704

PSQ01 Goal Understanding (8h)

0.692

PSQ02 Problem Understanding (8l)

0.685

PSQ17 Strategic Understanding(10d)

0.680

PSQ23 Reliability(8f)

0.604

PSQ18 Client Orientation(10f)

0.588

PSQ21 Professionalism(8j)

0.570

PSQ05 Networking(8d)

0.560

0.552

PSQ13 Effective Use of Results(8n)

0.506

0.590

0.545

Extraction Method: Principal Component Analysis. Rotation converged in 7 iterations. Rotation Method: Varimax with Kaiser Normalization. Source: SPSS Output. Amber = Cross Loadings.

As a principle, it is appropriate to remove crossloaders if each relevant factor also incorporates several other adequate to strong loaders, =>.50 (Costello and

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Osborne 2005, p.3). Since this condition applies, all three are eliminated but suggest scale refinement opportunities for future research.

5.5.5 EFA2: PSQ - Interpretation of the Four Factor Solution

In factor interpretation, variables with highest loadings are considered more important and should influence nomenclature (Churchill and Iacobucci 2005, p.581). Thus:

Factor 1 (Rapport) aggregates items from both primary source instruments. It focuses primarily on ‘relationships’ (15 = 0.866) and describes a ‘client orientated’ process (18). This runs from understanding (01/02) through strategic engagement (17) and responsiveness (20) to interaction (19) and relationship building (15). The label of ‘rapport’ (14, second at 0.841) captures this continuum most effectively; Factor 2 (Outcomes) likewise aggregates items from both major instruments.

It also describes a continuum: from initial consultancy

creativity (22) and idea generation (3) via organisational methodologies (5) and productive meetings (16) to the delivery of exploitable results (12). Since PBS clients often lack technical knowledge to determine what constitutes a ‘result’, this multi-layered view is comprehensible (Ojasolo 2001). To capture the full range, the neutral term, ‘Outcome’, is adopted; Factor 3 (Ethics) concerns principally opportunism vs. altruism (promises kept/not kept [26], speed of response to complaints [27]). Noting also the near-adherence of ‘reliability’ (23) this mode-of-delivery factor is labelled ‘ethics’; and Factor 4 (Expertise) integrates two high-scoring variables concerned with consultancy involvement in strategy development (07) and overall solutions (08). It assesses the attributed level of ‘expertise’.

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This interpretation is consistent with the tri-dimensional conceptualisations of both Gronroos’s (1984, 2000) Perceived Service Quality and Scruton’s (1994) philosophical base in which:

Outcomes + Expertise:

= technical quality = ‘knows that’;

Rapport:

= functional/social quality = ‘knows how’’;

Ethics:

= reputational quality = ‘knows why’.

It is also broadly consistent with the prior hypothesised allocation of factors versus Grönroos’s conceptualisation (Figure 5.5). In the graphic the (*) placeholders indicate the original positioning of the nine postulated factors.

Figure 5.5: Perceived Service Quality Updated: EFA Factors

* Opportunism/Altruism Reputational

* Results * Methodology * Involvement

Technical

Perceived Service Change Quality Expertise

* Service Functional

* Interaction Quality * Problem Identification * Network Capability

* Relationship Quality

Based on Gronroos (2000) – adapted Author (2008)

Three, arrowed, have moved: (1) problem identification and (2) network capability relocate to the functional dimension while (3) involvement emerges as an independent fourth (second technical) factor, ‘Expertise’. Consistent with (1) the resource-based view of service and (2) the knowledge-based view of service provision (Conner and Prahalad 1996; Vargo and Lusch 2004), ‘Expertise’ may represent a natural fourth PBS dimension of Perceived Service Quality.

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Finally, extrapolating the rotated explanation of variance scores, it is notable that ‘Rapport’ (or functional quality) explains most (Table 5.22):

Table 5.22: Total Variance Explained and Allocated Proportionately Rotation

Rapport Outcomes Ethics Expertise

Total 6.337 4.260 2.297 1.924

Adjusted (%) % of Variance

Cumulative %

Based 100%

28.804 19.363 10.44 8.744

28.804 48.166 58.607 67.351

42.77 28.75 15.50 12.98

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Source: SPSS Output.

100.00

Although the current topic is variance and not regression weights, this finding supports evidence that where technical quality is a given (or perhaps uncertain), functional attributes (i.e. Rapport) may dominate (Baker and Lamb 1993; Higgins and Ferguson 1991; Palmatier et al 2006).

5.5.6 Perceived Service Quality - EFA Factor Reliability

Finally to confirm factor reliability and internal consistency, an independent Cronbach’s α test reports that both the whole scale and four factor scales exceed the recommended minimum acceptance level of 0.60 (Hair et al 2005):

Whole Scale (19 items):

α = 0.930 (standardised = 0.940);

Factor 1 - Rapport (10 items):

α = 0.937 (0.938);

Factor 2 – Outcomes (five items):

α = 0.861 (0.865 [*]);

Factor 3 – Ethics (two items):

α = 0.629 (0.642); and

Factor 4 – Expertise (two items):

α = 0.759 (0.762).

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(*) Within the Factor-2 scale one item, methodologies, if removed, results in an increase in alpha (.870). This aligns with the earlier note of its lowest communality rating in the 22-item set (5.5.4). It is accordingly eliminated from subsequent analysis, leaving a final 18-item scale.

5.5.7 Summary

This section reports two exploratory factor analyses (EFAs). Consistent with the majority of the relevant literature (Costello and Osborne 2005), the researcher deployed PCA with Varimax rotation and Kaizer normalisation to analyse both (1) the eight-item Loyalty-Commitment and (2) initially 22-item Perceived Service Quality scales. Oblique comparisons were also employed.

For Loyalty-Commitment, the unidimensional solution (α = 0.904) explains adequate variance at 61.18%. Constrained oblique and orthogonal solutions also confirm the presence, as predicted by recent interpersonal loyalty literature, of a (non-significant) attitudinal-behavioural factor structure. For Perceived Service Quality, following refinement, a four factor-solution explains adequate variance (67.35%) for a final 18-item scale (α = 0.930).

5.6

Confirmatory Factor Analysis

5.6.1 Introduction

Where an exploratory factor analysis (EFA, 5.5) determines “the most likely factor structure” (Cramer 2003, p.28), a confirmatory factor analysis (CFA) tests “the probability that a particular or hypothesised factor structure is supported or confirmed by the data” (Cramer 2003, p28). This section:

Summarises key CFA principles, measures, guidelines and diagnostics (5.6.2);

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Describes two major CFA procedures for Perceived Service Quality (5.6.35.6.4) and Attitudes (Transactional Satisfaction, Cumulative Satisfaction, Trust and Perceived Value, 5.6.5-5.6.6); and finally Reports in brief a separate CFA (5.6.7) for the ultimate dependent, LoyaltyCommitment undertaken as a partial replication of Grayson and Ambler (1999).

The multiple CFA approach is necessitated in part by sample size limitations versus level of observations required by a structural equation modelling (SEM) technique such as CFA. Even so, this multiple approach is less than optimum and sets a ‘high tariff’ on fit indices as outlined next.

5.6.2 Principles, Measures, Guidelines and Diagnostics

An application of structural equation modelling (SEM), CFA addresses theory testing as opposed to theory building (Grimm and Yarnold 1995, p.109). Whereas EFA determines factors and loadings, CFA reports how well an advance specification matches reality (Hair et al 2005, p.774).

It identifies whether

covariances (or correlations) are consistent with a hypothesised factor structure. , Maximum likelihood estimation (MLE), running on AMOS v16, is adopted given its ability to produce “reliable results under many circumstances” (Hair et al 2005, p.743).

In this context, this study’s available sample (150 observations) limits the application and interpretation of CFA. Further, neither principal CFA in this section achieves the optimum required by the ideal 10-observations per parameter rule: Perceived Service Quality = 180 (18 x 10); Attitudes = 160 (16 x 10). However, both exceed the minimum 5:1 (4.9.5). CFA evaluation requires a variety of measures: 1. The likelihood chi-square (χ2) statistic provides the most fundamental test of overall fit (Hair et al 2005). An inferential test of the plausibility of a model explaining the data, χ2 is calculated from discrepancies between the © Bill Nichols 2009

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original and reproduced correlations among items. The larger the values the more likely that differences are not subject to chance (Cramer 2003, p32); 2. The goodness-of-fit (GFI) and… 3. Adjusted goodness-of-fit indices both adjust for numbers of parameters estimated, are scaled 0-1 and are analogous to R2 in multiple regression analyses (MRA). As a benchmark, .9 or greater indicates a good-fitting model (Hair et al 2005, p.747); 4. The comparative fit index (CFI), assesses fit relative to the most restrictive model, typically a null (Grimm and Yarnold 1995, p.114). “CFI values of <0.90 are not usually associated with a model that fits well” (Hair et al 2005, p.749). Smaller (12-30 variable) samples raise this threshold to 0.95 (Hair et al 2005); 5. The root mean square error of approximation (RMSEA) conversely expresses the lack of fit per degree of freedom due to reliability and model (mis-)specification. In larger (>500) samples, acceptable lack-of-fit is confirmed at <.10 (Cramer 2003, p34): smaller (12-30 variable) samples reduce the benchmark to <.08 (Hair et al 2005, p.753); and 6. Finally, the standardised root mean square residual (RMR) is the average of differences between sample correlations and estimated population correlations. With a range of 0-1, it represents the absolute value of the average fitted residuals for a given CFA model (Grimm and Yarnold 1995, p.114). In this study’s cases, values of <.08 with a CFI of >0.95 are desirable (Hair et al 2005, p.753). To investigate reported CFA inadequacies, diagnostic modification indices (MI) are estimated for each constrained, or fixed, parameter. They measure “the predicted decrease in χ2 if the particular parameter were freed and the model was reestimated” (Grimm and Yarnold 1995, p.114). While MIs greater than four indicate potential fit improvements (Hair et al 2005, p.797), amendments should be consistent with other residual diagnostics and relevant theory (Hair et al 2005).

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5.6.3 CFA Solution 1 – Perceived Service Quality

Applying these guidelines (5.6.2), the first CFA tests the postulated four-factor 18item Perceived Service Quality scale (5.5.4-5.5.6, Figure 5.6).

Figure 5.6 – 18-Item Perceived Service Quality CFA 1

03 Ideas

12 Exploit Results

16 Productive M

22 Creativity

02 Problem Under/d

07 Strategy

15 Relationships

24 Dependability

1 1 1 1 1 1

Expertise

14 Rapport

08 Solutions

19 AM Interaction

Outcomes

Rapport

20 Responsiveness 01 Goal Understand

17 Strategic U

Ethics

18 Client Orientation

21 Professionalism

1 26 Promises

27 Complaints

Source: Author (drawn from AMOS16 output).

To facilitate transparency and replication, results are reported over five principal iterations (Roman I-V, Table 5.23). Since the first iteration (I) did not exhibit acceptable fit (e.g.: χ2 = 266.931; df = 113; ρ = 0.000: GFI = .851; and RMSEA = .096), factor-indicator inter-correlations were eliminated progressively (Gerbing and Anderson 1988). This procedure is consistent with that adopted by Grayson and Ambler (1999) including a test (iteration IVB) of an alternative GLS (generalised least squares) methodology.

Interventions are founded on

modification indices, residual and factor score weighting data and supporting theoretical justification.

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The final iteration (V) complies with key guidelines (5.6.2) and demonstrates good measurement model fit (χ2 = 64.89; df = 59; ρ = .279: RMSEA = .026, CFI = .994, GFI = .936). Only its AGFI score is marginally inadequate (.892 vs. .900).

Table 5.23 PSQ - Confirmatory Factor Analysis - Summary of Iterations and Outcome Iteration No.

III

IVA

IVB

IVC

No. of Variables

+/- PSQ Variable(s)

Per EFA

018/017

014

……

…..

021

Methodology

MLE

GLS

MLE

Sample Size

150

141

150

χ2

266.931

218.601

153.184

105.656

98.947

96.643

64.89

113

Significance (ρ)

0.000

0.005

0.016

0.023

0.279

CMIN/df

2.362

2.231

1.824

1.488

1.394

1.363

1.100

GFI

0.853

0.871

0.894

0.915

0.905

0.913

0.936

AGFI

0.800

0.821

0.849

0.874

0.860

0.871

0.892

CFI

0.905

0.919

0.947

0.969

0.783

0.977

0.994

RMSR

0.080

0.077

0.076

0.074

0.103

0.065

0.070

RMSEA

0.096

0.091

0.074

0.057

0.051

0.050

0.026

Highest Residual

1.619

1.789

1.664

1.545

-1.592

1.166

all .000

1.634 all .000

Regression Weights all .000 all .000 all .000 all.000 Source: AMOS Output. Green highlights acceptability of iteration V.

Iteration-by-iteration item eliminations are as follows:

(I→II) Two: (1) account manager ‘displays sound strategic understanding in interaction’ (PSQ17) and (2) ‘is very client-orientated in interaction’ (PSQ18). A retained variable - ‘interactions with (account manager) are productive’ (PSQ19) - operates in their stead as an overlapping general statement;

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(II→III) One: account manager ‘maintains a good rapport’ (PSQ14) eliminated in favour of ‘developed a close working relationship’ (PSQ15). Again the latter offers a broader and overlapping semantic frame; (III→IV) One: ‘understand aims and goals’ (PSQ01) in favour of the richer connotation of ‘thoroughly understands issue or problem’ (PSQ02); and finally (IV-V) One: ‘thoroughly professional in all it does’ (PSQ023), a near crossloader in the prior EFA, eliminated given overall presence of PSQ-Ethics factor.

Figure 5.7 summarises retained measures for a 13-item Perceived Service Quality scale:

Figure 5.7 – Final 13-Item Perceived Service Quality Post-CFA 07 Strategy

1 03 Ideas

1 1 1

12 Exploit Results

15 Relationships 24 Dependability

Expertise

22 Creativity

1 1

16 Productive M

1 1

08 Solutions

Outcomes 1

19 AM Interaction

Rapport

02 Problem Under/d

20 Responsiveness

Ethics 1 26 Promises

27 Complaints

Source: Author (drawn from AMOS16 output).

Initial acceptability of iteration V is subject to two technical limitations. First the retention of (two) two-item factors may be justified on the basis of ‘significant’ © Bill Nichols 2009

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interfactoral relationships (Hair et al 2005, p.792). Second the deletion of five variables breaches guidance to avoid dropping “more than two out of every 15 measured variables” (Hair et al 2005, p.797). Removing items limits a model’s ability to explain variance in latent constructs (Nunnally 1978). In mitigation: (1) the scale is characterised by both high inter-correlations and overlapping itemconnotations which may result from the multiple sources employed and (2) there may be an underlying non-normal distribution. In the latter case each iteration is characterised by the presence of at least seven extreme multivariate outlier cases (as defined by the Mahalanobis distance statistic (ρ = <0.005) and discussed earlier (6.2.3)). Conversely neither their temporary removal (e.g. compare iteration IVC versus IVA, Table 5.23) nor trial application of the less sensitive generalised least squares (GLS) procedure (Hair et al 2005, p.743) achieves significant improvement (e.g. IVB vs. IVA, Table 5.23).

5.6.4 Reliability and Validity – Perceived Service Quality

General acceptability of the Perceived Service Quality CFA is supported on four validity counts. First convergent validity is confirmed given that:

With one limited exception all standardised factor loadings exceed .7 (each thereby explaining over 50% variance, noting that .71 squared equals .5); Three factors out of four exhibit: o Average variance extracted (AVE) - i.e. the average squared factor loading - adequately in excess of the recommended 0.5 threshold at which latent factor variance exceeds error variance (Hair et al 2005, p.777). Retention of the fourth is supported by theory although it fails marginally (48.40%); and o Good construct reliabilities (>.7), computed from the squared sum of factor loadings per factor and concomitant sum of error variance terms. The fourth (>.6) is acceptable (Hair et al 2005, p.778, Table 5.24).

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Second, discriminant validity is confirmed given that each factor’s AVE exceeds each of its three squared interfactor correlations (SICs). Each underlying latent factor, accordingly, explains its own measures better than it does others (Table 5.25).

Table 5.24 PSQ: Average Variance Extracted and Construct Reliabilities Factor Loadings Squared (SMCs) Factor Loadings

Rapport

Outcomes

Expertise

Ethics

Delta

27-Slow Response

0.753

0.567

0.247

26-Promises Kept

0.633

0.401

0.367

07-Strategy

0.825

0.681

0.175

08-Solutions

0.746

0.556

0.254

22-Creativity

0.871

0.758

0.129

03-Ideas

0.828

0.686

0.172

12-Exploitable Results 16-Productive Meetings

0.764

0.583

0.236

0.726

0.527

0.274

24-Dependability

0.884

0.781

0.116

19-AM Interaction

0.871

0.634

0.129

15-Relationships

0.795

0.571

0.205

20-Problem Under-ing

0.762

0.581

0.238

23-Responsiveness

0.717

0.513

0.283

Average Variance Extracted (AVE)

61.60%

63.85%

61.85%

48.40%

Construct Reliability 0.76 0.76 0.74 0.61 Source: AMOS Output. Factor colour coding maintained from measurement model drawings.

Third, nomological validity is supported because:

1. All covariance matrix relationships are significant and at a high level (ρ = >.001) excepting only PSQ-Ethics→PSQExpertise, (ρ = .039); and 2. The positive relationships between rapport, expertise and outcomes are consistent with established theory.

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The negative correlations between PSQ-Ethics (altruism) and other factors offer insight on the “elusive nature of relational dynamics” (Grayson and Ambler 1999, p.139). The latter failed to support their ‘dark side’ hypothesis that negative ethics (opportunism) acts as a suppressor mediator. However, this supportive evidence suggests that, consistent with general inferential theory (Hayes 2000), a client may interpret ethical signals as attempts to manipulate (Cronk 2005).

Table 5.25: Perceived Service Quality - Discriminant Validity Interfactor Correlations

SIC

Ethics

<-->

Expertise

-0.245

0.060

Ethics

<-->

Rapport

-0.685

0.469

Ethics

<-->

Outcomes

-0.648

0.420

Expertise

<-->

Rapport

0.403

0.162

Outcomes

<-->

Expertise

0.507

0.257

Outcomes

<-->

Rapport

0.781

0.610

Factor

AVE

Associated SICs

Rapport

0.616

greater than

0.469

0.162

0.610

Outcomes

0.639

greater than

0.420

0.257

0.610

Expertise

0.619

greater than

0.060

0.162

0.257

0.484

greater than

0.060

0.469

0.420

Ethics Source: AMOS Output.

Fourth and finally, face validity - given elimination of high connotatively-correlating variables only - is maintained by the four factors in line with prior theory (Chapter Two) and prior EFA explication (5.5.5).

5.6.5 CFA Solution 2 - Attitudes

The second CFA tests a four-construct attitudinal model (Trust, Transactional Satisfaction, Cumulative Satisfaction and Perceived Value, Figure 5.8). Reporting style follows the preceding CFA (Table 5.26).

Notwithstanding potential item

issues (CS1, T1/T5, T2 and PV4) identified in the case of three scales (Cumulative

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Satisfaction, Trust and Perceived Value), the first iteration (I) includes all 16 original items.

Figure 5.8 – Initial 16-Item Attitudes CFA 1

S1 Service

S2 Advice

S3 Proactivity

1 1 1 1

PV1 Value Money

S4 Tasks

1 1

Expertise

PV2 Equity PV3 Acceptable Fees

1 1 1

Perceived Value

PV4 – Fair Value T1 - Principle

T2 – Getting Right T3 AM Performs

Trust

T4 – Team Performs

1 1 1

T5 Decisions

CS1 Decisional

CS2 Hedonic

Cumulative Satisfaction

CS3 Utility

Source: Author (drawn from AMOS16 output).

Since the initial iteration (I) failed to report acceptable fit (e.g. χ2 = 208.700; df = 98; ρ = >.001; GFI = .834; and RMSEA = .086 although CFI = .950), alreadyquestionable items were removed progressively and consistent with the prior procedure (5.6.1ff), as follows:

(II) CS1; (III) T1/T5; and finally, (IV) Consistent with Grayson and Ambler (1999), T2 plus PV4. The resulting Iteration IV is at first sight very positive: (χ2 = 65.48; df = 36; ρ = .004), RMSEA = .079, CFI = .983 and GFI = .926, Table 5.26). It complies (almost) with Hair © Bill Nichols 2009

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et al’s (2005) ideal outcome for a 100-respondent, four-construct and 12-indicator procedure: “evidence of good fit would include an insignificant χ2 value, a CFI of at least .97 and a RMSEA of .08 or lower” (p.753).

Table 5.26: Attitudes - Confirmatory Factor Analysis - Summary of Principal Iterations and Outcome Iteration No.

III

No. of Variables

+/- Variable(s)

16 PreScale

CS1

T1+T5

PV4+T2

S2-S4

Methodology

MLE

Sample Size

150

χ2

208.700

179.852

152.852

65.479

32.985

Significance

0.000

0.004

0.017

CMIN/df

2.102

2.132

2.591

1.723

1.833

GFI

0.834

0.843

0.840

0.926

0.952

AGFI

0.752

0.776

0.754

0.871

0.903

CFI

0.950

0.953

0.952

0.983

0.987

RMSR

0.087

0.074

0.073

0.051

0.054

RMSEA

0.086

0.087

0.103

0.070

0.075

Highest Residual

1.835

1.273

1.664

1.080

1.094

Regression Weights all .000 all .000 all .000 all.000 all .000 Source: AMOS Output. Green highlights compliance in final iteration V.

Unfortunately iteration IV fails to demonstrate discriminant validity in the case of Transactional- (TS) and Cumulative Satisfaction (CS) only (SIC = 0.876 and CS AVE = .908 but TS AVE = 0.721). [In part-explication, the transactional scale, although demonstrating high internal reliability (α = 0.908), is previously unvalidated (4.4.3).]

Consequent further elimination of factor-indicator inter-correlations

(Gerbing and Anderson 1988) results in a consolidated Satisfaction scale.

The final eight-item three-construct outcome (iteration V) demonstrates adequate measurement model fit and complies well with guidelines (5.6.2): e.g. RMSEA = .075, CFI = .987, GFI = .952. Strictly, although often ignored, the likelihood ratio © Bill Nichols 2009

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significance (χ2 = 32.99; df = 19; ρ = .017), implies that the specification of the structural model is unable to reproduce the original covariance matrix. All itemloadings, conversely, are significant at .001.

Figure 5.9 summarises the final

retained measures. Figure 5.9: Final Eight-Item Attitude Scales Post-CFA 1 1 1

PV1 Value Money

Perceived Value

PV2 Equity PV3 Acceptable Fees

T3 AM Performs

Trust

1 T4 – Team Performs

1 1 1

S1 Service

CS2 Hedonic

Satisfaction

CS3 Utility

Source: Author (drawn from AMOS16 output). Original construct colour coding maintained.

5.6.6 CFA 2: Attitudes - Reliability and Validity

Construct validity in the second CFA is supported as follows.

First convergent

validity on three counts:

(1) All standardised factor loadings significantly exceed the required .7 threshold; (2) All AVEs exceed substantially the recommended .5 threshold (Hair et al 2005, p.777); and (3) All three constructs manifest good construct reliabilities (>.7) – computed from the squared sum of factor loadings for each factor and the concomitant sum of error variance terms (Table 5.27).

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Table 5.27: Attitudes - Average Variance Extracted and Construct Reliabilities Factor Loadings Squared (SMCs) Factor Loadings

Perceived Value

Satisfaction

Trust

Delta

CS3 – Utility

0.953

0.908

0.047

CS3 – Hedonic

0.953

0.908

0.047

S1 – Service

0.890

0.792

0.110

T3 - AM Performs

0.806

0.650

0.194

T4 - Team Performs

0.893

0.797

0.107

PV1 – Money

0.966

0.933

0.034

PV3 – Fees

0.815

0.664

0.185

PV2 – Equity

0.892

0.796

0.108

Average Variance Extracted (AVE)

79.77%

72.35%

86.95%

Construct Reliability 0.88 0.83 Source: AMOS Output. Original construct colour coding maintained.

0.95

Second, discriminant validity since in each case AVE exceeds all squared interfactor correlations (SICs) i.e. each underlying latent factor explains its own measures better than it does others (Table 5.28):

Table 5.28: Attitudes - Discriminant Validity Inter-Construct Correlations

SIC

Trust

<-->

Perceived Value

0.698

0.487

Trust

<-->

Satisfaction

0.736

0.542

Perceived Value

<-->

Satisfaction

0.843

0.711

Construct

AVE

Associated SICs

Trust

0.724

greater than

0.542

0.487

Perceived Value

0.798

greater than

0.487

0.711

Satisfaction Source: AMOS Output.

0.870

greater than

0.542

0.711

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Third, nomological validity is supported since (1) all covariance matrix relationships are positive and significant (ρ = .001) and (2) consistent with established theory (Chapter Two).

Fourth and finally, while an analysis of face validity highlights the successful extension of Perceived Value to embrace both general equity and fee-acceptability proves robust, there are two limiting caveats. First, the findings support calls to reconceptualise Moorman et al’s (1992) five-item Trust scale (Doney and Cannon 1997; Geyskens et al 1996; Grayson and Ambler 1999). The refined two-item scale, as noted, better renders a concept of ‘empowerment’. Second, the necessary merger of the Satisfaction scales demonstrates that further work is required to establish the potential for discriminated transactional and cumulative constructs. The final three-item scale integrates general utility, hedonic and overall service assessments.

5.6.7 A Note on Loyalty-Commitment

Finally, both to complete testing of major constructs and to accommodate sample size constraints, a third CFA assessed the construct validity of the LoyaltyCommitment scale. A partial replication of Grayson and Ambler’s (1999) principal CFA, it supports: (1) the elimination of four additional Loyalty-Commitment items identified as marginal earlier on the basis of their corrected item-total correlations and loadings (SMCs, 5.4.2); (2) a resulting four-item scale; and (3), by extension, overall generalisability of this study’s findings (Appendix Seven). For example, the fifth and last iteration matches Grayson and Ambler’s (1999) successful final solution on most counts: χ2 = 48.644; df = 29.000; ρ = .013; GFI = .940; CFI = .976 and RMSEA = .067).

Figure 5.11 visualises implications for the scale by (1)

repeating the semantic analysis technique earlier employed for both Trust and Transactional Satisfaction (5.4.3-5.4.4) and (2) identifying clearly the four core items. For convenience the SMC statistic is again x10.

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Figure 5.10: Loyalty-Commitment - Semantic Analysis Soft.. Attitude… Hard/Soft.. Behaviour.. Hard 8

7 6

Identification (Team)

Relative Attitude

Commitment (Firm)

Commitment (Relationship)

Influence

Advocacy Exclusive Consideration Switching Resistance

Public Advocacy Compulsion

Source: Author based on SPSS Output.

The full original scale maps to an extended continuum (left to right): from soft to hard attitudinal components (tones of blue) and then from soft to hard behavioural components (tones of red). This conceptualisation is also consistent with the prior Loyalty-Commitment EFA analysis (5.5.2-5.5.3) in which - in the Oblimin perspective - ‘Commitment to Relationship’ is the only attitudinalbehavioural cross-loader. It is appropriately at the construct core.

5.6.8 Summary

This section completes the first phase of the analysis process; reports the results of two principal CFAs; and supports a final 13-item four factor (PSQ-Ethics, PSQExpertise, PSQ-Outcomes and PSQ-Rapport) solution for Perceived Service Quality and three final attitudinal scales: Satisfaction (three-items), Perceived Value (three) and Trust, or empowerment, (two). It also provides limited support for a four-item Loyalty-Commitment scale in a third CFA and partial replication of a principal source CFA (Grayson and Ambler 1999). Although tangential to the main study, this CFA also provides limited support for overall generalisability (Appendix Seven).

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5.7

Hypothesis Revision

As a result of the merger of the two Satisfaction scales, H2A-B and H6 are eliminated from the hypothesis set for the principal convergence model and H4AB, H5 and H7 amended (Table 5.29). These changes do not affect the final and limited hypothesis set for the alternative Goodwill Model.

Table 5.29: Principal Convergence Model – Revised Hypothesis Set No.

Hypothesis

Goodwill Model

Perceived Service Quality (PSQ) will relate positively to Perceived Value (PV)

Retained

Disconfirmation will relate positively to PV

Retained

ELIMINATED

Eliminated

ELIMINATED

Eliminated

PSQ will relate positively to Trust

Retained

Disconfirmation will relate positively to Trust

Retained

PSQ will relate positively to Satisfaction (AMENDED)

Eliminated

Disconfirmation will relate positively to Satisfaction (AMENDED)

Eliminated

PV will relate positively to Satisfaction (AMENDED)

Eliminated

ELIMINATED

Eliminated

Trust will relate positively to Satisfaction (AMENDED).

Eliminated

PV will relate positively to Loyalty-Commitment.

Retained

Satisfaction will relate positively to Loyalty-Commitment.

Eliminated

Trust will relate positively to Loyalty-Commitment. Satisfaction will partly mediate the effects of Perceived Value on LoyaltyCommitment

Retained

Eliminated

Part-Retained Satisfaction and Perceived Value will jointly partly mediate the effects of with PV as 12 Perceived Service Quality on Loyalty-Commitment mediator Source: Author. Red = eliminations following EFA/CFA process. Amber = Amendments following EFA/CFA process.

5.8

Chapter Summary

This chapter provides foundations for remaining stages of analysis. It reports data preparation, key descriptives and the process of scale analysis and refinement. The latter includes two principal exploratory factor analysis (EFAs) and confirmatory factor analyses (CFAs) – both addressing Loyalty-Commitment and

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Perceived Service Quality. For convenience, principal findings, interventions and consequent amendments are summarised at Table 5.30:

Table 5.30 – Chapter Six - Summary of Major Findings/Interventions Items Cases Pre

5.2.2

Missing Data

154

5.2.3

151

5.2.4

Outliers NonCoverage

5.2.5

NonResponse Bias

5.2.6

Between Groups Comparison

5.2.7

Normality Tests

5.3.2

Descriptives

151

150

Potential limitation acknowledged due to constraints on validation

Classificatory variables CM2, CM4C and OC9 excluded.

5.4.7

150

Variables PSQ10/CS1 raise concerns for subsequent analysis Issues with leptokurtic distribution for PSQ10. Issues also with PSQ20 and T5 (carried down).

150

Size and service requirements consistent with population benchmark.

150

Items L8/L10 eliminated

Items L1/L3/L4/L6 identified as marginal to scale.

5.4.6

150

Major headline finding: client-consultancy mean relationship reduces to 3.5 years.

S1 - LoyaltyCommitment

5.4.5

151

94 remaining missing data points replaced by mean substitution Contextual considered response retention all cases.

Respondent management status and responsibilities consistent with population benchmarks.

5.4.1

5.4.4

151

Carried Down For Review

150

5.4.3

Items /Case s Post

Concern over technology client bias in current sample not supported.

Descriptives

S2 - Trust S3 – TransSatisfaction S4 - Cum Satisfaction S5 - Perceived Value S6 – Disconfirmation S7 - Perceived Service Quality

Immediate Actions

Three cases excluded Nine univariate and multivariate cases identified. One case fails responsibility criterion and is excluded.

5.3.3

5.4.2

Initial Scale α

0.904

0.795

0.909

items T1/T5 eliminated No amendments - Item S1/Service noted as highest SMC

4 3

0.921

Item CS1/Decision eliminated

0.931

No immediate amendments

0.889

No amendments

0.933

Five Items eliminated (PSQ06, 09, 10, 11 and 25).

Item T2 identified marginal to scale. Scale semantic frame places T3/T4 at core. Scale semantic frame places item S1 at core of domain.

Item PV4/Fair Value identified as scale outlier. Potential negative covariance issue highlighted Marginal status of PSQ04/Methodologies highlighted

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Ite ms/ Cas es Pre

5.5.2

EFA - LoyaltyCommitment

5.5.3

EFA - LoyaltyCommitment.. Options

5.5.4

EFA - Perceived Service Quality

5.5.5

EFA - PSQ Interpretation

5.6.2

PSQ Scale

5.6.4

Attitudes

5.6.6

LoyaltyCommitment /Grayson & Ambler

Initial Scale α

Immediate Actions EFA complies all criteria - singlefactor solution validated using PCA/Varimax

Items /Case s Post

Carried Down For Review

Constrained two factor solution confirms potential separation between attitudinal and behavioural factors

Not validated.

Four factor solution identified. Three additional cross-loading items eliminated (PSQ05, 13, 23). Alpha = 0.933

Scale subsequently reduced to 18 items by elimination of marginal item (PSQ04) resulting in Outcomes scale alpha increase to 0.870 (0.861)

Four-factor solution labelled: ethics, rapport, outcomes and expertise. Aligned with prior theory. Alphas = 0.629 - 0.937. 18

PSQ scale validated following CFA

Five highly correlated items eliminated.

12-item four construct solution validated following reduction to 8-item, 3 construct solution.

Satisfaction constructs merged for final analysis.

Part-replication supports overall validity of study, validates reduced 4-item scale for L-C vs Trust.

Supporting semantic analysis of L-C introduced.

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CHAPTER SIX – DATA ANALYSIS PART TWO 6.1

Introduction and Overview

The preceding Chapter Five reported initial phases of analysis including: data preparation, descriptives and the testing/validation of principal scales. Utilising these foundations, this chapter addresses the balance of analysis in three phases.

First it makes extended use of multiple regression analysis (MRA) to explore key relationships in the Convergent Model (Sections 6.2-6.10). Following initial reviews of both MRA principles (6.2) and the reporting format adopted (6.3), the sequence employs summated variables (based on Chapter Five output) and addresses:

The antecedence of Perceived Value (6.4 – MRA1), Trust (6.5 – MRA2), Satisfaction (6.6 – MRA3), and Loyalty-Commitment (6.7 – MRA4); and Two postulated mediations by: Satisfaction and/or Perceived Value wholly or partly of the effects of Perceived Service Quality on Loyalty-Commitment (6.8 – MRA5); and Satisfaction wholly or partly of the effects of Perceived Value on Loyalty-Commitment (6.9 – MRA6).

An intermediate tabulation of support for Convergent Model hypotheses completes this phase (6.10).

Second, this MRA sequence facilitates testing of ‘Convergent’ measurement (6.11) and structural models (6.12). A complementary procedure also addresses the ‘Goodwill’ competing model (6.13).

Third and finally, a closing section introduces moderation (6.14) and its potential both to enrich final practitioner guidance and facilitate evaluation of findings for generalisability (Hair et al 2005; MacKinnon 2008). A closing summary (6.15) acts © Bill Nichols 2009

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as a prelude to Chapter Seven’s discussion of results, implications and contributions to marketing practice.

6.2

Multiple Regression Analysis – Key Principles

A multiple regression analysis (MRA) investigates the relationship between one dependent, or criterion, variable and several independent, or predictor, variables (Cramer 2003, p.59; Hair et al 2005, p. 176). This initial section summarises the researcher’s choice of (1) estimation procedure and of appropriate measures for (2) model fit, (3) predictive accuracy and (4) validity. A compliance summary is provided at Table 6.1.

First the study adopts the ‘simultaneous’ MRA procedure. Arguably simplest and most widely practised, it has two key aspects. It is at once (A) confirmatory - i.e., given theoretical support, the researcher predetermines variable selection (Hair et al 2005, p.209) - and (B) simultaneous (Grimm and Yarnold 1995, p.52). Typically it requires a feasible ~20 observations per variable versus an ideal and infeasible ~50 for the ‘stepwise’ competitor (Hair et al 2005, p.196). The latter is also rejected because it selects predictors by level of influence - thereby maximising predictive accuracy but minimising researcher control (Hair et al 2005, p.214) - and, with small samples, may become sample-specific.

Second MRA model fit is determined by the F Ratio and its level of significance.

Third prediction accuracy is indicated by the adjusted coefficient of determination or R2 statistic (R2adj) (Hair et al 2005, p.185). Adjustment accounts for the quantum of variables and cases in a given model. For example: given (1) this study’s 150sample, (2) required significance level of <.05 and (3) acceptance of a power level of .80, a researcher expects to detect R2 levels of 12% or greater (Hair et al 2005). In addition, since the observed data points do not all fall on the regression line, the regression equation itself is an estimate and not a perfect indicator of association.

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A further check – degree of proximity – is provided by the standardised error of estimate (SEoE).

Fourth and finally, procedural validity is confirmed by non-violation of key assumptions both per variable and in the variate (Hair et al 2005, p.204). These include: (1) linearity of the phenomenon measured; (2) equality, or homoscedasticity, of variance of the error terms; (3) independence of error terms; and (4) normality of error term distribution. It is also important to check for outliers, collinearity and multicollinearity – the last evidenced by highly intercorrelated predictor variables (Churchill and Iacobucci 2005, p.527, Table 6.1).

Table 6.1: Confirming the Integrity of the Multiple Regression Analysis (MRA) Procedure Assumptions

Tests

References

Linearity of phenomenon observed

Review scatterplot of studentised (correspond to tvalues) or standardised residuals

Hair et al 2005, pp. 205-206

Linearity - individual variables

Review of partial regression plots. (Facilitates identification of outliers, influential observations)

Hair et al 2005, p. 207

Equality or Homoscedascticity of Error Term

Review of scatterplot of studentised or standardised residuals

Hair et al 2005

Independence of Error Terms

Durbin-Watson, scale 0-4. Values of >2 indicate negative correlations between adjacent residuals. (<2 = positive). Score of 2 = zero correlation. As a rule of thumb scores of <1 or >3 are cause for concern.

Field 2005

Normality of Error Term Distribution

Review of plot of standardised residuals vs. normal. Residual line should closely approximate the diagonal.

Hair et al 2005, p.208.

Correlations

Positively evidenced at > .3

Pallant 2002

Collinearity

Multicollinearity

Outliers Source: Author.

In collinearity diagnostics, a large condition index associated with an eigenvalue indicates high degree of collinearity. Source of concern prompted by correlations among predictors at > .7. Correlations where r > .9 should be considered problematic. Formal assessment by Tolerance (low values = concern) and inverse, VIF (high = concern) Review at > two standard deviations. In a normal distribution it is reasonable to expect 5% of cases to be more than two standard deviations from the regression line.

Brace, Kemp and Snelgar 2003

Grimm and Yarnold 1995, p.45; Hair et al 2005; Pallant 2002

Hair et al 2005.

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6.3

Reporting Format

Each multiple regression analysis (MRA) report adopts a common format and sequence:

(1) Hypothesis/hypotheses under test; (2) Summary of phases of analysis; (3) Results per phase; (4) Summary of results; and (5) Review of compliance.

The last is supported as appropriate by appendix data. For the final two mediation analyses (MRA5-6), a modified format reflects the nature of analysis conducted. Textual reporting follows Cramer’s American Psychology Association (APA) -based guidelines (2003, pp.66, 81). A complementary tabulated reporting format is also adopted which, in order to achieve parsimony of presentation, consolidates multiple iterations into single tables (e.g. Tables 6.2/6.3 in next 6.4).

Final

outcomes are highlighted green.

6.4

MRA1: The Antecedence of Perceived Value

6.4.1 Hypotheses

MRA1 tests the hypotheses that:

Both Perceived Service Quality (H1A) and Disconfirmation (H1B) will relate positively to Perceived Value (3.3.2).

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6.4.2 Summary of Phases of Analysis

Deploying a common criterion variable (Y), Perceived Value, MRA1 has three phases of analysis in which the predictor variable(s) is/are:

1. Perceived Service Quality (PSQ, X6); 2. The four PSQ factors - PSQ-Ethics (X1), PSQ-Expertise (X2), PSQ-Outcomes (X3) and PSQ-Rapport (X4); 3. The four factors plus Disconfirmation (X5).

6.4.3 Phase One: Perceived Service Quality and Perceived Value

Phase One confirms a positive relationship between PSQ (X6) and Perceived Value (Y) (H1A). After initial iterations, a final regression (iteration III) evidences:

Overall model relationship significance (F1, 144 = 85.027, ρ = <0.001); and Modest predictive accuracy (R2adj = .367 or 36.7%, Table 6.2).

The regression co-efficient for PSQ/X6 is also statistically significant (β = .61, t = 8.18, ρ = <0.001). Thus: for every unit increase in X6, Y will augment by 0.61 units (Table 6.3).

6.4.4 Phase Two: PSQ Factors and Perceived Value

By extension, Phase Two confirms positive relationships between three PSQ factors (excluding only PSQ-Expertise, X2) and Perceived Value (Y). Its second and final iteration (V) also evidences:

Overall relationship significance (F4, 140 = 44.113, ρ = <0.001); and In comparison to MRA1-Phase One, adequate and improved predictive accuracy (R2adj = .545 or 54.5%, Table 6.2).

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The three partial regression co-efficients for supported PSQ factors are also statistically significant:

PSQ-Ethics (X1) (β = 0.13, t = 1.88, ρ = <0.05); PSQ-Outcomes (X3) (β = 0.33, t = 4.18, ρ = <0.001); and PSQ-Rapport (X4) (β = 0.41, t = 5.26, ρ = <0.001).

Thus: in Phase Two, for every 1.0 unit increase in X1, X3, and X4, Y augments respectively by 0.13, 0.33 and 0.41 units (Table 7.3). Alternatively: of 54.5% variance explained in Perceived Value (Y), PSQ-Ethics (X1), PSQ-Outcomes (X3) and PSQ-Rapport (X4) account respectively for 14.4%, 37.7% and 47.9%.

6.4.5 Phase Three: Four Factors, Disconfirmation and Perceived Value

Finally Phase Three confirms a positive relationship (H1B) between Disconfirmation (X5) and Perceived Value (Y). Its second and final iteration (VII) evidences:

Overall relationship significance (F5, 138 = 40.251, ρ = <0.001); and In comparison to both prior phases, both adequate and further improved predictive accuracy (R2adj = .578 or 57.8%, Table 6.2).

Additionally, the introduction of Disconfirmation (X5) renders PSQ-Ethics (X1) - like PSQ-Expertise (X2) - non-significant. Remaining partial regression co-efficients are statistically significant:

PSQ-Outcomes/X3 (β = 0.24, t = 2.60, ρ = <0.01); PSQ-Rapport/X4 (β = 0.35, t = 4.06, ρ = <0.001); and Disconfirmation/X5 (β = 0.21, t = 2.22, ρ = <0.05).

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Thus, for every 1.0 unit increase in X3, X4 and X5, Y will augment respectively by 0.24, 0.35 and 0.21 units (Table 6.3). Alternatively:

of the 57.8% variance

explained in Perceived Value (Y), PSQ-Outcomes (X3) PSQ-Rapport (X4) and Disconfirmation (X5) account respectively for 30.1%, 43.5% and 26.4%.

6.4.6 MRA1: Results Summary

MRA1 findings support positive relationships between:

Both (1) Perceived Service Quality (H1A) and (2) Disconfirmation (H1B) and Perceived Value; and Additionally (3), once Disconfirmation is included in the regression, between two PSQ factors - PSQ-Outcomes and PSQ-Rapport – and Perceived Value.

Table 6.2 summarises final results per phase (highlighted green) for both model fit and predictive accuracy:

Phase One/Iteration III – Perceived Service Quality (PSQ); Phase Two/Iteration V – PSQ factors; and Phase Three/Iteration VII – both PSQ factors and Disconfirmation.

In addition, illustrative preliminary iterations for both Phases One (I) and Two (IV) demonstrate the transformation evidenced via the procedure.

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Table 6.2: Perceived Value Multiple Regression Analysis (MRA1) – Predictive Accuracy and Model Fit

Model Summary (b)

Sample (N)

150

Correlation Coefficient (R)

.558 2

(a)

III – Phase OnePSQ and PV

146

.609

(a)

.725

(c )

V– Phase Two Factors and PV

VII – Phase Three Factors, Dis and PV

145

144

.747

(c )

.770

(d)

Coefficient of Determination (R )

311

.371

.525

.558

.593

2adj

.307

.367

.512

.545

.578

1.074

.930

.816

.787

.756

Sum of Squares-Regression

77,260

73.487

104.067

109.509

115.012

Sum of Squares- Residual

170.846

124.456

93.876

86.886

78.863

Degrees of Freedom (df)

1, 148

1, 144

4, 141

4,140

5,138

F Ratio

66.929

85.027

39.077

44.113

40.251

(a)

(c )

(d)

Adjusted R2 (R

)

Std. Error of Estimate (SEE) Relationship (ANOVA)

(b)

Significance

.000

(a) Predictors: (Constant), PSQ (b) Dependent Variable: Perceived Value (c ) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise (d) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise, Disconfirmation

Source: SPSS Output.

Complementing Table 6.2, Table 6.3 reports similarly the partial standardised regression co-efficients achieved for principal iterations across all phases.

(d)

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Table 6.3: Perceived Value – MRA1 – Coefficients

Phase One III – PSQ/PV IV

Stand. Coeff (β) - PSQ (X6)

.558

.609

t-value

8.181

9.221

Significance:

.000

Coefficients

Phase Two VFactors -PV

Phase Three VII – Factors – Dis – PV

(a)

Stand. Coeff (β) - PSQ Ethics (X )

.121

.127

.109

t-value

1.731

1.875

1.658

.009

.050

.099

Stand. Coeff (β) - PSQ Expertise (X )

-.024

-.050

-.028

t-value

-0.371

-.794

-.449

Significance:

.710

.268

.654

Stand. Coeff (β) - PSQ Outcomes (X3)

.301

.332

.238

t-value

3.636

4.136

2.600

.000

.010

Stand. Coeff (β) - PSQ Rapport (X )

.419

.422

.345

t-value

5.029

5.256

4.055

.000

Significance: 2

Significance: 4

Significance: 5

Stand. Coeff (β) - Disconfirmation (X )

.209

t-value

2.216

Significance:

.028

(a) Dependent: Perceived Value Source: SPSS Output.

6.4.7 Regression Assumptions and Compliance

During MRA1’s aggregate seven iterations, a test and intervention sequence ensured procedural compliance. This includes inter alia:

(1) Principles of linearity and homoscedasticity by per-iteration checks of scatterplots for each regressor versus the criterion and per partial regression plot (Appendix Eight, Figure A8.1);

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(2) Normality of error distribution evidenced e.g. by both the normative histogram of standardised residuals and the normal probability plot (Appendix Eight, Figure A8.2); (3) Independence of error terms by near-ideal 2.0 Durbin Watson scores (Table 7.5 below); and (4) Predictability.

In the last case, all regressor-predictor correlations (non-significant PSQ-Expertise excepted, Table 6.4 highlighted yellow) are above the .3 guideline and the high +.6 scores represent an “ideal situation” (Hair et al 2005, p. 226, Table 6.4).

Table 6.4: Perceived Value (MRA1) Regressor-Predictor and Inter-Predictor Correlations

Phase One Final III

Phase Two Final V

Phase Three Final VII

Sample (N)

150

146

145

144

PV – PSQ

.558

.609

PV- PSQ Ethics

.484

.497

.504

PV - PSQ Expertise

.271

.267

.270

PV - PSQ Outcomes

.629

.650

.664

PV - PSQ Rapport

.677

.692

.702

PV – Disconfirmation

.686

PSQ Ethics-PSQ Expertise

.160

.167

PSQ Ethics-PSQ Outcomes

.473

.467

.466

PSQ Ethics - PSQ Rapport

.534

.530

.529

PSQ Ethics – Disconfirmation

.504

PSQ Expertise-PSQ Outcomes

.428

.441

.442

PSQ Expertise-PSQ Rapport

.349

.356

.357

PSQ Expertise-Disconfirmation PSQ Outcomes-PSQ Rapport

.242 .669

.664

PSQ Outcomes- Disconfirmation

.750

PSQ Rapport – Disconfirmation

.718

Significance all: <.001 except five <.01 and three (all related to PSQ Expertise) at <.05

Source: SPSS Output. Yellow values highlight non-significant PSQ-Expertise. Amber indicates correlations of concern.

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Two issues only arise. First, casewise diagnostics identify six exceptional cases (=>3.0 standardised residual values) in four iterations (Appendix Eight, Table A8.1). As a principle a “researcher is encouraged to delete truly exceptional observations…(in order to) ensure the most representative model for the sample data so that it will best reflect the population from which it was drawn” (Hair et al 2005, p.222). Their consequent removal results in modest improvement to both R2adj and β values (Table 6.2/Table 6.3 above). Each iteration (except II) also evidences additional cases which are >two standard deviations’ distance from the regression line but within the 5% norm (6.2). The most extreme instance (iteration VII) = 7/144 = 4.86%.

Second high inter-predictor scores are reported for PSQ Outcomes/PSQ Rapport and Disconfirmation (especially Phase Three, iteration VII, Table 6.4, highlighted amber). Although well below the guideline .9 risk level, these scores may indicate limited collinearity (Hair et al 2005; Pallant 2002) - a concern supported by SPSS collinearity diagnostic tables.

Over five iterations, these also reveal eight

eigenvalues of <.01 and condition indices of > 23.000.

Table 6.5: Perceived Value (MRA1) - Collinearity Diagnostics and Durbin-Watson I

III

VII

2.089

2.038

2.068

2.029

2.033

Coll-Tolerance-PSQ Ethics

.685

.691

.681

Coll-Tolerance-PSQ Expertise

.803

.793

.757

Coll-Tolerance-PSQ Outcomes

.488

.490

.349

Coll-Tolerance-PSQ Rapport

.484

.490

.405

Independence of Error Terms (Durbin-Watson)

Coll-Tolerance-Disconfirmation

.329

Coll-VIF-PSQ Ethics

1.460

1.447

1.468

Coll-VIF-PSQ Expertise

1.246

1.262

1.320

Coll-VIF-PSQ Outcomes

2.048

2.039

2.864

Coll-VIF-PSQ Rapport

2.063

2.042

2.468

Coll-VIF-Disconfirmation Source: SPSS Output.

3.042

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Concern, however, is obviated a) in part by prior Perceived Service Quality CFA (6.6.3-6.6.4) and b) by consistent multicollinearity assessment scores for tolerance (high = >.190, Hair et al 2005, p.190) and variance inflation factor (VIF, low = < 5,3). These collectively assess the degree to which an individual independent variable is explained by the set as a whole (Table 6.5).

6.5

MRA2 – The Antecedence of Trust

6.5.1 Hypotheses

MRA2 tests the hypotheses that:

Both Perceived Service Quality (H3A) and Disconfirmation (H3B) will relate positively to Trust (3.3.2).

6.5.2 Summary of Phases of Analysis

Deploying a common criterion variable, Trust(Y), the predictors for the three analysis phases are:

1. Perceived Service Quality (PSQ, X6); 2. The four PSQ factors: PSQ-Ethics (X1), PSQ-Expertise (X2), PSQ-Outcomes (X3) and PSQ-Rapport (X4); and 3. The four factors plus Disconfirmation (X5).

6.5.3 Phase One: Perceived Service Quality and Trust

MRA2’s first phase confirms a positive relationship between PSQ and Trust (H3A). After an initial iteration, the second (II) evidences both:

Overall model relationship significance (F1, 146 = 100.238, ρ = <0.001); and

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Adequate predictive accuracy (R2adj = .403 or 40.3%, Table 6.6).

The regression co-efficient for PSQ/X6 is statistically significant (β = 0.64, t =10.01, ρ = <0.001). Accordingly, for every unit increase in X6, Y will augment by 0.63 units (Table 6.7).

6.5.4 Phase Two: PSQ Factors and Trust

By extension, Phase Two (iteration III) demonstrates a positive relationship between three PSQ factors (again excluding PSQ-Expertise) and Trust. The result evidences both:

Overall model relationship significance (F4, 143 = 67.232, ρ = <0.001); and In comparison to Phase One, improved predictive accuracy (R2adj = .643 or 64.3%, Table 6.6).

Each partial regression co-efficient is also statistically significant:

PSQ-Ethics/X1 (β = 0.14, t = 2.39, ρ = <0.05); PSQ-Outcomes/X3 (β = 0.20, t = 2.73, ρ = <0.01); and PSQ-Rapport/X4 (β = 0.60, t = 8.37, ρ = <0.001, Table 6.7).

Thus, for every 1.0 unit increase in X1, X3, and X4, Y will augment by 0.15, 0.21 and 0.60 units respectively. Alternatively: of the 64.3% variance explained in Y, PSQEthics, PSQ-Outcomes and PSQ-Rapport account respectively for 15.2%, 21% and 63.7%.

6.5.5 Phase Three: PSQ Factors, Disconfirmation and Trust

Phase Three confirms a positive relationship between Disconfirmation and Trust (H3B).

Its second iteration (V) evidences both: overall model relationship

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significance (F5, 140 = 66.552, ρ = <0.001); and versus both Phases One and Two, further improvement in predictive accuracy (R2adj = .693 or 69.3%, Table 6.6). Notably, it also eliminates two further PSQ factors (PSQ-Ethics and PSQ Outcomes) as non-significant. The remaining partial regression co-efficients are statistically significant:

PSQ-Rapport/X4 (β = 0.55, t = 7.71, ρ = <0.001) and PSQ-

Disconfirmation/ X5 (β = 0.24, t = 3.03, ρ = <0.01). Thus, for every 1.0 unit increase in X4 and X5, Y augments by 0.55 and 0.24 units respectively (Table 6.7). Alternatively: of the 69.3% variance explained in Trust (Y), PSQ-Rapport (X4) and Disconfirmation (X5) account respectively for 69.33% and 30.67%.

6.5.6 Summary of Results

Following tabulations consolidate the three-phase MRA2 findings.

They support positive relationships between (1) Perceived Service Quality (H3A), (2) Disconfirmation (H3B) and (3), once Disconfirmation is included in the regression, between one principal factor, PSQ-Rapport, and Trust.

Table 6.6 summarises final per-phase results (highlighted green) for both model fit and predictive accuracy including: II – Perceived Service Quality (PSQ); III – PSQ factors; and V – both PSQ factors and Disconfirmation. Preliminary iterations for both Phases One (I) and Three (IV) demonstrate the transformation evidenced via the procedure.

Table 6.7 similarly reports the partial standardised regression co-efficients achieved for principal iterations across all phases.

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Table 6.6: Trust – Multiple Regression Analysis (MRA2) – Predictive Accuracy and Model Fit

Model Summary (b)

Sample (N)

150 (a)

Phase One II – PSQTrust

Phase Two III – FactorsTrust IV

148

.839

(d)

Coefficient of Determination (R2)

.386

.407

.653

.666

.703

Adjusted R2 (R2adj)

.382

.403

.643

.655

.693

Std.Error of Estimate (SEE)

.933

.876

.677

.666

.622

Sum of Squares-Regression

81.106

77.026

123.530

126.143

128.798

Sum of Squares- Residual

128.914

112.19

65.685

63.073

54.188

Degrees of Freedom (df)

1, 148

1, 146

4, 143

5, 142

5, 140

F Ratio

93.118

100.238

67.232

56.799

66.552

(c )

(d)

(b)

Significance (a) Predictors: (Constant), PSQ (b) Dependent Variable: Trust (c ) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise (d) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise, Disconfirmation

.816

146 (d)

.621

Source: SPSS Output.

.807

148 (c )

Correlation Coefficient (R)

Relationship (ANOVA)

.638

(a)

Phase Three V– Factors Dis Trust

.000

(a)

.000

(a)

.000

(d)

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Table 6.7: Trust Multiple Regression Analysis (MRA2) – Coefficients Phase Two III – Factors - Trust IV

Phase Three V – Factors – Dis Trust

Stand. Coeff (β) - PSQ Ethics (X1)

.142

.110

.089

t-value

2.388

1.850

1.566

Significance:

.018

.066

.120

Stand. Coeff (β) - PSQ Expertise(X2)

-.078

-.048

-0.03

t-value

-1.437

-0.880

-0.679

Significance:

.153

.381

.498

Stand. Coeff (β) - PSQ Outcomes (X3)

.197

.093

.073

t-value

2.733

1.119

0.937

Significance:

.007

.265

.350

Stand. Coeff (β) - PSQ Rapport (X4)

.597

.527

.547

t-value

8.365

6.956

7.708

Significance:

.000

Stand. Coeff (β) – Disconfirmation (X5)

.207

.242

t-value

2.425

3.030

Significance:

.017

.003

Phase One II – PSQTrust

Stand. Coeff (β) – PSQ (X6)

.621

.638

t-value

9.650

10.012

Significance:

.000

Coefficients

(a)

(a) Dependent: Trust Source: SPSS Output.

6.5.7 Regression Assumptions and Compliance

An MRA2 test and intervention sequence ensures procedural compliance. This includes inter alia: (1) principles of linearity and homoscedasticity by per-iteration check of scatterplots for each regressor versus the criterion (Y) and per partial regression plot (Appendix Nine, Figure A9.1); (2) normality of error distribution evidenced e.g. by both normative histogram of standardised residuals and normal

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probability plot (Appendix Nine, Figure A8.2); (3) independence of error terms by acceptable Durbin Watson scores (Table 6.9 below); and (4) predictability.

All regressor-predictor correlations are significantly above the .3 guideline (nonsignificant PSQ-Expertise excepted, Table 6.8 highlighted yellow).

Table 6.8: MRA2/Trust – RegressorPredictor and Inter-Predictor Correlations

Phase One II

Phase Two III

Phase III – V

Sample (N)

150

148

146

Trust – PSQ

.621

.638

Trust - PSQ Ethics

.542

.543

Trust - PSQ Expertise

.228

.243

Trust - PSQ Outcomes

.644

.658

Trust - PSQ Rapport

.781

.803

Trust – Disconfirmation

.705

.725

PSQ Ethics-PSQ Expertise

.155

.156

PSQ Ethics-PSQ Outcomes

.493

.491

PSQ Ethics - PSQ Rapport

.527

.526

.556

PSQ Ethics – Disconfirmation PSQ Expertise-PSQ Outcomes

.417

.416

PSQ Expertise-PSQ Rapport

.339

.204

.200

PSQ Expertise-Disconfirmation PSQ Outcomes-PSQ Rapport

.686

.683

PSQ Outcomes- Disconfirmation

.686

.752

.749

PSQ Rapport – Disconfirmation

.715

.704

Significance all: <.001 except five <.01 and three (all related to PSQ Expertise) at <.05

Source: SPSS Output. Non-significant PSQ-Expertise highlighted yellow. Correlations of concern hatched amber.

Two issues arise. First casewise diagnostics reveal four exceptional outliers (x2 @ iterations I/IV). Their elimination results in modest improvements to both R2adj and β scores (Table 6.6). All additional >2.0 standard deviations’ cases are within the 5% norm e.g. iteration V = 6/146 = 4.1% (Appendix Nine, Table A9.1). Second, although below the .9 level, some high inter-predictor scores may indicate limited © Bill Nichols 2009

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collinearity (Hair et al 2005; Pallant 2002, Table 6.8), a concern part-supported by the SPSS collinearity diagnostics tables. These reveal: (iteration III) one (23.801) and (V) two high condition indices (respectively 25.841 and 28.215) and three linked low eigenvalues, indicating potential multicollinearity. Concern, however, is obviated by a) the prior PSQ CFA and b) consistently acceptable multicollinearity assessment scores for tolerance (high) and the variance inflation factor (VIF, low) (Table 6.9).

Table 6.9: Trust MRA2 Collinearity and Durbin-Watson I

One (II)

Two (III)

Three (V)

2.093

2.071

1.903

1.945

1.881

Coll-Tolerance-PSQ Ethics

.684

.652

.651

Coll-Tolerance-PSQ Expertise

.813

.772

.773

Coll-Tolerance-PSQ Outcomes

.465

.339

.341

Coll-Tolerance-PSQ Rapport

.476

.408

.418

.324

.331

Independence of Error Watson)

Terms (Durbin-

Coll-Tolerance-Disconfirmation Coll-VIF-PSQ Ethics

1.462

1.533

1.535

Coll-VIF-PSQ Expertise

1.228

1.294

1.292

Coll-VIF-PSQ Outcomes

2.152

2.943

2.927

Coll-VIF-PSQ Rapport

2.098

2.449

2.388

3.088

3.017

Coll-VIF-Disconfirmation Source: SPSS Output.

6.6

MRA3 – The Antecedence of Satisfaction

6.6.1 Hypotheses

MRA3 tests the following hypotheses:

Perceived Service Quality (H4A), Disconfirmation (H4B), Perceived Value (H5) and Trust (H7) will all relate positively to Satisfaction (5.7, Table 5.29).

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6.6.2 Summary of Phases of Analysis

Deploying a common criterion variable, Satisfaction (Y), the regressors for the four MRA2 phases are: (1) Perceived Service Quality (PSQ, X6); (2) the four PSQ factors: PSQ-Ethics (X1), PSQ-Expertise (X2), PSQ-Outcomes (X3) and PSQ-Rapport (X4); (3) the four factors plus Disconfirmation (X5); and finally (4) the Phase Three factors plus Perceived Value (X7) and Trust (X8). It is acknowledged that, in Phase Four, an aggregate eight variables (8 x minimum 20) is at the limit of acceptability for a simultaneous multiple regression analysis where N=~150 (Hair et al 2005).

6.6.3 Phase One Results: PSQ and Satisfaction

Phase One confirms a positive relationship between PSQ and Satisfaction (H4A). Following two initial iterations, the third (III) evidences both:

Overall model relationship significance (F1, 144 = 186.438, ρ = <0.001); and Adequate predictive accuracy (R2adj = .561 or 56.1%, Table 6.10).

The regression co-efficient for PSQ/X6 is statistically significant (β = 0.75, t =13.65, ρ = <0.001). Accordingly, for every unit increase in X6, Y will augment by 0.75 units (Table 6.11).

6.6.4 Phase Two Results: PSQ-Factors and Satisfaction

By extension, Phase Two demonstrates (iteration IV) a positive relationship between three PSQ factors (again excluding PSQ-Expertise) and Satisfaction (Y). The result evidences both: overall model relationship significance (F4, 141 = 92.170, ρ = <0.001); and, versus Phase One, substantially-improved predictive accuracy (R2adj = .716 or 71.6%, Table 6.10). Each remaining partial regression co-efficient is also statistically significant: PSQ-Ethics/X1 (β = 0.14, t =2.55, ρ = <0.05); © Bill Nichols 2009

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PSQ-Outcomes/X3 (β = 0.41, t =6.30, ρ = <0.001); and PSQ-Rapport/X4 (β = 0.40, t = 6.41, ρ = <0.001, Table 6.11).

Thus, for every 1.0 unit increase in X1, X3, and X4, Y will augment by 0.14, 0.41 and 0.40 respectively (Table 6.10). Alternatively: of the 71.6% variance explained in Satisfaction, PSQ-Ethics, PSQ-Outcomes and PSQ-Rapport account respectively for 14.2%, 43.1% and 42.6%.

6.6.5 Phase Three Results: PSQ-Factors, Disconfirmation and Satisfaction

Phase Three confirms a positive relationship between Disconfirmation and Satisfaction (H4B).

The result (iteration VI) evidences both: overall model

relationship significance (F5, 139 = 99.145, ρ = <0.001); and, versus both Phases One and Two, a further improvement in predictive accuracy (R2adj = .773 or 77.3%, Table 6.10). Four partial regression co-efficients are statistically significant:

PSQ-Ethics/X1 (β = 0.12, t = 2.58, ρ = <0.05); PSQ-Outcomes/X3 (β = 0.17, t = 2.47, ρ = <0.05); PSQ-Rapport/X4 (β = 0.23, t = 3.61, ρ = <0.01); and Disconfirmation/ X5 (β = 0.44, t = 6.12, ρ = <0.01).

(Additionally PSQ-Expertise is only marginally insignificant, ρ = 0.066). Thus, for every 1.0 unit increase in X1, X3, X4 and X5, Y augments by 0.12, 0.17, 0.23 and 0.44 units respectively.

Alternatively: of 77.3% variance explained in Satisfaction (Y),

PSQ-Ethics (X1), PSQ-Outcomes (X3), PSQ-Rapport (X4) and Disconfirmation (X5) account respectively for 12.8%, 17.5%, 23.7% and 45.9%.

6.6.6 Phases One-Three: Results Summary

Following tabulations consolidate the findings of the first three phases of MRA3. They support positive relationships between (1) Perceived Service Quality (H3A),

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(2) Disconfirmation (H3B) and (3), when Disconfirmation is included in the regression, between three PSQ-factors (PSQ-Rapport, PSQ-Outcomes and PSQEthics) and Satisfaction. Table 6.10 summarises MRA3 Phase One-Three final results (highlighted green) for both model fit and predictive accuracy:

Table 6.10: Satisfaction MRA3 – Phases 1-3: Predictive Accuracy and Model Fit

Model Summary (b)

Phase One III – PSQ and PV

Phase TwoIV – Factors and PV V

Sample (N)

146

Correlation Coefficient (R)

.751 2

(a)

.851

Phase Three VI – Factors, Dis and PV

146 (c )

.875

145 (c )

.884

(d)

Coefficient of Determination (R )

.564

.723

.766

.781

2adj

.561

.716

.758

.773

.730

.588

.543

.512

Sum of Squares-Regression

99.357

127.382

134.874

129.793

Sum of Squares- Residual

76.741

48.717

41.225

36.393

Degrees of Freedom (df)

1, 144

4,141

5,140

5, 139

F Ratio

186.438

92.170

91.607

99.145

(C )

(c )

Adjusted R2 (R

)

Std.Error of Estimate (SEE)

Relationship (ANOVA) (b)

Significance

.000

(a)

.000

(d)

(a) Predictors: (Constant), PSQ (b) Dependent Variable: Satisfaction (c ) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise (d) Predictors: (Constant),PSQ-Rapport, PSQOutcomes, PSQ-Ethics, PSQ-Expertise, Disconfirmation

Source: SPSS Output.

The regressors include: III – Perceived Service Quality (PSQ); IV– PSQ factors; and VI – both PSQ factors and Disconfirmation. A preliminary iteration for Phase Three (V) demonstrates the transformation evidenced via the procedure.

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Table 6.11 reports similarly the partial standardised regression co-efficients achieved for principal iterations across all phases.

Table 6.11: Satisfaction MRA3: Coefficients Phase Two IV – Factors and PV V

Phase Three VI – Factors, Dis and PV

Stand. Coeff (β) - PSQ Ethics (X )

.135

.112

.124

t-value

2.55

2.277

2.585

.012

.024

.011

Stand. Coeff (β) - PSQ Expertise (X )

.048

.077

.084

t-value

0.958

1.643

1.851

.340

.103

.066

Stand. Coeff (β) - PSQ Outcomes (X )

.409

.226

.170

t-value

6.298

3.227

2.472

.000

.002

.014

Stand. Coeff (β) - PSQ Rapport (X )

.404

.256

.229

t-value

6.407

3.922

3.611

.000

.001

Stand. Coeff (β) - Disconfirmation (X )

.368

.443

t-value

5.044

6.122

Significance:

.000

Phase One III – PSQ and PV Coefficients

(a) 6

Stand. Coeff (β) - PSQ (X )

.751

t-value

13.654

Significance:

.000 1

Significance: 2

Significance: 3

Significance: 4

Significance: 5

(a) Dependent: Satisfaction Source: SPSS Output.

6.6.7 Phase Four: Antecedence including Trust and Perceived Value MRA3-Phase Four also supports a positive relationship between Perceived Value and Satisfaction (H5) but not the parallel Trust-Satisfaction relationship (H7).

reports both: overall model relationship significance (F7, 137 = 84.526, ρ = <0.001); and a further modest increment in predictive accuracy (R2adj = .802 or 80.2% versus Phase Three’s R2adj = .773 or 77.3%).

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Four of seven standardised partial regression coefficients (β) are significant (ρ = <0.05, Table 6.12).

The exclusions are Trust, PSQ-Ethics and PSQ-Rapport.

Notably, inclusion of the strongly PSQ-Rapport weighted construct of Perceived Value: (1) triggers re-constitution of the variate; (2) specifically eliminates PSQ Rapport; and (3), for the first time, incorporates the second technical factor, PSQExpertise. Further investigation of the significant role of Perceived Value follows (MRA6, 7.9).

Table 6.12: Satisfaction MRA 3: Phase Four Coefficient Values and Percentage of Variance Explained

Sig (ρ)

%var

PSQ-Ethics

.077

1.680

.095

PSQ-Expertise

.088

2.074

.040

10.96%

PSQ-Outcomes

.144

2.235

.027

17.93%

PSQ-Rapport

.115

1.713

.089

Disconfirmation

.336

4.721

.000

Trust

.103

1.781

.077

Phase Three - %var (VI) 12.80% 17.50% 23.70%

41.84%

45.90%

Perceived Value .235 4.117 .000 29.27% Source: SPSS Output. Green = significant partial regression co-efficients. Amber provides for comparison the penultimate Phase Three.

6.6.8 Regression Assumptions and Compliance

MRA3 incorporates an aggregate seven iterations.

A test and intervention

sequence again ensures procedural compliance throughout. This includes inter alia: (1) principles of linearity and homoscedasticity by per-iteration checks scatterplots for each regressor (X1-8) versus the criterion (Y) and per partial regression plot (Appendix Ten, Figure A10.1); (2) normality of error distribution evidenced e.g. by both normative histogram of standardised residuals and normal probability plot (Appendix Ten, Figure A10.2); (3) independence of error terms by acceptable Durbin Watson scores (Table 6.14 below); and (4) predictability. In the last case, all regressor-predictor correlations (PSQ-Expertise again excepted, Table 7.13 highlighted yellow) exceed the .3 guideline. © Bill Nichols 2009

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Table 6.13: Satisfaction MRA3: Phases 1-3 RegressorPredictor and Inter-Predictor Correlations

Sample (N)

Phase One III 146

Phase Two IV V 146 146

Phase Three VI 145

Satisfaction – PSQ

.751

Satisfaction - PSQ Ethics

.555

.563

Satisfaction- PSQ Expertise

.421

.416

Satisfaction - PSQ Outcomes

.772

.766

Satisfaction - PSQ Rapport

.770

.769

.812

.834

Satisfaction – Disconfirmation PSQ Ethics-PSQ Expertise

.221

.220

PSQ Ethics-PSQ Outcomes

.486

.487

PSQ Ethics - PSQ Rapport

.516

.517

PSQ Ethics – Disconfirmation

.497

.496

PSQ Expertise-PSQ Outcomes

.471

.467

PSQ Expertise-PSQ Rapport

.370

.365

.320

.321

.676

.671

PSQ Outcomes- Disconfirmation

.764

.772

PSQ Rapport – Disconfirmation

.743

.748

PSQ Expertise-Disconfirmation PSQ Outcomes-PSQ Rapport

.676

Significance all: <.001 except three at <.01 (all related to PSQ Expertise 1V, V, VI)

Source: SPSS Output. Amber highlights correlations of concern.

Two issues again arise. First, the removal of five exceptional outliers, reported over three iterations (I, II and V, Appendix Ten, Table A10.1), results in modest improvement to both R2adj and β scores.

Each iteration also evidences additional cases at >2.0 standard

deviations’ distance. The case aggregate twice violates the 5% norm (cases/sample e.g. IV = 9/146 = 6.1%). No individual case, however, approaches the three standard deviations’ line. Accordingly the researcher opted to retain all cases. Second, Phases One-Three evidence moderately high inter-predictor scores for PSQ Outcomes/PSQ Rapport and Disconfirmation (Table 7.13, highlighted amber). Although well below the .9 level, these scores may indicate limited collinearity

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(Hair et al 2005; Pallant 2002). Concern is supported by the SPSS collinearity diagnostics tables revealing: (iteration IV [one] and V/VI [two each]), five cases of low eigenvalues (<0.1) and high condition indices (>20.00), indicating potential multicollinearity. Concern, however, is obviated by a) the prior PSQ CFA and b) consistently acceptable multicollinearity assessment scores for tolerance (high) and the variance inflation factor (VIF, low) (Table 6.14).

Table 6.14: Satisfaction MRA3: Collinearity and Durbin Watson Independence of Error Terms (DurbinWatson)

III

1.758

1.887

1.952

1.953

Coll-Tolerance-PSQ Ethics

.698

.691

Coll-Tolerance-PSQ Expertise

.772

.760

Coll-Tolerance-PSQ Outcomes

.463

.339

Coll-Tolerance-PSQ Rapport

.492

.392

.691 .764 .332 .392 .300 1.447 1.308 3.001 2.552 3.332

Coll-Tolerance-Disconfirmation

.313

Coll-VIF-PSQ Ethics

1.433

1.446

Coll-VIF-PSQ Expertise

1.296

1.315

Coll-VIF-PSQ Outcomes

2.156

2.949

Coll-VIF-PSQ Rapport

2.031

2.548

Coll-VIF-Disconfirmation Source: SPSS Output.

6.7

3.189

Multiple Regression Analysis 4 – Loyalty-Commitment

6.7.1 Hypotheses

MRA4 tests the following hypotheses:

Perceived Value (H8), Trust (H9) and Satisfaction (H10) will all relate positively to Loyalty-Commitment (5.7, Table 5.29).

6.7.2 Summary of Phases of Analysis

MRA4 has two phases of analysis in which the common criterion variable (Y) is the final four-item Loyalty-Commitment (6.6.7). In Phase One (iterations I-III), the

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predictors are Perceived Value (X7), Trust (X8) and Satisfaction (X9) to which Phase Two (iteration IV) adds a fourth: Perceived Service Quality (PSQ/X6).

This Phase

Two test of a direct PSQ and Loyalty-Commitment relationship serves as a preliminary to the sixth, and final, regression analysis (MRA6) which explores mediation of the effects of PSQ. The test is consistent with recent presumption of a direct path (e.g. Hennig-Thurau and Klee 1997; Rauryen, Miller and Barrett 2007; 4.4.7).

6.7.3 Phase One Results

MRA4-Phase One confirms relationships between all three principal attitudes – Perceived Value (H8), Satisfaction (H9) and Trust (H10) – and Loyalty-Commitment. Following two initial iterations, it evidences (iteration III) both:

A significant overall model relationship (F3, 143 = 260.850, ρ = <0.001); and High predictive accuracy (R2adj = .842 or 84.2%, Table 6.15).

All partial regression co-efficients are also statistically significant:

Perceived Value/X7 (β = 0.31, t =5.75, ρ = <0.001); Trust/X8 (β = 0.12, t =2.47, ρ = <0.05); and Satisfaction X9 (β = 0.56, t =9.63, ρ = <0.001).

Accordingly, for every unit increase in X7, X8 and X9, Y will augment, respectively, by 0.31, 0.12 and 0.56 units (Table 6.16). Alternatively: of total Y variance explained (84.2%), Perceived Value/X7, Trust/X8 and Satisfaction/X9 account respectively for 30.94%, 12.29% and 56.75%.

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6.7.4 Phase Two Results

As theorised, Phase Two also confirms a direct relationship between PSQ (X6) and Loyalty-Commitment (Y). The regression (iteration IV) evidences:

A significant overall model relationship (F4, 142 = 208.463, ρ = <0.001); and Versus MRA4/iteration III, a small improvement in predictive accuracy (R2adj = .850 or 85.0%, Table 6.15).

Three of four partial regression co-efficients are also statistically significant:

PSQ/X6 (β = 0.14, t =2.96, ρ = <0.05); Perceived Value/X7 (β = 0.31, t =5.95, ρ = <0.001); and Satisfaction/X9 (β = 0.49, t = 7.72, ρ = <0.001).

Notably the procedure results in the marginal exclusion of Trust (ρ = <0.072). Thus, for every 1.0 unit increase in X6, X7, and X9, Y will augment by 0.14, 0.31 and 0.49 respectively. Alternatively: of the 85.0% variance explained in Y, PSQ/X6, Perceived Value/X7 and Satisfaction/X9 account respectively for 14.54%, 33.01% and 52.35%.

6.7.5. Results Summary

Following tabulations consolidate the two-phase MRA4 findings. They support positive relationships between (1) Perceived Value (H8), (2) initially Trust (H9) and (3) Satisfaction (H10) and Loyalty-Commitment. In addition, the incorporation of Perceived Service Quality (PSQ) in the variate confirms its own direct path to Loyalty-Commitment and (2) results in the marginal exclusion of Trust.

Table 6.15 summarises the results for both model fit and predictive accuracy for all four iterations conducted including: III – the final iteration without PSQ (highlighted green); and IV with PSQ added (highlighted blue).

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Table 6.15: Loyalty Commitment MRA 4: Predictive Accuracy and Model Fit

Model Summary (b)

Sample (N)

150

Correlation Coefficient (R)

.903 2

III – Final 3IV Attitudes (+PSQ)

II 148 (a)

.915

147 (a )

.920

147 (a )

.924

(c )

Coefficient of Determination (R )

.816

.839

.845

.854

2adj

.813

.836

.842

.850

.551

.511

.496

.483

Sum of Squares-Regression

197.397

195.817

192.159

194.201

Sum of Squares- Residual

44.419

37.573

35.144

33.071

Degrees of Freedom (df)

3,146

3,144

3,143

4,142

F Ratio

216.274

250.154

260.850

208.463

Adjusted R2 (R

)

Std.Error of Estimate (SEE)

Relationship (ANOVA) (b)

Significance

.000

(a)

.000

(a )

.000

(a )

.000

(c )

(a) Predictors: (Constant), Trust, Perceived Value and Satisfaction (b) Dependent Variable: Loyalty-Commitment (c ) Predictors: (Constant),Trust, Perceived Value, Satisfaction and Perceived Service Quality

Source: SPSS Output. Green = final three attitude model. Blue indicates incorporation of Perceived Service Quality.

Table 6.16 reports similarly partial standardised regression co-efficients achieved for the principal iterations across all three phases.

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Table 6.16 Loyalty-Commitment MRA 4: Coefficients

I Coefficients

III – Three Attitudes IV (+ Final PSQ)

(a) 6

Stand. Coeff (β) - PSQ (X )

.136

t-value

2.961

Significance:

.003 8

Stand. Coeff (β) - Trust (X )

.080

.106

.122

.089

t-value

1.513

2.116

2.471

1.811

.132

.036

.015

.072

Stand. Coeff (β) - Perceived Value (X )

.296

.288

.307

.309

t-value

5.151

5.330

5.754

5.945

.000

Stand. Coeff (β) - Satisfaction (X )

.592

.590

.563

.490

t-value

9.211

9.757

9.363

7.723

Significance:

.000

Significance: 7

Significance: 9

(a) Dependent: Loyalty-Commitment Source: SPSS Output. Green = final three attitude model. Blue = incorporation of Perceived Service Quality.

6.7.6 Regression Assumptions and Compliance

MRA4 incorporates an aggregate four iterations.

A tests and intervention

sequence again ensures procedural compliance confirming inter alia: (1) principles of linearity and homoscedasticity by per-iteration checks of scatterplots for each regressor versus the criterion (Y) and per partial regression plot (Appendix Eleven, Figure A11.1); (2) normality of error distribution evidenced e.g. by both normative histogram of standardised residuals and normal probability plot (Appendix Eleven, Figure A11.2); (3) independence of error terms by an acceptable Durbin Watson scores (Table 6.17); and (4) predictability for which all regressor-predictor correlations are significantly above the .3 guideline (Table 6.18).

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Table 6.17: Loyalty-Commitment MRA4 - Collinearity and Durbin Watson I

III – IV 3Att +PSQ

Independence of Error Terms (Durbin-Watson)

1.954

1.990

1.960

2.035

Coll-Tolerance-P/Value

.382

.379

.378

Coll-Tolerance-Satisfaction

.305

.299

.254

Coll-Tolerance-Trust

.450

.448

.444

.422

Coll-Tolerance-PSQ

.485

Coll-VIF- P/Value

2.620

2.618

2.638

Coll-VIF- Satisfaction

3.282

3.278

3.345

3.931

Coll-VIF- Trust

2.220

2.231

2.251

2.369

Coll-VIF-PSQ 2.059 Source: SPSS Output. Green = final three attitude model. Blue = incorporation of Perceived Service Quality.

Table 6.18: Loyalty-Commitment MRA4 - Correlations

Sample (N)

I 150

II 148

III – 3IV Att (+PSQ) 147 147

Loyalty-Commitment - P/Value

.808

.816

.825

Loyalty-Commitment – Satisfaction

.880

.892

Loyalty-Commitment – Trust

.704

.725

.735

Loyalty-Commitment – PSQ

.706

P/Value – Satisfaction

.778

.781

P/Value – Trust

.646

.648

P/Value – PSQ Satisfaction-Trust Satisfaction-PSQ

.557 .731

.732

.736

.736 .699

Trust-PSQ .623 Source: SPSS Output. Green = final three attitude model. Blue = incorporation of Perceived Service Quality.

Two issues only arise. They are discussed and addressed in more detail. First, the removal of three exceptional outliers (Appendix Eleven, Table A11.1) results in modest improvement to both R2adj and β scores. Each iteration is also

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characterised by five-six >2.0 standard deviations’ outliers which collectively, in no instance, violate the 5% norm.

Second, over 50% of inter-predictor correlations are in the range .7- .8. Although below .9, MRA4 evidences the highest concentration of such correlations in any MRA reported and may indicate collinearity (Hair et al 2005; Pallant 2002). Additionally, SPSS collinearity diagnostics report one case in each of the first three iterations (plus two in IV) with low eigenvalues (<0.15) and concomitant high condition indices (range 20:00-29:50). Although concern is partly mitigated by a) the prior CFAs and b) the consistent compliant multicollinearity assessment scores for tolerance and VIFs (Table 6.17 above), this subject is revisited in later analysis.

6.8

Mediation Analysis 1: MRA5 – Perceived Value and Satisfaction

6.8.1 Overview and Hypothesis

As noted (MRA3, 7.6), Perceived Value is corroborated as a major component of the Satisfaction variate. MRA5, accordingly, determines support for H11 (3.3.6):

Satisfaction (M) will act as a partial mediator of the effects of Perceived Value (X) on Loyalty-Commitment (Y) (summary Whittaker et al 2007).

To ensure consistency, MRA5 employs the preceding and closing MRA4 LoyaltyCommitment regression sample (N=147, 7.7).

6.8.2 Results and Compliance

To determine mediation (discussion 3.3.5) requires three inter-related regression analyses (I-III, MacKinnon 2008, p.57).

Using unstandardised β co-efficients

(MacKinnon 2008) and conducted sequentially, they supply path values as follows: (I) X→M = a; (II) X→Y= c; and (III) (X+M)→Y= b + c1.

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All three regressions report acceptable or high predictive accuracy and that overall model relationships are significant (Table 6.19). In the three-variable case, for example, a high R2adj = .835 or 83.5% is achieved while F2,139 = 358.804, ρ = <.001. Further the partial regression co-efficients for both Perceived Value (X) and Satisfaction (M) are also significant: respectively X (β = .34; t = 5.68; ρ = <.001) and M (β = .61; t = 10.11; ρ = <.001).

Table 6.19: Mediation MRA5: Value-Satisfaction Principal Results MRA5A

MRA5B

MRA5C

Criterion (Y)

Satisfaction

Loyalty-C

Predictor (X)

P/Value

Moderator (M)

N/A

Satisfaction

Sample

142

.681

.718

.838

Adjusted R

.678

.716

.835

St.Error of Estimate

.645

.655

.499

Durbin-Watson

2.094

2.081

1.978

F-Ratio

298.266

357.173

358.804

Significance (ρ)

<.001

1, 140

2, 139

Unstandardised β - Constant/(Confidence Interval)

1.758 (1.3202.196)

1.385 (.9401.830)

.223 (-.186.631)

Unstandardised β - P/Value

.719 .802)

.800 (.716.883)

.324 (.211.437)

N/A

.661 (.532.791)

Unstandardised β – Satisfaction Source: SPSS Output.

N/A

(.637-

Next compliance with key assumptions is confirmed e.g.:

Variable correlations: all inter-correlations are >.6 (Hair et al 2005): X→Y, r = .848; M→Y, r = .894; and X→M, r = .825, all ρ = <.001;

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Outliers: iteration I resulted in the necessary elimination of five cases (each at > three standard deviations from the regression line (Hair et al 2005); Normality of distribution of error terms: is supported at each stage by visual inspection of the output plots; Independence of error terms:

closely proximate at each stage to the

Durbin-Watson ideal of 2.0 (Table 6.19); and Multi-collinearity: mitigation is confirmed by appropriate VIF/tolerance scores (Grimm and Yarnold 1995; Pallant 2002) and preceding CFAs.

6.8.3 Analysis of Mediation

Further analysis confirms that:

1. Perceived Value (X) has statistically significant relationships (paths c and a respectively) with both: Loyalty-Commitment (Y) (c = .800; sc = .042; tc = 18.899; ρ = <.001); and Satisfaction (M) (a = .719; sc = .042; tc = 17.270; ρ = <.001). Each unit increase in X associates, therefore, with 0.8 and 0.72 unit augmentations in, respectively, Y and M; 2. When controlling for Perceived Value (X) on path b, the M-Y relationship is also statistically significant (b = .661; sc = .065; tc = 10.110; ρ = <.001). One unit’s increase in M associates with a 0.66 rise in Y; and 3. Finally, the adjusted effect (c1) of X on Y is also statistically significant (c1 = .324; sc = .057; tc = 5.680).

In summary, when M is introduced as an additional predictor, the value of path c is: (1) reduced (.800-.326 = .476); (2) confirmed by the ab calculation (.719*.661 =.476; and (3) remains significant (ρ = <.001).

Re-assurance is provided by computation of (upper and lower) confidence levels requires. This requires the standard error (S) which, in the case of ab, is the

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product of coefficient standard errors. The following intervals calculation confirms acceptability (MacKinnon 2008):

S = ab √t2a+t2b tatb S = (0.719)(0.661) √ 17.172+10.112 (17.27 x 10.11) S = 0.544 Therefore, LCL = 0.476-1.96(0.0544) UCL = 0.476+1.96(0.0544)

= 0.369; and = 0.583.

Accordingly (H11), partial mediation of Perceived Value (X) by Satisfaction (M) is supported. This mediated effect is assessed at 0.48 units per unit increase in X. By extension, the total (unstandardised) effects of Perceived Value (direct + indirect) are equal to .324 + .476 i.e. .800.

6.9

Mediation Analysis 2 – MRA6: Perceived Service Quality, Attitudes and Loyalty-Commitment

6.9.1 Introduction and Hypothesis

The preceding mediation analysis (6.8) confirms that Satisfaction is a partial mediator of Perceived Value (PV, H11) and that both possess a direct path to Loyalty-Commitment (L-C).

Such a path is also supported provisionally for

Perceived Service Quality (‘PSQ’, MRA4, 6.7).

Accordingly, this sixth and final

multiple regression analysis (MRA6) tests:

(H12) Both Perceived Value and Satisfaction will act as partial mediators of the effects of Perceived Service Quality on Loyalty-Commitment.

For convenience, this section retains the formula references employed earlier in Chapter Seven e.g. Satisfaction = X9. © Bill Nichols 2009

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6.9.2 Phases of Analysis

Phase One identifies the nature of direct relationships between the four confirmed PSQ predictor factors (Ethics, Expertise, Outcomes and Rapport) – plus Perceived Value and Satisfaction - and the criterion, Loyalty-Commitment.

Phase Two

explores the presence and nature of mediation by Perceived Value and Satisfaction.

6.9.3 Phase One – Results: PSQ Factors and Loyalty-Commitment

For the initial four-factor regression, MRA6-Phase One confirms both the significance of the overall model relationship (F6,140 = 134.759, ρ = <.001) and high predictive accuracy (R2adj = .846 or 84.6%, Table 6.20):

Table 6.20 – Mediation MRA6: Model Summary and ANOVA Model Summary(b)

Model 1

R .923(a)

R Square .852

Adjusted R Square .846

Std. Error of the Estimate .48949

Durbin-Watson 2.019

ANOVA(b)

Model 1

Regression

Sum of Squares 193.729

df 6

Mean Square 32.288

Residual

33.544

140

.240

Total

227.273

146

F 134.759

Sig. .000(a)

a Predictors: (Constant), PSQRapport5, PSQExpertise2, PSQEthics2, PV3PValue, PSQOutcomes4, S3Satisfaction b Dependent Variable: L4Loyalty

Source: SPSS Output.

Among partial regression co-efficients, in addition to Satisfaction (X9) and Perceived Value (X7), only PSQ-Rapport (X4) is statistically significant (β= .12; t = 2.30; ρ = >.05). Accordingly, for every unit increase in X9, X7 and X4, LoyaltyCommitment (Y) will augment, respectively, by 0.50, 0.31 and 0.12 units (Table 6.21). © Bill Nichols 2009

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Table 6.21: MRA6 – Preliminary Regression – Coefficients and Collinearity Statistics Coefficients (a)

Unstand

Stand

Std.Error

(Constant)

-.361

.265

Satisfaction

.528

Perceived Value

Sig.

T 1.360

.176

.074

.499

7.108

.301

.050

.314

PSQ-Ethics

-.028

.038

PSQ-Expertise

.027

PSQ-Outcomes PSQ-Rapport

Collinearity.. Tolerance

VIF

.000

0.214

4.674

.000

0.373

2.683

-.030

5.909 0.723

.471

0.602

1.661

.029

.033

0.931

.354

0.800

1.250

.099

.066

.081

1.502

.135

0.355

2.816

.160

.069

.121

2.298

.023

0.378

2.647

(a) Dependent Variable: L4Loyalty Source: SPSS Output.

Phase One also conforms to all necessary assumptions (6.3, Table 6.1) including e.g. (1) independence of error terms, (Durbin-Watson, Field 2005, Table 6.20), (2) multicollinearity (Tolerance/VIF, Pallant 2002, Table 7.3) and (3) absence of any outliers at >3 standard deviations with six at >2 which is equivalent to <5% of available cases (Hair et al 2005).

6.9.4 Results Phase Two: Mediation Analysis

Phase Two next determines the level (if any) of mediation of PSQ-Rapport by Perceived Value and/or Satisfaction. It requires a four-part equation set. Following the multiple mediator procedure defined by MacKinnon (2008, p.103ff), this requires four inter-related regressions.

Using unstandardised β co-efficients

(MacKinnon 2008) and conducted sequentially, they supply path values respectively as follows: (1) X→M1 = a1; (2) X→M2 = a2; (3) X→Y = c; and (X + M1 + M2) →Y = b1 + b2 +c where: X = PSQ-Rapport; M1 = Perceived Value; M2 = Satisfaction; and Y = Loyalty-Commitment (Figure 7.1).

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Figure 6.1: MRA6 – Joint Mediation • . Perceived Value (M1) a1

PSQRapport (X)

LoyaltyCommitment (Y)

c b2

a2 Satisfaction (M2)

LoyaltyCommitment

Source: Author based on MacKinnon (2008)

All four regressions report significant overall relationships and demonstrate acceptable predictive accuracy (Table 6.22). (Note, however, that the reported R2adj = .459 for PSQ-Rapport→Perceived Value (a1) is consistent with the lesser weight reported for PSQ Rapport in the Perceived Value regression [MRA1, 6.4] vs. its relationships with Trust and Satisfaction [MRA2/MRA3, 6.5-.6.6). All partial regression co-efficients are also significant (<.01, Table 6.22). Results are based on a common reduced sample (N=143). This follows elimination of four outliers (> three standard deviations) in initial iterations. Compliance with other assumptions (7.2, Table 6.1) is confirmed e.g. independence of error terms closely proximates the Durbin-Watson ideal of 2.0 (Table 6.22).

Table 6.23 confirms statistically significant relationships per path.

These

demonstrate that the value of path c is: (1) greatly reduced at c1 (.959 - .193 = .766); (2) confirmed by the a1b1 + a2b2 =calculation [= (.868)(.303) + (.920)(.548) = .262 + .504 = .766.]; and (3) remains significant (ρ =<.001) when both M1 and M2 are introduced as predictors. The mediated effects of PSQ-Rapport (X) on LoyaltyCommitment (Y) via Perceived Value (M1) and Satisfaction (M2) are estimated respectively at 0.26 and 0.50 units per unit increase in X.

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Table 6.22: Mediation MRA6: Principal Results of Regression Paths

Path

a2+b2+c1

Criterion (Y) Predictor (X)

P/Value PSQRapport

Satisfaction PSQRapport

Loyalty-C PSQRapport

Moderator (M1)

N/A

P/Value

Moderator (M2)

N/A

Satisfaction

Sample

143

.462

.610

.597

.827

.459

.608

.595

.823

St.Error of Estimate

.884

.694

.744

.491

Durbin-Watson

2.038

1.861

1.883

2.008

F-Ratio

121.274

221.254

209.430

221.157

Significance (ρ)

<.001

1, 141

1,141

3, 139

Unstandardised β – Constant Unstandardised β - PSQRapport

.135

.076

-0.091

-0174

.868

.920

.959

.193

Unstandardised β - Satisfaction

N/A

.547

Unstandardised β - P/Value

N/A

.302

Adjusted R

Source: SPSS Output

Table 6.23: Mediation MRA6 – Phase Two: Unstandardised Coefficients and Relationship Significance 1

PSQ-Rapport → P/Value (X-M ) 2

PSQ-Rapport → Satisfaction (X-M ) PSQ-Rapport → Loyalty-Commitment (X-Y) 1

P/Value → Loyalty-Commitment (M -Y) Satisfaction → Loyalty-Commitment 2 (M -Y) PSQ-Rapport → Loyalty-Commitment (X-Y) Source: SPSS Output.

Path

Unstand β

.868

.079

11.012

.000

.920

.062

14.875

.000

.959

.066

14.472

.000

.303

.054

5.644

.000

.548

.068

8.022

.000

.193

.072

2.678

.008

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Re-assurance is provided by applying the formulae previously employed in the first mediation, MRA5 (MacKinnon 2008, p.107). Thus: the standard errors of the two mediated paths are .053 (a1b1) and .064 (a2b2) and the respective confidence intervals an acceptable .159 - .365 (a1b1) and .379 - .629 (a2b2). Further “the mediated effect can be tested for its statistical significance by dividing the estimate of the mediated effect by its standard error” (MacKinnon 2008, p.108). The simpler format multivariate delta solution for this standard error is provided by:

S a1b1+a2b2 = √ S 2a1b1+ S 2a2b2 +2a1a2 Sb1b2

Given that the resulting absolute value of the ratio is 2.01 (>1.96), it follows that the mediated effect “is significantly different from zero at the .05 level of significance” (MacKinnon 2008, p108).

Thus: H12 is supported and Perceived Value and Satisfaction are confirmed as joint partial mediators of PSQ-Rapport.

6.10 Convergence Model - Hypothesis Summary: MRA Findings Based on the findings of the six multiple regression analyses (MRA1-6), the first part of this chapter supports 12 out of 13 remaining Convergent Model hypotheses. The unsupported exception is H7, the relationship between Trust and Satisfaction.

Table 6.24 summarises all provisional path validations.

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Table 6.24: Principal Convergence Model – Revised Hypothesis Set – Provisional Support No.

Hypothesis

Alternative Goodwill Model

Perceived Service Quality (PSQ) will relate positively to Perceived Value (PV)

Retained

Disconfirmation will relate positively to PV

Retained

ELIMIINATED

Eliminated

ELIMINATED

Eliminated

PSQ will relate positively to Trust

Retained

Disconfirmation will relate positively to Trust

Retained

PSQ will relate positively to Satisfaction (AMENDED)

Eliminated

Disconfirmation will relate positively to Satisfaction (AMENDED)

Eliminated

PV will relate positively to Satisfaction (AMENDED)

Eliminated

ELIMINATED

Eliminated

Trust will relate positively to Satisfaction (AMENDED).

Eliminated

PV will relate positively to Loyalty-Commitment.

Retained

Satisfaction will relate positively to Loyalty-Commitment.

Eliminated

Trust will relate positively to Loyalty-Commitment. Satisfaction will partly mediate the effects of Perceived Value on LoyaltyCommitment

Retained

Eliminated

Part-Retained Satisfaction and Perceived Value will jointly partly mediate the effects of with PV as 12 Perceived Service Quality on Loyalty-Commitment mediator Source: Author. Red (2A/2B/6) = eliminated following initial tests. Amber (7) = unsupported. Green (others) = provisionally supported following MRA sequence.

6.11 Convergent Model – Measurement Model Confirmatory Factor Analysis 6.11.1 Overview

As noted, this study adopts a ‘building-block’ approach to model validation. This accommodates (1) the relatively small sample size (N = 150) and (2) large variableset. Based on refinements provided by the prior Chapter Five and this chapter to date, it is now feasible to examine the Convergent Model (and, comparatively, the competing Goodwill model).

As a preliminary it is necessary to assess

measurement model fit and construct validity in order to eliminate - or minimise the twin risks of measurement- and specification error (Grimm and Yarnold 1995, © Bill Nichols 2009

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p. 88).

“Valid structural theory tests cannot be conducted with bad measures”

(Hair et al 2005, p.848). Confirmatory factor analysis (CFA) results are reported below (6.11.2) together with appropriate reliability and validity tests (6.11.3).

6.11.2 Final ‘Convergent Model’ CFA

The measurement model incorporates five latent constructs (ξ

1-5)

including PSQ-

Rapport (five items), Loyalty-Commitment (4), Perceived Value (3) and Satisfaction (3). Given prior theoretical support and its only marginal elimination (6.6.7), Trust (2) is also provisionally retained. A consequent risk of “interpretational confounding” – often resulting from instability associated with under-identified factors is acknowledged (Hair et al 2005, p.786).

Table 6.25 summarises, and

colour-codes by construct, the aggregate 17 measured variables (X

1-17).

This

coding applies also to the complementary AMOS model path diagram (Figure 6.2) and throughout the balance of this chapter.

Table 6.25: Final Model - Table of Variables Ref:

Observed/Measured

PV1 (X1)

Value for Money

PV2 (x2)

General Equity

PV3 (X3)

Fee Acceptability

CS2 (X4) CS3 (X5)

Utility (Cumulative Satisfaction) Pleasure (Cumulative Satisfaction)

S1 (X6)

General Service (Satisfaction)

L2 (X7)

Benevolent Influence

L5 (X8)

Relationship Commitment

L7 (X9)

Firm/Brand Commitment

L9 (X10)

Private Advocacy

PSQ02 (X14)

Problem Understanding

PSQ15 (X11)

Working Relationship

PSQ19 (X12)

Account Manager Interaction

PSQ20 (X13)

Responsiveness

PSQ24 (X15)

Dependability

T3 (X16)

Team Empowerment

T4 (X17)

Account Manager Empowerment

Ref:

Latent/Unobserved

Perceived Value

SAT

Satisfaction

Loyalty-Commitment

RAP

Perceived Service Quality (PSQ) – Rapport

Trust

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δ1 δ2

Figure 6.2 – Final Convergent Measurement Model

1 1

PV1 (x1) PV2 (x2)

δ5

δ6

δ7

δ10

δ11

1 1

L5 (Xx) L7 (X6)

1 1

PSQ15 (X8)

1 1 1

Φ3,1

δ14

δ15

Λx3,11 Λx3,12

Λx414

CS2 (X14)

PSQ Rapport ξ3

1 Λx4,13

Λx415

T3 (X16) T4 (X17)

Φ4,2 Φ5,2 Φ4,3

Satisfaction ξ4

Φ5,3

Φ5,1

Φ5,4

CS3 (X15)

Φ4,1

Φ3,2 Λx3,10

PSQ02 (X11)

S1 (X13)

δ17

Φ2,1

Λx3,9 1

PSQ20 (X10)

1 1

δ16

LoyaltyCommitment ξ2

Λx3,8

PSQ19 (X9)

PSQ24 (X12)

δ13

Λx2.4 1 Λx2.5 Λx2.6 Λx2.7

L9 (X7)

δ12

Perceived Value ξ1

Λx1,3

L2 (x4)

δ8 δ9

Λx1,2

PV3 (x3)

δ3

δ4

Λx1,1

Λx5,16 Λx5,17

Trust ξ5

Source: Author (based on AMOS16 path diagram).

As noted, for a sample/model of this size, a good-fit benchmark comprises: “an insignificant χ2 value, a CFI (comparative fit index) of at least .97 and a RMSEA (root mean squared error of approximation) of .08 or lower” (Hair et al 2005, p.753). It is also appropriate to: (1) add, in the role of goodness-of-fit index, a GFI of >.900 (Hair et al 2005); and (2) acknowledge that for χ2 a significant ρ-value may result even with good fit (Hair et al 2005). Against this benchmark, the first Convergent measurement model iteration (I) is positive on most counts: e.g. χ2 = 174.680, df = 109, ρ = 0.000; CFI = .972; and RMSEA = 0.64). Exceptions are χ2 significance and marginally-deficient GFI (Table 6.26).

To improve acceptability, five further iterations (II-VI), all reported, serve to identify iteration VI (highlighted green, Table 6.26) as the most adequate solution. It reports: χ2 = 120.840, df = 94, ρ = 0.033; CFI = .987; GFI = .905 and RMSEA =

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0.45. Notably, the target < 0.8 RMSEA obtains also within the confidence intervals (0.14-0.67).

Table 6.26 Final Convergent Measurement Model: CFA Iterations Iteration No.

III

Less L2/CS2

Less L2 /D = 143

Annotation

All

Less L2

Less L9

Less CS2

No. of Variables

Methodology

MLE

Sample Size (N)

150

143

χ2

174.680

141.640

157.252

153.949

121.327

120.840

109

Significance (ρ)

.000

.001

.000

.002

.033

CMIN/df

1.603

1.507

1.673

1.638

1.517

1.285

GFI

.882

.891

.886

.888

.904

.905

AGFI

.834

.857

.835

.838

.855

.862

CFI

.972

.979

.971

.979

.987

RMSR

.058

.053

.059

.060

.054

.044

RMSEA RMSEA Confidence

.064 .045081

.058 .037.077

0.67 .048.085

.065 .046.084

.059 .036.079

.045 .014.067

Source: AMOS Output. Amber = Best variable elimination; Green = repeated with 143 sample.

The solution is achieved based on modification indices, normality report and theoretical support. It requires elimination of: (1) one variable (‘L2-Influence’), with lowest regression weight; and (2) the most common seven extreme multivariate outliers (ρ = <0.005). Determined by the Mahalanobis D2 distance statistic, the latter “observations… are improbably far from the centroid under the hypothesis of normality” (Arbuckle 2007). They are co-incidentally the identical

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seven removed in the sixth multiple regression analysis (N=143, 7.9.4). This set is retained for the balance of analysis.

Although the solution does not comply fully with the ideal 10-observations per measured parameter rule (i.e. 10 x 17 = 170 vs. 143, Hair et al 2005), it is well above the minimum tariff of five. Additional analysis confirms absence of any major violations: e.g. (1) very large standard errors for one or more co-efficient; and/or (2) unreasonable estimates such a negative error variances etc. (Hair et al 2005).

Unfortunately, although consistent with prior theory, model adequacy is not supported by following reliability and validity tests.

6.11.3 Convergent Measurement Model CFA - Reliability and Validity

Based on iteration VI, support is confirmed for (1) content validity (based on prior theory), (2) nomological validity (prior empirical contributions and positive interconstruct correlations [φ]) and (3) convergent validity based on:

(1) All standardised factor loadings => .7 (i.e. .70 x.70 = .5 thereby explaining at least half the variance); (2) All AVEs = >0.5; and (3) All constructs exhibit >.7 estimates of construct reliability (CR) or an acceptable .67 (Trust) given that other criteria are fulfilled (Hair et al 2005, p. 807ff, Table 6.27)

However, discriminant validity is unsupported since only half of the estimates of average variance extracted per construct across the model exceed the “squared inter-construct correlations (SICs) associated with that factor” (Hair et al 2005, p.208).

Most egregiously, Loyalty-Commitment is in breach on all four counts

(Table 6.28).

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Table 6.27: Final Convergent Model - Convergent Validity Assessment Item Reliabilities

Delta

0.958

0.918

0.082

PV2

0.884

0.781

0.219

PV3

0.808

0.653

0.347

Ref:

PV1

SAT

PSQ

CS2

0.940

0.884

0.116

CS3

0.948

0.899

0.101

0.881

0.776

0.224

0.844

0.712

0.288

0.755

0.570

0.430

0.857

0.734

0.266

PSQ02

0.754

0.569

0.431

PSQ15

0.757

0.573

0.427

PSQ19

0.832

0.692

0.308

PSQ20

0.725

0.526

0.474

PSQ24

0.859

0.738

0.262

0.805

0.648

0.352

0.870

0.757

0.243

Variance Extracted

78.40% 85.28% 67.23% 61.95% 70.25%

Construct Reliability

0.92

0.95

0.86

0.89

0.67

Source: AMOS Output. Construct colour-coding maintained per AMOS path diagram per Figure 7.2.

Per initial tests, no obvious measurement error remedies are available.

For

example, the largest remaining standardised residual = 1.125 only. Similarly, freeing the parameter suggested by execution of the largest remaining MI (~12) makes no sense and is, in any case, deleterious. In parallel, potential specification error do not produce significant amelioration. These include elimination of Trust and, following initial discriminant analysis, stepwise removal of potentially influential cross-loading variables (e.g. L9-Advocacy, CS3-Utility, and PSQ19Account Manager Interaction).

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Table 6.28: Final Convergent Model - Discriminant Validity Assessment Part 1. InterConstruct Correlations Ref:

SAT

PSQ

0.000

0.825

0.899

0.769

0.706

SAT

0.825

0.000

0.940

0.860

0.751

0.899

0.940

0.000

0.912

0.838

PSQ

0.769

0.860

0.912

0.000

0.862

0.706

0.751

0.838

0.862

0.000

Part 2

AVE

SIC

0.784

0.000

0.681

0.808

0.591

0.498

SAT

0.853

0.681

0.000

0.884

0.740

0.564

0.672

0.808

0.884

0.000

0.832

0.702

PSQ

0.619

0.591

0.740

0.832

0.000

0.743

0.702

0.743

0.000

T 0.702 0.498 0.564 Source: AMOS Output. Bold red = breaches.

6.11.4 Summary

Based on this evidence of high non-discrimination between constructs (6.11.3), one is advised to reject the Final Convergent Model as ‘very unlikely’ (Grimm and Yarnold 1995, p.67). Nonetheless since the model has strong theoretical support for many aspects and validity in other dimensions, a structural model analysis is reported in brief in the following section (6.12) and its findings carried forward to the concluding discussion (Chapter Seven).

The CFA measurement model proof also corroborates three other propositions:

(1) “Consistency is a necessary but not sufficient condition for construct validity” (Nunnally 1978, p.103, 4.6.1ff). It fails here notwithstanding: (a) prior theory, (b) initial scale validations in which Cronbach αs all exceed the

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benchmark 0.8 (Carmines and Zeller 1999; 5.4.8) and (c) a supportive sequence of factor analyses (5.5ff). It fails, therefore, to demonstrate metatriangulation theory and integrate differing explicative traditions. (2) The evidence of prior theory which is ‘accrued’ over time - as opposed to structurally obtained – will likely generate consequent ‘fuzzy’ domains (Nunnally 1978, pp. 97-99); and (3) Third, given such overlapping domains and risks identified in literature review’s counterpoint (2.15) – e.g. model complexity, construct instability, natural language descriptors - are all upheld.

Following discussion of the structural Convergent Model (6.12), it remains to assess potential support for the alternative ‘Goodwill’ model (6.13).

6.12 Final Model – Structural Model Review Since early structural Convergent Model iterations result in the elimination of Trust - as the Trust→Loyalty-Commitment path is reported non-significant (ρ = .200) – the final iteration configures 18 exogenous and 17 endogenous variables. These include the unobserved PSQ-Rapport (ξ1) and Perceived Value, LoyaltyCommitment and Satisfaction (η1-3) constructs. Figure 6.3 summarises their interrelationships together with provisionally-supported hypothesis labels (Table 6.24 above). Apart from an (acceptable) significant χ2 (= 120.385; df = 71; ρ = .000; CMIN/df 1.696) and marginally-deficient GFI, the model complies with Hair et al’s criteria (2005, p.753, 7.2.2) and on other counts (Table 6.29). No standardised residual exceeds a value of 1.3.

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Figure 6.3 – Final Convergent Model – Structural View E

PSQ15 (X1)

PSQ19 (X2)

PSQ20 (X3)

PSQ02 (X4)

PSQ Rapport ξ1 H12

PSQ24 (X5)

H12 L5 (Y7)

H12

LoyaltyCommitment

E E E

1 1

PV1 (Y1) PV2 (Y2)

Perceived Value η1

E E

S1 (Y4) 1

L9 (Y9)

1 1

E E

H5,H11 1

L7 (Y8)

η3

PV3 (Y3)

Satisfaction

η2

CS2 (Y5) CS3 (Y6)

Source: Author (based on AMOS16 path diagram).

Table 6.29: Final Convergent Structural Model - Goodness of Fit Assessment Application

Abb.

Guidance

Outcome

Incremental Fit Index

CFI

> .95

0.974

Badness of Fit Index

RMSEA

<.08 with CFI of >.95

0.070

Absolute Fit Index

RMR

<.08 with CFI of >.95

0.062

Goodness of Fit Index

GFI

>.90

0.886

Green = confirmed Source: AMOS Output.

Amber = proximate solution.

The solution also exhibits parameter estimates which are all in the predicted direction and nontrivial. It implies support for five out of six tested hypotheses (Figure 6.4):

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Figure 6.4 – Final Structural Model (including Factor Loadings [SRWs] and Squared Multiple Correlations) E

PSQ15 (X1)

PSQ19 (X2)

E E

PSQ20 (X3)

.74 .79

PSQ Rapport ξ1

.77

PSQ02 (X4)

.88

PSQ24 (X5)

.75

PV1 (Y1)

.96

PV2 (Y2)

.88

PV3 (Y3)

.81

.31

.51 SMC=.56

SMC=.96

LoyaltyCommitment

η3

.34

Perceived Value η1

.84

L5 (Y7)

L7 (Y8)

L9 (Y9)

.79 .86

.40 .44 SMC=.79

.88

S1 (Y4)

CS2 (Y5)

.94

CS3 (Y6)

.95

Satisfaction

η2

Source: Author (based on AMOS16 diagram)

The model is accordingly:

1. Effective in explaining Loyalty-Commitment variance (SMC = .96); 2. Supportive of all relevant principal hypotheses including both mediations (H11/H12); 3. Confirms the marginal predominance, in terms of standardised direct effects (β = .402 vs. β = .336), of Satisfaction versus Perceived Value; and 4. Indicates a significant direct role for PSQ-Rapport (β = .310).

Accordingly, at least in terms of consistency of theory and outcomes, the Final Convergent Model is plausible (albeit invalidated). Its implications are considered further in Chapter Seven.

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6.13 The Competing ‘Goodwill’ Model 6.13.1 Overview and Context

The inadequacy of the Final Model appears attributable primarily to construct misspecification (Grimm and Yarnold 1995, p.88) for which the theoretical review’s counterpoint (2.15) identifies four possible sources:

(1) Model complexity; (2) Construct instability; (3) ‘Intra’-consumption observation; and (4) The nature of language and its connotations).

Redress requires (a) model refinement, (b) re-testing and, possibly (but outside this study’s scope), (c) fresh data collection (Hair et al 2005, p.759). Among potential refinement solutions, the counterpoint highlights Oliver’s (1999) conjecture regarding integrated Loyalty-Satisfaction (2.15.4). It is the basis of an alternative Goodwill or ‘Uni-Conative’ model - so named because it postulates a single endogenous variable of Loyalty-Satisfaction. This section describes the procedure adopted to generate the model (6.13.2) and reports:

Headline CFA findings (6.13.3); Convergent and discriminant validity tests (6.13.4); Critically content and nomological validity tests in the revised context (6.13.5); and Key findings for the structural model itself (6.13.6).

6.13.2 Alternative Model Development Procedure - Summary

In the interests of consistency, the researcher retained the Convergent Model’s final 17 measured-variable framework (6.11) and adopted a three-step

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development procedure. The first two steps are summarised in brief in this section: the third is reviewed next in detail (6.13.3). First a new exploratory factor analysis (EFA) confirmed:

In an unconstrained two-factor Varimax solution, loading of all seven final Loyalty-Satisfaction indicators to a common factor; and In a constrained four-factor Oblimin solution, designed to detect the underlying pattern, a potential four construct solution based on existing Perceived Value, PSQ-Rapport, Trust and the newly-labelled LoyaltySatisfaction constructs.

Second, in preparation for CFA, a discriminant analysis sequence identified three potentially influential cross-loaders:

L9-Advocacy, PSQ19-Account Manager

Interaction and, to a lesser extent, PSQ15-Relationships.

6.13.3 Uni-Conative Model: CFA Key Results

Following progressive elimination of potential cross-loaders, a compliant UniConative measurement model solution is achieved.

It fulfils Hair et al’s (2005,

p.753) criteria with two exceptions: (1) significant χ2 (= 133.135; df = 75; ρ = 0.000; CMIN/df = 1.875) which is duly noted and, with caution, set aside; and (2) an again marginally deficient GFI (Table 6.30).

Table 6.30: Uni-Conative Model - CFA Goodness of Fit Assessment Application

Abb.

Guidance

Outcome

Incremental Fit Index

CFI

> .95

.968

Badness of Fit Index

RMSEA

<.08 with CFI of >.95

.077

Absolute Fit Index

RMR

<.08 with CFI of >.95

.065

Goodness of Fit Index

GFI

>.90

.887

Green = confirmed Source: AMOS Output.

Amber = proximate solution.

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This outcome is adopted as an acceptable basis on which to proceed to tests of reliability and validity.

6.13.4 Uni-Conative Model – Convergent and Discriminant Validity

The Uni-Conative measurement model complies with convergent validity criteria. Notably the newly-integrated construct of Loyalty-Satisfaction achieves a high CR (.94) while the marginally deficient Trust (CR = .67) is acceptable (Table 6.31).

Table 6.31: Uni-Conative Model – Convergent Validity Assessment Ref:

Perceived Value

PV1

LoyaltySatis

Item Reliabilities

Delta

0.967

0.935

0.065

PV2

0.892

0.796

0.204

PV3

0.814

PSQ

Trust

0.663

0.337

CS2

0.944

0.891

0.109

CS3

0.954

0.910

0.090

0.881

0.776

0.224

0.746

0.557

0.443

0.762

0.581

0.419

0.829

0.687

0.313

0.742

0.551

0.449

PSQ20

0.721

0.520

0.480

PSQ24

0.919

0.845

0.155

PSQ02 PSQ15 PSQ19

0.819

0.671

0.329

0.878

0.771

0.229

Variance Extracted

79.78%

73.36% 63.83% 72.08%

Construct Reliability

0.92

0.94

0.84

0.67

Source: AMOS Output. Construct colour coding retained from AMOS path diagrams.

Second, in comparison to the Convergent Model, the model also achieves a substantial improvement in discrimination. It complies fully using the Campbell and Fiske (1959) formula by which results =<0.85 indicate the probability of © Bill Nichols 2009

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discrimination.

It is also nearly fully compliant based on the conservatively-

formulated comparison of average variance extracted (AVE) and relevant squared interconstruct correlations (SICs) (Hair et al 2005, Table 6.32).

Table 6.32 Uni-Conative: Discriminant Validity Assessment Variance Extracted

L-SAT

PSQ

0.798

0.000

0.736

0.487

0.576

L-SAT

0.734

0.736 (*1)

0.638

0.490

PSQ

0.638

0.487

0.000 0.638 (*2)

0.000

0.584

T 0.721 0.576 0.490 Source: AMOS Output – colour coding maintained. (*1) = 0.79 Campbell and Fiske (1959); (*2) = 0.71 Campbell and Fiske (1959).

0.584

0.000

PV SAT

6.13.5 Uni-Conative Model: Content and Nomological Validity

Although there are positive inter-construct correlations (part nomological validity), the significant model re-configuration requires that we address in detail both:

Content or face validity, “the extent to which an empirical measurement reflects a specific domain of content” (Carmines and Zeller 1999, p.20); and Nomological validity, i.e. “the extent to which predictions based on the concept which an instrument purports to measure are confirmed” (Thietart et al 2001, p.198).

These issues apply particularly to: (1) the newly-integrated six-item LoyaltySatisfaction construct; and (2) the reduced three-item PSQ-Rapport.

First an

appropriate content framework for Loyalty-Satisfaction – in the context of the alternative model’s underpinning theoretical conjecture - is provided by the four layers, or steps, of Oliver’s (1999) conceptual loyalty-ladder).

Successful

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reconciliation of these four layers with the six construct indicators supports underlying theoretical coherence (Table 6.33).

Table 6.33: Content Validity, The Loyalty-Ladder and the UniConative Construct of Loyalty-Satisfaction Layer

Description

readiness to demonstrate/acknowledge loyalty "Deeply held commitment to buy" Conative (pp.35-37) Develops on basis of 'cumulatively Affective satisfying usage' (p.35) "no deeper than mere performance" Cognitive (p.35) Source: Oliver (1999). Action

Indicators L2 - Benign influence on decision-making; CS2 – pleasure in service, hedonic satisfaction. L5 Commitment to the (consultancy) team; relationship and firm brand respectively CS3 – cumulative utilitarian satisfaction; L7 – commitment to firm/brand. S1 - Satisfaction with general service

Next, robust nomological validity for Loyalty-Satisfaction is demonstrated by each component indicator’s ability to predict relevant criterion variables (Hair et al 2005). To fulfil the criterion role, five items – not forming part of the Uni-Conative model – are available. Collectively four are the ‘loyalty-behaviours’ – identified by the prior semantic analysis (5.6.7) and summarised in soft-hard sequence: (1) L9 Advocacy (‘privately recommend to a colleague’); (2) L1 Exclusive Preference (‘consider only’); (3) L3 Switching Resistance (‘warmer than’); and (4) L10 Public Advocacy (willing to recommend publicly e.g. conference). A fifth item - CS1: ‘satisfied with decision to appoint’ – is suggested by additional semantic evaluation. Under conditions of intra-consumption observation (as opposed to post in its original Patterson and Spreng (1997) deployment), the item may metamorphosise from assignment-judgement into a global test of loyaltymaintenance. This application is consistent with both counterpoint principles (2.15) and the item’s original exclusion from the Cumulative Satisfaction scale (5.4.4). As a proto-loyalty-behavioural scale, the five items achieve a high and acceptable standardised α (= .827) – recalling that in scales > .80 “correlations are attenuated very little by random measurement error” (Carmines and Zeller 1999). The intentions-behaviours dichotomy is also consistent with: a principal distinction

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of the loyalty literature (2.3.4); (2) recent empirical support for the ladder conceptualisation (Evanschitzsky and Wunderlich 2006; Taylor, Hunter and Longfellow 2006); and specifically, the generalisation to loyalty, based on Perugini and Bagozzi (2001), of the model of goal-directed behaviours (MGB) (Taylor, Hunter and Longfellow 2006).

Using these five indicators as criteria, five consequent multiple regression analyses demonstrate that each loyalty-behaviour is predicted materially and significantly by two or more of the predictor variables (Table 6.34, only significant partial regression co-efficients reported).

Table 6.34: Uni-Conative Nomological Validity - Loyalty-Satisfaction as a Predictor of 'Loyalty' Behaviours Criterion Variables L9 – Advocacy Vs. Predictor.. Predictor.. L2 - Consider Only

R2 R

.649

.414

adj

Std E of E

DurbinWatson

ANOVA

.633

.831

1.895

F6,136 = 41.841, ρ = .000

.318

1.381

1.964

CS3 – Utility

.283 (2.197), ρ = .003

S1 - General Service

.304 (3.075), ρ = .030

F6,136 = 16.030, ρ = .000 L2 – Influence L5 - Relationship Commitment

L3 – Warmer

.432

.407

1.115

1.871

.717

.705

.722

2.115

.343

Source: SPSS Output

.314

1.705

2.021

.324 (2.888), ρ = .005 .360 (3.942), ρ = .000 .250 (2.260), ρ = .025

F6,136 = 57.450, ρ = .000 L2 – Influence L7 - Committed to Firm/Brand L5 - Relationship Commitment

L10 - Public Advocacy

.368 (3.942), ρ = .000

F6,136 = 17.242, ρ = .000 L2 – Influence L5 - Relationship Commitment

CS2 – Decision

Std β (t)

.199 (3.059), ρ = .003 .309 (4.186), ρ = .000 .195 (2.496), ρ = .014

F6,136 = 11.842, ρ = .000 L2 – Influence CS2 - Hedonic Satisfaction

.223 (2.254), ρ = .026 .391 (2.204), ρ = .029

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A final nomological step for Loyalty-Satisfaction reconciles these supportive findings graphically with the ‘Loyalty-Ladder’. It demonstrates, as theorised, that different predictors drive behaviours from different levels in the ladder (Table 6.35).

Table 6.35: Uni-Conative Nomological Relationships – Loyalty Behaviours and the Loyalty-Ladder Layer

Description readiness to demonstrate, acknowledge loyalty

Action Action

Conative

"Deeply held commitment to buy" (pp.35-37)

Variable(s) CS2 Pleasure with service; hedonic cumulative satisfaction L2 - Benign influence on decision-making

L9Adv

L2Pref

L4Swit

L10Padv

L7 Commitment to the consultancy team relationship

Develops on basis of 'cumulatively satisfying usage' (p.35)

L5 - Commitment to Firm/Brand CS3 - Utilitarian cumulative Affective satisfaction "no deeper than mere S1 - Satisfaction with general Cognitive performance" (p.35) service Source: SPSS Output. Green-highlighted squares are those in which the indicators are confirmed as behavioural predictors. Affective

For example:

(1) The cognitive base predicts the simplest, lowest-cost or ‘soft’ personal behaviour (L9 - Private Advocacy); (2) The highest levels of Loyalty-Satisfaction are required to drive the most extreme, ‘hard’ or action-oriented behaviour (L10 - Public Advocacy); (3) As anticipated and confirmed by the high R2 above (=.717), the global maintenance component (CS1 – Decisional Satisfaction) possesses the broadest relationship i.e. predicts three criteria across the middle of the ladder; and (4) Confirming Taylor et al’s conjecture (2006), the former satisfaction items play a dual role providing: (1) at the foot of the ladder a utilitarian indicator

CS1Dec

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of service interaction assessment; and (2) at the head, the (hedonic) emotional antecedent of significant loyalty behaviours (Table 6.35).

Similarly, content validity for PSQ-Rapport is supported by emerging PBS tolerance theory (Davies and Palihawadana 2006, 2.15.4). Its three confirmed performance predictors for ‘tolerance’, identified by discriminant analysis, are close proxies for the three remaining PSQ-Rapport items e.g.: constant account information (PSQ20Responsiveness); consistent work processes (PSQ24-Dependability); and strategic thinking

(PSQ02-Problem

Understanding).

Separately,

the

removal

‘relationships’ eliminates overlap with the criterion.

Finally the predictive relationships for the indicators of both PSQ-Rapport and Perceived Value are confirmed by additional multiple regression analysis (Table 6.36) – all partial regression co-efficients significant (<0.05). This analysis also highlights the critical role of PV1-‘Value-for-Money’ as the one common predictor of all the Loyalty-Satisfaction indicators.

Table 6.36: Loyalty-Ladder Items - Partial Regression Co-efficient Analysis R2adj Ρ

PV1 PV2 PV3 PSQ20 PSQ24 PSQ02

CS2

Hedonic Cumulative Satisfaction

.646

.000

.282

Benign Influence Reciprocation

.388

.000

.338

Relationship Commitment

.599

.000

.346

Brand/Firm Commitment

.510

.000

.378

CS3

Utilitarian Cumulative Satisfaction

.705

.000

.406

General Service Satisfaction

.612

.000

.293

Key: PV1

Value for Money

PV2

Equity - Justifies Investment

PV3

Fee Acceptability

PSQ20

Responsiveness

PSQ24

Dependability

PSQ02

Problem Understanding All regression weights ρ=<0.05

Source: SPSS Output.

.299

.225

.147

.303 .250

.283

.187 .167

.202

.178

.296

.202

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6.13.6 Uni-Conative Model: Structural Model Assessment

Having demonstrated the consistency of the Uni-Conative measurement model and data (6.13.4-5), a revised 14-item non-recursive structural model includes the following indicators per construct: Perceived Value (3), Trust (2), PSQ-Rapport (3) and Loyalty-Satisfaction (6). An antecedent role for Trust is also postulated given its prior formulation (5.4.2) as an assessment of empowerment attributes (Figure 5.3).

The model is based on prior corroborated hypotheses with two additions:

H13: PSQ-Rapport (Perceived Service Quality) will relate positively to Loyalty-Satisfaction; and H14: Perceived Value will mediate positively the effects of the relationship between Trust and Loyalty-Satisfaction.

The first is supported by prior findings in this study (6.9). The second, postulating a Trust-Value relationship, is predicated on: (1) recent findings in the clientagency/P2B stream (Davies and Palihawadana 2006; 2.15.4); and (2) empiric extensions of the European Customer Satisfaction Index (ECSI) in which both constructs are modelled as antecedents of loyalty (Ball, Coelho and Machas 2004; Table 6.37).

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Figure 6.5: Uni-Conative Path Model Trust H14

H10

Perceived Value

Loyalty – Satisfaction

H12B H13

PSQ – Rapport

Source: Author.

Table 6.37 Uni-Conative Model: Applicable Hypothesis Set No. H8 H10

H12B H13 H14

Hypothesis Perceived Value will relate positively to Loyalty-Satisfaction (Loyalty-Commitment) Trust will relate positive to Loyalty-Satisfaction (LoyaltyCommitment) Perceived Value will partly mediate the effects of Perceived Service Quality on Loyalty-Satisfaction (Loyalty-Commitment) Perceived Service Quality (PSQ-Rapport) will relate positively to Loyalty-Satisfaction (Loyalty-Commitment) Perceived Value will mediate the effects of Trust on LoyaltySatisfaction

Prior Tests (Chapter Six) Supported Partly supported

Supported Supported by findings for H12 N/A

Initial testing, employing the covariance matrix and maximum likelihood estimation (MLE), eliminates the Trust→Loyalty-Satisfaction path as non-significant (ρ = .335).

The resulting final structural Uni-Conative model is both consistent

with the prior measurement model and complies with Hair et al’s criteria (2005, p.753) to offer acceptable fit (Table 6.38). Limited but acceptable exceptions are:

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(1) χ2 significance (χ2 = 94.121; df = 75; ρ = 0.041; CMIN/df = 1.307) and (2) a recurrent marginal shortfall in GFI. There are no standardised residuals over an absolute value of 1.40.

Table 6.38: Uni-Conative Model: Structural Goodness-of-Fit Assessment Application

Abb.

Guidance

Outcome

Incremental Fit Index

CFI

.973

Badness of Fit Index

RMSEA

> .95 <.08 with CFI of >.95(and note reported confidence = .046-089)

Absolute Fit Index

RMR

<.08 with CFI of >.95

.059

Goodness of Fit Index

GFI

>.90

.893

.068

Green = confirmed Source: AMOS Output.

Amber = proximate solution.

The Uni-Conative model explains a satisfactory 58% of Perceived Value and a high 83% of Loyalty-Satisfaction. The regression weights for PSQ-Rapport (ξ1) and the equity-based Perceived Value (η1) on the paths determining Loyalty-Satisfaction are near equal (.50 and .48 respectively, Figure 6.6).

The dominant antecedent role, however, is assigned to PSQ-Rapport once the indirect effects of both Trust and PSQ-Rapport via Perceived Value are incorporated. Compliant standardised regression coefficients per variable and, for the principal endogenous constructs, variance explained and standardised direct, indirect and total effects for the key constructs are summarised at Figure 6.6/Table 6.38 respectively.

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Figure 6.6: ‘Uni-Conative’ – Final Structural Model (including Factor Loadings [SRWs] and Squared Multiple Correlations) PSQ Rapport ξ1 E

.73

PSQ20 (X3)

PSQ02 (X4)

PSQ24 (X5)

.76

.90

.50

.46 SMC=.58

.96

PV1 (Y1)

PV2 (Y2)

PV3 (Y3)

T3 (X6)

.81

T4(X7)

.87

SMC=.83

Perceived .48 Value η1

.82

LoyaltySatisfaction

η2

.88

.81

.33

Trust ξ2

Source: Author (based on AMOS path diagram).

Table 6.39: Uni-Conative Model - Standardised Effects Rapport

Trust

.464

.333

.000

.720

.160

.480

.000

Perceived Value

.464

.333

.000

Loyalty-Satisfaction

.497

.000

.480

.000

.223

.160

.000

Total Effects Perceived Value (Equity) Loyalty-Satisfaction (Relationship Orientation)

Direct Effects

Indirect Effects Perceived Value Loyalty-Satisfaction Source: AMOS Output.

.78 .82 .87 .93 .95

L5 (Y7)

L7 (Y8)

L2 (Y9)

S1 (Y4)

CS2 (Y5)

CS3 (Y6)

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6.13.7 Uni-Conative Structural Model: Summary

The findings reported (6.13.6) support all but one of the Uni-Conative model hypotheses (Table 6.40). In summary, they indicate that a client’s level of LoyaltySatisfaction (or ‘goodwill’) is a variable of his perceptions of:

PSQ-Rapport (i.e. the consultancy’s effectiveness of engagement); Perceived Value (i.e. his equity obtained); and Indirectly only, Trust (i.e. the level of empowerment achieved).

Table 6.40: Uni-Conative Model - Hypothesis Report No. H8 H10

H12B H13 H14

Hypothesis

Initial Tests

Final

Perceived Value will relate positively to LoyaltySatisfaction (Loyalty-Commitment) Trust will relate positive to Loyalty-Satisfaction (Loyalty-Commitment)

Supported Partly supported

Supported Not supported

Supported

N/A

Supported

Perceived Value will partly mediate the effects of Perceived Service Quality on Loyalty-Satisfaction (Loyalty-Commitment) Perceived Service Quality (PSQ-Rapport) will relate positive to Loyalty-Satisfaction (LoyaltyCommitment) Perceived Value will mediate the effects of Trust on Loyalty-Satisfaction

Thus, PSQ-Rapport and Perceived Value – the latter facilitated by both PSQRapport and Trust - are the principal drivers of Loyalty-Satisfaction. The implications of these findings are discussed in detail below (Chapter Seven).

6.14 Moderation Hypotheses and Generalisability 6.14.1 Overview

The closing brief section of this chapter explores the potential of moderation to extend the generalisability of the Uni-Conative model and enrich the practitioner

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value derived. It discusses in turn: the concept and application of moderation (6.14.2); formulates appropriate extension hypotheses (6.14.3); and summarises results and implications (6.14.4).

6.14.2 The Concept and Application of Moderation

Moderation helps establish the generalisability of findings (MacKinnon 2008, p.276). Specifically, a moderator variable “modifies the intensity” of a relationship (Thietart et al 2001, p.269). It “affects the direction and/or strength of the relation between an independent or predictor variable and dependent or criterion variable” (Baron and Kenny 1986, p.1174). If a moderator is also a significant predictor of the criterion, it is termed a quasi moderator: if not, a pure moderator (MacKinnon 2008). Additional practitioner insight, therefore, may be provided by identifying contextual moderation on one or more significant paths in the UniConative Model.

Moderation is widely demonstrated by employing hierarchical multiple regression analysis (Aiken and West 1991; Cramer 2003). Explanation is facilitated by lack of direct moderator-predictor and moderator-criterion relationships (Hair et al 2005). Procedurally the researcher: (1) subtracts the mean from each value to centre the two independents (X1c, X2c); (2) generates an additional interaction term by multiplying them together; and then (3) enters all three sequentially into a regression (Aiken and West 1991). “If the interaction term explains a significant increment in the variance (of the criterion), then a moderating effect is present” (Cramer 2003, p.75).

Interpretation of findings offers the principal challenge.

Conceptually, a

recommended solution is to trichotomise the two independents to produce a ninecell diagram (Aiken and West 1991). A common framework is high (=one standard deviation above the mean), medium (the mean) and low (one standard deviation below).

Where lines are not parallel (i.e. the slope changes), interaction is

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confirmed and its direction explained. This process is automated helpfully, and rendered graphically, by the Italassi 1.2 software (Provalis Research 1997ff).

6.14.3 Extension Moderation Hypotheses

Based on the analysis at Appendix 12, it is postulated that the relationship between:

Perceived Value and Loyalty-Satisfaction (PV-LS) will be o Stronger when Trust is low (H15A); o Weaker under conditions of extended Firm Relationship Duration (H15B); o Weaker under conditions of high Client-Buyer Experience (H15C). PSQ Rapport and Loyalty-Satisfaction (Rapport-LS) will be weaker under conditions of: o High Trust (H16A); o Extended Firm Relationship Duration (H16B); and o High Client-Buyer Experience (H16C).

In all, six cases of moderation are postulated.

6.14.4. Moderation Results

Of the six moderating cases, three are supported at the first step: i.e. in each instance, once added to the regression, the moderating variable is reported significant.

They are:

(1) Trust on the PV-LS path (<.001); (2) Client-Buyer

Experience on the PSQ-LS path (<.05); and (3) Trust on the PSQ-LS path (<.001).

At the second step - i.e. when the interaction term is added to the regression, an improvement in explanation is evidenced - only (1) and (2) are supported. Potentially this means that a unit-increase in:

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o Perceived Value will have greatest effects on Loyalty-Satisfaction when Trust is low (H15A); o PSQ-Rapport will have greatest effects on Loyalty-Satisfaction when ClientBuyer Experience is low (H16C).

Both findings would be consistent with prior theory. However, at the final third step, they both prove marginally insignificant (respectively = (1) .093 and (2) = .101). The outcome, nonetheless, indicates potential for further research to offer additional insights (Chapter Seven).

6.15 Chapter Summary The three-part Chapter Six completes the analysis section of this study.

In Part One (6.2-6.10), six multiple regression analyses (MRA1-6) provide individual support for all except one of the Convergent Model’s hypotheses, including both mediation hypotheses (H11-H12). The exception is the Trust→Satisfaction path (H7). In terms of the focal research question, and excluding mediated effects, there is a resulting 66:33 (Satisfaction vs. Perceived Value) explication of LoyaltyCommitment variance. At the attribute level, a dominant weighting for functional quality (PSQ-Rapport) and expectations management (Disconfirmation) is indicated.

In Part Two (6.11-6.13), structural equation modelling (SEM) analysis reports the principal Convergent Model of Loyalty-Commitment invalid on grounds of high non-discrimination between constructs. The finding fulfils a principal ground of the ‘null’ perspective in the theoretical review (2.15).

Nonetheless, on a

necessarily limited and circumstantial basis, the structural Convergent Model is consistent with both prior theory (Chapters Two) and initial findings (Chapter Five/Chapter 6.2-6.10).

With the exception of the postulated Trust→Loyalty-

Commitment path, all relationships are in the direction proposed and imply

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support for five of six final hypotheses.

Accordingly, and subject to further

research (Chapter Seven), the ‘convergent’ perspective offers a potential solution to the research questions concerning the nature and antecedence of loyalty.

Complementary analysis validates the postulated competing Uni-Conative Model and supports four out of five final hypotheses. The exception, consistent with MRA3, is the Trust→Loyalty-Satisfaction path.

All other relationships are

significant, in the proposed direction and support validation of the Uni-Conative model. They also indicate high explanatory power in terms of Loyalty-Satisfaction variance (SMC =.83).

Finally the brief Part Three (6.14) explores the generalising principle of moderation on key paths. Although it fails to support any proposed instance, the marginal non significance of two moderation cases - involving Trust and Firm Relationship Duration - indicates potential opportunities to generate practitioner insight (Chapter Seven).

The following final chapter discusses conclusions drawn from the analysis and assesses the research’s contributions to understanding loyalty-related phenomena in the field of Professional Business Services (PBS).

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CHAPTER SEVEN – CONTRIBUTIONS, CONCLUSIONS AND IMPLICATIONS 7.1

Overview

Following presentation of the data analysis (Chapters Five-Six), this chapter completes the research study.

It summarises its conclusions (7.2), their

implications for marketing management today (7.3), their limitations (7.4) and their overall contribution (7.5). Recommendations for future research are also incorporated (7.6) as well as a brief reflective epilogue. This last describes the personal learning obtained. It is presented to assist others embarking on a similar quest (7.7).

7.2

Conclusions

7.2.1 Introduction and Overview

Few social psychology journeys are linear. Complex social phenomena are not easily reducible to simple construct-sets and metrics (Tikkanen and Alajoutisjarvi 2002).

The principal Convergent Model attracts extensive theoretical

underpinning. It also obtains in this study provisional empirical support for 12 out of 13 final hypotheses (6.10, Table 6.24). Yet it is ultimately invalidated by high construct non-discrimination (6.11.3-6.11.4). In the language of dialectic, the thesis is rendered ‘determinate’.

Meanwhile its anti-thesis, the competing and more parsimonious Uni-Conative (Goodwill) Model is supported both by theory and by data analysis. It is:

1. Consistent with; a. Oliver’s (1999) uni-conative conjecture,

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b. The principles of the counterpoint (2.15), and c. The emerging P2B Trust-Value stream model in which a focal endogenous variable measures a clientâ&#x20AC;&#x2122;s reservoir of goodwill (Davies and Palihawadana 2006, 2.15.4). 2. Supported by; a. Both exploratory- and confirmatory factor analysis (EFA/CFA, 6.11), and b. A compliant final structural model which reports high and acceptable construct reliability (.94, Fornell and Larcker 1981) for the focal uni-conative Loyalty-Satisfaction variable, explains an acceptable 83% of variance (6.12) and supports four out of five final hypotheses (H8, H12B, H13 and H14, Table 6.36).

This outcome underpins 13 principal conclusions. Grouped under six headings, they are discussed in the remainder of this section:

(1) The domain, conceptualisation and superordination of Loyalty-Satisfaction (responding to RQ1, 7.2.2); (2) The direct antecedence of Loyalty-Satisfaction (RQ2A, 7.2.3); (3) The indirect antecedence of Loyalty-Satisfaction (RQ2B, 7.2.4); (4) Constructs and scales (7.2.5); (5) Model extensions (7.2.6); and finally (6) Implications of the inadequate Convergent Model (7.2.7).

Where appropriate, conclusions are based on evidence complementary to the UniConative structural model. For example, understanding of indirect antecedence (7.2.4) is enriched by insights derived from multiple regression analyses (6.4-6.9).

ÂŠ Bill Nichols 2009

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7.2.2 Conclusions 1-3: The Domain, Conceptualisation and Superordination of Loyalty-Satisfaction

First the PBS domain of the six-indicator Loyalty-Satisfaction construct (CR = .94) describes ‘goodwill’. That is, a favourably disposed attitude towards someone or something which entails expectations of (future) benefits (Egginton 1990; Tollington 1999; 2.15.4).

When observed intra-consumption, ‘goodwill’ is

evidenced by six indicators:

(1) General service satisfaction [item S1]; (2) Cumulative utilitarian satisfaction with the overall quality of service provided [CS2]; (3) Cumulative pleasure in the service provided [CS3]; (4) Commitment to respectively the consultancy relationship [L5]…. and (5) Firm-brand [L7]; and (6) Reciprocation of perceived benign influence [L2] (6.13.2).

The chosen ‘goodwill’ label is more appropriate connotatively than:

Either the neutral (buyer-seller literature) ‘relationship orientation’ which captures principally commitment (Kelley and Thibaut 1978) and closeness alone (Ganesan 1994); or The P2B ‘tolerance’ which “signals strongly valued relationships” (Davies and Palihawadana 2006, p. 383) but lacks the positive disposition implied by ‘goodwill’.

Second, this domain representation confirms that - under conditions of intraconsumption observation – the final functional sufficiency of a client’s attitude (retrospective ‘satisfaction’) and his behavioural intention (prospective ‘loyalty’) are simultaneous and co-terminous. This integration, and superordination, of ‘loyalty’ and ‘satisfaction’ is consistent with early descriptions of a single global attitudes e.g. of Satisfaction (Cardoso 1965). © Bill Nichols 2009

Among attitudes in this study, for

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example, Satisfaction exhibits the broadest and most balanced set of attribute relationships. It excludes only, and marginally, the second technical Perceived Service Quality (PSQ) factor, PSQ-Expertise (ρ = .066).

Third, the conceptualisation of ‘goodwill’ is consistent with both:



The general loyalty-ladder narrative (Evanschitzsky and Wunderlich 2006; Oliver 1999; Taylor, Hunter and Longfellow 2006); and



Recent findings that increasing loyalty associates with both decreasing cognitive effort and increasing affective involvement (Taylor et al 2006).

Broadly predictive nomological analysis versus criterion loyalty-behaviours confirms that ‘goodwill’ is generated via “an accumulative process, a step-by-step function” (Mascarenhas, Kesavan and Bernacchi 2006, p.403; Table 6.36). Individual

indicators,

turn,

reconcile

coherently

with

the

ladder

conceptualisation (Table 6.35). And, like the ladder, they ascend from a base of utilitarian satisfaction (items S1/CS3) via a core of commitment to a summit of hedonic satisfaction (CS2).

In summary (Conclusions 1-3 and responding to the first research question): in continuous service PBS, traditional ‘loyalty’ is a component of a richer domain of Loyalty-Satisfaction (‘client goodwill’) whose superordinate conceptualisation accords with Oliver’s (1999) loyalty-ladder.

7.2.3 Conclusions 4 - 6: The Direct Antecedence of Loyalty-Satisfaction

Generally, excluding indirect effects, the Uni-Conative model evidences nearequipoise in terms of Loyalty-Satisfaction’s two direct antecedent relationships: Perceived Value (H8, SRW = .48) and PSQ-Rapport (H12, .50, both ρ<.001, Figure 6.6). Both are also consistent with reported tolerance antecedence (Davies and Palihawadana 2006).

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Fourth, the critical role of Perceived Value - as both independent and mediator variable - is well-evidenced. The findings are:

(1) Corroborative of its previously well-supported relationships with both Satisfaction (McDougall and Levesque 2000; Patterson and Spreng 1997; 3.3.3) and Loyalty (e.g. Duravasula et al 2004; Yang and Peterson 2004, 3.3.4); (2) Consistent with complementary multiple regression analysis evidence from MRA3-4 (6.6-6.7) in which variance explanation by the newly-validated three item scale (CR = .92, 7.11.3) exceeds 30% across multiple iterations; and (3) Further supported by additional confirmatory multiple regression analyses which highlight item PV1-Value for Money, in particular, as the only common and significant (<0.05) predictor of all six Loyalty-Satisfaction indicators (Table 6.36).

The maintenance of Perceived Value (relationship equity) is, therefore, fundamental to the determination of Loyalty-Satisfaction (client goodwill).

Fifth the PSQ-Rapport path confirms a direct relationship between attribute perceptions and Loyalty-Satisfaction.

This finding: (1) corroborates recent

exchange-based theory (Harrison-Walker 2001; Hennig-Thurau and Klee 1997; Rauyruen, Miller and Barrett 2007); (2) is consistent with the new resource-based paradigm of service in which “economic exchange is fundamentally about service provision” (Vargo and Lusch 2004, p.326); and (3) highlights the value of advice to focus on the “sum of inter-related cognitive, affective, conative and behavioural components filtered by a general relative impression of the relationship” (Akerlund 2005, p.158).

Client perceptions of PSQ-Rapport (consultancy engagement)

therefore likewise play a significant direct role in determining the level of LoyaltySatisfaction (goodwill).

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Sixth, among service quality dimensions PSQ-Rapport plays an exclusive role in the final Uni-Conative Model. This primary status is supported specifically in MRA4 (6.7) and also in MRA1-2 (6.4-6.5). It is - by weighting - respectively the dominant and exclusive predictor of Perceived Value and Trust. In the latter case, PSQOutcomes, PSQ-Expertise and even PSQ-Ethics (albeit marginally, ρ = 0.066) are all non-significant. In aggregate the evidence (1) supports the early practitioner hypothesis in favour of ‘chemistry’ attributes and (2) corroborates findings that outcome ambiguity amplifies the importance of functional quality or service cues (Babakus and Boller 1992; Davies and Palihawadana 2006; Palmatier et al 2006). Ambiguity - contextually an environment of low service visibility and deferred benefits - characterises professional marketing/creative services e.g. PR Consultancy.

In summary (Conclusions 4-6 and responding to part of the second research question): in terms of direct effects, Loyalty-Satisfaction is a variable equally of both Perceived Value and PSQ-Rapport – the latter measured by responsiveness, dependability and strategic understanding.

7.2.4 Conclusions 7 - 10: Indirect Antecedence of Loyalty-Satisfaction

Seventh, the relationship between the two direct antecedents is rendered more complex since PSQ-Rapport also partly influences Loyalty-Satisfaction indirectly via Perceived Value.

I.e. the aggregate of the consultancy’s responsiveness,

dependability and strategic understanding also acts as a facilitator of perceptions of fairness and value.

Eighth, similarly, the level of a client’s Trust also influences Loyalty-Satisfaction wholly and indirectly via Perceived Value.

Consistent with both the P2B

perspective (2.15) and practitioner experience, this suggests that an empowered account manager and/or team have enhanced opportunities to demonstrate value/fairness.

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Ninth, the Uni-Conative model’s supported reciprocal Trust-PSQ-Rapport relationship indicates that, in a continuous service environment, responsive and dependable processes facilitate consultancy empowerment and vice versa – a case of so-called ‘win, win’. This finding is part supported by PSQ -Rapport’s confirmed role as the exclusive service quality attribute determinant of Trust (MRA2, 6.5).

Tenth, although not within the Uni-Conative structural model boundaries, the contextual influence of expectations management (Disconfirmation) on e.g. Trust (MRA2 = 30.67%, 6.5) and Perceived Value (MRA1- = 26.5%, 6.4) is well supported beyond its traditional Satisfaction relationship (MRA3, 6.6). Expectations are a proxy for socially normative beliefs – ‘how things should be’ (Fishbein and Ajzen 1975). They help determine the representational sufficiency, or appropriateness, of raw attitude (Cohen and Reed 2006). Their effects are generally reported as contextual (Halstead, Hartmann and Schmidt 1994; Voss, Parasuraman and Grewal 1998). The high weightings for Disconfirmation reported in this study associate with the already highlighted outcome ambiguity (Ojasolo 2001).

Accordingly:

confronted with uncertainty, in order to determine assessment, clients migrate to both service cues (Conclusion Six above) and expectations.

In summary (Conclusions 7-10 and completing the answer to the second research question): in terms of additional indirect antecedence (RQ2B), Loyalty-Satisfaction is a variable of (1) Trust, wholly via Perceived Value, (2) PSQ-Rapport, partly via Perceived Value, and (3) the management of expectations (Disconfirmation, by implication via Trust and Perceived Value).

7.2.5 Conclusions 11: Constructs and Scales

Underpinning the first ten conclusions (7.3.2-7.3.4), a number of scales representing antecedent constructs of Loyalty-Satisfaction were adapted and developed as part of the research study.

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(A) The three-item Perceived Value scale captures successfully a broader domain of relationship equity, demonstrates significant and direct influence on the Loyalty-Satisfaction outcome and complies with calls for enrichment of the traditional single-item ‘value-for-money’ operationalisation (e.g. Patterson and Spreng 1997; Woodall 2003); (B) The final three-item PSQ-Rapport scale measures successfully a domain of consultancy-to-client engagement and demonstrates significant and direct influence on the Loyalty-Satisfaction outcome. Its indicators are also consistent with the three confirmed performance indicators in emerging P2B/tolerance theory (Davies and Palihawadana 2006). Thus: ‘Constant account status information’ with responsiveness (PSQ20); ‘Consistent work processes’ with dependability (PSQ24); and ‘Strategic thinking’ with problem understanding (PSQ02) (Davies and Palihawadana 2006, p.399ff). (C) The final required adaptation to the PSQ-Rapport scale (the elimination of one item) places a limitation on the otherwise successful demonstration of a four-factor, 13-item scale for Perceived Service Quality (PSQ) in Professional Business Services (PBS). Grounded in items derived from two primary source scales (Grayson and Ambler 1999; Patterson and Spreng 1997),

consistent

with

tri-dimensional

(functional-technical-

reputational) theory (2.8, Gronroos 1982, 2000). All four factors offer coherent measurement relations (Echambardi, Campbell and Agarwal 2006; 5.5.5) and all (except PSQ-Ethics = .61) exhibit good construct reliabilities (Table 5.24); (D) The inadequacies highlighted (Grayson and Ambler 1999) in the postulated five-item Trust scale (Moorman et al 1992) are corroborated. The residual two-item scale successfully measures a more limited domain of consultancy ‘empowerment’ and demonstrates significant indirect influence on the Loyalty-Satisfaction outcome;

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(E) The postulated two-item Disconfirmation scale – an execution of the ‘better than, worse than’ version of the Additive Difference Model (Spreng and Page 2003; Tversky 1969) – is successfully replicated; and (F) The postulated deployment of complementary scales for transactional and cumulative versions of Satisfaction is unsupported.

7.2.6 Conclusions 12: Extensions

Twelfth, penultimately and in an extension to the main conclusions, the results also demonstrate significant relationships between all Loyalty-Satisfaction indicators and consequent loyalty-behaviours (Table 6.35).

A by-product of the

nomological validation of the Uni-Conative model, these findings are consistent with Oliver’s (1999 ‘loyalty-ladder’ conjectures. In summary: the more public and/or action-orientated a target behaviour so the higher up the ‘loyalty-ladder’ the consultancy needs to position the client’s positive perceptions. For example, at

the

base

utilitarian

satisfaction

sufficient

generate

private

recommendation: at the summit active relationship pleasure is required to instigate public recommendation. These extensions are incorporated at Figure 7.1:

Figure 7.1: The Indicators of LoyaltySatisfaction (Goodwill) and Loyalty Behaviours CS2 Hedonic Satisfaction L2- Benevolent Influence

L10 Public Advocacy

L5 Relationship Commitment

CS2 – Decision Satisfaction

L7 Brand Commitment

CS3 Utilitarian Satisfaction S1 – General Service

Source: Author.

L2 Exclusive Preference L3 – Preference L9 – Private Advocacy

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7.2.7 Conclusions 13: Out-takes - The Convergent Model

With unintentional irony, the thirteenth and final conclusion returns to the invalidated Convergent Model.

The outcome of a structured application of

metatriangulation theory, prior versions of the model are sometimes labelled ‘relationship quality’ or, alternatively ‘relationship orientation’ (2.14). It represents a distillation of emerging convergence theory (e.g. Beloucif, Donaldson and Kazanci 2004; Gustafsson, Johnson and Roos 2005; Lin and Ding 2005; Verhoef 2003).

Following the merger of transactional and cumulative Satisfaction constructs, substantial theoretical underpinning supports each remaining relationship in the model. The final hypothesis set (following withdrawal of H2 and H6) includes 10 hypotheses and thirteen final propositions (Table 5.29, 5.7). Based primarily on an extended sequence of multiple regression (MR) and MR-based mediation analyses, twelve (excluding only H7) are upheld provisionally (Table 6.24, 6.10). All key structural model relationships and parameter estimates are also in the predicted direction and nontrivial (6.3, Figure 6.3).

On this strictly limited basis it is

reasonable to conclude that: (1) the underlying theoretical framework possesses substance; and (2) one may have confidence in the majority of assumptions – many consistent with the Uni-Conative model.

Thus: provisionally an integrated attitudinal-behavioural construct of LoyaltyCommitment is the dependent: (1) (directly) of Satisfaction, Perceived Value and PSQ Rapport; and (2) (indirectly) of both Disconfirmation and three of the four factors (PSQ-Expertise, PSQ-Outcomes and PSQ-Rapport) of Perceived Service Quality.

However, thirteenth, the model’s inadequacy illustrates the approach described by Nunnally (1978, pp. 97-99) as the accrual of evidence over time resulting in fuzzy domains. Three distinct primary traditions - generating differing perspectives and using partly-incompatible language – are, when aggregated, characterised by a phenomenon of overlapping domains. Mis-specification occurs both at the natural © Bill Nichols 2009

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language level (Teas and Palan 1997) and in the rendering of complex social phenomena (Tikkanen and Alajoutisjarvi 2002). These key issues require further research (below 7.6).

7.3

Implications

and

Recommendations

for

Marketing

Management 7.3.1 Overview

Doctoral level research should demonstrate both an academic contribution to the theoretical knowledge base and substantive practice based implications. This section aims to fulfil the latter nomothetic requirement. It discusses insights for marketing management that are applicable to the development of customer retention strategies in professional business services (PBS, RQ3).

As this study confirms, PBS incumbency duration continues to decline making retention arguably the number one priority for PBS marketers.

Commentary

addresses: (1) the PBS environment; (2) four implications of the Uni-Conative Model – expectations management, engagement, equity and empowerment; and finally (3) the integrated construct of ‘goodwill’ (Loyalty-Satisfaction).

7.3.2 Environment: Strategic Analysis and Choice

A substantial body of service theory and research has emerged over the past 30 years. Major streams address topics such as service customisation and customer satisfaction). However, service studies have yet to either establish an overall empirically-supported paradigm or address specific taxonomic issues in a systematic or rigorous manner (Lovelock and Gummesson 2004; Vargo and Lusch 2004). Formal understanding of a service domain such as PBS, its implications (e.g. outcome ambiguity) and consequent appropriate marketing strategies remains limited.

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As demonstrated, the configuration of the PBS domain is unusual.

It is

characterised by: (1) the presence of expertise (vendor input/’know why’); (2) extended duration until benefits (process/’know how’); and (3) low visibility of deliverables (outputs/’know what’) (Lu 2002; Scruton 1994). Further (4) many PBS (e.g. PR, advertising and year-round legal/accountancy support) are also distinguished by continuous service provision. Yet, as confirmed in the literature review and by a 2008 ‘strategy workshop’ conducted by the author for 20 senior lawyers, most practitioners consider only the first issue: in effect technical quality (PSQ Outcomes/PSQ Expertise).

For clients, conversely, this configuration typically implies outcome ambiguity (Ojasolo 2001). Consequently, as demonstrated, they judge service primarily on the basis of overall equity (Perceived Value) and functional quality (PSQ-Rapport).

These contrasting perspectives define a major strategic choice for PBS marketers:

Either to: accept the ‘world as it is’ i.e. relationships operating under conditions of ambiguity, adopt a classic consolidation strategy and execute client retention practice recommendations as summarised in following sections (7.3.3-7.3.7); Or to: seek to eliminate the ‘ambiguity’ premise and adopt a focused differentiation strategy.

In the latter case, a starting point is provided by the finding that in the special case of management consultancy (MC): “clients’ over-riding concern and emphasis [is] on the technical (outcome) performance dimension” (Patterson and Spreng 1997, p.420). This position appears attributable to the ability of marketers to package MC in discrete and quantifiable assignments.

To achieve a similar metamorphosis in the nature of client evaluation beyond management consultancy will be attractive in some PBS market segments. The early 2000s evidence a strong pro-performance tide among UK/US PBS clients. © Bill Nichols 2009

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Calls for concrete deliverables, clear service-line definitions and the abandonment of traditional time-based in favour of value-based billing (TBB/VBB) have breached even the London ‘magic circle’ law-firms. This trend is likely (Winter 2008/2009) to accelerate under the triple moderation of:

(a) Global recession; (b) In the UK, the latest round of professional deregulation instigated by the advent of potentially cross-disciplinary LLPs (Limited Liability Partnerships); and (c) Not least, PBS commoditisation and increasing competition symbolised by the 2008 decision of the UK’s Number One supermarket chain, Tesco, to enter the legal services market.

Any PBS marketer (e.g. in PR Consultancy) tempted by this route will have the advantage of differentiation but confront major challenges such as the definition of ‘performance’ and the execution of value-based billing (VBB).

7.3.3 Expectations and Retention Management

For those PBS marketers who prefer to address the ‘world as it is’, this study offers further insights beginning with the potency, role and opportunities presented by the management of expectations.

The disconfirmation paradigm is a well-established tool. Although it requires careful calibration among over 50 available reference standards (Santos and Boote 2003, 3.2.8), its potency - at least in the case of Satisfaction management - is wellsupported (Szymansky and Henard 2001, 3.2.8).

This study extends that

application. It corroborates recent evidence that Disconfirmation is also a major predictor of other attitude components e.g. Trust and Perceived Value (MRA1-2, 7.4-7.5). It also implies (but does not test) its role as a major antecedent of the intentional construct of Loyalty-Satisfaction (MRA3, 7.6).

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The potency of effective expectations management is also widely-accepted by senior practitioners. Informal discussions confirm that client expectations management is a - probably the - imperative for any front-line account-handler (PR Panel 2005). Yet they indicate equally that its deployment is commonly tactical, individual and contingent. Each handler follows a personal process. For PBS marketers the challenge is to formalise and adopt the disconfirmation paradigm as a key component of firm-wide client retention strategy.

The validated Uni-

Conative model suggests two specific opportunities.

First, applying social exchange/equity principles and as demonstrated, LoyaltySatisfaction (client goodwill) associates positively with Perceived Value i.e. the perception that perceived (monetary and non-monetary) benefits outweigh costs (Smith and Bolton 2002; Yang and Petersen 2004). Disconfirmation is at least a major indirect source of determination. As such the planning and communication of expectations requires both:

Careful alignment â&#x20AC;&#x201C; or fit â&#x20AC;&#x201C; with the balance of available organisational resources and service delivery (Johnson and Scholes 1993); and Concomitant consistent presentation at all points of client interaction.

Second, knowledge creation and transfer occurs throughout (professional) services interaction (Conner and Prahalad 1996). it is also fundamental to the resourcebased service paradigm (Vargo and Lusch 2004). Complementing the specific attributes of PSQ-Rapport, expectations management provides the signalling process, or metadata, of intellectual capital. Under conditions of ambiguity, it also manages expectations of the process itself by which value is delivered.

7.3.4 Engagement: The Practical Side of Service Quality

The second insight for PBS marketers builds on the previous commentary. It focuses on the deliverable (service quality) which is framed by expectations setting. ÂŠ Bill Nichols 2009

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This study extends earlier PBS-specific conceptualisations and offers practitioners a helpful delivery framework. It is based on a tri-dimensional (technical, functional, reputational) conceptualisation of service quality and a four-factor service design and delivery framework (5.5.5/5.6.3). It highlights the potency of social and functional attributes (PSQ-Rapport) as a critical antecedent of added value given also that:

A firm is a “social community specialising in the speed and efficiency of the creation and transfer of knowledge” (Kogut and Zander 1996, p.503); and Social capital facilitates the creation of intellectual capital (Nahapiet and Ghoshal 1998).

The application is defined by considering the uncommon PBS environment. Typically PBS clients are absent during 95-99% of service consumption and may not understand the remainder.

To continue to buy (remain loyal), they must

constantly make leaps of faith. For the consultancy, every limited contact is an opportunity to demonstrate a benefit or achievement. As demonstrated by the indicators of the secondarily-influential PSQ-Outcomes, such benefits include not only final results but also interim outcomes e.g. productive meetings and creative idea generation. It is such creative skills that win accounts (Henke 1995).

But, conversely, such skills do not retain accounts (Henke 1995). In an ambiguous context, the consultancy should focus more on the 95% absence than on the 5% opportunity. It is “more important to excel at servicing the customer” than at service delivery (Maister 1993, p.70). To meet this need and consistent with similar items in Davies and Palihawadana’s (2006) ‘tolerance’ study, this study highlights a limited three-item range of essential social and functional attributes. The 95% absent client requires:

(Strategic) Problem Understanding or, fearing that his tasks and/or campaign objectives are ill-understood, he will be reluctant to make ‘leaps © Bill Nichols 2009

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of faith’.

Shared understanding facilitates goodwill whereas lack of

closeness accelerates dissatisfaction (Mitchell, Cataquet and Hague 1992); Responsiveness, meaning constant account status information (Davies and Palihawadana 2006).

In his own organisation, accurate and timely

knowledge will provide reassurance and confidence; Dependability:

finally (and most crucially in the tolerance study)

consistent work processes and presentational formats.

However

innovative they may be, heterogeneous project management methods, reports and documents inject uncertainty. They make him work harder. Such increased cognitive investment is inversely related to ‘loyalty’ and, by extension, client goodwill (Taylor, Hunter and Longfellow 2006, 7.3.2).

In short, doing the simple functional things well - and consistently firm-wide delivers competitive advantage in a PBS consolidation strategy.

7.3.5 Equity: A Broader Orientation

To extend the preceding discussion, if: (1) the consultancy’s work processes are inconsistent and (2) the client must ‘work harder’, then (3) his total investment increases and, in parallel, his equity, or total utility, decreases. This study confirms that in PBS Perceived Value is both a major direct antecedent of LoyaltySatisfaction (‘goodwill’) and part-mediator of critical social/functional attributes (PSQ-Rapport, 7.4.4).

Exploring this path, the third insight for PBS marketers addresses the nature of ‘value’ under conditions of ambiguity. In conventional B2C/B2B environments, the significant role of a predominantly monetarised (‘value-for-money’) version of Perceived Value as an antecedent of both ‘satisfaction’ and ‘loyalty’ is well supported (Bhattacharya and Singh 2008; Szymansky and Henard 2001; Woodall 2003).

This study validates a conceptualisation of Perceived Value that also

embraces both (1) fairness of fees and (2) overall equity, or total monetary and non-monetary utility (Zeithaml 1988). © Bill Nichols 2009

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This framework requires a consultancy to assess the entire service balance-sheet from the client’s perspective. To take a current example, a UK client can hire apparently similar PR consultant expertise in the range £1000-£2500 per day. Since performance ambiguity applies, the justification for higher rates (Perceived Value) depends on successful equity marketing (e.g. ‘hassle-free’ service) influenced by effective (1) expectations setting (Disconfirmation) and (2) consultancy engagement (PSQ Rapport).

7.3.6 Empowerment: A Limited Application of Trust

The fourth insight offers practitioners a ‘litmus test’ of an account’s health at any given point in time by exploring the client’s Trust.

Trust is a complex construct. In the relationship marketing stream, its role as a facilitator of ‘commitment’ and relational behaviours is well supported. This study demonstrates that:

A narrowly defined construct of Trust (the client’s empowerment of his account manager and/or team) is an indirect facilitator of LoyaltySatisfaction (client goodwill) mediated by Perceived Value; and that The account manager should facilitate, and (often contrary to service efficiency imperatives) willingly accept, every opportunity to increase empowerment.

This point will find resonance among practitioners. Increasing client micromanagement (=declining empowerment = increasing client investment/declining value) often signals impending account defenestration.

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7.3.7 Goodwill: A New Metric for Professional Business Services

Finally, many PBS organisations already employ a ‘satisfaction’ metric. It assesses organisational performance and, to some limited extent, predicts loyaltyintentions. This study introduces PBS marketers to a new metric for assessing the overall health of their organisation’s client-base: the superordinate construct of Loyalty-Satisfaction or ‘goodwill’.

Loyalty-Satisfaction aggregates three aspects of client assessment:

Utilitarian and hedonic components of cumulative satisfaction; Relationship and brand commitment; and, Reflecting

the

equity

judgement,

the

consultancy’s

perceived

altruism/opportunism.

It is also consistent conceptually with the ‘loyalty-ladder’ (Oliver 1999). In turn, the derived hierarchy of components provides diagnostics that help guide interventions to achieve desirable loyalty-behaviours (Taylor, Hunter and Longfellow 2006; Table 6.34). For example, PBS marketers who seek to:

Increase the incidence of private advocacy should focus on achieving satisfaction with the key performance components of service delivery and quality; Ensure exclusive preference should emphasise softer issues which demonstrate the consultancy’s altruism and engender relationship commitment; Win public advocacy or evangelism should concentrate on building a relationship which is pleasurable as well as effective (Figure 7.1 above).

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7.4

Limitations of the Research Study

7.4.1 Overview

This section addresses four areas of limitation: scope of model (7.4.2); methodology (7.4.3); sample and data collection issues arising from the study’s research design (7.4.4); and, finally, broader concerns that relate to the selected service category and overall generalisability of findings (7.4.5).

7.4.2 Model Scope

There are three principal scope issues:

1. Both Convergent and competing Uni-Conative models are restricted by the selected perspective of client cross-sectional intra-consumption. Neither addresses potentially complementary and enriching perspectives such as the effects of personality factors or the client-consultant dyadic; 2. More narrowly, the intra-consumption perspective also precludes the preand post- perspectives of prospective and lapsed clients; and finally 3. The client’s assessment of attributes (Perceived Service Quality) is deficient. In the absence of an appropriate (factor) scale, it does not incorporate indicators that – putatively under the heading of reputational quality address the client’s overall experience of the servicescape (Bitner 1992; Reimar and Kuehn 2005).

Although any one of these extensions would expand the models beyond intended aims and potentially induce respondent fatigue, explication is necessarily limited as a result.

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7.4.3 Methodological Issues

As noted the study follows a positivist epistemological presumption (4.2). This accords with the dominant tradition in each of three major theoretical streams. It also underpins the application of metatriangulation theory and,

to maximise

generalisability, supports a quantitative methodology (Runkel and McGrath 1972, 4.2.4). Although well-supported, this approach (as demonstrated by the ultimate inadequacies of the Convergent Model) attributed insufficient weight to the:

Contextual effects of the relative ‘novelty’ (Berthon et al 2002, p.420, 4.4.1) generated by research model parameters such as the professional business services’ (PBS) environment and intra-consumption observation; and Risk of overlapping domains in a field of complex social phenomena not easily reducible to simple metrics (Tikkanen and Alajoutisjarvi 2002).

A more prominent role for initial and formal phenomenological-qualitative research would have:

Questioned the presumptions of the tradition in regard to the status of constructs such as Loyalty and Satisfaction; Mitigated a residual risk that the compliant Uni-Conative Model may omit one or more significant factors in the PBS domain or antecedence of goodwill.

Finally, the very process of studying intra-purchase effects under conditions of continuous service provision may constitute a constructivist intervention i.e. it may have triggered unpredictable amendments to client behaviour/attitudes (Dholakia and Morwitz 2002).

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7.4.4 Sample and Data Collection Issues

First, a carefully-assessed convenience sample substituted the originally-planned probability sampling approach (4.9.6-4.9.7). Although validity is supported by data preparation and by e.g. the consistency achieved in the partial Grayson and Ambler (1999) replication (Appendix Seven), there are at least two negative effects:

On the one hand, it may be the source of the extensive incidence of univariate and multivariate outliers (Appendix Five). These affect a number of analytical procedures and may introduce inconsistencies into the final results; On the other, it limits generalisability.

Second, the final sample size (N=150) is only a minimum qualifying ‘tariff’ (4.9.5) and necessitates the ‘building block’ analytical procedures adopted. In practice, to address the level of complexity encountered effectively and parsimoniously, the maximum likelihood estimation (MLE) procedure requires 200 or more - say 30 variables x 10 cases = 300 (Hair et al 2005, DeCoster 1998). This necessarily reduces the confidence level attributable to the results.

Third and finally, the cross-sectional form of data collection imposes a clear but accepted limitation. Such studies do not explain relationships between observed phenomena but rather capture data at one point in time regarding the phenomenon under investigation (Churchill and Iacobucci 2005; Easterby-Smith et al 2002). Causation can only be demonstrated when the following applies to the variable of interest:

Concomitant variation; Evidence of clear temporal ordering; All other spurious influences controlled (Cook and Campbell 1979).

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Contextually this was the objective: to determine intra-consumption the nature of factors sustaining continued loyalty-purchasing. The findings offer no guidance future predictability – a requirement to be addressed longitudinally (further 7.6).

7.4.5 Service Category and Generalisability

The research question specifies Professional Business Services (PBS) as the focal market segment. Generalisability to at least the UK/US sub-group of Professional Creative Services (PCS) is supported by:

The adoption of a majority of components with prior validation in PBS contexts; Prior complementary findings in e.g. advertising and market research (Davies and Palihawadana 2006; Grayson and Ambler 1999; Moorman, Zaltman and Deshpande 1992); and The successful Grayson and Ambler replication (1999, Appendix Seven).

However, major differences in comparable results obtained from management consultancy (Patterson and Spreng 1997) suggest that broader generalisability requires an extension and replication in e.g. law, architecture and medical services (further 7.6).

Similarly although the principle of cross-border transfer is supported (Farley and Lehmann 1994, 4.8), recent evidence suggests that some germane measures (e.g. Perceived Service Quality and Satisfaction) are non-equivalent across cultures. This “would limit their usage across borders” (Ueltschy, Laroche, Eggert and Bindl 2007, p.418).

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7.5

Contributions of the Thesis

Chapter One described the thesis’s intended contribution. Having reviewed the conclusions, recommendations and limitations of the research study, it is relevant next to evaluate the work’s final contribution. In this context, a useful framework is provided by Summer (2001) who states that contributions may be “conceptual, empirical or methodological in nature” (p.408).

Conceptually the first goal of the thesis was to enhance understanding of the domain of ‘loyalty’ in Professional Business Services (PBS). The result indicates that traditional B2C/mainstream B2B construct distinctions (such as loyalty vs. satisfaction) may not apply in PBS contexts. It demonstrates that in PBS, in partconsistent with the P2B stream’s identification of ‘tolerance’, traditional loyalty components (e.g. relationship commitment) are part of a superordinate construct of Loyalty-Satisfaction or ‘goodwill’. By extension, the thesis provides a theoretical explanation of the nature and relationships of the key constructs which are antecedent to ‘Goodwill’ in the final Uni-Conative model. It therefore extends the understanding by marketing practitioners and academics of the nature and determinants of ‘Goodwill’.

Additionally, the study extends understanding of two key constructs in the PBS intra-purchase effects environment: Perceived Value (or relationship equity) by incorporating further reflective indicators in addition to the traditional single-item ‘value for money’; and the ‘rapport’ factor of Perceived Service Quality (PSQ) by corroborating items consistent with prior work in the P2B stream.

Empirically, the thesis contributes by testing both the inadequate Convergent and final Uni-Conative models, and their respective inter-construct relationships, with empiric data. In testing the overall PSQ and Loyalty-Satisfaction constructs, final 13 and six-item scales are developed which will assist PBS marketers to assess respectively client perceptions of (1) service attributes and (2) overall ‘goodwill’.

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Methodologically, the thesis contributes by demonstrating how understanding of key constructs may be enriched via the iterative interplay of both Varimax/orthogonal and Oblimin/oblique exploratory factor analysis approaches. It also demonstrates how an extended sequence of multiple regression analyses may partly address the limitations imposed by sample size.

7.6

Implications for Future Research

If, as this study suggests, the mean annual cost of ‘disloyalty’ for a typical UK PR consultancy is over £1 million in fees, then further direct research is merited on six principal counts.

First compliance with calls for replication (Berthon et al 2002; Wright and Kearns 1998) will:

Validate the Uni-Conative model in one or more non-creative PBS services (e.g. law, accountancy) and also internationally in order to extend its generalisability; Mitigate November’s (2004, p.47) prediction that it will become one of the 97% of academic contributions that are ‘academic clutter’ lacking independent verification; and Offer practitioners a practical tool that assesses the ‘goodwill’ of their client base and helps them manage desired behaviours.

Second, extension of a replication process to include a longitudinal study will establish the long-term predictive capability of the ‘goodwill’ construct vs. actual loyalty-behaviours (including the control factor of loyalty purchasing).

In the

context of average 42-month incumbency, both short- and mid-term benchmarks are desirable. To frame this investigation, both this study’s indicative exploratory factor analysis (EFA) and the Uni-Conative Model’s distinction between the

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conative ‘goodwill’ and loyalty-behaviours support the broad two dimensional conceptualisation of intrapersonal loyalty stream.

Third, the present model may benefit from improved explanation of variance and predictability given further investigation, enrichment or incorporation of four major parameters:

The PBS conceptualisation and role of Trust remains an enigma. The study corroborates inadequacies identified in the postulated five item Trust scale (Grayson and Ambler 1999). These may explain its non-significance in many tested contexts but should not eliminate it from further consideration. A validated, fully discriminated and appropriately customised PBS scale is urgently required; The dramaturgical consequences of PBS continuous service provision and their relationship to both attitude formation and behaviours require further investigation. In terms of service attributes, for example, emerging marketing theory emphasises the critical role played in many sectors by the creation of “engaging and lasting experiences for the customer” (Mascarenhas et al 2006). This offers a potential model for capturing and expanding the omitted ‘servicescape’ component of reputational quality (Reimar and Kuehn 2005).

One recent conceptualisation offers

‘atmosphere’ – the fifth of Five Qs (for quality) that also include object, processes, infrastructure and interaction (Zinedin 2006); Similarly it will be appropriate to test the extension of the Uni-Conative model by incorporating both ‘image’ and ‘communications’ in line with recent comparable calls to extend ECSI model (Ball, Coelho and Machas 2004); Finally service personalisation is a recently supported antecedent of ‘trust’, ‘satisfaction’ and ‘loyalty’ (Ball, Coelho and Vilares 2006). By definition, many PR Consultancy services are customised per client.

The high

respondent variance exhibited in this study may reflect undetected

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personalisation. Further investigation of this construct, as an extension of the Uni-Conative model, is also consistent with the current evolution of relationship marketing to one-to-one marketing.

Fourth, given that the ‘holy grail’ of reliable predictability of loyalty-behaviours may not be achievable within the parameters of the present Uni-Conative model, a major - and as yet unresearched opportunity - would test for the effects of client personality on P2B relationships. Recent B2C findings support direct relationships between a consumer’s personality orientation, emotional characteristics and selfreported satisfaction of the service experience (Gountas and Gountas 2007). Accepting the premise of the PBS buyer as a professional consumer (Wilson 2000) extends this paradigm, in principle, to P2B. Procedurally, a de facto analytical standard is provided by the Five Factor Model (McCrae & Costa 1991). It includes conscientiousness, agreeableness, neuroticism (widely identified as most potent), openness and extraversion.

Fifth and by extension, the personal relationship satisfaction literature suggests that, over time, client-consultant relationships will migrate from limited and social to complex and interpersonal (Hayes 2000). A dyadic study of this framework will further enrich understanding.

Sixth and finally, another potentially fruitful line of new research challenges the ‘cognitive presumption’ of principal attitudinal theories. Since these theories originate in multi-attribute utility theory (MAUT), their presumption is that clients base decisions on the consequences of their actions (the consequentiality perspective).

Most evidence relating to choice under risk or uncertainty,

accordingly, is based upon fundamentally cognitive theories (Loewenstein 2001). Conversely the emerging model of goal-directed behaviour (MGB) also takes emotions into account. If, as reported, cognitive effort is inversely related to ‘loyalty’ (Taylor, Hunter and Longfellow 2006), it follows that positive emotion may be ‘loyalty’s’ most significant antecedent.

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7.7

Epilogue: Learning and the Research Process

Any research process generates both first- and second-order learning: respectively what is achieved in relation to the research goals and what is gained by undertaking the research process itself.

In the first case, the contributions recorded here indicate a relative achievement although any assessment of absolute value is ultimately subjective.

In the second case, and a key insight of the process, those contributions are incomplete. The nature of double-loop learning is one of continuous refinement (Argyris 1999). What was intended as the conclusion to a 25-year quest only culminates in new challenges. For some, at the outset of a DBA/PhD process, such knowledge of ambiguity may prove a deterrent: for others perhaps, an added attraction.

The nature of this second-order change encompasses a shift from one orientation to another (Greenwood, Royston and Hinings 1988).

As so many doctoral

colleagues and friends assert like a litany: ‘if only I had known at the outset what I know today’. If only I had known, it would have transformed the approach to: (1) this dissertation; (2) most management and consulting projects and programmes over 25 years; and (3), more broadly, the conduct of personal experiential learning (Kolb 1984) in so many disciplines. If only.

Such learning also associates positively with confidence and scepticism. They reveal that the wonderful ‘Pandora’s Box’ of academic theory also carries baggage. Forty years of tradition may assert that ‘satisfaction’ and ‘loyalty’ are distinct constructs and, predominantly, require positivist investigation. But not, as this study shows, necessarily. If only.

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In the spirit of such scepticism, it remains (tongue-in-cheek) to dissent from the customary closing genuflection to the metaphorical ‘journey’ of research. This view is founded on the dominant linear exposition of history and, ultimately, Bishop Otto of Freising’s (1114-1158) famous first deployment of the label ‘progressus’ for the movement of history (Patrides 1972). The minority cyclical or recurrent view of history, which dates at least from Homer, is perhaps more apposite for timeless social phenomena. So, ‘we shall not cease from exploration’ but:

“… the end of all our exploring Will be to arrive where we started And know the place for the first time.” (T. S. Eliot [1944], Little Gidding, vv. 239-242).

More than 25 years after the conundrum of loyalty first arose, this most effectively captures the final sensation.

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APPENDIX ONE - INSTRUMENT

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APPENDIX TWO – INSTRUMENT ANALYSIS

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APPENDIX THREE – THE PR PANEL The ‘PR Panel’ acted as an expert advisory group at two principal stages in the development of this study. They reviewed and commented on: (1) drafts of the instrument (Appendix One); and (2) the outputs of the qualitative research conducted at Whiteoaks (4.2.3). The panel comprised:

Susy Brooks, then director of The Whiteoaks Consultancy, a Top 100 firm and Top 20 outside London; Gill Craig, co-founder and director (now chairman) of The Whiteoaks Consultancy; Jennifer Janson, director (now managing director) of Six Degrees – a mid-size firm comparable to The Whiteoaks Consultancy; James Kelliher, director (now managing director) of The Whiteoaks Consultancy; Stuart Smith, (then) chief executive of Edelman UK (a subsidiary of one of the world’s largest PR firms); and Adrian Wheeler, (then) chief executive GCI Europe (one of the top five panEuropean networks.

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APPENDIX FOUR – SAMPLE MAILING LETTERS 4.1

Sample First Mailing (Consultancy Clients – Whiteoaks Version)

Dear ******* UPCOMING CLIENT SURVEY – SPECIAL REQUEST FOR HELP Ever wanted to understand what makes your customers/clients tick when it comes to their perceptions of service, performance and value? Identify why only some remain loyal? We certainly do. Together with a number of other selected firms, Whiteoaks is about to participate in a ground-breaking Henley survey of client needs and perceptions. We believe its findings may also prove directly useful to you in relation to your own customers. So we’d very much like your help and feedback. Absolutely warts and all! And no matter the extent (or manner) of your experience with us over time. The immediate purpose of the study is twofold: a) to achieve a better understanding of how clients view the performance and value of professional service firms such as PR consultancies and b) to apply that information to deliver relevant improvements in our operations across the board. Every chosen participant is critical to the process to enable statistical validity. And by way of return? Naturally, we hope you’ll experience increased value in our performance over time! But we also offer two further benefits (OK, inducements!): A personal copy of a report summarising the study’s findings as they apply to IT and professional services as a whole; Your choice of a ‘thank you’ gift of wine or a £10 donation made on your behalf to a charity of your choice. I do very much hope that you will be willing to participate. To ensure confidentiality, the survey will be administered externally by an independent consultant, George Illingworth – formerly research manager at one of the UK’s leading mid size management consultancies Bourton Group (formerly Ingersoll). You’ll hear further from George in a week’s time. If, in the meantime, you have any queries and/or for any reason would prefer to opt out please don’t hesitate to call or email (jamesk@whiteoaks.co.uk). With best wishes

James Kelliher Director

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4.2

Second Phase Letter (Research Manager – Consultancy Clients – Whiteoaks Version)

Dear ******* WHITEOAKS CLIENT SURVEY – PARTICIPATION DETAILS I am writing further to the recent letter from Whiteoaks’ James Kelliher (copy attached) to provide further details regarding the survey. And I very much hope you will be able to help us with this important project. To make your participation as convenient as possible, you have three options: Hard Copy: simply complete and return the attached form to me in the pre-paid envelope supplied; Telephone: call me on +44 1926 ******* or email me at george.illingworth@btinternet.com and I will be delighted to arrange a time when I can walk you through the questionnaire over the ‘phone; Online: click on to http://www.henleymc.ac.uk/quest/PR.pdf in your browser and complete the questionnaire directly online; Whatever your choice, I would expect the survey to require approximately 15 minutes of your time. As James indicated my primary role is to ensure confidentiality for all participants and to administer the survey strictly in accordance with The Market Research Society’s code of practice. To this end I have allocated a unique personal code to each chosen participant. Its identity will be known only to me. Yours is Orange 100 and you will need it when you complete the form. Also on the front page, you’ll find two options for your choice of ‘thank you’ gift which I will arrange, and confirm, upon completion of the form. I am also available to answer any queries you may have and to provide any help you may need. Please don’t hesitate to get in touch at any point. With best wishes

George Illingworth Survey Administrator PS: Following requests during a test, I will also be emailing a copy of this letter to you in the next 48 hours. © Bill Nichols 2009

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APPENDIX FIVE - OUTLIERS

Table A5.1: Cases with Repeated Univariate Outliers (1) Item

Description

Cases Reported

CM1

Client-Buyer Experience

68,

CM2

Personal Relationship Duration

112, 138, 145

CM3

Firm Relationship Duration

45, 58, 107, 138

CM4a

Contact: Face-2-Face

39, 78

CM4b

Contact: Conference Calls

56,

CM4c

Contact: General Calls

69, 104, 117

CM4d

Contact: Emails

45, 69

OC5

Buyer Responsibility 2

13, 62, 92

OC6

Buyer Responsibility 3

82, 107, 108, 110

PSQ1`

Goal Understanding

54,

PSQ2

Problem Understanding

29,

PSQ9

Content Development

48,

PSQ10

Media Pitching

44, 48, 76

PSQ13

Results Effectiveness

59, 76, 113

PSQ15

Close Working Relationship

113,

PSQ17

Strategic Interaction

29, 94, 113

PSQ20

Responsiveness

113,

PSQ22

Creativity

80, 82, 120, 149

PSQ23 PSQ24

Reliability Dependability

113, 82, 113

PSQ18 Client Orientation 113, Source: SPSS Output. Bold underlined cases identify repeated instances.

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Table A5.2: Cases with Repeated Univariate Outliers (2) Item

Description

Cases Reported

LOY4

Relative Attitude

80, 112

LOY5

Exclusive Consideration

80, 113

LOY7

Exclusive Intentions

48, 80, 113

LOY8

Switching Intention

100, 107, 110

LOY9

Private Advocacy

80, 113, 137

DIS1

Disconfirmation 1

80, 144

DIS2

Disconfirmation 2

42, 80, 144

TR2

Perform Without Monitoring

113,

TR3

AM Performing Tasks

12, 113

TR4

Team Taking on Tasks

113, 150

TR5

Do Not Trust

146,

TS1

Satisfaction-Service

42, 48, 80, 113

TS2

Satisfaction-Advice Quality

16,

TS3

Satisfaction-Proactivity

113,

PV4

Fair Value

113,

CS1

Cumulative -Decision to Appoint

80, 99, 113

CS2

Cumulative – Pleasure

80,

CS3 Cumulative - Overall Service 80, Source: SPSS Output. Bold underlined cases identify repeated instances.

Table A5.3 Multivariate Outliers (Sample CFA Analysis) Mahalanobis D2

Case

D2/df (16)

107

56.911

0.000

3.557

144

53.869

0.000

3.367

109

51.746

0.000

3.234

113

51.231

0.000

3.202

47.828

0.000

2.989

108

44.745

0.000

2.797

149 41.333 Source: AMOS Output.

0.000

2.583

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APPENDIX SIX – PERCEIVED SERVICE QUALITY EFA SAMPLE ANTI-IMAGE CORRELATION MATRIX P01

P02

P03

P04

P05

P07 0.139

0.894

0.473

-0.473

0.897

0.022

-0.025

0.013 0.072

-0.092

0.911

-0.030

0.057

-0.080

-0.030

0.924

0.036

0.120 0.110

-0.013

0.022 0.025 0.072

0.057

0.036

0.945

0.002

-0.139

0.120

-0.110

0.002

0.057

-0.068

0.033

0.092

-0.104

-0.257

-0.061

-0.252

-0.088

-0.346

-0.283

0.098

0.078

0.188 0.133

0.106 0.150 0.282 0.078

0.838 0.541

0.234

-0.006

0.022

-0.043

-0.186

-0.016

-0.138

0.074 0.273

-0.017

-0.001

0.016 0.111

-0.215

0.044

-0.168

0.253

0.319 0.155

0.016

0.199

-0.068

0.048

-0.072

0.167

-0.087

-0.108

-0.037

-0.449

-0.101

-0.065

0.118

-0.099

0.110

-0.093

-0.027

0.171

0.316

0.101 0.203 0.039 0.159 0.095 0.194

0.081 0.028 0.252 0.080 0.215

-0.080

-0.056

0.074 0.058

-0.253

0.175

0.085

0.003

0.088

0.136 0.071 0.055 0.050

-0.092

-0.080

0.136

0.003 0.158 0.023 0.020 0.078 0.048 0.053 0.135

P08 0.057 0.071 0.068

0.092 0.055 0.104 0.257 0.150

P13 0.061 0.050 0.252 0.088 0.282 0.158

P14 -0.346 0.188

0.078 0.133

0.098 -0.078

0.022

0.023

0.020

0.070 0.191

-0.094

0.918

0.180

0.180 0.115

0.873

0.087

0.039 0.004

0.087 0.004 0.115 0.558

-0.558

0.000

0.075

0.012

0.244

0.097 0.152

0.045

0.036 0.133

0.010

0.096

-0.112

0.128 0.045

0.037

0.033 0.106 0.541 0.778 0.032 0.070 0.094

0.004 0.005

0.000 0.034 0.042

0.039 0.120 0.026 0.029 0.097 0.068

0.003 0.032 0.953 0.191

0.100 0.263 0.036 0.065 0.180 0.019 0.073 0.020 0.039

-0.283

P15

0.234 0.006

0.050

0.048 0.012 0.008

P12

0.039

0.014

-0.174 0.092

0.898 0.213 0.063 0.030 0.107 0.115 0.068 0.111

0.218 0.041 0.337

-0.200 -0.001

0.225 0.163

0.079

-0.231

0.157

0.014

0.089

0.034

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P16

P17

P18

P19

P20

P21

-0.043

-0.138

-0.215

0.253

-0.068

0.074

-0.273

0.319

-0.155

0.101

-0.186

-0.017

0.044

0.016

0.048

-0.016

-0.001

-0.168

0.199

-0.072

0.016

-0.111

0.081

-0.028

-0.252

0.167 0.203 0.087 0.108 0.080

-0.078

-0.048

0.053

-0.135

0.050

0.000

0.097

-0.152

0.004

0.005

0.075

0.045

0.100

-0.263

0.036

0.012

0.036

-0.133

0.096

0.128

0.244

0.010

0.014

-0.112

0.037

-0.213

0.063

-0.030

-0.107

-0.115

0.922

-0.100

0.037

-0.130

0.110

0.039 0.065 0.045 0.174 0.068 0.321

-0.100

0.931

-0.167

-0.409

0.007

0.037

-0.167

0.924

-0.368

-0.083

-0.130

-0.409

-0.368

0.909

-0.062

0.110

0.007

-0.083

-0.062

-0.321

0.070

-0.146

-0.246

0.064

-0.096

P22

P23

0.037 0.039 0.449 0.101 0.215 0.034 0.120 0.180

0.065 0.159

0.218 0.092 0.111 0.246

0.070 0.146

0.064 0.128 0.017

0.930

0.121 0.104

0.121

-0.104

0.929

0.137

-0.128

0.017

0.049

0.118

0.077

-0.073

-0.409

0.058

0.042

-0.091

-0.113

-0.199

0.137 0.160 0.036

-0.057

0.028

-0.126

0.100

0.082

0.085

-0.024

-0.007

-0.125

-0.038

0.917 0.014 0.044 0.006 0.136

0.156 0.350

0.049

P24

P26

P27

-0.093

0.316

0.253

-0.095

-0.194

0.175

0.118 0.099

-0.027

-0.080

0.085

0.171

-0.056

0.003

0.110 0.042 0.026

0.074

-0.058

0.048

-0.012

0.088 0.008

-0.029

0.097

0.019 0.041 0.200

-0.073

-0.020

0.068 0.039

-0.337

0.079

0.014

-0.001

-0.231

0.089

0.225 0.096

-0.163

0.157

0.034

0.058

-0.057

0.118

0.042

0.028

0.077 0.073 0.409 0.160 0.014

-0.091

-0.126

-0.113

0.100

-0.199

0.082

-0.036

0.156

-0.044

-0.006

0.085 0.024 0.007 0.125 0.038 0.350 0.136

0.925 0.170 0.168

-0.170

-0.168

0.011

0.955

-0.181

0.807

0.054 0.362

0.011

0.054

-0.362

0.888

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APPENDIX SEVEN – CONFIRMATORY FACTOR ANALYSIS – PARTIAL REPLICATION OF GRAYSON AND AMBLER (1999) Grayson and Ambler’s (‘G&A’, 1999) CFA is based on five constructs (Trust, ‘consultant involvement’, Commitment, ‘interaction quality’ and ‘utilisation’). All relevant items are available in the current study with the exception of the fifth and last construct which is unrepresented. Granted overall sample size constraints, Its replication serves to: (1) provide support for the validity of the Loyalty-Commitment scale; and (2) via limited replication (Berthon et al 2002; November 2004) extend this study’s generalisability.

In the data report (Table A7.1), it will be noted that the initial non-compliant CFA (Column II) is consistent with G&A’s own first attempt (Column I). The second iteration (Column IV): (1) tracks all item-changes executed by G&A except that it retains the full eight-item Loyalty-Commitment scale; and (2), as a result, fails to match G&A’s fit achieved (Column III). Only by applying analysis of factor loadings, factor scores and modification indices (MIs) and eliminating the four peripheral items is a matching result (Column V) achieved.

Table A7.1: Replication – Model Fit I

III

G&A 1999 Original Loy-3

Nichols 2008 Replication Loy-8

G&A 1999 Final Loy-2

Nichols 2008 Replication Loy-8

Nichols 2008 Revision Loy-4

χ2

476.440

379.090

37.960

164.820

48.644

160.000

186.000

34.000

73.000

29.000

0.000

0.290

0.000

0.013

N/Reported

2.038

N/Reported

2.258

1.677

GFI

0.810

0.804

0.970

0.866

0.940

AGFI

0.750

0.756

0.940

0.807

0.886

CFI

0.810

0.897

0.990

0.924

0.976

RMSEA

0.100

0.083

0.024

0.092

0.067

CMIN/df

B i l l N i c h o l s A p p e n d i c e s … … … … … … … … … … … … … P a g e | 356 As regards construct validity, convergent validity is evidenced by: (1) all factor loadings being significant; (2) construct reliabilities high and in range 0.76-0.83; and (3) average variance extracted (AVE) indicating that in each case the variance captured is greater than that due to measurement error (Table A7.2). Second, supporting discriminant validity, the AVEs for both Loyalty-Commitment and Trust correctly exceed their squared correlations. Third and fourth, nomological and content validity are supported by the nature of the replication of the prior study (Grayson and Ambler 1999).

Table A7.2: Replication: Average Variance Extracted and Construct Reliabilities Factor Loadings

Factor Loadings Squared (SMCs) LoyInvolveInter4 ment action Trust

De lta

L2 Influence

0.724

0.524

0.276

L5 Comm Relationship

0.883

0.780

0.117

L7 Comm Firm

0.812

0.659

0.188

L9 Advocacy

0.835

0.697

0.165

PSQ06 Planning

0.646

0.417

PSQ18 Strategy

0.938

0.880

T3 - AM Performs

0.812

0.659

0.188

T4 - Team Performs

0.887

0.787

0.113

PSQ16 Productive Meets

0.623

0.388

0.377

PSQ18 - Client Orientation

0.812

0.659

0.188

Average Variance Extracted (AVE) Construct Reliability

0.354 0.062

64.86%

52.37%

72.31%

66.51%

0.76

0.83

0.77

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APPENDIX EIGHT – MULTIPLE REGRESSION ANALYSIS 1 (MRA1): PERCEIVED VALUE-ILLUSTRATIVE SUPPORTING DATA This section provides supporting data for the first multiple regression analysis (MRA1, 7.4). Specifically Figure A8.1 supports assumptions of linearity and homoscedasticity by the lack of violations in a sample scatterplot of residuals (predicted vs. actual) and in an illustrative partial regression plot (from iteration VII):

Figure A8.1: Perceived Value MRA1 – Linearity and Homoscedasticity

Source: SPSS Output.

Figure A8.2 demonstrates normality of error distribution as evidenced e.g. by both the normative histogram of standardised residuals and the normal probability plot.

Table A8.1 summarises casewise diagnostics and case eliminations over the seven iterations.

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Figure A8.2: Perceived Value MRA1 – Normality of Error Distribution

Source: SPSS Output.

Table A8.1: MRA1 Perceived Value - Casewise Diagnostics (a) Eliminated After…. Iteration #1

Std. Residual PV

Case

Predicted Residual

-3.480

3.00

6.739

-3.739

Iteration #1

(b)

-2.944

1.00

4.163

-3.163

Iteration #2

(c)

120

-3.253

1.00

4.495

-3.495

Iteration #2

(c )

137

-3.188

2.00

5.063

3.063

Iteration #3

(d)

None

Iteration #4

(e)

119

-2.960

3.00

4.973

-1.973

Iteration #5

(f)

None

Iteration #6

(g)

131

-3.016

3.67

6.018

-2.352

Iteration #7

(h)

None

(a) Dependent: Perceived Value (b) Plus one other (-2.786) (c ) No others (d) One other only (-2.153) (e) Five in range 2.050 - -2.712 . (f) Six in range (-2.879 - 2.088) (g) Five in range (2.057 - 2.718) (h) Seven in range (-2.056 - 2.842) Source: SPSS Output.

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APPENDIX NINE: MULTIPLE REGRESSION ANALYSIS 2 – TRUSTSUPPORTING DATA This section provides supporting data for the second multiple regression analysis (MRA2, 7.5). Specifically Figure A9.1 supports assumptions of linearity and homoscedasticity by the lack of violations in a sample scatterplot of residuals (predicted vs. actual) and in an illustrative partial regression plot (from iteration V):

Figure A9.1: MRA2 Trust – Linearity and Homoscedasticity

Source: SPSS Output.

Figure A9.2 demonstrates normality of error distribution as evidenced e.g. by both the normative histogram of standardised residuals and the normal probability plot.

Figure A9.2: Trust MRA2 – Normality of Error Distribution

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Table A9.1 summarises casewise diagnostics and case eliminations over the five iterations:

Table A9.1: MRA2 Trust Casewise Diagnostics (a) Eliminated After…. Iteration #1

Case

Std.Resid Trust

Predicted Residual

-3.158

2.00

4.947

-2.947

Iteration #1

(b)

137

-2.987

2.50

5.288

-2.788

Iteration #2

(c )

None

Iteration #3

(d)

None

Iteration #4

3.264

5.50

3.325

2.175

Iteration #4

(e)

119

-2.960

3.00

4.973

-1.973

Iteration #5

(f)

None

(a) Dependent: Trust (b) Plus six others range (2.2112.725) (c ) Six in range (2.283-2.942) (d) Five in range -2.055 - 2.928 (e) Five in range -2.001 - -2.327 (f) Six in range -2.132 - -2.476 Source: SPSS Output.

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APPENDIX TEN: MULTIPLE REGRESSION ANALYSIS 3 – SATISFACTIONSUPPORTING DATA

This section provides supporting data for the third multiple regression analysis (MRA3, 7.6). Specifically Figure A10.1 supports assumptions of linearity and homoscedasticity by the lack of violations in a sample scatterplot of residuals (predicted vs. actual) and in an illustrative partial regression plot (from iteration VI):

Figure A10.1: Satisfaction MRA3 – Linearity and Homoscedasticity

Source: SPSS Output.

Figure A10.2 demonstrates normality of error distribution as evidenced e.g. by both the normative histogram of standardised residuals and the normal probability plot (iteration VI). Table A10.1 summarises casewise diagnostics and case eliminations over the first six iterations (Phases One-Three).

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Figure A10.2: Satisfaction MRA3 – Normality of Error Distribution

Source: SPSS Output.

Table A10.1: MRA3 Satisfaction

Casewise Diagnostics (a)

Eliminated After….

Case Std.Resid

Iteration #1

Satis Predicted

-3.435

4.33

7.225

Iteration #1

(b)

-3.496

1.33

4.277

Iteration #2

(c )

144

-3.048

3.00

5.361

Iteration #2

(c )

-2.923

3.00

5.264

Iteration #3

(d)

None

Iteration #4

(e)

None

Iteration #5

(f)

-3.893

2.33

4.446

Iteration #6

(g)

None

(a) Dependent: Satisfaction (b) Plus six others (2.048 - -2.759) (c ) Plus six others (-2.029 - 2.563) (d) Eight cases range -2.076 - -2.761 (e) Nine in range 2.149 - -2.780 (f) Five in range (2.057 - -2.638) (g) Five in range (-2.27 - -2.700) Source: SPSS Output.

Additionally regards Phase Four (iteration VII) it should be noted that: (1) casewise diagnostics report no significant >3.0 standard deviations’ outliers and only four cases at >2.0 (range: 2.012 - -2.898). These latter do not violate the 5% norm; regards independence of error terms, Durbin-Watson reports a near-ideal 1.976.

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APPENDIX ELEVEN: MULTIPLE REGRESSIONS ANALYSIS 4 – LOYALTYCOMMITMENT-SUPPORTING DATA This section provides supporting data for the fourth multiple regression analysis (MRA4, 7.7). Specifically Figure A11.1 supports assumptions of linearity and homoscedasticity by the lack of violations in a sample scatterplot of residuals (predicted vs. actual) and in an illustrative partial regression plot (from iteration IV):

Figure A11.1: Loyalty-Commitment MRA4 – Linearity and Homoscedasticity

Source: SPSS Output.

Figure A11.2 demonstrates normality of error distribution as evidenced e.g. by both the normative histogram of standardised residuals and the normal probability plot (iteration IV). Table A11.1 summarises casewise diagnostics and case eliminations over the four iterations (Phases One-Two).

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Figure A11.2: Loyalty-Commitment MRA 4– Normality of Error Distribution

Source: SPSS Output.

Table A11.1: MRA4 Loyalty-Commitment

Casewise Diagnostics (a)

Eliminated After….

Case Std.Resid Loyalty Predicted Residual

Iteration #1

3.487

7.00

5.077

1.923

Iteration #1

(b)

-3.180

3.00

4.754

-1.754

Iteration #2

(c )

125

-3.014

3.00

4.539

-1.539

Iteration #3

(d)

None

(e)

None

Iteration #4 (a) Dependent: LoyaltyCommitment (b) Plus five others (2.051- -2.758) (c ) Plus five others (-2.018 - 2.731) (d) Six cases range (-2.055 - 2.831) (e) Five in range 2.024 - -2.509 Source: SPSS Output.

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APPENDIX TWELVE – MODERATION BACKGROUND The final complementary analysis of moderation on the key paths in the UniConative model is limited by (1) absence or scarcity in most cases of theoretical underpinning and (2) elimination of two of the four postulated classificatory moderating variables during initial data preparation [They are: CM2-Personal Relationship Duration and CM4-Interaction Frequency.] These limitations acknowledged, this appendix described the generation of hypotheses for the potential roles of: (1) CM1 – Client-Buyer Experience [CBE], (2) CM3 – Firm Relationship Duration [FRD]) and (3), perhaps most significantly, Trust.

A12.1 The Role of Trust

Prior findings in this study suggest at best only a peripheral role for Trust. It is nonsignificant in both Final and Univariate Models. Earlier it was reported as (1) both a limited (β = .103) and non-significant (ρ = .077) direct antecedent of Satisfaction (6.7.2, Table 6.20); and (2), if PSQ is excluded, a weak but significant predictor (β = .122; ρ = .015) of Loyalty-Commitment.

Yet, conversely, prior theory identifies Trust as “crucial for the long-term development of the firm” (Gummesson 1981, p.31). It describes the construct as a complex Janus-like attitude: one which encompasses both reflective, or backward, evaluation of attributes (Zinedin and Johnson 2000) and forward looking acceptance of risk and obligation (Tan and Sutherland 2004). Accordingly, the multi-dimensional application of Trust may emerge with greater clarity as a moderator.

A12.2 Moderators, Perceived Value and Loyalty-Satisfaction

Relationships between Perceived Value (PV), Trust and both Loyalty-Commitment (LC) and Satisfaction are well-supported in the literature (3.7) and corroborated in this study. The PV→Loyalty-Satisfaction path, conversely, lacks prior evidence.

B i l l N i c h o l s A p p e n d i c e s … … … … … … … … … … … … … P a g e | 366 Proceeding, accordingly, deductively two useful initial observations are: (1) occasional non-linearity in the Satisfaction→L-C path (Bennett and Rundle-Thiele 2004; Fornell et al 1996; Johnson, Nader and Fornell 2001; Mittal and Kamakura 2001); and (2) systematic differences in repurchase rates among different customer groups (Mittal and Kamakura 2001, p.131). Granted some of these observations may arise due to both constructs’ operational heterogeneity (Giese and Cote 2003; Oliver 1999), they are also a confirmed function of a wide range of personal and situational moderators (Homburg and Giering 2001; Oliver 1999). It is reasonable to deduce further that they are indicative of general conditions in the transition from attitude (e.g. PV) to current intention (e.g. Loyalty-Satisfaction).

In this context on this path, a moderating role for Trust is consistent with: (1) its prior postulated role as a test of functional sufficiency (Cohen and Reed 2006; (2) emerging Trust-Satisfaction theory in the convergence or relationship quality stream (Beloucif, Donaldson and Kazanci 2004; Lin and Ding 2005); and (3) emerging TrustValue theory in the P2B tolerance literature (Davies and Palihawadana 2006).

Conceptually the level of Trust is a store of goodwill. It will mitigate the effects of major changes in, say, the assessment of PV. Following Cohen and Reed’s (2006) test: (1) PV may assure representational sufficiency i.e. it is “well-formed and coherent… as opposed to some hazy, vague thought” (2006, p.11); but (2) Trust is required to secure functional sufficiency i.e. that the underlying value is closely aligned with the proposed behaviour. Illustratively: I may be highly dissatisfied as a result of a short-term service failure but because I trust the consultancy it will have only a very minor impact on my loyalty.

By extension, a complementary moderating role for the level of client-consultancy familiarity, operationalised here by Firm Relationship Duration (FRD) is also supported (at least in terms of attribute→attitude). Generally, the dynamics of attitude formation change over relationship lifetimes (Johnson, Herrman and Huber 2006; Wilson 1995). One meta-analysis confirms a weak but significant FRD→attitudes relationship (r = .13) (Palmatier et al 2006). Specifically P2B stream © Bill Nichols 2009

B i l l N i c h o l s A p p e n d i c e s … … … … … … … … … … … … … P a g e | 367 evidence, for example, confirms that: (1) a client’s emphasis “shifts dramatically (over time) to an (account-retaining) focus on performance dimensions involving the agency’s ability to get results” (Henke 1995, p.25); and (2) by changing account manager or team a consultancy may refresh, or re-set, client perceptions (Henke 1995).

Third and finally, practitioner anecdote suggests that professional client-buyers, in equal measure, will be less gratified and/or perturbed by service success/failure than their amateur counterparts. Limited empiric corroboration for the potency of ClientBuyer Experience (CBE) is available (Davies and Palihawadana 2006; Patterson and Spreng 1997). Accordingly by extension: the relationship between PSQ-Rapport and LoyaltySatisfaction will be weakest under conditions of high Trust, extended Firm Relationship Duration and high Client Buyer-Experience.

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Abbreviations

ACSI

American Customer Satisfaction

AVE

Average Variance Extracted

B2B

Business-To-Business

B2C

Business-To-Consumer

CBB

Consumer Buying Behaviour

CEBR Centre for Economic and Business Research CFA

Confirmatory Factor Analysis

CIPR

(United Kingdom) Chartered Institute of Public Relations

Construct Reliability

DMU Decision-Making Unit EFA

Exploratory Factor Analysis

ECSI

European Customer Satisfaction Index

GLS

Generalised Least Squares

KMV

Key Mediated Variable

MCA

(United Kingdom) Management Consultancies Association

MLE

Maximum Likelihcod Estimation

MRA

Multiple Regression Analysis

NPV

Net Present Value

OBB

Organisational Buying Behaviour

P&L

Profit and Loss (Statement)

P2B

Professional-To-Business

PBB

Professional Buying Behaviour

PBC

Perceived Behavioural Control

PBS

Professional Business Services

PPE

Post-Purchase Effects

Public Relations

PRCA (United Kingdom) Public Relations Consultants Association RATER An acronym for the final five-factor SERVQUAL scale (responsiveness, assurance, tangibles, empathy and reliability) © Bill Nichols 2009

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Structural Equation Modelling

SIC

Squared Inter-Correlation

SNB

Social Normative Belief

TRA

Theory of Reasoned Action

TpB

Theory of Planned Behaviour

USCI

(United States) Census of Service Industries

Acrobat Reader 4.x required! Click here or go to www.adobe.com to download. Henley Management College

A Study of Client Perceptions of UK PR Consultancies, Their Performance and Value.

Completing this Questionnaire This questionnaire is about you, your firm and your firm’s PR consultancy. Specifically it seeks to help us understand your perceptions of three things: The work undertaken by your current – sometimes called ‘incumbent’ – PR consultancy; Your interaction and relationship with the firm; Your interaction and relationship with the account manager and the team who work on the account. In most cases, you will need only to circle a number to indicate your opinion. There are a couple of key points to note. First, the term ‘account manager’ is used throughout to denote your principal day-to-day contact at the PR consultancy. (In practice terms vary widely: others include e.g. ‘account director’, ‘team-leader’, ‘account supervisor’). Second, a frequent reference point is the last occasion on which you formally/informally reviewed/appointed/re-appointed your current PR consultancy. If you have any queries on these or any other points before/during questionnaire completion please contacted our research consultant, George Illingworth, Tel: (+44) 1926 511183. who will be delighted to help. Finally two preliminaries.

Please indicate your personal reference Please indicate your ‘thank you’ preference by inserting either ‘Wine’ or your preferred charity.

Strictly Private & Confidential Henley Management College 2005 based on an original instrument by Paul Patterson, University of Wollongong, NSW.

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SECTION ONE - BACKGROUND Q1

Which of these categories best describes the principal need currently addressed by your PR Consultancy? Please select only ONE

Communications strategy/planning

Marketing/marketing support

Market research

General media relations

Analyst relations

Investor/financial relations

Corporate social responsibility

Public affairs

Event/exhibition management

Other please specify

What is the approximate number of full-time employees in your organisation in the UK? Number:

Which of the following categories best describes your position in your organisation? Please select only ONE Director/CEO Senior Management Middle Management Department Member

In which one of the following industries is your organisation principally engaged? Please select only ONE Manufacturing/engineering

Technology/IT/Telecommunications

Fmcg

Retail/distribution

Financial services

Professional services

Public sector

Other please specify

How long have you/your firm worked with your present/incumbent PR Consultancy?

(a) You:

Years

Months

(b) Firm:

Years

Months

SECTION TWO - INVOLVEMENT, PERFORMANCE AND INTERACTION Q6

When you are working with the PR Consultancy's account team, how much contact do you have (whether instigated by either side) via? (a) Face to face meetings (per month) (b) Formal audio or video-conference calls (per week) ) (c) Telephone calls - made & received (per week) (d) Emails - sent & received (per day) 3851279993

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Thinking about your PR activities as a whole, how important are the involvement of your PR Consultancy's account manager and account team in delivering each of the following activity areas? Please circle ONE number for each statement. Scale: 1=Not at All; 7=Very Important.

Statement

Very Important

Not at all

a. Defining campaign objectives.

b. Researching, developing and recommending strategy.

c. Designing, and recommending, creative/proactive solutions.

d. Identifying, researching and creating relevant content (e.g. releases, features, case studies etc).

e. Pitching stories and positions to media and other relevant audiences (e.g. analysts)

f. Project management of the campaign including events.

Please indicate your opinion of various statements about your PR consultancy's performance. Please circle ONE number for each statement. Scale: 1 = Strongly Disagree; 7 = Strongly Agree Strongly Disagree

Statement: The PR Consultancy

Strongly Agree

a. Consistently produces results which we are able to exploit

b. Uses the most up-to-date methodologies (e.g. strategy, evaluation) in their work

c. Responds promptly when we contact them

d. Makes good use of its contacts, both national and international, in order to help us come up with solutions

e. Shows some creativity in solving our problems

f. Is reliable in meeting deadlines

g. Has established and maintains a good rapport with relevant staff in our organisation

h. Consistently makes sure they understand our aims and goals

i. Comes up with innovative ideas and solutions

j. Is thoroughly professional in all it does

k. Has developed a close working relationship with our staff

l. Ensures it thoroughly understands the issue or problem before commencing work on any campaign/project

m. Is dependable

n. Produces results which will enable us to increase our organisation's marketing effectiveness

Thinking about the consultancy's recent work in the last few monthsâ&#x20AC;Ś. Please circle ONE number for each of the following two questions. (a) How closely did its overall performance meet the expectations you held at the time you last reviewed the firm? Extremely different to what I expected

Exactly what I expected

(b) How do you feel about the firm's performance over the period since your last review? Very bad/very negative

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Very good/very positive

Page 3 of 8

Q10

Considering your personal interaction with the PR Consultancy, please indicate the degree to which you agree/disagree with the following statements. Please circle ONE number. Scale: 1=Strongly Disagree; 4=Neither Agree nor Disagree; 7=Strongly Agree

Statement

Strongly Disagree

Neither agree nor disagree

Strongly Agree

a. If I or someone in my department could not be reached by our account manager, I would be willing to let him/her take important PR decisions without my involvement.

b. My meetings with the account team produce creative insights and new ideas.

c. I trust the account team to get the job done right without having to monitor their progress continuously.

d. Our account manager displays a sound strategic understanding of our business in his/her interaction with me.

e. I trust my account manager to perform tasks on my behalf which I haven't got the time or specialist knowledge to carry out personally.

f. Our account manager is very client-orientated in his/her interactions with us.

g. I am confident that my account manager and team can take on jobs on behalf of my staff if they lack the time or expertise to carry them out themselves.

h. My interactions with our account manager are productive. i. I generally do not trust our account manager.

Q11

Thinking about your experience in recent months with your PR consultancy, how satisfied today are you with: Scale: 1 = Very Dissatisfied; 7 = Very Satisfied

Statement

Very Dissatisfied

Very Satisfied

a. The service delivered by your account team.

b. The quality of advice.

c. The degree of proactivity.

d. The quality of completed tasks (e.g. copywriting, event management).

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SECTION THREE: OVERALL OPINIONS Q12

Listed below are six features relating to PR consultancies and the services they offer. We would like to know how important each of these features is to you when you review and evaluate their performance. Please allocate a total of 100 percent among the eight features according to how important each feature is to you. The more important a feature is to you, the more percentage points you should allocate to it. Please ensure that the percentages you allocate to the six features add up to 100%. a. Results: ability to deliver effectively the results of the service for which it was appointed

b. Understanding: willingness to understand thoroughly your needs, aims and goals

c. Expertise: demonstrated expertise, knowledge and methodologies

d. Contacts: willingness to use its network of national and international contacts to help you

e: Service: willingness to provide prompt, dependable service

f: Relationships: ability to establish a good rapport and understanding with your staff

Please ensure that the percentages you have allocated to the six features add up to 100%.

Q13

Reflecting now on your overall opinions of your PR consultancy and its performance over the full lifetime of yours and your firm's relationship with them, to what extent do you AGREE or DISAGREE with the following statements. Scale: 1=Strongly Disagree; 7=Strongly Agree

Statement

Strongly Disagree

Strongly Agree

a. Considering the fees we are paying and the results we are obtaining, I believe we are receiving value for money.

b. What we are getting back justifies what we are putting in.

c. The consultancy's fees are acceptable.

d. Compared to what I know of competitive alternatives, we are obtaining fair value.

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Q14

Based on your overall knowledge, experience of, and feelings towards, your PR consultancy and taking everything into account, are you: Scale 1 =Definitely Not; 7 =Definitely Yes

Statement

Definitely Not

Definitely Yes

a. Willing to recommend the firm to a colleague in a non-competitive company with similar needs.

b. Committed to your relationship with your PR consultancy account manager and team.

c. Expecting to consider only this PR consultancy to handle any additional project requirements in the short-to-medium term (next 3-6 months).

d. Satisfied with your/your firm's decision to appoint/re-appoint the PR consultancy.

e. Treating your account manager/account team as a part of your department.

f. Sometimes concerned that your account team alters the facts slightly to meet their own needs.

g. Feeling pleased with what you have received

h. Caring about the future of your working relationship with your account manager.

i. Sometimes concerned that your account team promises to do things without actually doing them later.

j. Committed to continuing to work with the PR firm for the foreseeable future.

k. Feeling satisfied with what you have received

l. Concerned that your PR consultancy responds to complaints slowly and ineffectively.

m. Locked into a relationship over which you have little control yourself.

n. Feeling warmer/more positive about the firm than about other PR consultancies which you have worked with or are familiar with.

o. Of the view that the PR firm's influence on your decision-making and implementation has been, and continues to be, significant and beneficial.

p. Willing for you/your firm to appear in a published case study or public conference presentation to recommend the PR consultancy.

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SECTION FIVE: BACKGROUND DETAILS Q15

During your career, approximately how many PR consultancies have you worked with? Number:

Q16

During your relationship with your present incumbent PR Consultancy, how many different account managers have you worked with? Number:

Q17

Taking into account the level of involvement of other staff (if any), to what degree are you personally responsible and accountable to your company for ensuring that the current PR Consultancy's programme is carried out successfully? %

Q18

Concerning your views about the PR Consultancy, how representative are they of the views of your colleagues who (if any) are involved in managing and working with the PR consultancy on a regular basis? %

Q19

How frequently do you review formally the progress made by your PR Consultancy? Please select only ONE Quarterly

Q20

Every Two Years

Six-Monthly

Every Three Years

Annually

Other please specify

At the last review, how much personal responsibility did you have for entering into or renewing the relationship with the PR Consultancy? Partly/On a Day-to-Day basis/as part of a wider management or departmental group

None at all

Completely for all aspects including the commercial relationship

You have now completed the main part of this questionnaire. Thank you for your help and co-operation which is greatly appreciated. If you would like to: Receive a summary of the research findings please tick this box Make further comments about any aspect of your perceptions of your PR Consultancy and its work with you, please use the Comments section below Participate in an entirely optional last general question about your views of the world of PR Consultancy in general, please scroll down to the final page (Q21). COMMENTS

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OPTIONAL QUESTION - YOUR VIEWS OF PR CONSULTANCY IN GENERAL Q21

Based on your own experiences please indicate your opinion of PR consultancies in general. Please circle ONE number for each item listed. PR consultancies in general:

Have a poor reputation

Have an excellent reputation

Provide poor value for money

Provide excellent value for money

Always provide workable solutions

Only sometimes provide workable solutions

Don't always meet deadlines

Always meet deadlines

Provide recommendations which are often difficult to implement

Provide recommendations which are easy to implement

Are often less than professional in what they do

Are thoroughly professional in all they do

Often don't keep the client well informed

Keep the client well informed

Only sometimes provide an objective and unbiased assessment

Always provide an objective and unbiased assessment

Don't always put experienced people on the programme

Only put experienced people on the programme

Often don't really understand the issues at each stage before moving on in the programme

Always understand the issues at each stage before moving on in the programme

Are only sometimes academically well qualified

Are always academically well-qualified

Only sometimes display high integrity

Always display high integrity

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Loyalty and PBS - Instrument (Field Use 10/06-02/07)

Author: Bill Nichols Page 1 of 9

INSTRUMENT ANALYSIS

Column Abbreviations

Frequent Abbreviations

Update: August 2006

Q = Question Number C = Construct/Filter Number

P&S - Patterson & Spreng (1997) MZD - Moorman, Zaltman & Deshpande (1992, 1993)

V = Variable H = Specific Relevant Hypotheses

G&A - Grayson & Ambler (1999) P&S Adapt - Patterson & Spreng (1997) with minimal contextual VarPR - Language/terminological variation for PR industryonly

P - Source P&S G - Source G&A O - Other Source R - Reference Code

Pan - PR Group Panel (2005)

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

Author: Bill Nichols Page 2 of 9

1 F1

1 N/A

2 F2

2 N/A

CONSTRUCT DEFINITION AND SOURCE "Problem area being addressed by the Filter 1/Principal Service Need of Client (management consultancy) Firm firm" (P&S ) Firm size by number of employees in relevant Filter 2/Firm Size by Employee Numbers territory, UK (P&S)

3 F3

3 N/A

Filter 3/Hierarchical Position

Management hierarchy categorisation (P&S)

4 F4

4 N/A

Filter 4/Industry Classification

15 F5

69 N/A

F5/PR Firms Worked With

Industry type/PR customised In which… is your organisation (Nichols/Panel 2005) principally engaged? Respondent experience metric Firms in career

16 F6

17 F7

18 F8

1 1

1 F6

CONSTRUCT

OPERATIONALISATION

QUESTION FORMAT

Source and Notes

"The principal need being addressed by the PR consultancy firm"

Which of the following categories best describes the principal need being addressed by the PR Consultancy firm….

P&S/VarPR

"approximate number of employees in…"

What is the approximate number of full-time employees

P&S Adapt

Self-assessed 'best describes your position'

Which of the following categories best describes your position in the organisation?

P&S

Account managers in current PR F6/ Account Managers Worked With Respondent interaction level Consultancy relationship Respondent's responsibility for managing the PR firm

F7/ Management Responsibility

Representativeness of the respondent's views vs. the remainder of F8 Representativeness Buying/Managing Group of View (P&S)

% Weighting Format

"In which ONE of the following industries is your Adapted from G&A to PR organisation principally engaged? industry taxonomy How many PR consultancies have you worked with in your career? P&S/PR Adapt

During your relationship with your incumbent PR consultancy, how many account managers have you worked with? How much responsibility do you personally have for ensuring that the PR programme is carried out? Concerning your views about the PR consultancy, how representative are they of the views of your colleagues (if any ) who are involved in managing the PR consultancy?

Prompted by G&A future directions. A possible 'dark side' variable (Nichols/Panel 2005)

P&S PR/Adapt

Prompted by P&S. Adapted to meet PR consultancy operating conditions (Nichols/Panel 2005)

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

19 F9

1 F9

CONSTRUCT

CONSTRUCT DEFINITION AND SOURCE

F9 Frequency of Formal Review

Metric for formal review cycle

Six-item scale from quarterly to triennial

Respondent's responsibility for appointing the PR firm

Single item seven-point bipolar scale… self-assessed representativeness, 1=Notat-all, 4=Day-to Day, 7=Full including How much responsibility did you have for commercial. appointing/re-appointing the PR firm?

P&S PR/Adapt

Time worked with your present/incumbent PR consultancy

How long have you worked with… (tripartite taxonomy, less than one year etc)

Prompted by G&A discussion of variation in construct relationships over time

How long has your firm worked with…

Prompted by G&A discussion of variation in construct relationships over time

F10 Appointment

20 F 10

F 11

F 12

74 N/A

6a7-to6f F 13 12 2B

F 10 Responsibility

F11 - Personal Relationship 1 F 11 Duration

F1 12

Author: Bill Nichols Page 3 of 9

F12 - Firm Relationship Duration

F13 - Frequency of 6 F 13 Interaction

Time worked with PR consultancy by client source (Nichols 2005)

OPERATIONALISATION

QUESTION FORMAT How frequently do you formally review the progress made by your PR Consultancy?

Time worked with PR consultancy by client firm (Nichols 2005)

Time worked with present/incumbent PR consultancy.

The frequency of interaction between the client and the consultancy account team during their relationship (Nichols 2005). Based on the fact that the customer typically enters into a formal relationship with the service provider and, subsequently, consumes or uses the service (continuously or intermittently) for an extended period. (Bolton & Lemon 1999 )

Aggregate measure based on quantity of 1) Meetings, 2) Audio or When you are working with the PR Consultancy, Videoconference calls 3) Telephone how much contact (whether instigated by either calls, 4) Emails. party) do you have via:…..

Source and Notes P&S adapted to meet PR consultancy operating conditions (Nichols/Panel 2005)

New operationalisation (Nichols/Panel 2005). Prompted by G&A analysis of dark side constructs

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

14c jn o C1A

14a p C1B

CONSTRUCT

LS1- C1A - Loyalty 34 Intentions

LA1- C1B-Loyalty 12 Advocacy

CONSTRUCT DEFINITION AND SOURCE

Loyalty is a mixed cognitive, affective and conative construct (Jacoby & Chestnut 1978), which captures a buyer’s overall attachment or deep commitment to a product, service, brand or organisation (Oliver 1999). It aggregates a buyer’s current patronage, intention to future patronage, relative attitude between a home and alternative brands... [Next]

…..willingness to act as advocate (Oliver 1999)

Author: Bill Nichols Page 4 of 9

OPERATIONALISATION

Scale: Definitely Yes - Definitely No (Patterson et al 1997 ). Four item seven point bipolar scale, Definitely Not... Definitely Yes • Positive view of the consultancy’s past and continuing influence (cognitive G&A 1999; MZD 1992); • Feeling warmer/more positive about this PR firm than others with which I am familiar (affective, Oliver 1997); • Intention to continue working with (conative, Oliver 1997); • Will only consider for additional project work (action, Patterson & Spreng 1997; Oliver 1997, Gremler & Brown 1996);

Scale: Definitely Yes - Definitely No (Patterson et al 1997 ). Six-item seven point bipolar scale, Definitely Not... Definitely Yes (two items) 1 - Informally recommend (word of mouth); 2 Formally recommend (e.g. on a public platform/case study)

QUESTION FORMAT

Source and Notes

Based on your knowledge of, and feelings towards, the PR consultancy today, would you: I - Of the view that the PR firm's influence on your decision-making and implementation has been, and continues to be, significant and beneficial; II - Expect to consider only this firm for additional projects; III - Intend only to use; IV - Feeling warmer/more positive about this PR firm than about other PR firms which you have worked with or are familiar with.

I - Based on G&A suggestion, Author/Panel 2005 II - P&S/PR Panel Adapt III - Based on Oliver 1997/Author-Panel 2005; IV - Based on Oliver 1997.

Based on your knowledge of, and feelings towards, the PR consultancy today, would you: I - (Two items) 1 - Be willing to recommend them privately to a colleague in a non-competitve company with similar needs; II - Be willing for you/your company to appear in a published case study or conference presentation recommending 1 - P&S; the firm; II - Nichols/Panel 2005.

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

10a ce g i C2

14b ej m C3

CONSTRUCT

TRT 1TRT 5 C2 - Trust

CMT 1 1-4 C3 - Commitment

CONSTRUCT DEFINITION AND SOURCE

Author: Bill Nichols Page 5 of 9

OPERATIONALISATION

QUESTION FORMAT

Source and Notes

I - If I or someone in my department could not be reached by our account manager, I would be willing to let him/her take important PR decisions without my involvement; II - I trust the consultancy’s account team to get the job done right without having to monitor their progress Five-item scale embracing trust in continuously; III - I trust my account manager generally, trust to take consultancy account manager to do things I "both confidence in an decisions without reference, trust in haven’t got the time or the specialist knowledge to exchange partner (the account team to get job done right, in do myself; IV -.I have psychological component) account manager to take on tasks client confidence in my consultancy account manager to and a willingness to rely on unable to perform and clients' do things that people in my department haven’t an exchange partner (the colleagues unable to perform (MZD, got the time or the specialist knowledge to do sociological component)" G&A). Seven-point bipolar scale, themselves. V - I generally do not trust our G&A/adapted to PR (MZD 1993, p92). strongly disagree... strongly agree. consultancy account manager. consultancy

An enduring desire to maintain a valued relationship (MZD, 1992), the affective component and/or a compulsion to remain in a relationship by virtue of cost, lack of alternatives or dependence (Sharma and Patterson 2000, Fullerton 2003),the continuance component.

Four item seven-point bipolar scale (definitely not, definitely yes), three parts based on G&A adapation of original MZD conceptualisation. The new fourth item is intended to capture the continuance component.

I - Committed to relationship with; II Treat as part of your team; III Care about relationship with; IV Locked into a relationship over which you have I-III G&A, little control. Nichols/Panel 2005.

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

10b df h C4

7a-f C5

14f, I,l C6

11ad C7

CONSTRUCT

CONSTRUCT DEFINITION AND SOURCE

Author: Bill Nichols Page 6 of 9

OPERATIONALISATION

QUESTION FORMAT

The degree to which clients view client-consultant interactions as productive, insightful and clientorientated (MZD 1992)

Four-item scale embracing creative insights and new ideas, strategic understanding, client orientation and productivity (MZD, G&A). Seven-point bipolar scale, strongly disagree... strongly agree.

I - My meetings with the consultancy account team produce creative insights and new ideas. II Our account manager displays a sound strategic understanding of our business in his/her interaction with me. III - Our account manager is very client-orientated in his/her interactions with us. IV - My interactions with our consultancy account G&A adapted for PR manager are productive. Consultancy

CON 1CON C5 - Consultant 26 Involvement

The extent to which a client involves, and works with, the PR account manager and/or consultancy team in the conception, strategic development and tactical implementation of PR campaigns (adapted from MZD 1992).

Thinking about your PR activities as a whole, how important are the involvement of your account manager and account team in delivering each of Six item seven-point bipolar scale (not at the following activity areas: a) campaign all important, very important) based on objectives, b) strategy, c) creative/proactive I-IV adapted for PR G&A adapation of original MZD solutions, d) creating relevant content, e) pitching consultancy from G&A; conceptualisation. stories and positions, f) project management. V-VI Nichols/Panel 2005.

OPP 3 1-3 SAT 1SAT 44

"self-interest seeking with guile" (Morgan & Hunt 1994) A client's summary affective response of varying intensity with a time-specific point of determination and limited duration which is directed

Three item seven point Likert scale. Two I - Economical with the truth; items sourced from Morgan & Hunt II - Failure to keep promises (1994) based on John III - Improper andabout slow your handling of complaints. Four dimensions based(1984) on Oliver's VS-VD: Thinking experience in recent (1997) service guidelines. I - months with the PR consultancy, how satisfied are Service Satisfaction; you with: III - Advice Satisfaction The service delivered by the consultant or team III - Proactivity Satisfaction; working on your account;

PQI 1- C4 - Perceived PQI Quality of 4 Interaction

C6 - Opportunistic Behaviour C7 - Satisfaction (TransactionSpecific)

Source and Notes

1-II Morgan & Hunt 1994 III suggested by Ball, Coelho & Machas 2004 New PR industry-specific operationalisation (Nichols/Panel 2005 ) following Oliver's guidelines (1997)

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

14, d, g, k C8

13ad C9

198aton C10 32

8a& n

19& 32

CONSTRUCT

CONSTRUCT DEFINITION AND SOURCE

The Client's stored evaluation of his or her purchase or consumption experience to CU date with a product or M1- C8 - Satisfaction service provider (Olsen & 3 (Cumulative format) Johnson 2003 )

A client's aggregate monetary and non-monetary judgement of net utility (Rust 2000 ) based on "perceptions of what is received and what is given" (Zeithaml 1988 ) PV1both "hedonic and utilitarian" 3 PV4 C9 - Perceived Value (Oliver 1997 ).

Author: Bill Nichols Page 7 of 9

OPERATIONALISATION

I - Satisfaction; II - Pleasure; III - Wisdom of decision to appoint. Three-item seven point bipolar scale... definitely not... definitely yes.

C10 - Perceived Performance or Perceived Service N/A Quality

A client's "perceptions of actual service performance" (PZB 1985, 1988; Gronroos 1982 )

I - Value for money; II - Fairness of treatment; III - Equality of inputs and outputs; IV - Compared to the promotion of competitive offerings. Four item, seven-point bipolar scale, strongly disagree... strongly agree Reflecting on the period since you completed the last questionnaire, how well has the PR consultancy performed on various aspects of the programme (P&A/PR Adapt). 14-item seven point

OUT 1OUT 2 Outcomes

Reflecting the core or promised service: "The consultants deliver the results for which they were commissioned" (P&S )

The consultancy's ability to deliver effectively the results of the service for which it was appointed (P&S ).

QUESTION FORMAT

Source and Notes

Taking everything into account, how do you feel about what you have received from the PR consultancy and its team to date during the course of the campaign(s): I - Very satisfied/dissatisfied; II Very pleased/displeased; III We made a wise decision to appoint them/We P&S adapted for PR made an ill-judged decision to appoint them; consultancy A-D: With reference to the most recent projects and tasks in the Programme: I - Considering the fees we are paying and the results we are obtaining, I believe we are receiving value for money; I - P&S 1997 adapted to PR II - I regard the consultancy's fees as being industry; II acceptable; III Hellier et al 2003; What we are getting out justifies what we are III - Based on Olsen & putting in; Johnson 2003; IV - Compared to what I know of competitive IV - Yang & Peterson 2004. Average of 14 attributes (Please indicate the degree to which you AGREE or DISAGREE with EACH of the following statements. "The PR Consultancy hasâ&#x20AC;Ś"

SEE BELOW

I D-A: Produce results which we are able to exploit; II DA: Produce results which will enable us to increase our organisation's marketing effectiveness;

I-P&S unadapted; II - P&S adapted to replace 'operational' with 'marketing';

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

8b, e, i

8c, f, j, m

8g, 8k

8h, 8l

CONSTRUCT

CONSTRUCT DEFINITION AND SOURCE

Author: Bill Nichols Page 8 of 9

OPERATIONALISATION

QUESTION FORMAT

I-D:A: Use the most up-to-date methodologies (e.g. strategy/evaluation) in their work; II-D:A: Show some creativity in solving our problems; IIII-III: P&S adapted to PR D:A: Comes up with innovative ideas and solutions consultancy

Source and Notes

MET 1MET 3 Methodology

The modus operandi of, and creativity exhibited by, the consultancy (P&S/VarPR) .

The consultancy's demonstrated expertise, knowledge and methodologies (P&S ).

21, 24, 28, 31

SER 1SER 4 Service

"Reliability involves consistency of performance and dependability (PZB 1985 ). "It is defined as the ability to perform the promised service accurately and dependably" (Z&B 1996 ). "Responsiveness concerns the willingness or readiness of employees to provide service. It involves timeliness of service (PZB 1985 ).

I-D-A: Respond promptly when we contact them; The consultancy's willingness to provide II - Reliable in meeting deadlines prompt, reliable, responsive and III - Dependable professional service (P&S) . IV - Thoroughly professional in all they do

25, 29

REL 1REL 2 Relationship Quality

"The (quality of) rapport and relationship developed The consultancy's ability to build between service provider and understanding and rapport with the client" (P&al 1997 ). client team (P&S)

!-D:A: Develop a close working relationship with our staff; II- P&S adapted to PR D:A: Establish a good rapport with... consultancy

PRB 1PRB Problem 2 Identification

Clients' view that the consultancy should "take time to familiarise themselves with the firm's goals and thoroughly understand the nature of the The consultancy's willingness to problem before commencing understand thoroughly (your) needs, any work" (P&S). aims and goals (P&S ).

I-D-A: Consistently makes sure they understand our aims and goals II-D-A: Ensures they thoroughly understand the issue or problem before commencing work on any P&S adapted to PR campaign or project. consultancy

20, 23, 27

26, 30

10B 11A

P&S Adapt to PR consultancy with reference to Parasuraman et al (1988)

Loyalty and PBS - Instrument (Field Use 10/06-02/07)

12af C11 9a9b

4752

CONSTRUCT DEFINITION AND SOURCE

OPERATIONALISATION

QUESTION FORMAT

GNT 1 Global Networking

Clients' expectations that the consultancy will have access to a global network of contacts and sources of information (P&S) .

The consultancy's willingness to use its network of local, industry and international contacts to help us (adapted to national PR consultancy from P&S)

I-D:A: Make good use of their network of contacts to add P&S adapted to PR value and come up with solutions. consultancy

Perceived Performance WD Weighting 1-6 Dimensions

Performance: critically neutral, the cognitive 'what' not the 'why' of pyschological processing (Oliver 1997 ). Weighting: based on expectancy value theory defining an individual's attitude by reference to an importance-weighted evaluation of specific service Using a 100-point allocation to construct dimensions (Cohen, Fishbein a weighted scale for the importance of & Ahtola 1972 ). the performance dimensions

Performance: Please assess various aspects of your PR consultancy's performance thinking about the last few months in particular. Weighting: Listed below are eight features relating to PR consultancies and the services they offer. I would like to know how important each of these P&S adapted to PR features is to you. consultancy

A client's cognitive comparison of Expectations versus actual post-purchase evaluation of Performance (Churchill & Surprenant 1982; Oliver 1980 )

I - How closely did overall performance meet expectations (extremely different… exactly) IIHow do you feel about… (very bad…. Very good)

10B 21 11A

CONSTRUCT

C11 2 DIS1 Disconfirmation

Author: Bill Nichols Page 9 of 9

Based on ADM (Spreng & Page 2003). Two components: closeness to expectations, goodness/badness. Both items seven-point bipolar format.

Source and Notes

Based on Spreng & Page (2003)