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Journal of Human Resource Costing & Accounting Emerald Article: Strategic human resource utility analysis Jens Rowold, Martina Mรถnninghoff

Article information: To cite this document: Jens Rowold, Martina Mรถnninghoff, (2005),"Strategic human resource utility analysis", Journal of Human Resource Costing & Accounting, Vol. 9 Iss: 1 pp. 10 - 25 Permanent link to this document: Downloaded on: 21-10-2012 References: This document contains references to 34 other documents Citations: This document has been cited by 4 other documents To copy this document:

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Strategic human resource utility analysis Jens Rowold and Martina Mo¨nninghoff University of Mu¨nster, Mu¨nster, Germany

10 Abstract

Purpose – As the implementation and acceptance of utility analyses are afflicted by several problems, this paper sets out to describe how to circumvent these problems by implementing a new framework for utility analysis. Design/methodology/approach – The HC BRidgee model, developed by Boudreau and Ramstad, was implemented to determine the utility of assessment centers within a call center company. Findings – The results demonstrate the utility of the assessment centers and the usefulness of the HC BRidgee model. Research limitations/implications – Future research should clarify under which conditions human resource specialists can communicate effectively and reach an optimal decision within the HC BRidgee model of utility analysis. Practical implications – It is highlighted how human resource experts can assist in using utility analyses (as a component of HR strategy) for decision-making processes concerning limited organizational resources. Originality/value – To demonstrate the usefulness and value of the HC BRidgee model for both researchers and practitioners. Keywords Human resource management, Selection, Call centres, Germany Paper type Research paper

Journal of Human Resource Costing & Accounting Vol. 9 No. 1, 2005 pp. 10-25 q Emerald Group Publishing Limited 1401-338X DOI 10.1108/14013380510636676

Decision makers in organizations are increasingly challenged with the task of saving expenses and resources. Less money will be available for personnel selection and development in the future (Quin˜ones and Ehrenstein, 1997). At the same time, psychologists in human resource (HR) departments are required to work “strategically”. This means that in the context of personnel selection and development, measures that are related unambiguously to business objectives are to be favored (Cascio, 2000). Current HR and human capital (Nafukho et al., 2004) approaches stress the importance of integrating HR interventions in this value-added chain. The focus is increasingly set on an economic approach towards the strategic HR process. Hence, HR departments should continuously produce documentation for the effectiveness and efficiency of selection procedures and interventions carried out (Morrow et al., 1997). Drawing on utility analysis of two assessment centers located within a single organization, the present work describes an empirical case study. Therewith, it is demonstrated under which conditions utility analyses can fulfill the demands described above. In the first part of the paper, current approaches towards utility analyses and associated problems are discussed. A recent theoretical model named HC BRidgee (Boudreau and Ramstad, 2002), which integrates utility analysis into a strategic HR framework, is drawn upon to solve each of the problems. In the second, empirical part, this framework model is applied and its success is reported. In contrast to the majority of earlier empirical studies, in this work the parameters of utility

analysis are determined empirically and take into account intra-and inter-individual differences between employees (e.g. working hours, performance, fluctuation). Utility analysis Utility analysis is a method for determining the usefulness of personnel selection procedures in monetary units (Boudreau, 1990). Most models describing utility analyses are based on the multiplicative combination of four central parameters: (1) the standard deviation of job performance (SDy) expressed in monetary units; (2) the validity of the selection procedure rxy; (3) the number of applicants hired (N); and (4) the average test score of the applicants selected (compared to those not selected). The benefit of a selection procedure increases proportionally to these parameters. In order to determine the utility, the costs of the procedure are subtracted from the benefits. More recent models of analysis take into consideration the average retention period of employees, the share of variable costs, the number of employees who drop out, interest and taxes, differentiations of costs, and changes in these parameters over time (Boudreau and Berger, 1985; Boudreau, 2004; Cascio, 2000). For the respective utility analysis, these parameters need to be determined. In contrast to estimates, empirically determined parameters not only raise the acceptance of the results in the respective organization, but also augment the validity of the procedure (Sturmann, 2000). Although the method of utility analysis has been known since the 1950s, its utilization is not yet completely established in organizations (Boudreau, 1990). The practice of utility analysis is afflicted with several problems, each of which will be discussed in the following sections. Problems connected to utility analysis Firstly, the methods of utility analysis as well as the modalities of implementation are not sufficiently known by HR executives and other decision makers in organizations (“acquaintance problem”; Macan and Highhouse, 1994; Boudreau, 1988). This is partly due to the complexity and variety of more recent models (Fitz-Ens, 1995; Roth and Bobko, 1997). Secondly, the empirical determination of the necessary parameters is problematic at least, and as a consequence the results of utility analyses are looked at as merely estimates (“estimation of parameters problem”). In particular, the standard deviation of performance is a problem often discussed in the literature. Estimating this parameter is problematic because it directly implies the translation of job performance into economic figures (e.g. dollars, euros). SDy is rarely determined empirically for utility analyses. Although a great number of different estimation procedures have been suggested (Cascio, 2000), the standard deviation of job performance in particular remains the “Achilles’ heel” of utility analysis (Boudreau and Ramstad, 2002). Yet, the estimation of this parameter is of crucial significance for the results of the utility analysis. Already small variations of this parameter have a considerable impact on final results (Becker and Huselid, 1992), which is why methodologies of estimation of

Human resource utility analysis




the SDy are often criticized within the framework of utility analysis (Greer and Cascio, 1987). Next, the relationship between a chosen performance criterion y and organizational goals is critical for utility analysis. If, for example, the selection of applicants in an insurance company depends on their selling skills, it needs to be defined how these abilities should be operationalized and how they contribute to the organizational goals (Sturmann, 2000). While for the example of the insurance company relationships are relatively clear, considerable effort has to be invested for the choice and empirical examination of postulated relationships in other occupational fields and industries. Decision makers and psychologists are often challenged by the quest for performance criteria that quantifiable contribute to strategic goals (Boudreau and Ramstad, 2001). To make matters worse, often several criteria need to be taken into account. For example, managers are required to master several performance criteria such as planning, motivating employees, problem solving, etc., for successful working. For the identification of relevant criteria, decision makers from different backgrounds are required to work together and share their expert knowledge. It is necessary to demonstrate why multi-modal criteria contribute to – partly divergent – organizational goals. Consequently, empirical evaluation of the proposed relationships is needed. Due to high complexity and high demands on resources, the evaluation of personnel selection procedures is not integrated into a strategically oriented HR management (Boudreau and Ramstad, 2002). This constitutes the third problem of utility analysis (“problem of identifying strategic criteria”). It has been stated that utility analysis is a method that can help decision makers to reach the right conclusions concerning financial resources. In reality, however, utility analysis is often performed in isolation, separate from organizational decision processes. The results of utility analyses are not taken into consideration, e.g. for the decision to proceed with or decline from the evaluated personnel selection strategy (“isolated consideration problem”). One of the primary goals of utility analysis is to help to form rational decisions concerning the allocation of funds in HR departments or whole organizations. Up to now, these decision-making processes have been strongly neglected in research into utility analysis (Arvey and Murphy, 1998). As a result of this neglect, decision makers have only been insufficiently included into the process of analysis (“problem of the integration of decision makers”). Upper management especially maintains an interest in using the results of utility analysis for making decisions. In general, just the final results are presented to decision makers. They react – partly because of their ignorance and unexpectedly high monetary benefits – with skepticism or rejection (Roth, 1994). A possible further explanation for this fact is that by presenting monetary values to decision makers, the impression is conveyed that they are supposed to be persuaded (Cranshaw, 1997). Carson et al. (1998) also found support for the fact that results of utility analyses are more easily accepted if they are presented briefly and simplified with regard to the content. Even then, however, acceptance is not guaranteed. To summarize, it can be stated that on the one hand utility analyses are required by decision makers, but on the other hand the results are neither accepted nor utilized. There is a major need for research about this discrepancy (Highhouse, 1996). To raise the acceptance of the results of utility analyses, it was suggested to include consistently all parties involved and employees affected (Cascio, 2000). This mainly

includes upper management and representatives of the personnel department. During the planning and evaluation phase, and especially during the phase where the appropriate utility analysis model is chosen, it has to be agreed which (if necessary modified) model is to be used and which different parameters are to be determined. These measures can drastically increase the internal validity of the utility analysis. The fact that results of the utility analysis are often not utilized by organizational decision makers constitutes the final problem (“implementation problem�). As described above, a main cause for this problem is that upper management is not involved with planning and design of utility analyses and lacks a detailed understanding of them, resulting in a lack of acceptance, especially if the results seem improbably high. In most of the empirical literature no information about the utilization and the implementation of the results of the utility analysis is provided. The exception is Morrow et al. (1997), who carried out utility analyses for all the personnel development programs of an industrial organization. Although their results were accepted by management, trainings that clearly had a high profit were later cancelled by organizational decision makers. This underlines the importance of research for the decision-making processes taking place when using the results of utility analyses. While we know relatively much about the different methods of utility analysis, only the analysis of the processes essential for the practical planning, use, and implementation of utility analyses can lead to a more effective utilization of the utility analysis (Boudreau and Ramstad, 2001). Table I lists the six problems connected to utility analysis. These problems demonstrate how strongly the potential effectiveness of the utility analysis is limited in Problem

How the HC BRidgee model addresses the problem

Acquaintance problem

Presenting UA models and processes to organizational decision makers, identifying the most appropriate model Empirical assessment of parameters necessary for UA, in cooperation with the respective organizational experts (e.g. finances, marketing, HR, etc.) Identifying strategic criteria derived from organizational goals, in communication with top management and organizational experts Embedding UA in a decision-making process, wherein utility analysis is used as a method for deciding on organizational resources Building a task force which cooperates from the start with other decision makers in the organization, communicates goals, processes, and strategies of UA to decision makers prior to UA itself, takes their concerns and suggestions into account, and presents results of UA for final decision Task force helps with the implementation of results of UA, evaluates the process of UA and suggests improvements for future UA within the respective organization

Estimation of parameters problem

Problem of identifying strategic criteria Isolated consideration problem Problem of integrating decision makers

Implementation problem

Human resource utility analysis


Table I. Overview of problems associated with utility analyses (UA) and how they are addressed by the HC BRidgee framework



practice. Nevertheless, recent conceptual considerations show how these problems can be avoided by an integration of utility analysis into HR strategy. The HC BRidgee framework In order to illustrate how the method of utility analysis can be applied and used more effectively, we resort to the framework of Boudreau and Ramstad (2001, 2002). As a detailed description of the framework is beyond the scope of the present article, the interested reader is referred to the original literature. Boudreau and Ramstad (2001, 2002) characterize their HC BRidgee model as a framework for a strategically oriented management of human capital (Boudreau et al., 2001). For the first time, the method of utility analysis is defined as an integrative component of the HR strategy of an organization. Moreover, the HR strategy is based on the explication of strategic advantages (e.g. abilities of the employees), derived from the respective organization goals. For the acceptance and utilization of subsequent results of the utility analysis, the HC BRidgee model focuses on three points. Firstly, the efficiency of personnel selection programs is exactly determined. The model of Boudreau and Ramstad rests on several proven cost-oriented methods (Phillips, 1997; Cascio, 2000), which consider HR interventions mainly as investments in employees. This approach towards personnel selection programs accommodates decision makers, who understand similar ways of thinking from controlling and accounting models. Secondly, the relationship between personnel selection measures and the desired knowledge, skills, and abilities (KSA) of the employees is explored. Here the model picks up classical questions of industrial and organizational psychology, for instance, evaluating the effectiveness of personnel selection procedures. In addition, however, the strategic utilization of measures is attended to in the HC BRidgee model. A psychological exercise or test utilized in the selection procedure is judged to be useful if it predicts the pre-assigned KSAs of the employees. Thirdly, the question is raised whether the analyzed KSAs have a clear, measurable impact on the important processes and strategic advantages of the organization (cf. Sturmann, 2000). Seen from a long-term perspective, newly hired employees should have the specific KSAs that help the organization to be successful. Within the framework of Boudreau and Ramstad, the three components are integrated in the process of utility analysis. Important differences between earlier approaches to utility analysis and the HR BRidgee model should be noted. Earlier approaches to utility analysis focused mainly on the effectiveness of HR interventions. However, the problems of utility analysis described above demonstrated that earlier models of utility analysis were too narrow in focus. Thus, the BRidgee model expanded the view by taking the impact and efficiency of HR interventions into account. The logic implied within the BRidgee model emphasizes the predominance of the identification of sustainable strategic advantages and which KSAs of certain groups of employees are important to achieve these advantages. The next link in the chain is to design effective HR interventions that ensure high degrees of these identified KSA. Finally, these interventions should be implemented efficiently. Discussing and explicating this three-step value-added chain can help organizations to clarify the strategic goals for and implementation of:

. . .

HR interventions themselves; the evaluation of these interventions; and utility analysis.

For the implementation of the BRidgee model, decision makers should be involved at all stages. The continuous integration of decision makers into all phases of the utility analysis process has the advantage that expert knowledge about situation-specific parameters can increasingly be used for the formulation and execution of a specific model of utility analysis. The utilization of this knowledge raises the internal validity of the parameters. In summary, the advantage of the model described is that decision makers receive valid internal and external information about the allocation of resources. The logic of utilizing the available information is given by the framework model. At the same time, the strategic aspect of decisions is stressed within the organizational context. While previous theoretical and practical models contained only parts of the model introduced above, the HC Bridgee model explicates and integrates these components and utilizes them in the actual decision-making process at the strategic level. Table I provides a brief summary of the problems connected with utility analysis and how the HC Bridgee model attempts to solve these problems. Based on an empirical case study, the purpose of the present work is to demonstrate for the first time how the model of Boudreau and Ramstad (2001, 2002) solves the problems of utility analyses as outlined above. The entire planning, realization and result utilization of the utility analysis was carried out in cooperation with decision makers of the organization. Utility analysis was considered an integral component of the organization-specific HR strategy. Therewith, the present investigation attempts to demonstrate how two personnel selection procedures in a service organization provide clear contributions to the organizational goals. The case study To demonstrate the usefulness of the HC BRidgee framework for utility analyses, the remainder of this paper reports a case study in which the HC BRidgee model has been implemented. As the contextual boundaries had consequences for conducting the utility analyses, we start with a brief description of the organization in which the studies were located. The organization under focus In an out-sourced call center organization, which is operating throughout Germany, assessment centers (AC) were implemented for the selection of new employees. In inbound call centers, the employees (call center agents, CCA) receive phone calls from customers and services of different kinds (technical hotlines, complaint management, etc.). These inbound call centers, in which the employees are called by customers, can be seen alongside outbound call centers, where employees actively call customers and where the major task is selling of products, services, etc. The organization examined in this study included inbound as well as outbound call centers (departments). As a consequence of different task requirements, inbound ACs were to be distinguished from outbound ACs.

Human resource utility analysis




Description of the AC The in-house ACs lasted half a day. For the successful work of the CCA, above all, high resiliency, social competence, and language skills were deemed important (Konradt et al., 2003). Performance on these dimensions was assessed by two observers through phone-supported role-plays in the ACs. Resilience was additionally surveyed by the German attention-load test “d2” (Brickenkamp, 1981). As computer knowledge was basic for the computer-supported work of a CCA, computer skills were tested with the help of a test developed in the organization. The AC elements described were used for the selection for inbound as well as outbound CCAs, though the requirement profiles of these both groups differed. After their respective ACs, the selected employees were assigned to different departments within the call center company. Forming the task force The strategic goal provided by top management was to control future costs while keeping the quality of personnel selection at high levels. A task force was formed which had the primary goals of evaluating the monetary profit (i.e. utility) of the ACs as well as providing information for subsequent decisions about possible improvements in the personnel selection strategy. For this reason, the utility analyses were embedded in a decision-making process relevant to the organization. The fifth problem outlined above (“problem of the isolated consideration”, cf. Table I) was thereby solved. The task force included representatives of the HR department of the organization who were not engaged in the AC process, as well as the authors of the present study. The task force was to solve the problems of utility analyses outlined above. For this reason, it was ensured that each of the implemented steps of analysis was derived directly from the HR strategy of the organization. The strategically oriented model of Boudreau and Ramstad (2002) served as a basis for the respective steps of utility analyses. As the task force consisted of HR decision makers and, moreover, cooperated in consultation with other decision makers in the organization (cf. below), another problem (“integrating decision makers”) was solved by the current approach. While the task force reviewed and discussed different approaches to utility analyses (“acquaintance problem”, cf. Table I), it became obvious that the temporal resolution of earlier models described in the literature was too low for the present study. As typical service organizations, call centers encounter an extremely dynamic market situation and typically handle specific, short-lived projects. In the organization examined in this study, project time was often limited to a time-span of between six and 12 months. As a consequence, the more appropriate temporal frame for staffing and hiring was based on months and not on years. Also, CCAs typically serve in a business for several (between ten and 20) months (Dormann and Zijlstra, 2003). Utility analyses explicitly need to take these specific features into consideration (Boudreau and Ramstad, 2002). While recent utility analyses were set out for a time frame of several years, the current study was based on a time frame of several months. The utility model Utility analysis was based on the model of Boudreau (1983). As an advancement of the early models of Brogden, Cronbach and Gleser (BCG), it is still useful for current problems in organizational decision-making. Viewed from a theoretical perspective,

models exist that are more detailed in their evaluation of the use of personnel selection procedures (DeCorte and Lievens, 2003). However, with increasing model complexity, expenditure of the procedures as well as estimation problems also increase. In addition, according to discussions of the task force, the model used here was the model which: . satisfies the specific requirements of organization; . complements (see below) the theory of Boudreau and Ramstad (2001, 2002); and . is most appropriate for an application to in-house decision making. The specification of Boudreau’s (1983) model is as follows: DU ¼


N k r xy zx SDy ð1 þ V k Þð1 2 TAX k Þ½1=ð1 þ ik Þk 




N bt CV ð1 2 TAX k Þ½1=ð1 þ ik Þk 




CFð1 2 TAX k Þ½1=ð1 þ ik Þk ;


with N k ¼ N k21 þ N at 2 N st : This model illustrates the incremental utility of an AC in comparison to a random selection. In the first line of equation (1) the utility is estimated, from which variable costs (line 2) and the fixed costs (line 3) are subtracted. Table II gives a definition for each parameter of the equations. Parameter calculation To ensure high acceptance of the results of utility analysis, the task force aimed at an empirical calculation of the parameters. As will be described in the following paragraphs, the calculation of the parameters was performed in a way that a maximum validity of the parameters was accomplished from the point of view of the organization as well as from the point of view of the authors (“determination problem”). Description of the performance indicators. In general, CCAs work efficiently when customer phone calls are handled as quickly as possible. This implies that hard skills, i.e. quantity, are valued higher than soft skills (social competences), i.e. quality. In the call center organization, the performance indicator “absolute number of phone calls per hour” was assessed electronically and reported to the CCA as performance feedback, a fact that underlines the relevance of this criterion. The performance indicator was also preferred with regard to the strategic adjustment of the utility analysis according to the theory of Boudreau and Ramstad (2002). That is, it contributed directly to the organizational goals (high impact). An essential strategic purpose of the organization was remaining competitive while having low personnel expenditures. For this reason, phone calls were required to be as brief as possible. As a consequence, the personnel selection process of the organization aimed to select CCAs who have the respective abilities and attitudes (“problem of the strategic criterion choice”).

Human resource utility analysis






Empirical values

DU k F Nk

Incremental utility of the selection procedure Future time period (in months) Effective period of the selection procedure Number of current employees in period k (cf. equation (2)) Number of employees newly hired in period t Number of employees separated in period t Incremental validity of the selection procedure Average standardized test score of newly hired employees Standard deviation of job performance, expressed in monetary units Proportion of variable costs in period k Discount rate in period k Tax rate in period k Number of applicants in period t Variable costs per applicant Fixed costs (implementing costs plus maintenance costs per year)

cf. Table V k ¼ 1; . . . ; F months 18 months cf. Table III

Nat Nst rxy ¯zx SDy

Table II. Parameter values

Vk ik TAXk Nbt CV CF

cf. Table III cf. Table III Inbound, 0.19; outbound, 0.20 0.7980 Inbound, e408.91; outbound, e619.85 0 0.0089 (constant across periods) 0.40 (constant across periods) cf. Table III e145.49; cf. Table IV cf. Table IV

For a random sample of N ¼ 135 (inbound) and N ¼ 158 (outbound) performance indicators were assessed empirically and used for parameter calculation (see below). At the same time, the “absolute number of working hours per month” was collected as a second indicator. This indicator was taken into consideration as it strongly varies strongly inter- and intra-individually. The indicators were collected three months after the AC took place. Important requirements of utility analyses, such as the normal distribution of the data, were met. Number of employees and time frame. The absolute number of applicants, of newly hired employees, of separations, as well as of the CCAs employed in the respective month (i.e. actual workforce) were taken into consideration in order to accommodate the relatively high dynamics of the job market and other characteristics of the service branch. In contrast to previous investigations, these figures were not only included in the utility model, but had furthermore been exactly determined empirically on a monthly basis (see Table III). The choice of a time frame for the utility analysis should be based on the average retention period of the employees in the company (Boudreau, 1990). Baumgartner et al. (2002) reported an average retention period of a CCA of 26 months. However, after consultation with organizational experts, the retention period for the current organization was set at 18 months in order to allow for a rather conservative calculation. Consequently, performance and data related to the AC were collected over a period of 18 months in a longitudinal study. Predictive validity Rxy. The analyses carried out in this study were part of a summative and formative evaluation of the ACs. Prior to this study, outcomes of the AC exercises and tests were utilized for a decision about the employment or refusal of the respective applicant. Implementing linear combination, each AC component was assigned identical weight. In the context of the present study, regression analysis allowed for an examination of the linear combination of all AC components. Regression

Newly hired Separated employees Nai employees Nsi Period Applicants Nbi Total workforce Nk (kth month) Inbound Outbound Inbound Outbound Inbound Outbound Inbound Outbound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

30 112 76 45 88 22 4 0 0 0 0 8 7 8 0 0 4 24

20 30 30 33 44 4 0 0 0 0 0 16 5 14 0 2 6 12

0 31 69 32 65 33 2 1 0 0 3 9 2 5 0 0 0 15

0 21 18 7 33 8 0 0 0 0 10 5 10 15 17 9 0 9

0 2 4 3 13 15 15 18 34 14 3 14 9 2 4 3 0 1

0 3 1 4 2 5 4 7 5 0 1 3 4 7 3 5 1 3

0 29 94 122 174 192 179 162 128 114 114 109 102 105 101 98 98 112

0 18 35 37 68 71 67 60 55 55 64 66 72 80 94 98 97 103

analysis resulted in a correlation of Rxy ¼ 0:19 (attenuation-corrected) for the inbound AC and 0.20 for the outbound AC. The calculated empirical linear combination was reported to the AC observers in order to optimize their decision-making in future ACs. With regard to the HC BRidgee framework of Boudreau and Ramstad (2002), the described calculation of validity allows for conclusions about the effectiveness of the personnel selection procedure (cf. Results). Standard deviation of performance SDy. The standard deviation of performance (in euros) could be determined empirically (“estimation problem of parameters”). In a first step, the average monthly performance of all CCAs was centered (MW ¼ 1, SD ¼ 1). As a second step, a product was formed from this centered performance and the monthly wage of the respective CCA. The monthly wage was calculated as a product of a steady hourly wage and the actual working hours per month of the respective CCA. Before aggregating data across call centers, data were standardized within the respective call center. In the end, the standard deviation of the performance in euros could be calculated by descriptive analyses of the data of all CCAs. The results were SDy ¼ e408:91 (inbound) and SDy ¼ e619:85 (outbound). These standard deviations lie within approximately 60 percent of the wage for inbound as well as for outbound. This value falls within in the upper third of the often quoted estimate (40-70 percent) from Schmidt et al. (1982). Our approach differed from earlier utility analyses, because inter-individual features as the actual working hours were explicitly included in the calculation. Proportion of variable costs V. The salary, i.e. the least sum of money that CCA was worth for the organization, constituted the basis for our calculations of SDy. Because the organization realized profit, however, it was assumed that the actual value of a CCA for his/her organization was higher. The wage therefore constitutes a conservative estimate of the actual value of an employee for the organization; in it,

Human resource utility analysis


Table III. Empirical values for applicants, newly hired employees, separated employees, and total workforce in time period k



non-wage labor costs, etc., were already taken into consideration. Hence, in contrast to earlier utility analyses, the proportion of the variable costs V was to be neglected (Cascio, 2000). Average standardized score of employed applicants. The average standardized performance score of employed applicants (in comparison to those not employed) ¯zx was calculated from w/p, where p refers to the selection rate and w to the ordinate height of the normal distribution function at the corresponding interception with the x-coordinate. For rejected applicants, no AC results were available due to secrecy obligations. As a consequence, ¯zx was estimated, which is standard for utility analyses. Nevertheless, analyses of organizational data allowed – in comparison to previous studies – the empirical assessment of the selection rate (p ¼ 0:5001). Costs. Unlike in previous empirical investigations, an effort was made to calculate fixed and variable costs. In accordance with the model of Boudreau and Ramstad (2001, 2002), a more precise analysis of the efficiency of the personnel selection procedure was performed. In addition, the number of applicants was taken into consideration for the calculation of the costs, whereas in previous analyses the calculation was based on the number of AC participants, which is already pre-selected by the analysis of applicants’ documents or by telephone interviews prior to the AC (DeCorte, 2000). In summary, a more realistic cost calculation was possible (cf. Martin and Raju, 1992). The task force analyzed all matters of expense, which were identified by means of half-structured interviews with five experts in the organization (marketing executive, human resource executive, trainer, strategic human resources executive, and financial executive). The task of the experts was to determine the respective costs, based on up-to-date organizational figures. The results of these interviews were used by the task force for the calculation of the parameters of the utility model. Table IV gives a specification of the fixed and variable costs of the ACs. Because elements of the inbound AC were identical to the outbound AC, the costs for the two kinds of AC were identical. As a part of the fixed costs, the implementation expenses of the AC are displayed first (Table IV, row 1a). Because certain costs resulted annually (firstly in month 1, last in month 13; cf. Table V), these were assumed for every year (Table IV, row 1b). Table IV further displays the variable costs per applicant. The relatively high costs of establishing telephone contact were remarkable. The time-consuming interface between potential applicants and the call center company offering the AC included an information service (hotline), consultation, entering applicant and participant data into personnel data banks, designing advertisements, and also tasks of personnel marketing. Taken together, the variable costs were quite low – corresponding to the short duration of the AC. As described above, the number of the AC participants was determined empirically. In addition, from the point of view of the task force, every applicant not invited to the AC or not selected caused additional costs, e.g. through formulating and mailing a letter of refusal. Hence, the number of applicants was taken into consideration in the respective matters of expense (cf. Martin and Raju, 1992). Taxes and interest rate. In the present utility analysis, these two parameters were determined by organizational financial experts. The tax rate was specified at 40 percent, and the inflation-adjusted interest rate at i ¼ 0:0089 per month. The established task force made increased efforts to solve theoretical and practical problems known from previous utility analyses. It was possible to solve the majority of

Human resource utility analysis

(1) Fixed costs (1a) Implementation (one-time) costs Administrative survey Role play Tests (e.g. computer skill test) Sum (1b) Maintenance costs (per year) Reviewing the AC Telephone equipment Consumable material Sum

200 600 2,600 3,400 100 1,500 42 1642

(2) Variable costs (2a) Preparing the AC Invitation letter, stamps Telephone contact/hotline Preparing the rooms (2b) Execution of AC Administrative survey Guideline for participants Tests Preparing materials Observers Rental fee (2c) Post-processing the AC Test results (including standard report) Notification letter, stamps Sum

Period (kth month) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sum

Inbound 0 940 3,056 3,952 5,587 6,110 5,646 5,065 3,967 3,502 3,456 3,274 3,036 3,098 2,953 2,854 2,829 3,197

Gains Outbound 0 921 1,799 1,913 3,484 3,606 3,372 2,993 2,720 2,696 3,085 3,154 3,413 3,761 4,384 4,555 4,468 4,698


0.85 102.07 1.83 0.28 0.28 1.13 1.59 16.44 11.60 9.13 0.29 145.49

Inbound 4,335 4,845 3,259 1,913 3,707 919 166 0 0 0 0 317 1,161 311 0 0 152 892

Costs Outbound 3,898 1,298 1,286 1,403 1,854 167 0 0 0 0 0 633 1,082 545 0 76 227 451

Table IV. Fixed and variable costs (e)

Utility DU Inbound Outbound 24,335 23,905 2 203 2,040 1,880 5,192 5,481 5,065 3,967 3,502 3,456 2,958 1,875 2,787 2,953 2,854 2,678 2,305 40,549

2 3,898 2 377 513 510 1,630 3,439 3,372 2,993 2,720 2,696 3,085 2,521 2,331 3,216 4,384 4,478 4,241 4,247 42,101

Table V. Results of utility analysis (e)



the problems outlined above (problems 1-5; cf. Table I). The “implementation problem” will be addressed in turn. Results The results of both utility analyses are displayed in Table V. Altogether, a positive utility evolved for the two ACs. In addition, the ratio between costs and gains (i.e. input-output ratio) can be used to interpret the utility of the two ACs. Therefore, for both inbound as well as outbound, the respective total costs (input) were compared to total gains (output) from Table V. The input-output ratios were approximately 1:2.8 (inbound) and 1:4.3 (outbound). Thus, in addition to utility, the input-output ratios also demonstrated positive effects for the two ACs evaluated. A closer examination of Table V demonstrates the temporal development of utility. In the first two or three time periods (i.e. months), costs exceed gains. In the following months, the utility of the staff-selection procedure consistently rose over time. Altogether, a positive utility resulted for both ACs after the end of the 18-month period. Consequently, results were presented to organizational decision makers. Because of the previous integration of these decision makers into the process of the utility analysis, the results were accepted without reservation. The ACs were evaluated as efficient and effective. Possible cost reductions in the area were rejected. In addition, it was deemed important to achieve higher validity of the ACs. Currently, meta-analyses (Hunter and Hunter, 1984; Schmidt and Hunter, 1998) are reviewed in order to determine which tests (e.g. personality or intelligence) might be employed for a possible enhancement of validity. Altogether, the conducted utility analyses have been accepted and implemented (“implementation problem”). Discussion The present investigation reports for the first time in detail on the integration of utility analyses in the organizational decision context. Implementing the HC BRidgee model of Boudreau and Ramstad (2002), close attention was paid to the integration of the utility analysis into strategic HR management. The model intends that the planning, realization, and use of the results of the utility analyses are carried out in collaboration with decision makers in the respective organization. Problems of earlier approaches of utility analyses could be avoided using this framework model. Firstly, the analyses yielded a positive utility of the ACs. Secondly, the results were not only accepted by the decision makers, but also used for decision processes and associated further actions. The present investigation therefore provides a first example of an integration of utility analysis in the organizational decision process and closes a gap in the research literature. Therewith, a new branch of research develops in addition to earlier utility research, which has focused on the advancement of models and calculation methods for parameters. In contrast, the new branch of research is more concerned with the integration of the utility analysis into the organizational decision-making context. In this regard, several important aspects should be mentioned. Firstly, it should be clarified why the results of utility analyses are rarely accepted. How do knowledge and attitudes of involved HR experts and decision makers impact the process of utility analysis? It is of special interest to discover to what extent knowledge relevant for the decision is:

. . .

communicated; accepted by the recipients; and used for subsequent decision making (Roth and Bobko, 1997).

With respect to utility analyses, certain organizational experts only have access to their own expert knowledge: as a rule of thumb, psychologists are mostly experts for HR activities, while managers are experts for accounting, marketing, etc. Under which conditions can both expert groups communicate effectively and reach an optimal decision? Secondly, it is of interest to discover which conditions are necessary for the successful realization of utility analysis. The model of Boudreau and Ramstad (2001, 2002) offers a first orientation about the crucial points important for the acceptance of utility analyses. Though our results confirm the utility of this model, they are not immediately transferable to other organizations. Future research has to show to what extent the model is successful in other contexts (Sturmann, 2000). The two lines of research can help to achieve two goals. On the one hand, they deepen our understanding of in-house decision-making processes. On the other hand, it will be possible to provide psychologists with the knowledge and tools necessary to positively influence decision processes about economic resources.

References Arvey, R.D. and Murphy, K.R. (1998), “Performance evaluation in work settings”, Annual Review of Psychology, Vol. 49, pp. 141-68. Baumgartner, M., Good, K. and Udris, I. (2002), CALL – Call Centers in der Schweiz: Psychologische Untersuchungen in 14 Organisationen, Institut fu¨r Arbeitspsychologie, Eidgeno¨ssische Technische Hochschule, Zu¨rich. Becker, B.E. and Huselid, M.A. (1992), “Direct estimates of SDy and the implications for utility analysis”, Journal of Applied Psychology, Vol. 77, pp. 227-33. Boudreau, J.W. (1983), “Effects of employee flows on utility analysis of human resource productivity improvement programs”, Journal of Applied Psychology, Vol. 68, pp. 396-406. Boudreau, J.W. (1988), “Utility analysis”, in Dyer, L. (Ed.), Human Resource Management. Evolving Roles & Responsibilities, The Bureau of National Affairs, Washington, DC, pp. 125-86. Boudreau, J.W. (1990), “Utility analysis for decisions in human resource management”, in Dunnette, M. and Houghs, L.M. (Eds), Handbook of Industrial and Organizational Psychology, 2nd ed., Consulting Psychologists Press, Palo Alto, CA, pp. 621-745. Boudreau, J.W. (2004), “Effects of employee flows on utility analysis of human resource productivity improvement programs”, Journal of Applied Psychology, Vol. 68, pp. 396-406. Boudreau, J.W. and Berger, C.J. (1985), “Decision-theoretic utility analysis applied to employee separations and acquisitions”, Journal of Applied Psychology, Vol. 70, pp. 581-612. Boudreau, J.W. and Ramstad, P.M. (2001), “Strategic I/O psychology and utility analysis”, in Borman, W., Klimoski, R. and Ilgen, D. (Eds), Handbook of Industrial and Organisational Psychology, Wiley, New York, NY. Boudreau, J.W. and Ramstad, P.M. (2002), Strategic I/O Psychology and the Role of Utility Analysis Models, Cornell University, Ithaca, NY.

Human resource utility analysis




Boudreau, J.W., Dunford, B.B. and Ramstad, P.M. (2001), “The human capital impact on e-business: the case of Encyclopedia Britannica”, in Pal, N. and Ray, M. (Eds), Pushing the Digital Frontier, AMACOM, New York, NY, pp. 192-221. Brickenkamp, R. (1981), Test d2. Aufmerksamkeits-Belastungs-Test, Hogrefe, Go¨ttingen. Carson, K.P., Becker, J.S. and Henderson, J.A. (1998), “Is utility really futile? A failure to replicate and an extension”, Journal of Applied Psychology, Vol. 83, pp. 84-96. Cascio, W.F. (2000), Costing Human Resources: The Financial Impact of Behavior in Organizations, 4th ed., South-Western College Publishing, Cincinnati, OH. Cranshaw, S.F. (1997), “Lo! The stimulus speaks: the insider’s view on Whyte and Latham’s ‘The futility of utility analysis’”, Personnel Psychology, Vol. 50, pp. 611-15. DeCorte, W. (2000), “Using order statistics to assess the sampling variability of personnel selection utility estimates”, Journal of Applied Statistics, Vol. 27, pp. 703-13. DeCorte, W. and Lievens, F. (2003), “A practical procedure to estimate the quality and the adverse impact of single-stage selection decisions”, International Journal of Selection and Assessment, Vol. 11, pp. 87-95. Dormann, C. and Zijlstra, F. (2003), “Call centers: high on technology – high on emotions”, European Journal of Work and Organizational Psychology, Vol. 12, pp. 305-11. Fitz-Ens, J. (1995), How to Measure Human Resource Management, McGraw-Hill, New York, NY. Greer, O.L. and Cascio, W.F. (1987), “Is cost accounting the answer? Comparison of two behaviorally based methods for estimating the standard deviation of job performance in dollars with a cost-accounting-based approach”, Journal of Applied Psychology, Vol. 72, pp. 588-95. Highhouse, S. (1996), “The utility estimate as a communication device: practical questions and research directions”, Journal of Business and Psychology, Vol. 11, pp. 85-100. Hunter, J.E. and Hunter, R.F. (1984), “Validity and utility of alternative predictors of job performance”, Psychological Bulletin, Vol. 96, pp. 72-98. Konradt, U., Hertel, G. and Joder, K. (2003), “Web-based assessment of call center agents: development and validation of a computerized instrument”, International Journal of Selection and Assessment, Vol. 11, pp. 184-93. Macan, T.H. and Highhouse, S. (1994), “Communicating the utility of human resource activities: a survey of I/O and HR professionals”, Journal of Business and Psychology, Vol. 8, pp. 524-36. Martin, S.L. and Raju, N.S. (1992), “Determining cutoff scores that optimize utility: a recognition of recruiting costs”, Journal of Applied Psychology, Vol. 77, pp. 15-23. Morrow, C.C., Jarrett, M.Q. and Rupinski, M.T. (1997), “An investigation of the effect and economic utility of corporate-wide training”, Personnel Psychology, Vol. 50, pp. 91-119. Nafukho, F.M., Hairston, N.R. and Brooks, K. (2004), “Human capital theory: implications for human resource development”, Human Resource Development International, Vol. 7, pp. 545-51. Phillips, J.J. (1997), Handbook of Training Evaluation and Measurement Methods, 3rd ed., Butterworth-Heinemann, Woburn. ˜ Quinones, M.A. and Ehrenstein, A. (1997), Training for a Rapidly Changing Workplace: Applications of Psychological Research, American Psychological Association, Washington, DC.

Roth, P.L. (1994), “Multi-attribute utility analysis using the Promes approach”, Journal of Business and Psychology, Vol. 9, pp. 69-80. Roth, P.L. and Bobko, P. (1997), “A research agenda for multi-attribute utility analysis in human resource management”, Human Resource Management Review, Vol. 7, pp. 341-68. Schmidt, F.L. and Hunter, J.E. (1998), “The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 85 years of research findings”, Psychological Bulletin, Vol. 124, pp. 262-74. Schmidt, F.L., Hunter, J.E. and Pearlman, K. (1982), “Assessing the economic impact of personnel programs on workforce productivity”, Personnel Psychology, Vol. 35, pp. 333-47. Sturmann, M.C. (2000), “Implications of utility analysis adjustments for estimates of human resource intervention value”, Journal of Management, Vol. 26, pp. 281-99.

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