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International Journal of Economics, Commerce and Research (IJECR) ISSN 2250-0006 Vol. 3, Issue 4, Oct 2013, 29-36 Š TJPRC Pvt. Ltd.

CATALOGUING OF ISSUES AFFECT ON OVERALL OPINION MAKING FOR SELLING OF LIFE INSURANCE PRODUCTS BY THE AGENT SILENDER SINGH Assistant Professor, Department of Commerce, Chaudhary Devi Lal University, Sirsa, Haryana, India

ABSTRACT This paper has mainly focused to study influential variables which have affects on overall opinion making for selling of life insurance products by the agent. Factor analysis is employed on data of 33 items that have the most impinge on to individual policies to be sold to customer (term policy, whole life policy, endowment life policy, money back policy and unit linked plans). The findings indicate that factor 3 is at the top by which agents makes their opinion for selling policies (Mean= 4.94) followed by factor 11 client interest in mind (Mean=4.91). On the contrary, they do not consider try to sale more and more ignoring wants, facter-7, (Mean=2.42) and self made rosy picture of plan for selling factor-4, (Mean=2.53). Overall, the analysis provides an understanding the agents differ significantly by age, education and experience as agent-wise on intrinsic motivation, sale with creating company’s goodwill and deemed similar vale of company and self. On contrary, except annual commission from commission, agents do not differ related to factor, customer best interest and need. The results are important for insurance actuaries, companies, regulators, agents and development officers and also help in increasing the retention of customers.

KEYWORDS: Term Policy, Whole Life Policy, Endowment Life Policy, Money Back Policy, Unit Linked Plans, Factor Analysis, Actuaries, Regulator, Retention

INTRODUCTION In purchase of life insurance policies family is influenced by policy features, company, agent and different media. Whence, household acquire relevant knowledge about the products, companies and agents and then provides information to agent about socio-economic structure of his family. Subsequently, s/he makes the relation of faith for purchasing. If the provided demographic information to agent and acquired knowledge about policy purchased by household are not properly matches, after some time it may be lapsed. Such problem is increasing throughout and adversely effects on life insurance companies. After the opening of the life insurance sector, it has been rising continuously. As per Shri S. B. Mathur of Life Insurance Council, most of the private players refused to admit mis-selling of policies, the Insurance Regulatory And Development Authority (IRDA) data shows out of 1,794 complaints registered by the life insurance industry in 2009, 17.45% of the complaints relate to the wrong plan and term allotted the highest among all categories of complaints. Life Insurance Corporation of India has been reported of having 17 to 18% lapsation of life insurance policies in during the years 2000-07(Kannan R. et. al. 2008). So it is need to analysis the problem related to opinion making of agents for selling of the policy.

REVIEW OF LITERATURE The study has review earlier studies related to area of life insurance policy lapsation. Theoretical Review The emergency fund hypothesis (EFH) (Outreville, 1990; Kuo, Tsai and Chen, 2003; Kim, 2005) argues that


30

Silender Singh

individual tends to lapse a life insurance policy when faced with economic hardship. The interest rate hypothesis (IRH) (Pesando, 1974; Kuo, Tsai, and Chen, 2003) stated that policy owner may be willing to remove funds from a life insurance policy (either by way of loan or surrender) in order to take advantage of higher market rates. The policy replacements hypothesis (PRH) (Outreville, 1990; Russell, 1997; Carson and Forster, 2000) states that policy lapses may occur simply because the policyholder has identified a more attractive policy with better terms or rates. Empirical Review (Smith, Michael L., 1982) selling skill is the ability the agent employs to consummate a sale successfully. This skill can be developed through essentially two broad means: formal training and sales experience gained through exercising the selling job over time (Darmon, 1992). As reported by Kimball (1994), relationships and emotions are the keys to success in selling. People buy from salespeople they like and trust. This fact makes price and the company brand insignificant when clients make a buying decision, hence increasing the importance of a producer. The insurance company can distribute its products only if consumers buy them and consumers can be expected to buy life insurance only if agents sell it to them (Oakes, 1990). Lawhon (1995) found that most salespeople who fail within the first year. In some fields of selling, this figure can run 99 percent and even higher. The prospective agents who successfully passed the selection and licensure process leave prematurely because of poor sales. The company either terminates the relationship or the producer leaves voluntarily because of insufficient income. Further, (Nik Kamariah 1995) found that when insurance agent not makes the customer-oriented behaviour, sold policies after some time may lapse. After the sale, agents provide follow-up service and help customers make policy changes in response to changing needs. This would justify the importance of continuous research to satisfy the customers in this dynamic marketing industry. It is readily apparent that investigation of customer-orientation behaviour in life insurance industry is accentuated. Selling is a high-pressure job, filled with emotional highs and lows. Its shortcomings among others include: "It's a lonely job, much like that of an athlete or a performer". Life insurance selling is a creative specialty that needs to be complemented with consultative selling to achieve the desired result. Research Gap Review of earlier studies have shown that EFH, IRH and PRH hypothesis has not considered that role of decision making of agent before selling of the policy. Same as empirical studies have shown that they have take up the problem of getting desired results, skill of selling, training, and failure of agent related issues. But, none has checked issues affect on overall opinion making for selling of life insurance products by the agent. So, the study has taken up the issues of opinion making for selling of life insurance products consequently responsible for lapsation and categorisation on the basis of precedence. Objective: To categorise items used in opinion making for selling by agent and effect of his demographic variables on items in the factors.

RESEARCH METHODOLOGY Hypothesis There is no significant effect of demographic variables of agent on items in the factors related to his opinion making for selling. Reliability and Validity The response on 33 items chosen of agents used generally for opinion making before selling of policy were


31

Cataloguing of Issues Affect on Overall Opinion Making for Selling of Life Insurance Products by the Agent

collected on 5-point Likert scale from 5 for completely agree, 4 for slightly agree, 3 for neutral, 2 for slightly disagree and to 1 for completely disagree. The interactive Cronbach’s Alpha values for reliability in responses of responents were found 0.725. The Content Validity Ration (CVR) above 0.80 is significant (0.60 ≤ significant) of the present study. It means items in questions contains in questionnaires cover the content of the research significantly as by Kapoor D.R. and Saigal P. (2013). Size of Sample, Data Collection Method and Tool Non-Probabilistic convenience-cum-judgement sampling was used and responses of 160 agents having 20 per cent or above (statistically abnormal condition) policies had lapsed out of total sold policies and working at branch level in four districts i.e. Sirsa, Rohtak, Karnal and Panchkula of Haryana state were taken through well structured questionnaire. Data Analysis Strategy To analysis and interpret mean, standard deviation, factor analysis has been applied. For conformation of descriptive statistics F-test Statistic is used. The correlation matrix of 33 reaction items which were developed to know the overall opinion making for selling by agents and the present study has found that there are 10 loading greater than .634 correlation (greater than .400 correlation) between variables; it is reliable regardless of the sample size, (F. Andy and M. Jeremy, 2010). To test the appropriateness of factor analysis technique the correlation between the variables are checked and Keiser-Meyer-Olkin (KMO) measure of sample adequacy is also used for the same. The population correlation matrix is an identity matrix, is rejected by Bartlett’s Test of Sphericity. The approximate Chi-square value is 18224.985 with 528 degree of freedom, which is significant at 0.05 levels. The value of KMO statistic, 0.621, is also large than 0.6. Further, PCA method is used for extraction of variable for the component (factor) concerned. The extraction communalities, averagely for each variable has been found 0.804 which is the amount of variance a variable share with all the other variables being considered. It is also the proportion of variance explained by the common factors. Theoretically, sample size is enough to calculate factor analysis. The reproduced correlation matrix of overall items in opinion making of agent towards selling of policies has shown that (20%) non-redundant residuals with absolute values greater than 0.05, indicating an acceptable model fit.

ANALYSIS AND INTERPRETATION Table 1: Total Variance Explained Initial Eigenvalues Component Total 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

5.193 4.211 3.319 2.818 2.212 1.978 1.788 1.608 1.283 1.125 1.027 .875 .806 .737 .653 .575 .420 .408 .366

% of Variance 15.735 12.761 10.056 8.540 6.704 5.993 5.419 4.872 3.888 3.409 3.113 2.651 2.442 2.234 1.979 1.744 1.273 1.237 1.109

Cumulative % 15.735 28.496 38.552 47.093 53.796 59.789 65.208 70.080 73.968 77.376 80.489 83.140 85.582 87.816 89.795 91.539 92.812 94.049 95.158

Extraction Sums of Squared Loadings % of Cumulative Total Variance % 5.193 15.735 15.735 4.211 12.761 28.496 3.319 10.056 38.552 2.818 8.540 47.093 2.212 6.704 53.796 1.978 5.993 59.789 1.788 5.419 65.208 1.608 4.872 70.080 1.283 3.888 73.968 1.125 3.409 77.376 1.027 3.113 80.489

Rotation Sums of Squared Loadings Total 4.371 3.304 2.566 2.561 2.544 2.261 2.172 2.128 1.911 1.450 1.295

% of Variance 13.245 10.011 7.776 7.759 7.710 6.850 6.580 6.447 5.790 4.394 3.924

Cumulative % 13.245 23.256 31.032 38.791 46.502 53.352 59.932 66.380 72.170 76.564 80.489


32

Silender Singh

Table 1: Contd., 20 21 22 23 24 25 26 27 28 29 30 31 32 33

.317 .252 .220 .194 .138 .124 .089 .086 .066 .058 .039 .014 .001 3.904E-15

.960 .763 .667 .587 .418 .375 .269 .261 .201 .175 .118 .044 .003 1.183E-14

96.118 96.881 97.548 98.135 98.553 98.929 99.198 99.459 99.660 99.835 99.953 99.997 100.000 100.000

Extraction Method: Principal Component Analysis Source: Primary (Data processed through SPSS 18.0) In the Table 1, shows that Eigenvalue greater than 1.0 (default option) result in 11 factors being extracted. The scree plot associated with this analysis is given in Exhibit 5.2 shows that a distinct break occurs at 11 factors. Finally, from the cumulative percentage of variance accounted for 80.489 of the total variance by these 11 extracted variables. Table 2: Factor Pattern Matrix Reaction Items V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33

Component Matrixa 1

2

3

4

5

6

-.081 -.239 -.264 .520 .523 .437 .178 .299 -.441 -.103 -.177 -.253 -.275 -.093 -.355 .402 -.408 -.276 -.026 -.114 -.163 -.190 -.421 -.186 -.128 -.508 .067 -.053 -.030 .792 .779 .858 .864

-.222 .207 .296 .235 .135 .225 .056 .318 .653 .583 -.050 .222 .121 .390 .254 .239 .409 .299 .265 .321 .338 .539 .700 .467 -.098 .094 .439 .221 .689 .359 .387 .274 .257

-.017 .099 .129 .143 .481 -.011 .806 .517 .032 -.033 .087 -.302 -.428 -.589 -.340 .397 .202 -.177 .168 -.237 -.322 .200 .162 .445 .488 .205 -.108 .381 -.056 -.344 -.334 -.109 -.116

.091 -.546 -.534 .298 .262 .411 -.035 -.143 -.163 .518 .218 -.314 -.011 .003 -.043 -.164 -.200 .023 .174 .599 .610 .372 -.103 -.211 .274 -.132 -.297 .311 .009 -.149 -.158 -.215 -.211

-.111 .651 .630 .441 .024 .276 .108 -.190 -.097 -.098 -.060 .158 -.223 -.120 -.175 .089 -.035 -.181 -.244 .495 .452 -.045 -.014 .055 -.214 -.136 -.361 -.314 -.210 -.043 -.035 -.104 -.110

.141 -.066 -.053 .062 .076 .217 .178 .111 .115 -.059 .199 .393 .045 .068 .231 .089 .280 -.002 .539 -.005 .025 -.157 .014 -.451 .313 .604 -.410 -.505 -.188 .068 .070 .144 .146

7

Rotated Component Matrixa 8

9

10

11

-.318 .240 -.043 .705 .288 -.053 -.053 .102 .246 -.058 .005 -.083 .081 .203 .045 .165 .428 .233 .032 -.073 .079 .299 -.236 -.196 .091 .320 .292 .304 .054 -.172 .125 .163 -.044 -.065 .179 -.183 .269 -.057 -.156 -.112 .067 -.079 .257 .071 .035 -.270 -.102 .099 .142 -.240 -.382 -.016 .656 -.188 .177 -.259 .495 -.115 -.100 -.136 .346 .173 .389 -.065 .022 -.279 .437 -.016 -.006 .255 .393 -.127 -.014 -.053 .176 .195 -.371 .140 -.074 .362 .223 .024 -.417 -.067 -.121 .611 .161 -.033 .019 .296 .167 -.253 .110 .207 -.447 .072 -.227 -.229 .022 .067 .036 -.188 -.162 .045 .074 -.394 -.169 -.065 -.186 -.004 -.198 -.141 .033 -.197 .073 -.023 .151 .160 .019 -.006 -.153 -.213 .033 .182 .291 -.074 .036 -.133 .133 -.074 .057 -.100 .079 .082 -.224 .191 .208 -.067 .392 -.078 -.009 .153 -.145 .007 .214 -.217 -.071 -.089 .019 .025 -.202 -.080 -.092 .012 .051 .076 -.177 .034 .133 .041 .065 -.176 .035 .135 .018

1

2

3

4

5

6

-.053 -.055 -.040 .223 .189 .216 -.079 .198 -.044 .046 -.169 -.035 -.107 .204 -.026 .433 -.183 -.110 .096 -.038 -.045 -.062 -.062 -.163 -.158 -.289 .283 -.225 .220 .942 .943 .937 .937

-.082 .027 .074 .007 .077 -.065 .035 .354 .505 .629 .056 -.031 .034 .239 -.018 .010 .200 .122 .074 .052 .069 .652 .617 .675 .002 -.037 .569 .614 .628 .059 .072 -.046 -.055

-.014 .944 .946 .075 -.276 -.077 .133 -.057 .350 -.156 -.070 .223 -.080 -.104 .005 .241 .203 -.028 -.098 .047 .027 -.092 .295 .391 -.151 .130 .058 -.194 -.012 -.078 -.057 -.016 -.029

-.044 -.054 .034 .272 .703 .099 .818 .525 -.042 -.174 -.019 -.057 -.387 -.220 -.138 .498 .296 .037 -.008 -.042 -.123 .148 .158 .190 .414 .194 -.307 .078 .126 -.036 -.014 .096 .080

-.038 .004 .050 .153 .028 .198 -.182 -.321 .032 .439 -.036 -.056 -.125 .139 .148 -.013 .070 .056 .032 .922 .913 .418 .188 -.192 .064 -.056 -.276 -.086 .131 .054 .067 -.119 -.127

-.088 -.086 .024 -.094 -.168 .083 -.095 -.222 .441 -.102 -.056 .062 .605 .311 .683 .129 .284 .804 .066 .060 .107 -.173 .183 .005 -.051 .140 .130 .033 .330 -.127 -.100 -.001 -.022

7

8

9

-.041 .105 .014 .008 .085 -.009 -.032 .065 -.016 .853 .047 -.108 .351 -.013 -.079 .844 -.017 .123 .183 -.169 .095 .129 .171 .153 -.077 .114 .333 .162 .033 .301 .064 .034 .040 .025 .870 .125 .171 .125 .022 .002 .686 -.229 -.183 .091 .198 -.031 -.435 .008 -.221 .266 .431 .063 -.001 -.029 .143 -.110 .873 .139 -.028 -.004 .147 .037 .010 -.147 .049 .079 -.268 .204 .194 .092 -.095 -.138 -.204 -.264 .310 -.252 .326 .623 -.084 -.106 .012 .243 -.362 -.030 -.048 .196 -.098 .078 .169 -.097 .068 .165 -.099 .163 -.141 .056 .166 -.138 .064

10

11

.037 -.061 -.044 .024 -.149 .060 .010 -.006 .213 .159 .851 -.007 .171 .128 -.007 .185 -.345 -.186 .036 -.069 -.002 .218 .225 -.022 .315 .057 -.194 -.343 -.106 -.046 -.043 -.069 -.071

.895 -.006 -.028 -.015 -.058 -.052 .023 -.091 -.038 .070 .046 -.011 -.086 .219 -.087 -.126 -.162 .015 -.024 -.044 .000 -.112 -.203 -.074 .373 .181 -.150 .320 .051 -.033 -.039 -.012 -.012

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. 11 components extracted Table 2 shows the component matrix or factor matrix. It is also called factor pattern matrix. It shows the coefficient used to express the standardized variables in the terms of the factors. These coefficients, the factors loadings, represent the correlations between the factors and variables. A coefficient with a large absolute value indicates that the factor and the variable are closely related. The left portion of this Table 2, namely component matrix indicates the


33

Cataloguing of Issues Affect on Overall Opinion Making for Selling of Life Insurance Products by the Agent

relationship between the factors and individual variables; it seldom results in factors that can be interpreted, because factors are correlated with many variables. Therefore right portion of the Table namely rotated component matrix is useful for interpreting the factors. The rotation is made by commonly used methods, Varimax procedure. This is an orthogonal (unrelated) method of rotation that minimise the number of variables with high loading on a factor, thereby enhancing the interpretability of the factors. Interpretation of facilitated by identifying the variables that have large loadings on the same factor, that factor can be interpreted in terms of the variables that load high on it. For the purpose of interpretation, each factor was composed of variable that loaded 0.30 or higher on that factor. In case, where variables loaded 0.30 or above on two factors, each variable was assigned to the factor where it had the highest loading. The maximum of each row (ignoring the sign) indicates the respective variable belongs to the respective component (Table 2). Table 3: Factors Interpretation the Overall Opinion Making for Selling by Agent Factor

F1

F2

F3

F4

Factor Interpretation (% of Variance Explained

.943

Variables Included in the Factor 31

.942

30

.937

32

.937

33

.675

24

.652

22

.629

10

.628

29

.617 .614

23 28

.569

27

.505

9

.946

3

.944

2

.818

7

.703

5

.525

8

Loading

Intrinsic Motivation (15.735)

Self-Monitoring and effort for success of company (12.761)

Client’s best interest and need (10.056)

Self Made Rosy Picture of Plan before Client for Sale (8.540)

F5

Influence on Prospective Customer (6.704)

.498 .922 .913 .804

16 20 21 18

F6

Sale with Creating Company’s goodwill (5.993)

.683

15

.605

13

.853

4

F7

Try to Sale More and More Ignoring Wants (5.419)

F8

Deemed Similar Vale of Company and Self (4.872)

F9

F10 F11

Behaviour in Gathering (3.888)

Brainwave to Perform Best By Company (3.409) Client Interest in Mind (3.113)

.844

6

.870 .686

12 14

.873

19

.623

26

.431

17

.851

11

.895

1

Source: Primary, (Data Processed through SPSS 18.0)

Factor I feel satisfied when I do my job well-V31 When I do my work well, it gives me a feeling of accomplishment-V30 When I perform my job well, it contributes to my personal growth and development-V32 My job increases my feeling of self- esteem-V33 I will not change my opinion (or the way I do things)in order to please someone or win their favour-V24 In a group of people, I am the centre of attraction-V22 I am willing to accept almost any type of work in order to keep working in this company-V10 I can be friendly towards others even though I really dislike them -V29 I am not very good at making other persons like meV-23 I can tell a lie without fear if it is for a good purpose-N28 I feel a bit strange in public and do not give as good impression as I should-V27 I am willing to put in a great deal of effort beyond that is normally expected in order to help my company to be successful-V9 I try to recommend the insurance plan best suited to the client’s needs-V3 I try to suit the client’s need with an appropriate policy-V2 I paint a rosy picture of my insurance plan to make the sound as good as possible-V7 I stretch the truth in describing my insurance plan to a clientV5 I make recommendations to my client based on what I think could be sold, not on the basis of my client’s long term satisfaction-V8 I find it difficult to copy the behavior of others-V16 I attempt to influence or entertain others-V20 I am able to influence people’s feelings-V21 I can argue for ideas that suitable for sale-V18 I am extremely glad that I chose this company to work in comparison to other companies-V15 I talk highly about my company to my friend as a great place to work-V13 I offer and try to sell more policies rather than satisfying the client’s needs-V4 I try to convince my client to buy a policy more than what is actually needed-V6 I really care about the future of my company-V12 My values and my company’s values are similar-V14 I can make speeches on topics that I don’t know about without preparation-V19 At social gatherings, I let other tell the jokes and stories-V26 At parties and social gathering, I do not attempt to do or say something that other will like-V17 My company really inspires me to perform to the best of my ability-V11 I keep in mind the client’s best interest -V1


34

Silender Singh

Table 4: Confirmatory Statistics of Factors Influencing Overall Influence on Households for Purchase of Policy

Factor

Intrinsic Motivation (F1) I feel satisfied when I do my job 31 well-V31 When I do my work well, it gives 30 me a feeling of accomplishmentV30 When I perform my job well, it 32 contributes to my personal growth and development-V32 My job increases my feeling of 33 self- esteem-V33 Self-Monitoring and effort for success of company (F2) I will not change my opinion (or the way I do things)in order to 24 please someone or win their favour-V24 In a group of people, I am the 22 centre of attraction-V22 I am willing to accept almost any 10 type of work in order to keep working in this company-V10 I can be friendly towards others 29 even though I really dislike them V29 I am not very good at making other 23 persons like meV-23 I can tell a lie without fear if it is 28 for a good purpose-N28 I feel a bit strange in public and do 27 not give as good impression as I should-V27 I am willing to put in a great deal of effort beyond that is normally 9 expected in order to help my company to be successful-V9 Needs based recommendation and selling (F3) I try to recommend the insurance 3 plan best suited to the client’s needs-V3 I try to suit the client’s need with 2 an appropriate policy-V2 Self Made Rosy Picture of Plan before Client for Sale (F4) I paint a rosy picture of my 7 insurance plan to make the sound as good as possible-V7 I stretch the truth in describing my 5 insurance plan to a client-V5 I make recommendations to my client based on what I think could 8 be sold, not on the basis of my client’s long term satisfaction-V8 I find it difficult to copy the 16 behavior of others-V16 Influence on Prospective Customer (F 5) I attempt to influence or entertain 20 others-V20 I am able to influence people’s 21 feelings-V21 Sale with Creating Company’s goodwill (F6) I can argue for ideas that suitable 18 for sale-V18 I am extremely glad that I chose this company to work in 15 comparison to other companiesV15 I talk highly about my company to 13 my friend as a great place to workV13 Try to Sale More and More Ignoring Wants (F7) I offer and try to sell more policies 4 rather than satisfying the client’s needs-V4

4.67(4)

F 7.226

Sig .000*

F 22.386

Sig .000*

Confirmatory Statistics Annual Income from Experience as Commission on Life Life Insurance Insurance Products Agent (df=3, 156) (df=5, 154) F Sig F Sig 9.372 .000* 2.696 .023

4.58

8.476

.000*

20.980

.000*

13.097

.000*

2.499

.033

.634

.427

4.60

7.896

.000*

21.703

.000*

12.141

.000*

2.407

.039

.428

.514

4.61

5.546

.001*

18.572

.000*

5.840

.001*

2.730

.022

8.450

.004*

4.61

5.294

.002*

18.976

.000*

5.528

.001*

2.751

.021

7.945

.005*

3.91(8)

1.975

.120

3.869

.011

3.129

.027

4.882

.000*

7.077

.009*

4.15

.896

.445

2.379

.072

3.027

.031

5.035

.000*

.194

.660

4.07

1.822

.145

2.470

.064

.613

.608

6.388

.000*

5.168

.024

4.22

3.820

.011

5.530

.001*

1.801

.149

1.093

.367

14.095

.000*

4.22

.963

.412

1.782

.153

1.972

.120

3.480

.005*

5.986

.016

3.89

1.878

.135

4.674

.004*

1.850

.140

5.085

.000*

5.370

.022

3.77

.964

.411

1.743

.160

3.618

.015

4.113

.002*

.154

.695

2.67

3.050

.030

1.448

.231

1.589

.194

3.158

.010

1.136

.288

4.30

1.257

.291

3.249

.023

4.334

.006*

2.372

.042

6.372

.013

4.94(1)

.859

.464

3.908

.010

3.206

.025

8.042

.000*

1.477

.226

4.94

1.044

.375

3.478

.017

2.385

.071

7.177

.000*

.726

.395

4.95

.633

.595

4.016

.009*

3.881

.010

8.127

.000*

2.323

.129

2.53(10)

.393

.758

3.895

.010

2.771

.043

5.659

.000*

.256

.613

2.55

.170

.916

1.101

.351

4.132

.007*

6.613

.000*

.989

.321

2.03

1.540

.206

4.207

.007*

1.785

.152

2.727

.022

.519

.472

2.04

.846

.471

3.959

.009*

2.831

.040

3.905

.002*

.975

.325

Mean of the Variabl e

Age (df=3, 156)

Education (df=3, 156)

Other Side Business (df=1, 158) F 3.419

Sig .066

3.50

5.679

.001*

.654

.581

2.087

.104

9.970

.000*

12.276

.001*

4.66(5)

.943

.422

1.423

.238

1.226

.302

.952

.449

.155

.695

4.67

1.412

.241

1.242

.296

1.385

.249

.844

.521

.411

.523

4.65

.500

.683

1.475

.223

.968

.409

1.063

.383

.016

.900

4.78(3)

5.535

.001*

3.540

.016

.852

.468

.741

.594

5.971

.016

4.67

5.038

.002*

1.006

.392

.651

.584

1.404

.226

4.060

.046

4.94

4.859

.003*

2.908

.036

2.750

.045

.852

.515

8.318

.004*

4.73

1.550

.204

2.588

.055

.108

.955

1.678

.143

.343

.559

2.42(11)

2.130

.099

2.343

.075

6.291

.000*

1.646

.151

.089

.766

2.51

1.392

.247

2.988

.033

3.018

.032

3.317

.007*

.939

.334


35

Cataloguing of Issues Affect on Overall Opinion Making for Selling of Life Insurance Products by the Agent

Table 4: Contd., I try to convince my client to buy a 6 policy more than what is actually needed-V6 Deemed Similar Vale of Company and Self (F8) I really care about the future of my 12 company-V12 My values and my company’s 14 values are similar-V14 Behaviour in Gathering (F9) I can make speeches on topics that 19 I don’t know about without preparation-V19 At social gatherings, I let other tell 26 the jokes and stories-V26 At parties and social gathering, I 17 do not attempt to do or say something that other will like-V17 Brainwave to Perform Best By Company (F10) My company really inspires me to 11 perform to the best of my abilityV11 Client Interest in Mind (F11) I keep in mind the client’s best 1 interest V1

2.33

2.395

.070

1.271

.286

10.512

.000*

1.096

.365

.223

.638

4.58(6)

5.587

.001*

.727

.537

3.795

.012

5.444

.000*

19.124

.000*

4.42

6.513

.000*

1.769

.155

2.608

.054

6.339

.000*

23.334

.000*

4.73

2.739

.045

.239

.869

3.139

.027

2.466

.035

7.606

.007*

3.27(9)

.567

.637

3.923

.010

3.457

.018

2.475

.035

.077

.782

2.94

.876

.455

.954

.416

5.145

.002*

1.497

.194

1.661

.199

3.28

1.366

.255

3.906

.010

3.169

.026

4.367

.001*

3.739

.055

3.60

.594

.620

7.268

.000*

3.828

.011

4.040

.002*

.060

.807

4.50(7)

1.431

.236

4.578

.004*

4.354

.006*

2.137

.064

1.270

.261

4.50

1.431

.236

4.578

.004*

4.354

.006*

2.137

.064

1.270

.261

4.91(2)

.798

.497

.612

.608

.280

.840

.632

.676

.861

.355

4.91

.798

.497

.612

.608

.280

.840

.632

.676

.861

.355

Note: Value in the parenthesis shows rank, *Significant at 0.01 Source: Primary, (Data Processed through SPSS 18.0) After interpretation of the factors, Table 3 enlisted the rating of the factors on the basis of their importance and also depicts the results through ANOVA. It depicts that factor 3 is at the top by which agents makes their opinion for selling policies (Mean= 4.85) followed by factor 11 client interest in mind (Mean=4.91). On the contrary, they do not consider try to sale more and more ignoring wants, facter-7, (Mean=2.42) and self made rosy picture of plan for selling factor-6, (Mean=2.53). As far as F-statistics (ANOVA) is concerned, Table 4 shows that age-wise agents are significantly differ towards opinion making for selling life insurance policies under study on items I feel satisfied when I do my job well, when I do my work well, it gives me a feeling of accomplishment, when I perform my job well, it contributes to my personal growth and development, my job increases my feeling of self- esteem, I find it difficult to copy the behavior of others, I can argue for ideas that suitable for sale, I am extremely glad that I chose this company to work in comparison to other companies and I really care about the future of my company at 0.01 significant level with degree of freedom=3 of demographic characteristic of the agents and rejecting the null hypothesis.

CONCLUSIONS AND SUGGESTIONS Conclusions It is found that factor 3 related to client needs based recommendation and selling is at the top by which agents makes their opinion for selling policies (Mean= 4.94), but rejects it when he think about his annual income from commission, followed by factor 11 client interest in mind (Mean=4.91). On the contrary, they do not try to sale more and more policies by ignoring wants, factor-7 (2.42), but item of this factor, I offer and try to sell more policies rather than satisfying the client’s needs, has rejected when agent think about his income from commission and in case of self made rosy picture of plan for selling factor-4, (Mean=2.53) it is also concluded that for increasing their income from commission agent experience have significant effect their claim was reject by F-test. Agents overall demographic variables have significant effect but income from commission has profound role on all factors responsible for opinion making before sale of policies. Agents make the rosy story to sell their product so that income from commission may be increased.


36

Silender Singh

Suggestions It is recommended that Insurance Regulatory and development authority should define a minimum code of conduct so that rosy picture may not be produced by agent for selling. Rosy picture for a long time will not be able to increase the goodwill of companies and commission of agents. It will be harmful to life insurance thought. Further Area of Research The study has cover only agents having 20 per cent or above (statistically abnormal condition) policies had lapsed out of total sold policies of five life insurance companies working in four districts of Haryana. It may be extended with more items used in opinion making before selling of life insurance policy. Further, a study may be conducted to know the successful story of opinion making of the agent having no policy lapse.

REFERENCES 1.

Carson, James M. and Mark D. Forster, (2000) Suitability and Life Insurance Policy Replacement,” Journal of Insurance Regulation, Vol. 18, No. 4, pp. 427-447.

2.

Darmon, Rene Y. (1992) Effective Human Resource Management in the Sales Force. Westport, Connecticut: Quorum Books.

3.

F. Andy, M. Jeremy (2010) Discovering Statistics Using SAS, SAGE Publication India Pvt. Ltd. New Delhi, pp. 541-593.

4.

Kannan R., Sarma K. P., Rao A. V., Sarma S. K. (2008) Lapsation and its impact on Indian Life Insurance Industry (2002-07), Insurance regulatory and Development Authority, Occassional Paper; 1/2008, P. 4.

5.

Kapoor D.R. , Saigal P. (2013) Research Mehtodology Methods and Techniques, Regal Publications, New Delhi110027, P. 319.

6.

Kim, Changki, (2005) Modelling Surrender and Lapse Rates with Economic Variables. North American Actuarial Journal, Vol.9, No. 4, pp. 56-70.

7.

Kimball, Bob. (1994) AMA Handbook for Successful Selling. Lincolnwood, Ill.

8.

Kuo, Weiyu, Chenghsien Tsai, and Wei-Kuang Chen, (2003) An Empirical Study on the Lapse Rate: The Cointegration Approach. Journal of Risk and Insurance, Vol. 70, No. 3, pp. 489-508.

9.

Pesando, James E., (1974) “The Interest Sensitivity of the Flow of Funds through Life Insurance Companies: An Econometric Analysis,” Journal of Finance, Vol. 29, No. 4 pp. 1105-1121.

10. Lawhon, John F. (1995) The Selling Bible- For People in the Business of Selling. Tulsa, Okla. 11. Oakes, Guy. (1990) The Soul of the Salesman—The Moral Ethos of Personal Sales. Atlantic Highlands, N.J. 12. Outreville, J. Francois, (1990) “Whole-Life Insurance Lapse Rates and the Emergency Fund Hypothesis,” Insurance: Mathematics and Economics, Vol. 9, pp. 249-255. 13. Russell, David T., (1997) “An Empirical Analysis of Life Insurance Policyholder Surrender Activity,” Dissertation. 14. Smith, Michael L., 1982, “The Life Insurance Policy as an Options Package,” Journal of Risk and Insurance, 49(4): 583-601.


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