immpact of mmaarrkkeettiinngg...

Impact of Marketing Strategies on the Customer Behaviour of the LIC

279

CChhaapptteerr 55

IIMMPPAACCTT OOFF MMAARRKKEETTIINNGG SSTTRRAATTEEGGIIEESS OONN TTHHEE CCUUSSTTOOMMEERR BBEEHHAAVVIIOOUURR OOFF TTHHEE LLIICC

5.1 Sample Profile and Life Insurance Characteristics 5.2 Financial Awareness/Knowledge/Habits 5.3 Analysis of Customer Purchasing Behaviour 5.4 Customer Perception on Promotional Strategies of LIC 5.5 Customer Satisfaction on the Products and Services of the LIC 5.6 Customer Satisfaction on the Services of LIC Agents 5.7 Knowledge and Behavioural Pattern of Agents in Marketing Life

Insurance Products 5.8 Customer Perception on Brand Image and evaluation of Brand

Trust, Brand Loyalty, Customer Satisfaction and its impact on Brand Equity

5.9 Evaluation on the Relative Importance of Features and Benefits of Policies of LIC

5.10 Conclusions

The success of a marketing strategy can be examined from the

perceptions of its customers on its marketing practices and policies. The

analysis of perceptions of policyholders on the marketing practices and

policies of the Life Insurance Corporation of India will be helpful in the

formulation and implementation of marketing strategies. The level of

awareness of policyholders on various long-term saving instruments

available, especially with specific reference to life insurance products and

services, has much relevance in identifying their characteristic behaviour

towards investments in Life Insurance. The basic motive behind holding life

policies has significance in designing life insurance products. Factors like the

Co

nt

en

ts

Chapter 5

280

source of knowledge which the customer depends upon to know life

insurance, the decision maker in the family as to the purchase of life

insurance, the most influencing element to choose the LIC to buy life

insurance policy, etc., reveal the buyer characteristics as to life insurance

commitment. The best measure of effectiveness of promotional efforts of any

organisation is evaluation of customer responses towards its utility. The level

of influence of the promotional tools and strategies on customer buying

behaviour is also analysed. The customer satisfaction towards the products

and services of the LIC, the services of individual Agents, customer

perception towards knowledge of agents on the organisation, industry,

products and services, and customer needs and attitude will provide a

comprehensive framework on evaluation of the effectiveness of marketing

strategies implemented by the LIC. The study is based on sample survey

among 530 policyholders selected at random from 5 divisions of the Life

Insurance Corporation of India in Kerala.

This chapter analyses the impact of awareness and knowledge on

financial products, especially life insurance products, among policyholders

and their perception on the promotional strategies, level of satisfaction with

the products and services of the LIC, and services of Life Insurance Agents,

and Brand Image, Brand Trust, Brand Loyalty and Brand Equity of the

customers of the Life Insurance Corporation of India in Kerala.

The analysis is presented in nine parts.

1) Profile of sample policyholders and their life insurance characteristics

2) Financial awareness, knowledge and habits

3) Life insurance purchasing behaviour

4) Customer perception on promotional strategies of the LIC


281

5) Customer satisfaction on the products and services of the LIC

6) Customer satisfaction on the services of LIC agents

7) Customer perception on knowledge, behavioural pattern and

selection motive of agents

8) Customer evaluation of brand image, trust, loyalty and equity of

the LIC

9) Customer evaluation on relative importance of features/ benefits

of policies of the LIC

5.1 Sample Profile and Life Insurance Characteristics

The profile of sample explaining the demographic and occupational

features of policyholders is presented in the Table given below.

Chapter 5

282

Table 5.1 Demographic Profile of the selected policyholders of the LIC

Categories Frequency % Cumulative % Area Rural 368 69.4 69.4

Urban 162 30.6 100 Gender Male 334 63 63

Female 196 37 100 Marital Status Married 387 73 73

Unmarried 139 26.2 99.2 Others 4 0.8 100

Education Up to primary 48 9.1 9.1 Secondary/higher secondary 151 28.5 37.5 Degree 125 23.6 61.1 Post graduate 184 34.7 95.8 Others 22 4.2 100

Family Structure

Nuclear 430 81.1 81.1 Extended 44 8.3 89.4 Joint 56 10.6 100

Earning Members In Family

1 207 39.1 39.1 2 228 43.0 82.1 3 45 8.5 90.6 4 31 5.8 96.4 5 and above 19 3.6 100

Monthly Income

Up to 10000 173 32.6 32.6 10001-20000 150 28.3 60.9 20001-30000 80 15.1 76.0 30001-40000 73 13.8 89.8 Above 40000 54 10.2 100

Age ≤ 20 7 1.3 1.3 21-30 185 34.9 36.2 31-40 118 22.3 58.5 41-50 127 24.0 82.5 ≥ 51 93 17.5 100

Occupation Agriculture 25 4.7 4.7 Business and self employed 79 14.9 19.6 Government service 157 29.6 49.2 Private service 87 16.4 65.7 NRI/Foreign Employed 33 6.2 71.9 Others 149 28.1 100

Family Income

Up to 25000 276 52.1 52.1 25001-50000 155 29.2 81.3 50001-75000 43 8.1 89.4 75001-100000 36 6.8 96.2 Above 100001 20 3.8 100

Source: Field Survey


283

Table 5.1 exhibits the profile of the sample respondents selected for the

study. It is observed that 69.4 per cent of the respondents belong to rural areas

and 30.6 per cent belong to urban areas. The gender-wise classification shows

that out of the 530 selected policyholders, males constitute 63 per cent and

females come to 37 per cent. From the Table it can be seen that 73 per cent

respondents are married. As to educational qualification 34.7 per cent are post-

graduates, and 23.6 per cent are graduates. Also, it may be seen that 28.5 per cent

have completed school education while 9.1 per cent have primary education.

Professionally qualified policyholders constitute 4.2 per cent. Considering the

type of family, 81.1 per cent are nuclear families. It is also observed that

families having more than 2 earning members constitute 17.9 per cent. The

monthly income classification of the respondents reveals that 60.9 per cent of

the policyholders have monthly income up to ` 20000, and 10.2 per cent have

monthly income above ` 40000. The majority of the policyholders

(81.2 per cent) belong to the age group of 21 to 50. The classification of the

sample based on their occupation shows that the majority of the selected

policyholders belong to government service (29.6 per cent), followed by

private service (16.4 per cent) and business and self- employed (14.9 per cent),

while the group of others constitutes 28.1 per cent, comprising house wives,

daily wage earners and students.

5.1.1 Monthly Income and Family Income of Policyholders

The level of monthly income of respondents and their family income

have importance in deciding investment portfolio and profile of family. The

output of cross tabulation showing interrelation (nature and direction) between

the monthly income and family income of respondents is presented below.

Chapter 5

284

Table 5.2 Cross Tabulation of Monthly Income with Family Income

Monthly Income

Family Income Total ≤ 25000 25001-

50000 50001-75000

75001-100000 ≥100001

UPTO 10000 143(51.8) 22(14.2) 4(9.3) 3(8.3) 1(5) 173(32.6)

10001-20000 116(42) 25(16.1) 3(7) 2(5.6) 4(20) 150(28.3)

20001-30000 17(6.2) 55(35.5) 3(7) 3(8.3) 2(10) 80(15.1)

30001-40000 0(0) 43(27.7) 19(44.2) 7(19.4) 4(20) 73(13.8)

> 40000 0(0) 10(6.5) 14(32.6) 21(58.3) 9(45) 54(10.2)

Total 276 (100) 155(100) 43(100) 36(100) 20(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective columns

To evaluate the statistical significance of the association between

monthly income and family income of respondents, a Log linear Multinomial

Model test was attempted, with the following hypotheses.

H0: There is no dependence between the monthly income and family income

of respondent policyholders

H1: There is dependence between the monthly income and family income of

respondent policyholders

The result of Log linear Multinomial Model are exhibited in Table 5.3.

Table 5.3 Loglinear Multinomial model Goodness-of-Fit Tests

Value Df Sig. Likelihood Ratio 408.885 16 0.000* Pearson Chi-Square 413.978 16 0.000*

Source: Primary Data *Significant at 5 per cent level of significance


285

The result was found to be significant with LR=408.885, χ2 =413.978,

P=0.000<0.05. Hence the relationship explained above is statistically

significant. Therefore it can be concluded that there is relationship between the

monthly income and family income of respondents

5.1.2 Life Insurance Characteristics of Policyholders

The profile of respondents in terms of number of policies purchased,

policies bought based on premium, types of policies preferred, sum assured of

policies subscribed, mode and means of premium payment and the amount of

saving and amount of saving spent towards premium, is discussed below. The

attitude and approach of policyholders on the selection of life insurance as a

means of investment can be explored from the analysis. The preference among

policyholders as to the usage of available methods towards transacting the

different aspects of investment in life insurance is also identified through the

following Tables.

Table 5.4 Total Policies Purchased

Number of Policies Purchased 1 2 3 4 5 6 and

above Number of Policyholders 322 91 59 24 19 15 Per cent 60.8 17.2 11.1 4.5 3.6 2.8 Cumulative Per cent 60.8 78.0 89.1 93.6 97.2 100

Source: Primary Data

The Table shows that most of the sample policyholders (60.8 per cent)

had only one policy. A majority of the sample respondents (89.1 per cent) had

3 or less than 3 policies. The higher the number of polices purchased, the

lower the percentage of respondents belonging to the group. It shows the

indifference of policyholders towards holding more policies.

Chapter 5

286

Table 5.5.1 Number of Polices purchased based on premium (Single Premium, Level Premium and Limited Premium)

No of Policies 0 1 2 3 4 & more Total

Single Premium 462(87.1) 49(9.2) 12(2.3) 3(0.6) 4 (0.8) 530(100)

Level Premium 25(4.7) 319(60.3) 94(17.7) 49(9.2) 43 (8.1) 530(100)

Limited Premium 508(95.9) 17(3.2) 5(0.9) 0(0) 0(0) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

Table 5.5.2 Nature of Policy held by Policyholders

Single Premium Policy Only

Level Premium Policy Only

Limited Premium Policy Only

More Than One Type Of Policy

15(2.8) 447(84.3) 7(1.3) 61(11.6) Source: Primary Data Note: Figures in parenthesis represent percentage to total respondents

The Tables reveal that 87.1 per cent of sample had no single premium

policy, and 95.9 per cent of sample had no limited premium policy, while it

was 4.7 per cent in the case of level premium policy. This shows that

policyholders mostly prefer level premium policy to the other two types of

policies. While evaluating the number of policies, too, the sample policy

holders having more than two policies in the case of single premium policy

and limited premium policy is negligible compared to the number of level

premium policy. This indicates the preference of policy holders towards level

premium policy over the other two types of policies. The percentages of

policyholders having only single premium policy, level premium policy, and

limited premium policy are 2.8, 84.3, and 1.3 respectively. The figure also

points out the preference of the sample policyholders towards level premium

policy over the other two types of policies. While single premium policies

involve huge initial commitment, limited premium policies need high

premium, as the policies charge premium for a short period with coverage for


287

longer periods. It might be the reason for the preference of policyholders of

level premium policies over the other two types of policies. Possibly a policy

system may help in changing this situation, which should address an optimum

for the single premium policy and limited premium policy.

Table 5.6 Types of Policies Purchased

Name of Policy Number of policyholders

Total Having the policy

Not having the policy

Jeevan Anand 181(34.2) 349(65.8) 530(100)

Money Back Plans 152(28.7) 378(71.3) 530(100)

New Bima Gold 74(14) 456(86) 530(100)

Endowment Table 14 72(13.6) 458(86.4) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

The Table explains that the most preferred policy is Jeevan Anand,

followed by Money Back Plans, New Bima Gold, and Endowment Table 14.

Out of 20 policies, those policies having 10 per cent or more subscription are

enlisted.

Table 5.7 Sum Assured of All Policies

Sum Assured Number of Policyholders Percentage Cumulative

Percentage Up to 1 Lakh 225 42.5 42.5 1Lakh To 5 Lakh 247 46.6 89.1 6Lakh To 10 Lakh 49 9.2 98.3 11Lakh To 15 Lakh 4 .8 99.1 Above 15 Lakh 5 .9 100


Chapter 5

288

The Table reveals that the vast majority (89.1 per cent) of the sample

policyholders are holding policies worth less than or equal to 5 Lakh. While

reading along with the number of policies taken, it is clear that most of the

sample respondents have taken less number of policies (89.1 per cent have less

than or equal to 3 policies) with low sum assured (less than or equal to 5 Lakh).

Table 5.8 Premium Payment Period

Premium Payment Period Number of Policyholders Per cent Monthly 186 35.1 Quarterly 172 32.5 Half Yearly 39 7.4 Yearly 57 10.8 More Than One Mode 61 11.5 Single Premium 15 2.8 Total 530 100


The Table shows that the most preferred means of premium payment

among the sample respondents are Monthly and Quarterly, as 67.6 per cent

respondents belong to both these groups.

Table 5.9 Mode of Premium Payment

Premium Payment Period Number of Policyholders Percentage Agents 263 49.6 Directly In Office 123 23.2 Premium Collection Points 24 4.5 Salary Savings Scheme 43 8.1 More Than One Mode 43 8.1 Others 34 6.4 Total 530 100



289

It is clear from the Table that a large percentage (49.6 per cent) of the

sample respondents prefer payment of premium through Agents, followed by

Payment Directly in Office (23.2 per cent).

Table 5.10 Average Savings per Month

Savings per month

Number of Policyholders Per cent Cumulative

Per cent Below 5000 297 56.0 56.0 5001-10000 142 26.8 82.8 10001-15000 52 9.8 92.6 15001-20000 26 4.9 97.5 Above 20000 13 2.5 100.0


The Table shows that the majority (82.8 per cent) of the sample

policyholders have a saving rate below` 10000pm.

Table 5. 11 Amounts Spent on Life Insurance Premium per Annum

Amount spent (annually)on life insurance premium Frequency Per cent Cumulative

per cent Below 5000 220 41.5 41.5 5001-10000 168 31.7 73.2 10001-15000 85 16.0 89.2 15001-20000 24 4.5 93.8 Above 20000 33 6.2 100.0


The observed fact is that 73.2 per cent of the sample respondents spend

only an amount less than or equal to ` 10000 pa for investment in LIC

policies.

Chapter 5

290

5.1.3 Nature of relationship between Number of Polices Purchased and Sum Assured of All Policies The analysis of relationship between the number of policies purchased

and the total sum assured of such policies signifies the attitude of

policyholders towards holding life insurance policies. In order to assess the

relationship between the number of policies and sum assured of policies

subscribed, a cross tabulation of the variables has been done. The results of

cross tabulation between the policies subscribed by the respondents in the

sample survey and the sum assured of all policies subscribed by them are

presented in the following Table.

Table 5.12 Cross Tabulation of Number of Policies with Sum Assured of All Policies

No. of Policies

Sum Assured Of All Policies (in Lakh) Total

Upto 1 1 to 5 6 to 10 11 to 15 Above 15

1 203(90.2) 108(43.7) 10(20.4) 0(0) 1(20) 322(60.8)

2 18(8) 65(26.3) 5(10.2) 1(25) 2(40) 91(17.2)

3 4(1.8) 45(18.2) 10(20.4) 0(0) 0(0) 59(11.1)

4 0(0) 11(4.5) 11(22.4) 2(50) 0(0) 24(4.5)

5 0(0) 11(4.5) 8(16.3) 0(0) 0(0) 19(3.6)

≥ 6 0(0) 7(2.8) 5(10.2) 1(25) 2(40) 15(2.8)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

It is seen from the Table that from among the 530 policyholders

considered for the purpose of the study, 60.80 per cent hold one policy.

Among the policyholders holding one policy, 63 per cent have policies worth

one lakh. It is also evident that 89.1 per cent of the sample respondents have

subscribed to less than or equal to 3 policies and 89.05 per cent have


291

subscribed to policies worth 5 lakhs or less. It seems that people do not

prefer holding more number of policies and investing huge amounts in life

insurance.

To evaluate the statistical significance, if any, of the relationship

between the number of policies subscribed and the sum assured of policies

subscribed, a Loglinear Multinomial Model was attempted to test the

following hypotheses.

H0: There is no dependence between the number of policies and the sum

assured of polices subscribed.

H1: There is dependence between the number of policies and the sum


The result of the Loglinear Model are exhibited in the following Table.

Table 5.13 Loglinear Multinomial Model Goodness-of-Fit Tests

Value Df Sig.

Likelihood Ratio 235.47 20 0.000

Pearson Chi-Square Model: Multinomial 262.26 20 0.000* Source: Primary Data Significant at 5 per cent level of significance

The result was found to be significant with LR = 235.470, χ2 =262.260,

p<0.05. Hence the relationship explained above is statistically significant.

Therefore it can be concluded that there is relationship between the number

of policies and the sum assured of policies subscribed. The detailed

outcomes of the Loglinear Model can be explained by the following results.

Chapter 5

292

Table 5.13 (a) Parameter Estimates

Parameter(No. of Policies Purchased) Estimate Std.

Error Z Sig.

1 3.067 .264 11.609 .000*

2 1.803 .279 6.469 .000*

3 1.369 .289 4.736 .000*

4 .470 .329 1.428 .153

5 .236 .345 .684 .494

6 and above 0b . . . Source: Primary Data * Significant at 5 Per cent level of significance

The parameter “Number of Policies Purchased 6 and above” is set to

zero for relative evaluation. From the above Table, it can be observed that,

when compared to the number of policies purchased 6 and above, the

majority of policyholders prefer having One Policy Followed By Two, Three,

Four and Five Policies.(Z= 11.609, 6.469 and 4.736) where p<0.05 in the

first 3 cases. In other words, the number of policyholders purchasing one

policy is more than three times that of customers who purchase six/more

policies.

Table 5.13 (b) Parameter Estimates

Parameter (Sum Assured of Policies) Estimate Std.

Error Z Sig.

Up to 1Lakh 3.807 .452 8.420 .000* 1Lakh to 5 Lakh 3.900 .452 8.635 .000* 6 Lakh to 10 Lakh 2.282 .469 4.862 .000* 11 Lakh to 15 Lakh -.223 .671 -.333 .739 Above 15 Lakh 0b . . .

Source: Primary Data * Significant at 5 per cent level of significance


293

The parameter “sum assured of all policies above 15 Lakh” is set to zero

for relative evaluation. From the Table , it can be inferred that, when compared

to the sum assured of polices “above 15 Lakh” sums assured of policies up to

1 Lakh and 1 Lakh to 5 Lakh are the mostly preferred amounts of investment

by respondents in the sample survey (Z=8.420, 8.635, p<0.05). While reading

the two Tables given above, it can be seen that most respondents prefer to

have less number of policies with low sum assured. Further, the alternative

hypothesis is validated, thereby saying that the dependence between the two

categories is true to the extent of the observation made earlier.

5.1.4 Relationship of Total Policy Purchased, Sum Assured of all policies, Period of premium payment and Mode of Premium Payment, with Area, Occupation and Family Structure The selected policyholders belonging to different areas (Rural and Urban),

Occupations (Agriculture, Business and Self employment, Government Service,

Private Service, NRI/ foreign employment and Others), and Family Structure

(Nuclear, Extended and Joint) might have difference in preference as to

number of policies purchased, sum assured of policies purchased, period

chosen for payment of premium, and mode of premium payment. The analysis

of the relationship among these variables will be helpful in identifying the

attitudes and preferences of policyholders in this regard, which thereby effect

changes in the marketing policy framework.

5.1.4.1 Relationship of Total Policy Purchased with Area

In order to assess the relationship between areas of residence and

policies bought, a cross- tabulation of the variables has been done. In relation

to change in area of residence, the preference of policyholders as to number of

policies purchased might show difference. The interrelations between the two

variables are illustrated in the Table given below.

Chapter 5

294

Table 5.14 Cross Tabulation of Total Policy purchased with Area

Area Total Policy Purchased

Total 1 2 3 4 5 6

Rural 232(63.1) 58(15.8) 40(10.9) 16(4.3) 13(3.5) 9(2.4) 368(100) Urban 90(55.6) 33(20.4) 19(11.7) 8(4.9) 6(3.7) 6(3.7) 162(100) Total 322(60.8) 91(17.2) 59(11.1) 24(4.5) 19(3.6) 15(2.8) 530(100)

Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

The Table shows that from among 530 policyholders considered for the

purpose of the study, 60.80 per cent hold one policy and 89.1 per cent hold 3

or less than 3 policies. While considering rural (89.8 per cent) and urban areas

(87.7 per cent), the sample policyholders prefer to hold less than or equal to 3

policies. The majority of policyholders in both rural and urban areas hold one

policy. It highlights that preference of holding number of shares doesn’t differ

with area of residence.

To evaluate the statistical significance, if any, of the relationship between

number of policies subscribed and sum assured of polices subscribed, a Loglinear

Multinomial Model was attempted to test the following hypotheses.

H0: There is no dependence between the number of policies and area of

residence.

H1: There is dependence between the number of policies and area of

residence.

The results of Loglinear Model are exhibited in following Table.


Value Df Sig. Likelihood Ratio 3.172 5 .673 Pearson Chi-Square Model: Multinomial 3.229 5 .665 Source: Primary Data


295

The result was found to be not significant with LR=3.172, χ2 =3.229,

p>0.05. Hence the relationship explained above is not statistically significant.

Therefore it can be concluded that, irrespective of area of residence, policyholders

are having the same pattern and attitude towards holding life insurance policy.

5.1.4.2 Relationship of Total Policy with Occupation

The analysis of relationship between occupation of policyholders and

number of policies held exposes the preference of occupational groups towards

holding more number of life insurance policies. In order to assess the relationship

between the number of policies held and occupational groups, a cross-tabulation

of the variables has been done. The results of the cross-tabulation between the

number of policies subscribed by the respondents in the sample survey and their

occupational groups are presented in the following Table.

Table 5.16 Cross-Tabulation of Total Policy with Occupation

Total Policy

Purchased

Occupation Total

AC BSE GS PS NRI/FE Others

1 17(68) 49(62) 59(37.6) 60(69) 16(48.5) 121(81.2) 322(60.8)

2 4(16) 12(15.2) 41(26.1) 11(12.6) 9(27.3) 14(9.4) 91(17.2)

3 1(4) 9(11.4) 30(19.1) 7(8) 3(9.1) 9(6) 59(11.1)

4 1(4) 4(5.1) 11(7) 5(5.7) 0(0) 3(2) 24(4.5)

5 2(8) 0(0) 9(5.7) 2(2.3) 4(12.1) 2(1.3) 19(3.6)

≥ 6 0(0) 5(6.3) 7(4.5) 2(2.3) 1(3) 0(0) 15(2.8)

Total 25(100) 79(100) 157 87(100) 33(100) 149(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

While evaluating the cumulative percentage of number of policies held,

the government service group is found to show preference for holding more

Chapter 5

296

policies than other groups. In most of the occupational groups, a high

percentage of respondents has subscribed to one policy. Coming down to

increased number of polices (more than one) respondents belonging to

Government Service show preference towards holding higher number of

policies.


between the number of policies subscribed and the occupational groups of the

respondents, a Loglinear Multinomial Model was attempted to test the


H0: There is no dependence between the number of policies and the sum


H1: There is dependence between the number of policies and the sum


The results of the Loglinear Model test are exhibited in Table 5.17.


Value Df Sig.

Likelihood Ratio 94.190 25 .000

Pearson Chi-Square Model: Multinomial 87.748 25 0.000* Source: Primary Data * Significant at 5 per cent level of Significance

The result was found to be significant with LR= 94.190, χ2 =87.748,


Therefore, it can be concluded that the number of policies purchased differs

among different occupational groups of respondents. The following Table will

explain the fact in detail.


297


Parameter(Occupation) Estimate Std. Error Z Sig. Agriculture -1.785 .216 -8.259 0.000* Business and Self Employment -.634 .139 -4.559 0.000* Government Service .052 .114 .457 .647 Private Service -.538 .135 -3.988 0.000* NRI/Foreign Employment -1.507 .192 -7.835 0.000* Others 0b . . .


The parameter “others” is set to Zero for relative evaluation. The above

Table illustrates that except in the case of respondents belonging to Government

Service, all respondents from other groups seem to hold lesser number of policies

compared to those in group of Others’ in the order of Government Service,

Others, Private Service, Business and Self- Employment and Agriculture.

5.1.4.3 Relationship of Total Policy with Family Structure

The nature of life insurance policies held and the family structure of

respondents are cross- tabulated to see the relationship, if any, between the

two. The preference of policyholders belonging to different family structures

as to holding a specific number of policies is revealed in the following table.

Table 5.18 Cross Tabulation of Total Policy with Family Structure

Total Policy Purchased

Family Structure Total Nuclear Extended Joint

1 273(63.5) 25(56.8) 24(42.9) 322(60.8) 2 67(15.6) 9(20.5) 15(26.8) 91(17.2) 3 50(11.6) 3(6.8) 6(10.7) 59(11.1) 4 16(3.7) 3(6.8) 5(8.9) 24(4.5) 5 15(3.5) 1(2.3) 3(5.4) 19(3.6) ≥ 6 9(2.1) 3(6.8) 3(5.4) 15(2.8)

Total 430(100) 44(100) 56(100) 530(100) Source: Primary data Note: Figures in parenthesis represent percentage to total in respective rows

Chapter 5

298

It is seen from the Table that, from among 530 policyholders

considered for the purpose of study, 430, i.e., 81.13 per cent belong to

nuclear families. Among all the three groups of family structures, a

somewhat same pattern is found as to holding a specific number of policies.

Except in the case of extended families where 6 and above policies are held

by 3 respondents, it is seen that there is a tendency to hold less number of

policies.


between number of policies subscribed and family structure groups, a

Loglinear Multinomial Model was attempted to test the following hypotheses.

H0: There is no dependence between number of policies subscribed and

family structure groups.

H1: There is dependence between number of policies subscribed and family

structure groups.


Value Df Sig. Likelihood Ratio 15.685 10 0.109 Pearson Chi-Square Model: Multinomial 17.155 10 0.071

Source: Primary data

The result was found to be not significant with LR= 15.685, χ2=.071,


Therefore, it can be concluded that, irrespective of different family structure,

policyholders have a similar pattern and attitude towards holding life insurance

policies.


299

5.1.4.4 Relationship of Sum Assured of All Policies with Area

The following Table analyses the relationship between the sum

assured of policies with respect to Area through a cross-tabulation. It

exhibits the preference of policyholders towards holding policies worth

large/small sums assured, with respect to area of residence.

Table 5.20 Cross- Tabulation of Sum Assured Of All Policies with Area

Sum Assured Of All Policies Area Total Rural Urban Up to 1 Lakh 172(46.7) 53(32.7) 225(42.5) 1 Lakh To 5 Lakh 160(43.5) 87(53.7) 247(46.6) 6 Lakh To 10 Lakh 30(8.2) 19(11.7) 49(9.2) 11 Lakh To 15 Lakh 4(1.1) 0(0) 4(0.8) Above 15 Lakh 2(0.5) 3(1.9) 5(0.9) Total 368(100) 162(100) 530(100)

Source: Primary data Note: Figures in parenthesis represent percentage to total in respective rows

The Table shows that the large majority (89.1 per cent) respondents hold

policies worth 5 lakh or less than that. Compared to urban areas, policyholders

belonging to rural areas hold more policies worth smaller sums assured. It

might be due to the financial status or level of awareness on features and

benefits of life insurance policies.


between sum assured of policies and area of residence, a Loglinear


H0: There is no dependence between sum assured of policies and area of

residence.

H1: There is dependence between sum assured of policies and area of

residence.

Chapter 5

300

Table 5.21 Multinomial Loglinear goodness of fit

Value Df Sig


Pearson Chi-Square Model : Multinomial 13.092 4 .011* Source: Primary data * Significant at 5 per cent level of Significance

The result was found to be significant with LR= 14.181, χ2=13.092,


Therefore, by rejecting the null hypothesis it can be concluded that with

respect to change in area of residence, there is significant difference as to the

sum assured of polices held by respondents.

The following Tables as to parameter estimates will make the fact clear.

Table 5.21(a) Parameter Estimates

Parameter(Area) Estimate Std. Error Z Sig.

Rural .820 .094 8.702 .000 *

Urban 0b . . . Source: Primary Data * Significant at 5 per cent level of significance

The parameter “urban” is set to zero for relative evaluation. From the

above Table, it can be observed that, the relationship being significant,

compared to policyholders in urban areas, the preference of policyholders in

rural areas holding policies with higher volume is lower (Z= 8.702) where

p<0.05.

5.1.4.5 Relationship of Sum Assured Of All Policies with Occupation

The Table shows the interrelationship between occupation of respondents

and sum assured of policies held by them. The preference of policyholders


301

based on nature of occupation and sum assured of policies has much relevance

in identifying the attitude of particular segments of policyholders towards

holding policies with lower or higher value.

Table 5.22 Cross Tabulation of Sum Assured Of All Policies with Occupation

Sum assured

of all policies

Occupation

Total AC BSE GS PS NRI/FE Others

Up to 1 Lakh 13(52) 32(40.5) 35(22.3) 42(48.3) 7(21.2) 96(64.4) 225(42.5)

1Lakh To 5 Lakh 11(44) 41(51.9) 92(58.6) 37(42.5) 18(54.5) 48(32.2) 247(46.6)

6Lakh To 10 Lakh 1(4) 4(5.1) 25(15.9) 8(9.2) 7(21.2) 4(2.7) 49(9.2)

11Lakh To 15 Lakh 0(0) 1(1.3) 3(1.9) 0(0) 0(0) 0(0) 4(0.8)

Above 15 Lakh 0(0) 1(1.3) 2(1.3) 0(0) 1(3) 1(0.7) 5(0.9)

Total 25(100) 79(100) 157(100) 87(100) 33(100) 149(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

Even among all categories of occupation, the pattern of preference of

respondents as to holding policies up to 5 Lakh is found to be the same, in the

case of government servants, NRI/FE and business/self employed higher

percentage of respondents belong to the second category i.e. 1 Lakh to 5 Lakh

as to the sum assured of all polices subscribed. Among these three, the

percentage is higher in the case of Government service as they belong to the

regular and stable earning group.


between the number of policies subscribed and the sum assured of polices

Chapter 5

302

subscribed, a Loglinear Multinomial Model Test was attempted to test the


H0: There is no dependence between the sum assured of policies and

occupation.

H1: There is no dependence between the sum assured of policies and

occupation.

The results of the Loglinear Model Test are exhibited in the following Table.

Table 5.23 Loglinear Multinomial Model Test of Goodness of Fit

Value Df Sig.

Likelihood Ratio 83.899 20 0.000

Pearson Chi-Square Model: Multinomial 79.625 20 0.000* Source: Primary data *Significant at 5 per cent level of significance

The result was found to be not significant with LR= 83.899, χ2= 79.625,

p <0.05. Hence the relationship explained above is statistically significant.

Therefore, with the rejection of null hypothesis it can be concluded that the

preference towards holding policies with different sums assured varies among

different occupational groups.

5.1.4.6 Relationship of Sum Assured of All Policies with Family Structure

The result of cross-tabulation on the sum assured of all policies with the

family structure of the respondents is depicted below. The preference of

particular groups of family structures towards holding polices with different

sums assured of policies can be assessed by the process which is worthwhile in

deciding the market segment in marketing life insurance.


303

Table 5.24 Cross -Tabulation of Sum Assured Of All Policies with Family Structure

Sum Assured Of All Policies

Family Structure Total

Nuclear Extended Joint Up to 1 Lakh 192(44.7) 19(43.2) 14(25) 225(42.5) 1 Lakh To 5 Lakh 200(46.5) 18(40.9) 29(51.8) 247(46.6) 6 Lakh To 10 Lakh 33(7.7) 5(11.4) 11(19.6) 49(9.2) 11 Lakh To 15 Lakh 2(0.4) 2(4.5) 0(0) 4(0.8) Above 15 Lakh 3(0.7) 0(0) 2(3.6) 5(0.9) Total 430(100) 44(100) 56(100) 530(100)


The Table reveals that respondents belonging to joint structure family

groups have high preference towards holding policies with sums assured

from 1 to 5 Lakh. In both the other cases, higher preference is observed

towards policies worth 1 Lakh or less. Considering the sum assured of

policies up to 5 Lakh, the largest number (83 per cent) is from the nuclear

family group.


between the sum assured of policies subscribed and the family structure

groups of the respondents, a Loglinear Multinomial Model was attempted to

test the following hypotheses.

H0: There is no dependence between the sum assured of policies and the

family structure of respondents.

H1 There is dependence between the sum assured of policies and the family

structure of respondents.

The results of the Loglinear Model are exhibited in the following Table.

Chapter 5

304

Table 5.25 Loglinear Multinomial Model Goodness of Fit Tests

Value Df Sig. Likelihood Ratio 20.951 8 0.007 Pearson Chi-Square Model: Multinomial 27.12 8 0.001*


The result was found to be significant with LR= 20.951, χ2 = 27.120,


Therefore, by rejecting the null hypothesis it can be concluded that there is

relationship between the sum assured of policies subscribed and the family



Parameter(Family Structure) Estimate Std.

Error Z Sig.

Nuclear 2.038 .142 14.349 0.000* Extended -.241 .201 -1.197 .231 Joint 0b . . .

Source: Primary Data * Significant at 5 Per cent level of significance

The parameter “Joint family” is set to Zero, i.e., taken as the base for

relative evaluation. Among the family structure groups, value for nuclear

family is found to be significant with p<0.05 with high Z value 14.349. It

means that in respect to joint family structure group, respondents in the

nuclear family structure group hold policies 2 times worth the sum assured.

5.1.4.7 Relationship of Premium Payment Period with Area

The Tables exhibit the interrelationship between the area of residence of

the respondents and the choice opted by them for payment of premium. The


305

preference of the mode of premium payment in relation to area may depend on

the nature of their earning, which is helpful in designing suitable marketing

policies in the respective areas. The result of cross- tabulation between the

variables is outlined below.

Table 5.26 Cross Tabulation of Area with Premium Payment Period

Area Premium Payment Period

Total Monthly Quarterly Half

Yearly Yearly More Than One Mode

Single Premium

Rural 129(35.1) 125(34) 27(7.3) 33(9) 42(11.4) 12(3.3) 368(100)

Urban 57(35.2) 47(29) 12(7.4) 24(14.8) 19(11.7) 3(1.9) 162(100) Total 186(35.1) 172(32.5) 39(7.4) 57(10.8) 61(11.5) 15(2.8) 530(100) Source: Primary data Note: Figures in parenthesis represent percentage to total in respective rows

Table 5.26 reveals that a somewhat similar proportion of respondents

prefer any method of premium payment irrespective of their area. It indicates

that there is no specific choice among rural and urban respondents as to

selection of a particular method of premium payment. The nature of earning

and frequency and regularity of earning are the factors that might be

influencing the policyholders in choosing a particular means of premium

payment. It can also be concluded that such factors have little impact on

deciding the means of premium payment.


between the number of policies subscribed and the occupational groups of

respondents, a Loglinear Multinomial Model was attempted to test the

following Hypotheses.

H0: There is no dependence between area and premium payment period.

H1: There is dependence between area and premium payment period.

Chapter 5

306

The results of Loglinear Model are exhibited in the following Table.


Value Df Sig. Likelihood Ratio 5.113 5 0.402 Pearson Chi-Square Model: Multinomial 5.227 5 0.389


The result was found to be not significant with LR= 5.113, χ2= 5.227,

p>0.05. Hence, the relationship explained above is not statistically significant.

Therefore, by accepting the null hypothesis it can be concluded that the area of

residence of respondents has only very little influence over the selection of a

particular mode for premium payment.

5.1.4.8 Relationship of Premium Payment Period with Occupation

The relationship between the occupational status of respondents and

their preference towards particular means for payment of premium is cross-

tabulated and the output is presented below. The analysis will also be helpful

in extracting the nature of relationship between the regularity and stability of

earning and the choice of a particular period of premium payment.

Table 5.28 Cross tabulation of Premium Payment Period with Occupation

Premium Payment Period

Occupation Total AC BSE GS PS NRI/FE Others

Monthly 7(28) 29(36.7) 70(44.6) 23(26.4) 1(3) 56(37.6) 186(35.1) Quarterly 10(40) 25(31.6) 32(20.4) 38(43.7) 14(42.4) 53(35.6) 172(32.5) Half Yearly 2(8) 9(11.4) 4(2.5) 4(4.6) 5(15.2) 15(10.1) 39(7.4) Yearly 4(16) 9(11.4) 10(6.4) 10(11.5) 11(33.3) 13(8.7) 57(10.8) More Than One Mode 1(4) 6(7.6) 36(22.9) 10(11.5) 1(3) 7(4.7) 61(11.5)

Single Premium 1(4) 1(1.3) 5(3.2) 2(2.3) 1(3) 5(3.4) 15(2.8) Total 25(100) 79(100) 157(100) 87(100) 33(100) 149(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows


307

The Table reveals that monthly payment of premium is opted mostly

(44.60 per cent) by the occupational category of government service,

followed by business and self- employed groups. In the case of private

service and NRI/FE category, the most opted means of premium payment is

quarterly. Considering all the options among all the occupational

categories, the two most selected means for payment of premium are

monthly and quarterly.


between the number of policies subscribed and occupational groups of

respondents, a Loglinear Multinomial Model was attempted to test the following

hypotheses.

H0: There is no dependence between occupation and premium payment

period.

H1: There is dependence between occupation and premium payment period.

Table 5.29 Loglinear Multinomial Test of Goodness of Fit

Value Df Sig. Likelihood Ratio 91.315 25 0.000 Pearson Chi-Square Model : Multinomial 89.365 25 0.000*


The result was found to be significant with LR= 91.315, χ2=.000,


Therefore, by validating the alternative hypothesis it can be concluded that the

preference of different modes of premium payment among respondents vary

among the 5 categories of occupation significantly.

Chapter 5

308


Parameter (Premium Payment Period) Estimate Std. Error Z Sig.

Monthly 2.518 .268 9.380 0.000* Quarterly 2.439 .269 9.061 0.000* Half Yearly .956 .304 3.145 0.002* Yearly 1.335 .290 4.601 0.000* More than One Mode 1.403 .288 4.868 0.000* Single Premium 0b . . .

Source: Primary data * Significant at 5 per cent level of significance

The difference in the means opted for payment of premium is significant

in all cases. The parameter of Single Premium is set to zero for relative

evaluation. From the above Table, it can be observed that the number of

policyholders who have opted monthly premium is almost 2.5 times to those

policyholders who opt single premium (Z= 9.380, 9.061, 4.868, 4.601 and

3.145) where p<0.05 in all cases.

5.1.4.9 Relationship of Premium Payment Period with Family Structure

The relationship between premium payment period and family structure

of respondents is cross- tabulated and the output is presented below. The

preference existing among different family structure groups as to means of

premium payment may differ, as each family structure has its own

characteristics as to number of members, nature of earning income, disposable

income on hand, etc.


309

Table 5.30 Cross Tabulation of Premium Payment Period with Family Structure

Payment Premium Period Family Structure

Total Nuclear Extended Joint

Monthly 148(34.4) 17(38.6) 21(37.5) 186(35.1) Quarterly 143(33.3) 10(22.7) 19(33.9) 172(32.5) Half Yearly 34(7.9) 3(6.8) 2(3.6) 39(7.4) Yearly 52(12.1) 4(9.1) 1(1.8) 57(10.8) More Than One Mode 41(9.5) 9(20.5) 11(19.6) 61(11.5) Single Premium 12(2.8) 1(2.3) 2(3.6) 15(2.8) Total 430(100) 44(100) 56(100) 530(100)


While considering the different types of family structure groups, it can

be seen that monthly and quarterly payments are the most opted means chosen

for premium payment in the order specified. The Table also reveals that a very

meagre percentage of respondents have opted single premium means for

premium payment which signifies the non-preference of respondents towards

such policies.


between the number of policies subscribed and the occupational groups of

respondents, a Loglinear Multinomial Model was attempted to test the following

hypotheses.

H0: There is no dependence between family structure and premium payment

period.

H1: There is dependence between family structure and premium payment

period.

Chapter 5

310

Table 5.31 Loglinear Multinomial test of Goodness-of-Fit

Value Df Sig. Likelihood Ratio 17.488 10 .064 Pearson Chi-Square Model: Multinomial 15.907 10 .102 Source: Primary Data

The result was found to be not significant with LR= 17.488, χ2=.064,


Therefore, by accepting the null hypothesis it can be concluded that among

different family structure groups, the preference for different modes for

premium payment is similar.

5.1.4.10 Relationship of Mode of Premium Payment with Area

The Table 5.31 exhibits the dependence between the area and method

selected by respondents for payment of premium. With regard to change in

area, the mode of payment of premium may differ, based on availability of

service of agent in collecting premium, nearness/convenience of branch office

or premium collection points, the nature of earning, knowledge on various

options etc.

Table 5.32 Cross- Tabulation of Area with Mode of Premium Payment

Area

Mode of Premium Payment

Total Individual Agents

Directly In Office

Premium Collection Points

Salary Savings Scheme

More Than One

Mode

Others

Rural 192(52.2) 84(22.8) 14(3.8) 26(7.1) 31(8.4) 21(5.7) 368(100) Urban 71(43.8) 39(24.1) 10(6.2) 17(10.5) 12(7.4) 13(8) 162(100) Total 263(49.6) 123(23.2) 24(4.5) 43(8.1) 43(8.1) 34(6.4) 530(100)



311

In rural and urban areas, a majority of the respondents depend on agents

(52.2, 43.8 per cent) and office (22.8, 24.1 per cent) for payment of premium.

Considering the whole area, the most preferred modes of premium payment

among sample respondents are payments through agents and office.

To evaluate the statistical significance, if any, of the relationship between

the area and the mode of payment of premium, a Loglinear Multinomial Model

was attempted to test the following hypotheses.

H0: There is no dependence between area and mode of premium payment.

H1: There is dependence between area and mode of premium payment.


Value Df Sig. Likelihood Ratio 6.421 5 0.267 Pearson Chi-Square Data Model: Multinomial 6.587 5 0.253


Since the result is LR= 6.421, χ2= 0.253, p>0.05, the relationship

explained above is not statistically significant. Therefore, by accepting the null

hypothesis it can be concluded that between rural and urban areas, the

preference for different methods of premium payment is similar.

5.1.4.11 Relationship of Mode of Premium Payment with Occupation

The relationship between respondents belonging to different occupational

groups and different modes of premium payment are cross-tabulated to see the

relationship, if any, among the variables and the output is presented below.

The result is of much significance to management in designing marketing

policies as to mode of premium collection.

Chapter 5

312

Table 5.34 Cross- Tabulation of Mode of Premium Payment with Occupation

Mode Of Premium Payment

Occupation

AC BSE GS PS NRI/FE Others Total

Agents 15(60) 54(68.4) 32(20.4) 45(51.7) 14(42.4) 103(69.1) 263(48.9)

Directly at Office 5(20) 17(21.5) 41(26.10) 27(31) 6(18.2) 27(18.1) 123(23.2)

Premium Collection Points

1(4) 1(1.3) 7(4.5) 3(3.4) 2(6.1) 10(6.7) 24(4.5)

Salary Savings Scheme 0(0) 0(0) 43(27.4) 0(0) 0(0) 0(0) 43(8.1)

More Than One Mode 1(4) 6(7.6) 22(14) 5(5.7) 4(12.1) 5(3.4) 43(8.1)

Others 3(12) 1(1.3) 12(7.6) 7(8) 7(21.2) 4(2.7) 34(6.4)

Total 25(100) 79(100) 157(100) 87(100) 33(100) 149(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective columns

While considering the preference of respondents over different modes of

premium payment, it can be seen that except in the government service group,

the most preferred mode is agents. In the case of respondents belonging to

government service, salary saving scheme is found to be the more

(27.40 per cent) opted mode than others. It also highlights that 73.60 per cent

among respondents in the group opt other modes for premium payment.


between occupation and mode of payment of premium, a Loglinear


H0: There is no dependence between occupation and mode of premium

payment.

H1: There is dependence between occupation and mode of premium payment.


313


Value Df Sig. Likelihood Ratio 172.68 25 0.000 Pearson Chi-Square Model: Multinomial 170.7 25 0.000*


Since the result is LR= 172.680, χ2=.000, p< 0.05, the relationship

explained above is statistically significant. Therefore, it can be concluded that

the preferences of respondents on the choice of mode of premium payment

vary over different occupation groups.


Parameter (Mode of Premium Payment) Estimate Std.

Error Z Sig.

Agents 2.046 .182 11.225 0.000* Directly at Office 1.286 .194 6.636 0.000* Premium Collection Points -.348 .267 -1.306 .191 Salary Savings Scheme .235 .229 1.023 .306 More than One mode .235 .229 1.023 .306 Others 0b . . .

Source: Primary Data * Significant at 5 per cent level of Significance

The parameter “others” is set to zero for relative evaluation. The values

for the first two modes of premium payment are found to be significant. Apart

from that, they are the most preferred means for premium payment, i.e., 2 and

1.2 times that of the mode “Others”. The Z value 11.225 for agents indicates

that it is the most preferred mode of premium payment followed by office,

salary savings scheme and more than one mode (Z value 6.636, 1.023, 1.023

respectively).

Chapter 5

314

5.1.4.12 Relationship of Mode of Premium Payment with Family Structure

The preferences of respondents belonging to different family structure

groups towards listed modes of premium payment are cross tabulated and the

output is presented below.

Table 5.36 Cross Tabulation of Mode of Premium Payment with Family Structure

Mode of Premium Payment

Family Structure Total

Nuclear Extended Joint Agents 224(52.1) 19(43.2) 20(35.7) 263(49.6) Directly at Office 105(24.4) 7(15.9) 11(19.6) 123(23.2) Premium Collection Points 22(5.1) 0(0) 2(3.6) 24(4.5) Salary Savings Scheme 26(6) 8(18.2) 9(16.1) 43(8.1) More Than One Mode 29(6.7) 6(13.6) 8(14.3) 43(8.1) Others 24(5.6) 4(9.1) 6(10.7) 34(6.4) Total 430(100) 44(100) 56(100) 530(100)

Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective columns

The most preferred modes for payment of premium among all family

structure groups are through agents, and office payment . Among these two,

the first choice is higher among all the respondents from the 3 family structure

groups. While considering all the 3 groups as a whole, payments through

agents comes to 49.60 per cent among all modes of premium payment.


between family structure and mode of payment of premium, a Loglinear


H0: There is no dependence between family structure and mode of premium

payment.

H1: There is dependence between family structure and mode of premium

payment.


315


Value Df Sig.


Pearson Chi-Square Model : Multinomial 27.110 10 0.003* Source: Primary Data * Significant at 5 per cent level of significance

The result was found to be significant with LR= 26.190, χ2=.003,

p< 0.05. Hence, the relationship explained above is statistically significant.

Therefore, it can be concluded that the preferences of respondents on the

choice of mode of premium payment vary over different family structure

groups.

5.2 Financial Awareness/Knowledge/Habits

The awareness and knowledge on financial products and services,

especially life insurance products and services on the part of policyholders

play a prominent role in the success of marketing strategies. The awareness

and knowledge are the prerequisites for access and utilization of the products

and services offered by any form of organisation. While the level of awareness

on various financial products and services is measured on a five point scale

with a score ranging from 5 to 1, designating very high level to very poor level

of awareness, the level of awareness on different aspects of purchase and

servicing of products and services of LIC is measured on a seven point scale

with a score ranging from 7 to 1 representing very high to very poor level of

awareness. The respondents were also requested to identify the name of the

policy held by them. A comparative evaluation of investment in life policies of

LIC and the other identified means of investment was done to identify the

investment preference of policyholders. The perception of policyholders as to

Chapter 5

316

adequacy of coverage on life insurance and the reason for inadequate coverage

were also examined to assess the fundamental force behind investment

commitment in life insurance policies.

5.2.1 Level of Awareness of Long- Term Saving Instruments /Assets (LTSIA)

The level of awareness on various long- term saving instruments/assets

decides the investment behaviour of individuals. From marketing point of

view, it is very important to identify the level of awareness among policyholders

as to the different saving instruments/assets available in the financial market.

The level of awareness on long- term saving instruments/ assets may vary

from urban to rural areas. It is very important from the marketing point of

view to see if these responses show any differences between rural and urban

population. Since data provides ranks given by respondents, a non-parametric

test is used.

The Mann-Whitney U test is the most popular of the two-independent-

samples tests. The Mann-Whitney Test is one of the most powerful of the

nonparametric tests for comparing two populations. It is used to test the null

hypothesis that two populations have identical distribution functions against

the alternative hypothesis that the two distribution functions differ only with

respect to location (median), if at all.

The Mann-Whitney test does not require the assumption that the

differences between the two samples are normally distributed. In many

applications, the Mann-Whitney Test is used in place of the two sample

independent t-test when the normality assumption is questionable. This test

can also be applied when the observations in a sample of data are ranks, that

is, ordinal data rather than direct measurements. The results are reported

below:


317

Table 5.38 Descriptive Statistics on Awareness of Selected Long -Term Saving Instruments/ Assets

Financial Instruments Mean(n=530) Std. Deviation Bank Fixed Deposit (BFD) 4.2717 0.88985 Life Insurance (LI) 2.9821 1.55450 Capital Market Products (CMP) 2.4302 3.49849 Government Schemes (GSS) 2.8289 3.25257 Chit Funds (CF) 3.1547 1.23536 Post Office Savings (POS) 3.4736 1.14549 Gold/Jewellery (GJ) 3.7038 1.12750 Real Estate (RE) 3.1113 1.33757


Table 5.39 Mean Ranks of Awareness of Selected Long -Term Savings Instruments/ Assets

Saving Instruments/ Assets

Area Rural (n=368) Urban(n=162)

Mean rank

Sum of Ranks

Mean rank

Sum of Ranks

Bank Fixed Deposit 266.65 98127.50 262.89 42587.50 Life Insurance 260.09 95712.00 277.80 45003.00 Capital Market Products 255.98 94202.00 287.12 46513.00 Government Schemes 257.01 94580.50 284.78 46134.50 Chit Funds 265.14 97571.50 266.32 43143.50 Post Office Savings 261.38 96188.00 274.86 44527.00 Gold/Jewellery 263.95 97135.00 269.01 43580.00 Real Estate 263.69 97038.50 269.61 43676.50


The selected Long –Term saving Instruments/Assets (LTSIA) comprise

Bank Fixed Deposits (BFD), Life Insurance (LI), Capital Market Products

(CMP), Government Schemes (GSS), Chit Funds (CF), Post Office Savings

(POS), Gold/Jewellery (G/J) and Real Estate (RE).

Chapter 5

318

The hypothesis can be stated thus:

H0: There is no difference between Rural and Urban populations in their

median responses for selected LTSIA.

H1: There is difference between Rural and Urban populations in their median

responses for selected LTSIA.

Table 5.40 Man Whitney Test

BFD LI CMP GSS CF POS G/J RE

Mann-Whitney

U 29384.500 27816.000 26306.000 26684.500 29675.500 28292.000 29239.000 29142.500

Wilcoxon W 42587.500 95712.000 94202.000 94580.500 97571.500 96188.000 97135.000 97038.500

Z -.285 -1.251 -2.164 -1.932 -.084 -.967 -.364 -.419

Asymp. Sig.

(2-tailed) 0.775 0.211 0.030* 0.053 0.933 0.333 0.716 0.675

Grouping variable: Area Source: Primary Data * Significant at 5 per cent level of significance

Table 5.37 shows that among the selected Long- Term Savings

Instruments/Assets, the level of awareness is found to be high for Bank

Fixed Deposit ( BFD), Gold/Jewellery (G/J), Post Office Savings (POS ), Chit

Funds (CF), Real Estate (RE), Life Insurance (LI), Government Schemes

(GSS) and Capital Market Products (CMP) in the order. The Mean Ranks

Table 5.38 clearly points out that Bank Fixed Deposit (BFD), Chit funds (CF),

Gold/Jewellery (G/J) in Rural areas and Capital Market Products (CMP),

Government Schemes (GSS), Life insurance (LI) in Urban areas have the top

ranks in order. Table 5.39 reveals that among the selected Long-Term Savings

Instruments/Assets (LTSIA), the hypothesis as to Capital Market Products

(CMP) is rejected as its p value is 0.030 (p<0.05), while that of others is not


319

rejected as their respective p values are 0.775, 0.211, 0.053, 0.933, 0.333,

0.716 and 0.675 (p>.05). It may be concluded that there is significant variation

between rural and urban areas with regard to awareness on Capital Market

Products, i.e., while respondents are having high level of awareness on Capital

Market Products in urban areas (mean score 287.12), the case is the reverse in

rural areas (mean score 255.98).

This problem is again considered among the Occupation groups by

Kruskal-Wallis test as the category (Occupation) consists of more than 2

groups.

The hypotheses can be stated for each case as:

H0: There is no difference in median responses for selected long-term saving

instruments/ assets (LTSIA) among occupation groups.

H1: There is difference in median responses for selected long term saving

instruments/assets (LTSIA) among occupation groups.

Table 5.41 Occupation-wise Mean Ranks of Awareness on Selected Long Term Saving Instruments/Assets

Long Term Saving Instruments/Assets AC BSE GSS PS NRI/

FE Others

N=530 25 79 157 87 33 149 Bank Fixed Deposit 241.94 260.92 262.82 265.20 274.71 272.84 Life Insurance 271.80 248.65 280.28 241.35 303.53 263.48 Capital Market Products 244.96 271.09 305.42 251.02 279.97 229.16 Government Schemes 226.96 216.44 350.66 251.28 246.62 220.73 Chit Funds 311.56 286.12 266.65 282.44 176.77 255.39 Post Office Savings 304.78 243.17 286.33 257.42 218.17 264.00 Gold/Jewellery 227.42 248.99 262.48 269.15 282.92 277.83 Real Estate 235.90 275.19 257.29 289.90 291.08 254.07


Chapter 5

320

Table 5.42 Kruskal Wallis Test

BFD LI CMP GSS CF POS G/J RE

Chi-Square 1.406 6.954 20.841 72.864 17.477 10.349 4.276 5.919 Df 5 5 5 5 5 5 5 5 Asymp. Sig. 0.924 0.224 0.001* 0.000* 0.004* 0.066 0.51 0.314

Grouping variable: occupation Source: Primary Data * Significant at 5 per cent level of significance

From Table 5.40 it can be inferred that respondents belonging to the

classes of Agriculturists (AC) and Business and Self employed (BSE) have

better awareness on Chit Funds (CF), while Government Servants (GSS),

Private Servants (PS), NRI/Foreign Employed (NRI/FE) and Others have

better awareness on Government Schemes, Real Estate, Life insurance and

Gold/ Jewellery respectively. Table 5.41 states that the hypotheses, except for

Bank Fixed Deposit, Life Insurance, Post Office Savings, Gold /Jewellery and

Real Estate, are not rejected as the p values are seen to be 0.924, 0.224, 0.066,

0.510 and 0.314 (p>0.05) respectively while the hypotheses for Capital Market

products, Government Schemes and Chit funds are rejected as their p values are

0.001, 0.000, 0.004 where (p<0.05). It shows that the levels of awareness of

respondents belonging to different occupational groups vary significantly on

Capital Market Products, Government Schemes and Chit Funds.

This problem is considered among the Family structure groups using

Kruskal-Wallis test as there are more than two groups.


H0: There is no difference in median responses for selected long-term saving

instruments/assets among family structure groups.

H1: There is difference in median responses for selected long- term saving

instruments/assets among family structure groups.


321

Table 5.43 Family Structure- wise Mean Ranks on Awareness of Selected Long- Term Saving Instruments/ Assets

Long Term saving Instruments/Assets Nuclear Extended Joint

n=530 430 44 56 Bank Fixed Deposit 271.26 249.16 234.08 Life Insurance 269.98 228.18 260.44 Capital Market Products 262.92 255.33 293.34 Government Schemes 261.58 270.01 292.04 Chit Funds 268.14 242.8 263.1 Post Office Savings 267.22 261.34 255.55 Gold/Jewellery 265.66 275.55 256.38 Real Estate 264.28 260.34 278.92



BFD LI CMP GSS CF POS G/J RE Chi-Square 4.156 3.164 2.184 2.018 1.174 0.347 0.418 0.53 Df 2 2 2 2 2 2 2 2 Asymp. Sig. 0.125 0.206 0.336 0.364 0.556 0.841 0.811 0.767 Grouping variable: family structure Source: Primary Data

It can be inferred from Table 5.44 that Bank Fixed Deposits,

Gold/Jewellery and Capital Market Products have higher Mean ranks among

Nuclear, Extended and Joint family structure groups respectively. It shows

their higher level of awareness in the selected Long-Term Saving

Instruments/assets. Table 5.44 proves that none of the eight hypotheses is

rejected as the p values are seen to be 0.125, 0.206, 0.336, 0.364, 0.556,

0.841, 0.811 and 0.767 (p>0.05) respectively. It highlights that the level of

awareness of selected long-term saving instruments/assets does not vary

significantly the among three family structure groups.

Chapter 5

322

5.2.2 Awareness of Life Insurance Products and Services

The level of awareness on different elements of service marketing mix

influences the purchase behaviour and decision of customers. The awareness on

products and services is a prerequisite to build better Brand image, Brand trust,

Brand loyalty and, ultimately, Brand equity. The level of awareness also influences

decisions of prospective policyholders as to the number of policies purchased, level

of satisfaction towards products and services delivered rendered, etc.

5.2.2.1 Product Awareness

A life insurance product is a bundle of utilities. Awareness on insurance

product comprises the awareness on its features and benefits, terms and

conditions, various documents to be submitted to initiate insurance contract,

and ability to assess the insurable amount and type of policy as to his or her

requirement or suitability of policy. The better the awareness on policy from

the part of customers, the easier to market the life insurance product and

ensure higher persistency ratio of policies.

5.2.2.1.1 Two- Way ANOVA of Product Awareness (PA) by Area and Occupation

The variations of Product Awareness (PA) are analysed with Two- Way

ANOVA by the categories area and occupation and the output is presented in

Table 5.45, 5.46 and 5.47.

Table 5.45 Area- wise Estimated Marginal Means-PA 1.Area

Dependent Variable: Product Awareness 95% Confidence Interval

Area Mean Std. Error Lower Bound Upper Bound Rural 21.805 0.382 21.056 22.555 Urban 22.501 0.548 21.424 23.578



323

Table 5.46 Occupation- wise Estimated Marginal Means-PA 2.Occupation

Dependent Variable: Product Awareness

Occupation Mean Std. Error

95% Confidence Interval Lower Bound

Upper Bound

Agriculture 23.2 1.281 20.683 25.717 Business & Self- Employed 21.001 0.713 19.601 22.401 Govt Service 21.695 0.51 20.693 22.698 Private Service 22.904 0.674 21.579 24.228 NRI/Foreign Employed 21.792 1.092 19.646 23.938 Others 22.327 0.532 21.282 23.372


Table 5.47 Two -Way ANOVA-PA

Tests of Between-Subjects Effects Dependent Variable: Product Awareness

Source Type I Sum of Squares Df Mean

Square F Sig.

Area 46.951 1 46.951 1.199 .274 Occupation 216.461 5 43.292 1.106 .356 Error 20478.241 523 39.155 Total 20741.653 529


The test of mean variation of the scores for product awareness among

rural and urban areas and among different occupational groups using Two-

Way ANOVA shows that the area-wise and occupation-wise variations of the

mean scores are not statistically significant at 5 per cent level of significance

(value of F 1.199 and 1.106, Df 1 and 5 with p=0.274 and 0.356>0.05). As

per Tables 5.45, 5.46 and 5.47, there is no significant difference in the means

Chapter 5

324

scores on product awareness between rural and urban areas and among

different occupational groups. Therefore, it may be concluded that awareness

of product does not differ significantly among respondents in rural and urban

areas and among different occupational groups.

5.2.2.1.2 Two-Way ANOVA of Product Awareness (PA) by Area and Family Structure

The variations of product awareness (PA) with regard to area and family

structure are analysed with Two-Way ANOVA and the output is presented in

Tables 5.48, 5.49 and 5.50.

Table 5.48 Area- wise Estimated Marginal Means-PA

1. Area Dependent Variable: Product Awareness

Area Mean Std. Error 95% Confidence Interval

Lower Bound Upper Bound Rural 20.956 .463 20.046 21.866 Urban 21.582 .599 20.406 22.758


Table 5.49 Family Structure -wise Estimated Marginal Means-PA

2. Family Structure Dependent Variable: Product Awareness

Family structure Mean Std. Error

95% Confidence Interval Lower Bound Upper Bound

Nuclear 22.299 .320 21.670 22.928 Extended 19.719 .947 17.858 21.580 Joint 21.788 .844 20.131 23.446



325

Table 5.50 Two-Way ANOVA-PA Tests of Between-Subjects Effects



Square F Sig.

Area 46.951 1 46.951 1.209 0.272 Family Structure 269.418 2 134.709 3.469 0.031* Error 20425.284 526 38.831 Total 20741.653 529


The mean variation of the scores for product awareness among rural and

urban areas and different family structure groups is tested by Two-Way

ANOVA, which shows that the area-wise variation of the mean scores is not

statistically significant at 5 per cent level of significance (value of F 1.209

6 Df 1 and with p=0.272>0.05) but family structure- wise variation of the mean

scores is statistically significant at 5 per cent level of significance (value of

F 3.469 Df 2 with p=0.031<0.05). As per Tables 5.48, 5.49 and 5.50, product

awareness does not have significant difference between rural and urban areas

while in case of family structure the difference is significant. Therefore, it may be

concluded that area- wise awareness of product is similar among policyholders

but not among family structure groups, i.e., level of product awareness is high in

the case of nuclear family structure groups ( mean score 22.299).

5.2.2.1.3 Two-Way ANOVA of Product Awareness (PA) by Family Structure and Occupation

The variations of product awareness (PA) by family structure and

occupation are analysed with Two-Way ANOVA and the output is presented

in Tables 5.51, 5.52 and 5.53.

Chapter 5

326

Table 5.51 Family Structure wise- Estimated Marginal Means-PA 1. Family Structure


Family Structure Mean Std.

Error


Upper Bound



Table 5.52 Occupation wise- Estimate of Marginal Means-PA 2. Occupation




Upper Bound

Agriculture 22.011 1.293 19.471 24.551 Business & Self- Employed 19.983 .799 18.413 21.554 Govt Service 20.907 .568 19.790 22.023 Private Service 22.075 .741 20.620 23.530 NRI/Foreign Employed 20.983 1.137 18.749 23.216 Others 21.276 .648 20.002 22.549


Table 5.53 Two-Way ANOVA-PA Test of Between Subject Effects



Square F Sig.

Family Structure 272.308 2 136.154 3.509 0.031* Occupation 212.543 5 42.509 1.095 0.362 Error 20256.802 522 38.806 Total 20741.653 529



327

In order to test the mean variation of the scores for product awareness

among different family structure groups and different occupational categories ,

Two-Way ANOVA is used, which indicates that family structure - wise

variations of the mean scores are statistically significant at 5 per cent level of

significance (value of f 3.509 Df 2 and with p=0.031<0.05), while occupation

- wise variations of the mean scores are not statistically significant at 5 per cent

level of significance (value of f 1.095 Df 5 with p=0.362>0.05). As per

Tables 5.51, 5.52 and 5.53, it can be understood that the difference in product

awareness is significant among different family structure groups but not in the

case of occupation-wise categories. Therefore, it may be concluded that the

level of awareness among different occupational categories is almost similar

among selected policyholders but not for policyholders among different family

structure groups, i.e., the level of product awareness is high in the case of

nuclear (mean score 22.273) family structure followed by joint and extended


5.2.2.2 Awareness of Price/Premium

Price or premium payment on policies includes elements like the various

modes of payment of premium as through Individual Agents, in Office, at

Premium Collection Points, Online, etc; various periods for payment like

Monthly, Quarterly, Half-Yearly, etc; the facilities available like Days of

grace, Riders, etc; Mortality table and its usage, Rebates and Extras on Mode

of Payment, Rebate on Large sums assured and the rates of return (including

Bonus, Loyalty additions, etc) on policy. Awareness of these features benefits

both LIC and customers, as the customers can avail themselves of discounts on

certain modes of payment, while LIC is less strained as to cost and effort in

collecting premium if collected in lump annually or biannually.

Chapter 5

328

5.2.2.2.1 Two-Way ANOVA of Awareness on Price/Premium (APP) by Area and Occupation

The variations in the awareness of Price/Premium (APP) of Life

insurance products are analysed with Two-Way ANOVA by the categories

of area and occupation and the output is presented in Tables 5.54, 5.55

and 5.56.

Table 5.54 Area -wise Estimated Marginal Means-APP

1. Area Dependent Variable: Awareness of Price /Premium




Table 5.55 Occupation -wise Estimated Marginal Means-APP

2. Occupation Dependent Variable: Awareness of Price/ Premium


95% Confidence Interval

Lower Bound

Upper Bound

Agriculture 25.585 1.438 22.760 28.410 Business & Self -Employed 25.241 .800 23.670 26.812 Govt Service 27.139 .573 26.015 28.264 Private Service 27.257 .756 25.771 28.743 NRI/Foreign Employed 26.898 1.226 24.490 29.306 Others 26.034 .597 24.861 27.207



329

Table 5.56 Two-Way ANOVA - APP

Tests Of Between-Subjects Effects Dependent Variable: Awareness of Price /Premium


Square F Sig.

Area 9.248 1 9.248 .188 .665

Occupation 295.104 5 59.021 1.197 .309 Error 25786.772 523 49.305 Total 26091.124 529


To test the mean variation of the scores for awareness of price/premium

between rural and urban areas and among different occupational groups,

Two-Way ANOVA is used and it is found that area and occupation-wise

variations of the mean scores are not statistically significant at 5 per cent

level of significance (value of F .188 and 1.197 Df 1 and 5 with p=0.665

and 0.309 >0.05). As per Tables 5.54, 5.55 and 5.56, there is no significant

difference across rural and urban areas and among different occupational

groups with regard to awareness of price/premium. Therefore, it may be

concluded that awareness of price/premium among selected policyholders is

almost similar in rural and urban areas and among different occupational

groups.

5.2.2.2.2 Two-Way ANOVA on Awareness of Price (APP) by Area and Family Structure

The variations of awareness on price/premium (APP) by area and family


Tables 5.57, 5.58 and 5.59.

Chapter 5

330

Table 5.57 Area -wise Estimated Marginal Means-APP

1. Area

Dependent Variable: Awareness of Price /Premium


Lower Bound Upper Bound

Rural 25.716 .521 24.692 26.739

Urban 25.989 .674 24.666 27.312 Source: Primary Data

Table 5.58 Family Structure- wise Estimated Marginal Means-APP

2. Family Structure

Dependent Variable: Awareness of Price/ Premium




Nuclear 26.724 .360 26.016 27.431

Extended 24.306 1.065 22.213 26.399

Joint 26.528 .949 24.663 28.393 Source: Primary Data

Table 5.59 Two-Way ANOVA – APP

Tests of Between-Subjects Effects

Dependent Variable: Awareness of Price /Premium


Square F Sig.

Area 9.248 1 9.248 0.188 .665

Family Structure 233.333 2 116.667 2.374 .094

Error 25848.543 526 49.142

Total 26091.124 529 Source: Primary data


331

The mean variations of the scores for awareness on price/premium are

tested using Two-Way ANOVA between rural and urban areas and among

different family structure groups and it is found that area and family

structure - wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F .188 and 2.374 Df 1 and 2 with

p=0.665>0.01 and p=.094>0.05). As per tables 5.57, 5.58 and 5.59, there is no

significant difference among rural and urban areas and family structure- wise

as to awareness on price/premium. Therefore, it may be concluded that

selected policyholders in rural and urban areas and different family structure

groups are similar on the awareness on price/premium.

5.2.2.2.3 Two-Way ANOVA on Awareness of Price (APP) by Family Structure and Occupation

The variations of awareness of price/premium (APP) by family structure

and occupation are analysed with Two-Way ANOVA and the output is

presented in Tables 5.60, 5.61 and 5.62.

Table 5.60 Family Structure -wise Estimated Marginal Means-APP

1. Family Structure

Dependent Variable: Awareness of Price/ Premium




Nuclear 26.607 .409 25.803 27.411

Extended 23.858 1.087 21.722 25.993

Joint 26.048 .975 24.132 27.964 Source: Primary data

Chapter 5

332

Table 5.61 Occupation -wise Estimated Marginal Means-APP

2. Occupation Dependent Variable: Awareness of Price/ Premium



Upper Bound

Agriculture 24.617 1.450 21.767 27.466 Business & Self - Employed 24.294 .897 22.533 26.056 Govt Service 26.423 .637 25.171 27.675 Private Service 26.462 .831 24.830 28.094 NRI/Foreign Employed 26.143 1.275 23.638 28.649 Others 25.086 .727 23.657 26.515


Table 5.62 Two-Way ANOVA – APP

Tests of Between-Subjects Effects Dependent Variable: Awareness of Price/ Premium


Square F Sig.

Family Structure 234.181 2 117.090 2.398 .092 Occupation 369.063 5 73.813 1.512 .184 Error 25487.880 522 48.827 Total 26091.124 529


Two-Way ANOVA is used to test the mean variation of the scores for

awareness of price/premium among different family structure groups and

different occupational categories, and it is seen that family structure and

occupation-wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F 2.398 and 1.512 Df 2 and 5

with p=0.092>0.05 and p=0.184> 0.05). As per Tables 5.60, 5.61 and 5.62,


333

there is no significant difference among different categories of family structure

and occupation as to awareness on price/premium. Therefore, it may be

concluded that awareness of price/premium among different family structure

groups and occupational categories is similar.

5.2.2.3 Awareness of Distribution Channels

The means through which the products and services are delivered or

rendered is called Channel of distribution. The nature, quality, speed, cost,

efficiency, reach and security of service differ among channels. In an

organisation in the true sense, the channel selected to serve customers should

be able to deliver confidence and trust in products and services, ensuring cost-

effectiveness along with contribution to business growth. The awareness of

multiple channels along with its costs and benefits, service standards, types of

services available will be helpful in ensuring effective servicing of policy.

5.2.2.3.1 Two-Way ANOVA on Awareness of Distribution Channels (ADC) by Area and Occupation

The variations in awareness of distribution channels (ADC) by area and


in the following Tables.

Table 5.63 Area- wise Estimated Marginal Means-ADC

1. Area

Dependent Variable: Awareness of Distribution Channels



Rural 15.400 .300 14.811 15.989

Urban 15.338 .431 14.492 16.185 Source: Primary data

Chapter 5

334

Table 5.64 Occupation -wise Estimated Marginal Means -ADC

2. Occupation Dependent Variable: Awareness of Distribution Channels



Upper Bound

Agriculture 16.652 1.007 14.673 18.630 Business & Self - Employed 14.685 .560 13.585 15.785 Govt Service 15.269 .401 14.482 16.057 Private Service 15.419 .530 14.378 16.459 NRI/Foreign Employed 15.264 .859 13.578 16.951 Others 14.925 .418 14.103 15.746


Table 5.65 Two-Way ANOVA – ADC

Tests of Between-Subjects Effects Dependent Variable: Awareness of Distribution Channels


Square F Sig.

Area 1.563 1 1.563 .065 .799 Occupation 89.277 5 17.855 .738 .595 Error 12649.668 523 24.187 Total 12740.508 529


Tables 5.63, 5.64 and 5.65 show the awareness of distribution channels

of the LIC among selected policyholders based on the area of residence and

their occupational category. Two-Way ANOVA is used to test the mean

variations of the scores for awareness on distribution channels between rural

and urban areas and different occupational groups and it is found that area-


335

wise and occupation-wise variations of the mean scores are not statistically

significant at 5 per cent level of significance (value of F 0.065 and 0.7386 Df

1 and 5 with p=0.799 and 0.595>0.05). The marginal means Table makes it

clear that policyholders pertaining to agriculture are more aware about

distribution channels. Therefore it may be concluded that the level of awareness

of distribution channels does not differ significantly between respondents of

rural and urban areas and among different occupational groups.

5.2.2.3.2 Two-Way ANOVA on Awareness of Distribution Channels (ADC) by Area and Family Structure

The variations in awareness of distribution channels (ADC) are analysed

with Two-Way ANOVA in two categories, area and family structure, and the

output is presented in the following Tables.

Table 5.66 Area- wise Estimated Marginal Means-ADC

1. Area Dependent Variable: Awareness of Distribution Channels

Area Mean Std. Error


Rural 14.643 .363 13.929 15.357 Urban 14.511 .470 13.589 15.434


Table 5.67 Family Structure -wise Estimated Marginal Means-ADC

2. Family Structure Dependent Variable: Awareness of Distribution Channels

Family Structure Mean Std. Error




Chapter 5

336

Table 5.68 Two-Way ANOVA - ADC



Square F Sig.

Area 1.563 1 1.563 .065 0.798 Family Structure 171.846 2 85.923 3.596 .028* Error 12567.099 526 23.892 Total 12740.508 529


Tables 5.66, 5.67 and 5.68 show the level of awareness on distribution

channels of selected policyholders based on the area of residence and family

structure groups. To test the mean variations of the scores for awareness on

distribution channels between rural and urban areas and among different

family structure groups, Two-Way ANOVA is used and it is found that

the area-wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F 0.065 Df 1 and with p=0.798>0.05),

but family structure- wise variations of the mean scores are statistically

significant at 5 per cent level of significance (value of F 3.596 Df 2 with

p=0.028<0.05). As given in Tables 5.66, 5.67 and 5.68, there is no significant

difference between rural and urban areas as to awareness on distribution

channels while the difference is significant in the case of family structure

groups. To conclude, based on area of residence, the awareness on distribution

channels is found to be similar but among respondents belonging to different

family structure groups, the level of awareness on distribution channels is high

in the case of the nuclear family structure group with mean score of 15.364

followed by joint (mean score 15.077) and extended (mean score 13.291)



337

5.2.2.3.3 Two-Way ANOVA on Awareness of Distribution Channels (ADC) by Family Structure and Occupation

The variations on awareness of distribution channels by two categories,

family structure and occupation, are analysed with Two-Way ANOVA and the


Table 5.69 Family Structure -wise Estimated Marginal Means-ADC

1. Family Structure

Dependent Variable: Awareness of Distribution Channels




Nuclear 15.603 .286 15.041 16.165

Extended 13.453 .760 11.959 14.946


Table 5.70 Occupation- wise Estimated Marginal Means-ADC

2. Occupation

Dependent Variable: Awareness On Distribution Channels




Agriculture 15.969 1.014 13.976 17.962

Business & self -employed 13.969 .627 12.737 15.201

Govt service 14.735 .446 13.860 15.611

Private service 14.811 .581 13.669 15.952

NRI/FE 14.690 .892 12.938 16.443

Others 14.217 .509 13.218 15.216 Source: Primary data

Chapter 5

338

Table 5.71 Two-Way ANOVA - ADC



Square F Sig.

Family structure 171.463 2 85.732 3.590 .028* Occupation 103.653 5 20.730 .868 .502 Error 12465.392 522 23.880 Total 12740.508 529


To test the mean variations, of the scores for awareness on distribution

channels among different family structure groups and different occupational

categories, Two-Way ANOVA is used and it is found that family structure-

wise variations of the mean scores are statistically significant at 5 per cent

level of significance (value of F 3.509 Df 2 with p=0.028<0.05), but

occupation - wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F .868 Df 5 with p=0.502>0.05).

As per Tables 5.69, 5.70 and 5.71, there is significant difference among

different categories of family structure as to awareness on distribution

channels, while in the case of occupation- wise categories, the difference is not

significant. It may be concluded that the level of awareness does not vary

significantly among respondents belonging to different occupational categories

but there is significant difference among different family structure groups,

i.e., the level of awareness on distribution channels is high in the case of the

nuclear family structure group (with mean score of 15.603), followed by

joint (mean score 15.140) and extended (13.453) family structure groups.


339

5.2.2.4 Level of Awareness of Process (AP)

The term “process” implies various systems and procedures involved in

rendering service to customer. Simply “process” includes the various

interactions that take place between the insurance agent and the office and

the customer in the process of selling the policy to the customer till the

settlement of claims. The awareness on process comprises knowledge on

various forms to be filled in, documents and certificates to be attached to

forms, citizen chart of authorities and functions, renewal, surrender of

policy or registering complaints on defective service, Free Look Period,

modification and conversion of policy, time limit for service, claim settlement

procedures, etc .

5.2.2.4.1 Two-Way ANOVA on Awareness of Process (AP) by Area and Occupation

The variation on awareness on process by area and occupation is

analysed with Two-Way ANOVA and the output is presented in Tables 5.72,

5.73 and 5.74.

Table 5.72 Area -wise Estimated Marginal Means-AP

1. Area Dependent Variable: Awareness of Process




Chapter 5

340

Table 5.73 Occupation- wise Estimated Marginal Means -AP

2. Occupation Dependent Variable: Awareness of Process

Regroup Of Occupation Mean Std. Error


Upper Bound

Agriculture 28.901 1.954 25.063 32.739 Business & Self- Employed 28.723 1.086 26.589 30.857 Govt Service 27.965 .778 26.436 29.493 Private Service 28.607 1.028 26.588 30.626 NRI/Foreign Employed 29.498 1.665 26.226 32.769 Others 27.862 .811 26.268 29.456


Table 5.74 Two-Way ANOVA – AP

Tests of Between-Subjects Effects Dependent Variable: Awareness of Process


Square F Sig.



Tables 5.72, 5.73 and 5.74 show the level of awareness of process

among the selected policyholders, based on their area of residence and

occupation. To test the mean variations of the scores for awareness on process

between rural and urban areas and different occupational groups, Two-Way

ANOVA is used and it is observed that area and occupation -wise variations of

the mean scores are not statistically significant at 5 per cent level of


341

significance (value of F .013 and .274 Df 1 and 5 with p=0.908 and 0.927>0.05).

As such, it can be concluded that there is no significant difference between

rural and urban areas and among different occupational groups as to awareness

on process, i.e., the awareness of process of selected respondents is similar

either area-wise or occupation-wise.

5.2.2.4.2 Two-Way ANOVA on Awareness of Process (AP) by Area and Family Structure

The variations on awareness of process by area and family structure is

analysed with Two-Way ANOVA and the output is presented in the following

Tables .

Table 5.75 Area- wise Estimated Marginal Means-AP

1. Area Dependent Variable: Awareness of Process




Table 5.76 Family Structure- wise Estimated Marginal Means-AP

2. Family Structure Dependent Variable: Awareness of Process



Nuclear 28.630 .487 27.672 29.587 Extended 25.269 1.441 22.439 28.100 Joint 28.095 1.284 25.574 30.617


Chapter 5

342

Table 5.77 Two-Way ANOVA - AP


Dependent Variable: Awareness of Process


Square F Sig.

Area 1.208 1 1.208 .013 .908

Family structure 453.064 2 226.532 2.520 .081

Error 47274.839 526 89.876

Total 47729.111 529 Source: Primary Data

Tables 5.75, 5.76 and 5.77 show the awareness of process in between

rural and urban areas and among different family structure groups of the

selected policyholders. To test the mean variations of the scores for awareness

of process between rural and urban areas and among different family

structures, Two-Way ANOVA is used and it is found that area and family

structure-wise variations of the mean scores are not statistically significant at 5

per cent level of significance (value of F .013 and 2.520 Df 1 and 2 with

p=0.908>0.05 and p=.081>0.05). It can also be observed that there is no

significant difference based on area and occupation as to awareness on

process. Therefore, it may be concluded that selected policyholders belonging

to rural and urban areas and different occupational groups do not differ very

much in their level of awareness of process.

5.2.2.4.3 Two-Way ANOVA on Awareness on Process (AP) by Family Structure and Occupation

The variations on awareness of process by family structure and occupation

are analysed with Two-Way ANOVA and the output is presented in the

following Tables.


343

Table 5.78 Family Structure -wise Estimated Marginal Means-AP

1. Family Structure Dependent Variable: Awareness of Process





Table 5.79 Occupation- wise Estimated Marginal Means-AP

2. Occupation Dependent Variable: Awareness of Process



Agriculture 27.864 1.973 23.988 31.739 Business & Self Employed

27.638 1.22 25.243 30.034

Govt Service 27.151 0.867 25.448 28.854 Private Service 27.682 1.13 25.462 29.902 NRI/Foreign Employed 28.638 1.735 25.23 32.046 Others 26.797 0.989 24.854 28.74


Table 5.80 Two-Way ANOVA – AP

Tests of Between-Subjects Effects Dependent Variable: Awareness of Process


Square F Sig.

Family structure 452.457 2 226.229 2.505 .083 Occupation 128.996 5 25.799 .286 .921 Error 47147.658 522 90.321 Total 47729.111 529


Chapter 5

344

Tables 5.78, 5.79 and 5.80 indicate the awareness of “process” among

selected policyholders, based on their family structure group and occupation.

To test the mean variation of the scores for awareness on process among

different family structure groups and occupational categories, Two-Way

ANOVA is used and it is found that family structure and occupation wise-


level of significance (value of F 2.505 and 0.286 Df 2 and 5 with p=0.083

and 0.921> 0.05). It can also be observed from the Tables that there is no

significant difference among different categories of family structure and

occupation as to awareness of process. Therefore, it may be concluded that the

levels of awareness of process are similar among different family structure

groups and occupational categories.

5.2.2.5 Awareness of Promotion (APN)

Promotional efforts of the LIC through various media facilitate

customers to have better understanding on the features and benefits of policies.

The awareness of promotional efforts and activities includes awareness of the

media of promotion, message or content, nature and truthfulness in conveying

message etc. The efforts taken to understand and evaluate the promotional

efforts of the LIC vary among policyholders.

5.2.2.5.1 Two-Way ANOVA on Awareness of Promotion (APN) by Area and Occupation

The following Tables present the output of the analysis of variance on

awareness of promotion (APN) by two categories, area and occupation, using

Two-Way ANOVA.


345

Table 5.81 Area -wise Estimated Marginal Means-APN

1. Area Dependent Variable: Awareness of Promotion




Table 5.82 Occupation- wise Estimated Marginal Means-APN

2. Occupation Dependent Variable: Awareness of Promotion



Agriculture 16.611 1.123 14.405 18.816 Business & Self- Employed

14.657 .624 13.430 15.883

Govt Service 15.595 .447 14.717 16.473 Private Service 15.470 .591 14.310 16.630 NRI/Foreign employed 14.906 .957 13.025 16.786 Others 15.302 .466 14.386 16.218


Table 5.83 Two-Way ANOVA – APN

Tests of Between-Subjects Effects Dependent Variable: Awareness of Promotion


Square F Sig.



Chapter 5

346

Tables 5.81, 5.82 and 5.83 present the awareness of promotion among

selected policyholders based on area and occupation. To test the mean

variations of the scores for awareness of promotion between rural and urban

areas and among different occupational groups, Two-Way ANOVA is used

and it is found that area- wise and occupation-wise variations of the mean

scores are not statistically significant at 5 per cent level of significance (value

of F .061 and .630 Df 1 and 5 with p=0.805 and 0.677>0.05). The Tables also

reveal that there is no significant difference between rural and urban areas

and among different occupational groups as to awareness of promotion.

Therefore, it can be inferred that respondents pertaining to rural and urban

areas and different occupational groups do not have difference in their levels

of awareness of promotion.

5.2.2.5.2 Two-Way ANOVA on Awareness of Promotion (APN) by Area and Family Structure

The following Tables present the output of the analysis of area and

family structure- wise variance on awareness as to promotion (APN) using

Two-Way ANOVA.

Table 5.84 Area- wise Estimated Marginal Means-APN

1.Area

Dependent Variable: Awareness of Promotion



Rural 15.049 .406 14.251 15.847



347

Table 5.85 Family Structure- wise Estimated Marginal Means-APN

2.Family Structure Dependent Variable: Awareness of Promotion

Family structure Mean Std. Error 95% confidence interval

Lower bound Upper bound Nuclear 15.404 .281 14.852 15.956 Extended 13.982 .831 12.350 15.613 Joint 15.959 .740 14.505 17.413


Table 5.86 Two-Way ANOVA - APN

Tests of Between-Subjects Effects Dependent Variable: Awareness of Promotion


Square F Sig.

Area 1.829 1 1.829 .061 0.805 Family structure 104.414 2 52.207 1.748 0.175 Error 15712.868 526 29.872 Total 15819.111 529


Tables 5.84, 5.85 and 5.86 show the awareness of promotion among

selected policyholders based on area and family structure. The mean variations

of the scores for awareness of promotion using Two-Way ANOVA between

rural and urban areas and among different family structure groups reveal that

the variations of the mean scores are not statistically significant at 5 per cent level

of significance (value of F .061 and 1.748 Df 1 and 2 with p=0.805>0.05 and

p=0.175>0.05). The difference in the levels of awareness of promotion

between rural and urban areas and among different family structure groups is

also not significant as exhibited in the Tables referred to above. It leads to the

conclusion that the selected policyholders do not differ much in their level of

Chapter 5

348

awareness of promotion either by area of residence or by the family structure

group they belong to.

5.2.2.5.3 Two-Way ANOVA on Awareness on Promotion (APN) by Family Structure and Occupation


awareness of promotion (APN) by family structure and occupation, using

Two-Way ANOVA.

Table 5.87 Family Structure - wise Estimated Marginal Means-APN

1.Family Structure Dependent Variable: Awareness of Promotion





Table 5.88 Occupation- wise Estimated Marginal Means-APN

2.Occupation Dependent Variable: Awareness of Promotion



Agriculture 16.152 1.136 13.921 18.383 Business & Self- Employed 14.301 .702 12.922 15.680 Govt Service 15.299 .499 14.318 16.279 Private Service 15.171 .651 13.893 16.449 NRI/Foreign Employed 14.671 .999 12.709 16.632 Others 14.964 .569 13.846 16.083



349

Table 5.89 Two-Way ANOVA -APN


Dependent Variable: Awareness On Promotion


Square F Sig.


Occupation 90.102 5 18.020 .602 .698

Error 15624.733 522 29.932


To test the mean variations of the scores for awareness on promotion

among different family structure groups and different occupational categories,

as given in Tables 5.87, 5.88 and 5.89, Two-Way ANOVA is used and it is

found that family structure and occupation- wise variations of the mean scores

are not statistically significant at 5 per cent level of significance (value of

F 1.742 and 0.602 Df 2 and 5 with p=0.176>0.05 and p=0.698> 0.05). As per

the Tables, it is seen that there is no significant difference among different

categories of family structure and occupation as to awareness of promotion.

Therefore, it may be concluded that the selected policyholders have similar

levels of awareness of promotion either by their group of family structure or

by their occupation.

5.2.2.6 Awareness of People (APE)

The people servicing the policy comprise the direct employees of the

concern (employees on the rolls) and the individual agents (employees off the

rolls) who make personal contacts with customers. The attitude, quality of

service and efficiency in handling customer requirements decide the direction

Chapter 5

350

of the organisation itself. To get customers delighted, the marketing personnel

are to be kept well- trained, knowledgeable, enabling them to render quick and

prompt service pleasantly.

5.2.2.6.1 Two-Way ANOVA on Awareness of People (APE) by Area and Occupation


awareness of people (APE) by area and occupation using Two-Way ANOVA.

Table 5.90 Area- wise Estimated Marginal Means-APE

1.Area

Dependent Variable: Awareness of People



Rural 14.792 .318 14.167 15.417


Table 5.91 Occupation- wise Estimated Marginal Means-APE

2.Occupation Dependent Variable: Awareness of People




14.107 .594 12.940 15.274

Govt Service 14.392 .426 13.556 15.228 Private Service 14.399 .562 13.295 15.503 NRI/foreign Employed 15.332 .911 13.543 17.122 Others 14.295 .444 13.423 15.167



351

Table 5.92 Two-Way ANOVA -APE

Tests Of Between-Subjects Effects



Square F Sig.

Area 23.834 1 23.834 .875 .350

Occupation 42.674 5 8.535 .313 .905

Error 14243.962 523 27.235

Total 14310.470 529 Source : Primary Data

Tables 5.90, 5.91 and 5.92 show the awareness of people servicing

policy among selected policyholders, as to their area of residence and

occupation. To test the mean variations of the scores for awareness on people

servicing policy between rural and urban areas and among different

occupational groups, Two-Way ANOVA is used and it is found that area and

occupation- wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F.875 and .313 Df 1 and 5 with

p=0.350 and 0.905>0.05). As per the Tables referred to above, there is no

significant difference among the respondents as to awareness of people

servicing policy by area of residence and occupational category. Therefore, it

may be concluded that awareness of people servicing policy of selected

policyholders is similar as to their area of residence and occupation.

5.2.2.6.2 Two-Way ANOVA on Awareness of People (APE) by Area and Family Structure


family structure-wise variance on awareness of people servicing policy (APE),

using Two-Way ANOVA.

Chapter 5

352

Table 5.93 Area- wise Estimated Marginal Means-APE

1.Area





Rural 14.417 .387 13.656 15.177


Table 5.94 Family Structure- wise Estimated Marginal Means-APE

2.Family Structure



Error 95% Confidence Interval


Nuclear 14.477 .268 13.952 15.003

Extended 13.700 .791 12.146 15.255


Table 5.95 Two-Way ANOVA -APE

Tests of Between Subjects Effects



Square F Sig.

Area 23.834 1 23.834 .879 .349

Family Structure 24.156 2 12.078 .445 .641

Error 14262.480 526 27.115



353

Tables 5.93, 5.94 and 5.95 present the awareness of people servicing

policy among the selected policyholders, based on area of residence and

family structure. Two-Way ANOVA is used to test the mean variations of the

scores for awareness of people servicing policy between rural and urban areas

and among different family structure groups, which indicates that the area

and family structure- wise variations of the mean scores are not statistically

significant at 5 per cent level of significance (value of F 0.879 and 0.445 Df

1 and 2 with p=0.349>0.05 and p=.641>0.05). As per the Tables referred to

above, there is no significant difference as to awareness of people servicing

policy, based on area and family structure. Therefore, it may be concluded

that awareness of people servicing policy among selected policyholders is

similar as to their area of residence and occupation.

5.2.2.6.3 Two-Way ANOVA on Awareness of People (APE) by Family Structure and Occupation

The following Tables present output of the analysis of family structure

and occupation- wise variance on awareness of people (APE) using Two-Way

ANOVA.

Table 5.96 Family Structure- wise Estimated Marginal Means-APE

1.Family Structure


Family structure Mean Std.



Nuclear 14.742 .306 14.141 15.343

Extended 13.923 .812 12.327 15.519


Chapter 5

354

Table 5.97 Occupation wise Estimated Marginal Means-APE

2.Occupation Dependent Variable: Awareness of People



Upper Bound


13.929 .670 12.612 15.246

Govt service 14.274 .476 13.338 15.210 Private service 14.227 .621 13.007 15.447 NRI/FE 15.189 .953 13.316 17.062 Others 14.146 .544 13.078 15.213


Table 5.98 Two-Way ANOVA –APE

Tests of Between-Subjects Effects Dependent Variable: Awareness of People


Square F Sig.

Family structure 23.652 2 11.826 .434 .648 Occupation 46.920 5 9.384 .344 .886

Error 14239.898 522 27.279 Total 14310.470 529


To test the mean variations of the scores for awareness of people

servicing policy among different family structure groups and occupational

categories, Two-Way ANOVA is used and it is found that family structure and


at 5 per cent level of significance (value of F 0.434 and 0.344 Df 2 and 5 with


355

p=0.648>0.05 and p=0.886> 0.05). As per Tables 5.96, 5.97 and 5.98, there is

no significant difference among different categories of family structure and

occupation as to awareness of people servicing policy. Therefore, it may be

concluded that selected respondents do not differ in respect of awareness of

people servicing policy either by family structure or by their occupational group.

5.2.2.7 Awareness of Physical Evidence

Physical evidence of an organisation refers to the environment in which

the service is to be delivered and the place where the customer interacts with

the firm, and any tangible parts that facilitate performance or communication

of the service. The availability of better physical evidence will be helpful to an

organisation in enhancing its organisational image. It will create a positive

outlook on the part of customers on the organisation, as such elements enable

the customer to obtain efficient service.

5.2.2.7.1 Two-Way ANOVA on Awareness of Physical Evidence by Area and Occupation.

The following Tables present the output of analysis of area and

occupation-wise variance on awareness of physical evidence (APLE), using

Two-Way ANOVA.

Table 5.99 Area- wise Estimated Marginal Means-APLE

1.Area Dependent Variable: Awareness of Physical Evidence




Chapter 5

356

Table 5.100 Occupation -wise Estimated Marginal Means-APLE

2.Occupation Dependent Variable: Awareness of Physical Evidence



Agriculture 14.631 1.150 12.372 16.890 Business & Self - Employed

14.867 .639 13.611 16.123

Govt Service 14.896 .458 13.996 15.795 Private Service 14.819 .605 13.631 16.008 NRI/Foreign Employed 14.184 .980 12.259 16.110 Others 15.010 .477 14.073 15.948


Table 5.101 Two-Way ANOVA -APLE

Tests of Between-Subjects Effects Dependent Variable: Awareness of Physical Evidence


Square F Sig.



Tables 5.99, 5.100 and 5.101 present the level of awareness of physical

evidence among selected policyholders, based on area of residence and

occupation. In order to test the mean variation of the scores for awareness of

physical evidence between rural and urban areas and different occupational

groups, Two-Way ANOVA is used and it is observed that area and



357

at 5 per cent level of significance (value of F.193 and .127 Df 1 and 5 with

p=0.660 and 0.986>0.05). It also establishes that there is no significant

difference between rural and urban areas and among different occupational

groups as to awareness of physical evidence. Therefore, it may be concluded

that based on area of residence and occupation, the level of awareness of

physical evidence among selected policyholders is similar.

5.2.2.7.2 Two-Way ANOVA on Awareness of Physical Evidence (APLE) by Area and Family Structure


family structure- wise variance on awareness of physical evidence (APLE)

using Two-Way ANOVA.

Table 5.102 Area -wise Estimated Marginal Means-APLE

1.Area Dependent Variable: Awareness of Physical Evidence




Table 5.103 Family Structure wise Estimate of Marginal Means-APLE

2.Family structure Dependent Variable: Awareness of Physical Evidence



Lower Bound Upper Bound Nuclear 15.010 .287 14.446 15.573 Extended 13.182 .848 11.516 14.848 Joint 14.945 .756 13.461 16.429


Chapter 5

358

Table 5.104 Two-Way ANOVA –APLE



Square F Sig.

Area 6.092 1 6.092 .196 .658 Family Structure 133.844 2 66.922 2.150 .118 Error 16374.434 526 31.130 Total 16514.370 529


Two-Way ANOVA is used to test the mean variation of the scores for

awareness of physical evidence between rural and urban areas and among

different family structure groups, and it is found that area and family structure-

wise variations of the mean scores are not statistically significant at 5 per cent

level of significance (value of F .196 and 2.150 Df 1 and 2 with p=0.658>0.05

and p=0.118>0.05). As per Tables 5.102, 5.103 and 5.104, there is no

significant difference by area and family structure as to awareness of physical

evidence. Based on these facts, it may be concluded that the level of awareness

of physical evidence among selected policyholders in relation to their area of

residence and family structure group is almost similar.

5.2.2.7.3 Two-Way ANOVA on Awareness of Physical Evidence (APLE) by Family Structure and Occupation

The variations on awareness of physical evidence (APLE) by family

structure and occupation are analysed using Two-Way ANOVA and the output

is presented in the following Tables.


359

Table 5.105 Family Structure -wise Estimated Marginal Means-APLE

1.Family structure Dependent Variable: Awareness of Physical Evidence





Table 5.106 Occupation -wise Estimated Marginal Means-APLE

2.Occupation Dependent Variable: Awareness of Physical Evidence



Agriculture 13.967 1.162 11.684 16.250 Business & Self -Employed

14.282 .718 12.871 15.693

Govt service 14.439 .511 13.435 15.442 Private service 14.333 .666 13.025 15.640 NRI/FE 13.739 1.022 11.731 15.746 Others 14.427 .583 13.282 15.572


Table 5.107 Two-Way ANOVA -APLE



Square F Sig.

Family Structure 134.287 2 67.143 2.142 .118 Occupation 18.251 5 3.650 .116 .989 Error 16361.832 522 31.345 Total 16514.370 529


Tables 5.105, 5.106 and 5.107 show the level of awareness of physical

evidence among selected policyholders based on their family structure and

Chapter 5

360

occupation. The test of the mean variations of the scores for awareness on

physical evidence among different family structure groups and occupational

categories using Two-Way ANOVA reveals that family structure and occupation-

wise variations of the mean scores are not statistically significant at 5 per cent

level of significance (value of F 2.142 and 0.116 Df 2 and 5 with p=0.118>0.05

and p=0.989> 0.05). The Tables make it clear that the difference among different

categories of family structure and occupation as to awareness of physical evidence

are not significant. It pinpoints that the level of awareness as to physical evidence

are almost similar among different family structure and occupational groups.

5.2.4 Recall Rate on LIC Policies

Recall rate means the ability to remember the name of policy at any

point of time. The respondents were asked to specify whether they could recall

the name of policy/policies subscribed by them. It is the prerequisite for sale

of any product and indicates how well the product of the LIC could position in

the minds of its policyholders. The higher the percentage, the better it is. Apart

from this, the identification of reasons/factors behind the recall will be much

useful in product design and development.

Table 5.108 Recall Rate of Policies-Binomial Test

Category N Observed Prop.

Test Prop.

Asymp. Sig. (2-tailed)

Remember names of any LIC policies?

Yes 327(61.7) .62 .50 0.000* No 203(38.3) .38

Total 530 1.00 a. Based on Z Approximation. Source: Primary Data*significant at 5per cent level of significance

Table 5.108 clearly states that a higher percentage of the sample

respondents i.e., 61.7 per cent, could recall the names of their LIC policies.


361

The difference in the proportion of policyholders’ recall of the policies is

found to be significant statistically (p=000<0.05) in the Binomial test).

Respondents who could recall the name of policy/policies were

requested to state the names of the policies they could recall either heard or

subscribed. Three options were given for enlisting the type of policies that

came into their mind while thinking of products of the LIC. The Table

presented below outlines the picture.

Table 5.109 Mostly Recalled Policies of the LIC

Name of policy recalled

Frequency Total* Per cent

Choice-1 Choice-2 Choice-3 Jeevan Anand 152 55 35 242 74.01 Money Back Plans 31 31 47 109 33.33 Jeevan Saral 25 56 27 108 33.03 New Bima Gold 24 22 35 81 24.77 Jeevan Arogya 9 21 23 53 16.21 Jeevan Surabhi 13 13 20 46 14.07 Jeevan Tarang 6 21 15 42 12.84 Child Future Plan 6 10 14 30 9.17 New Jan Raksha 2 6 20 28 8.56 Endowment Plans 4 15 7 26 7.95

Source: Primary Data ( * ) Note: out of 327 respondents to recall a name of policy, the figure shows the number of respondents recalling the specific policy

Three options were given to respondents to enable those who might be

remembering more than one policy. The total recall rate (sum of three choices) on

each policy as a whole is considered for evaluation. In this sense, Jeevan Anand

seems to enjoy the major preference with 74.01 per cent. The second most

recalled type of policy is Money Back Plans, the third being Jeevan Saral. All

other policies of the LIC fall very back in their preferences i.e. New Bima Gold

Chapter 5

362

showing 24.77 per cent and Jeevan Arogya 16.21 per cent are far below the first

three types of policies. Respondents who marked just “first choice” and “first and

second choices” were also included for analysis. The output should be read in the

sense that 38.3 per cent respondents did not enlist any name of policy/policies.

5.2.5 Comparison of Investment in LIC with Listed Financial Products/ Assets

The analysis attempts to identify the preferences of policyholders

towards investment in LIC compared to other mostly opted financial

products/assets by the general public. The respondents were given two

options: if they felt that investment in LIC was better than other listed

financial products, they could respond, “Yes”; otherwise, they could record

“No”. The analysis will be of immense utility to assess the priority given by

prospective investors to various financial products, and this information will

help in designing products in tune with customer requirements. The result of

the analysis is presented in Table 5.110.

Table 5.110 Preference towards Listed Financial Products/Assets

Financial Products Investment In LIC is Better Yes No Total

Bank Fixed Deposits 269(50.8) 261(49.2) (530)100 Mutual Fund 253(47.7) 277(52.3) (530)100 Post Office Savings 281(53.0) 249(47.0) (530)100 Provident Fund Investment 253(47.7) 277(52.3) (530)100 Chitty 266(50.2) 264(49.8) (530)100 ULIPS 240(45.3) 290(54.7) (530)100 Govt Treasury Deposit 233(44.0) 297(56.0) (530)100 Gold/Jewellery 202(38.1) 328(61.9) (530)100 Real Estate 198(37.4) 332(62.6) (530)100 Securities/Shares 266(50.2) 264(49.8) (530)100



363

The tabular presentation states that out of the ten investment options listed,

all except four (Bank Fixed Deposit, Post Office Savings, Chitty and

Securities/Shares) are considered to be better options for investment compared to

investment in Life insurance. The difference in perception as to the superiority

of one investment to any other is evident in the case of investments in

Gold/Jewellery and Real Estate.

5.2.5.1 Relationship of Listed Financial Products with Sum Assured of All Policies

The Table shows the relationship between preferences for investment in

life insurance over listed financial products, against different groups of sum

assured of all policies subscribed by respondents. The row “Yes” indicates the

percentage of respondents who did prefer the investment in life insurance

(LIC) better over the listed financial products. (“YES” implies investment in

LIC is better than listed financial products and “NO” implies investment in

listed financial products/assets is better than LIC).

The listed financial products include:-

1) Bank Fixed Deposits (BFD)

2) Mutual Funds (MF)

3) Post Office Savings (POS)

4) Provident Fund Investment (PFI)

5) Chitty ( CHITTY)

6) ULIPS (ULIPS)

7) Government Treasury Deposit (GTD)

8) Gold/Jewellery (GJ)

9) Real Estate (RE)

10) Securities/Shares (SS)

Chapter 5

364

Table 5.111 Cross Tabulation of Listed Financial Products/Assets versus LIC with Sum Assured of All Policies (SAAP)

Is investment in LIC better

than

Sum Assured of All Policies(SAAP) (in Lakh) Total Upto 1 1 to 5 6 to 10 11 to 15 Above 15

BFD Yes 122(54.2) 119(48.2) 23(46.9) 2(50) 3(60) 269(50.8) No 103(45.8) 128(51.8) 26(53.1) 2(50) 2(40) 261(49.2)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

MF Yes 109(48.4) 116(47) 25(51) 0(0) 3(60) 253(47.7) No 116(51.6) 131(53) 24(49) 4(100) 2(40) 277(52.3)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

POS Yes 122(54.2) 136(55.1) 21(42.9) 1(25) 1(20) 281(53) No 103(45.8) 111(44.9) 28(57.1) 3(75) 4(80) 249(47)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

PFI Yes 115(51.1) 117(47.4) 18(36.7) 1(25) 2(40) 253(47.7) No 110(48.9) 130(52.6) 31(63.3) 3(75) 3(60) 277(52.3)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

CHITTY Yes 117(52) 125(50.6) 21(42.9) 1(25) 2(40) 266(50.2) No 108(48) 122(49.4) 28(57.1) 3(75) 3(60) 264(49.8)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

ULIPS Yes 107(47.6) 107(43.3) 24(49) 1(25) 1(20) 240(45.3) No 118(52.4) 140(56.7) 25(51) 3(75) 4(80) 290(54.7)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

GTD Yes 99(44) 110(44.5) 21(42.9) 2(50) 1(20) 233(44) No 126(56) 137(55.5) 28(57.1) 2(50) 4(80) 297(56)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

G/J Yes 94(41.8) 87(35.2) 17(34.7) 2(50) 2(40) 202(38.1) No 131(58.2) 160(64.8) 32(65.3) 2(50) 3(60) 328(61.9)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

RE Yes 101(44.9) 78(31.6) 16(32.7) 1(25) 2(40) 198(37.4) No 124(55.1) 169(68.4) 33(67.3) 3(75) 3(60) 332(62.6)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100)

SS Yes 112(49.8) 121(49) 29(59.2) 1(25) 3(60) 266(50.2) No 113(50.2) 126(51) 20(40.8) 3(75) 2(40) 264(49.8)

Total 225(100) 247(100) 49(100) 4(100) 5(100) 530(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows


365


between the sum assured of all policies and preference towards investment in

listed financial products, a Loglinear Multinomial Model was attempted to test

the following hypotheses.

H0: There is no dependence between sum assured of all policies and preference

towards investment in LIC over listed financial products/ assets.

H1: There is dependence between sum assured of all policies and preference

towards investment in LIC over listed financial products/assets.

Table 5.112 Loglinear Multinomial Test of Goodness-of-Fit Test

Value Df Sig. Bank Fixed Deposits Likelihood Ratio 2.199 4 0.699

Pearson Chi-Square 2.196 4 0.700 Mutual Funds Likelihood Ratio 5.809 4 0.214

Pearson Chi-Square 4.271 4 0.371 Post Office Savings Likelihood Ratio 6.185 4 0.186

Pearson Chi-Square 6.025 4 0.197 Provident Fund Investment

Likelihood Ratio 4.447 4 0.349 Pearson Chi-Square 4.366 4 0.359

Chitty Likelihood Ratio 2.64 4 0.620 Pearson Chi-Square 2.589 4 0.629

ULIPS Likelihood Ratio 3.23 4 0.520 Pearson Chi-Square 3.078 4 0.545

Government Treasury Deposit

Likelihood Ratio 1.389 4 0.846 Pearson Chi-Square 1.282 4 0.864

Gold/Jewellery Likelihood Ratio 2.637 4 0.620 Pearson Chi-Square 2.646 4 0.619

Real Estate Likelihood Ratio 9.705 4 0.046 Pearson Chi-Square 9.717 4 0.045*

Securities/Shares Likelihood Ratio 3.008 4 0.556 Pearson Chi-Square 2.951 4 0.566


Chapter 5

366

The test was found to be not significant in all cases except the case of

real estates. Therefore, it can be concluded that the preferences of respondents

to investment in LIC over listed financial products do not vary over with those

having policies, except in the case of real estates. It means that the higher the

disposable income in hand, the higher the preference of respondents to invest

in real estate.

5.2.6 Adequacy of Coverage on Life Insurance

The worth of life insurance policy one should take to cover his or her

future needs (including most dependent ones in family) can be assessed by the

most popularly used Human Life Value (HLV) Concept. Here, the basis for

finding adequacy of coverage is calculated based on the framework available

at http://www.myinsuranceclub.com. The procedure runs as follows. After

identifying the age group one belongs to, the score against the age group in

the Table is multiplied with his or her annual income. If the sum assured of

policies taken is greater than the amount, it is said that one is adequately

covered under life insurance, otherwise, not.

Table 5.113 Customer Perception on Adequacy of Coverage in Life Insurance

Choice Frequency Per cent

Yes 94 17.7

No 436 82.3 Source: Primary Data

It is observed that 82.3 per cent of the sample respondents do feel that

they are not adequately covered under life insurance. The figures also match

with the data available on life Insurance, i.e., on an average 20 per cent of

insurable Indian population is insured under life insurance.


367

5.2.7 Reasons for Inadequate Coverage under Life Insurance

The policyholders might have multiple reasons behind lesser preference or

for lower investment in life insurance. The major reasons that have influenced

policyholders are analysed using the frequency Table as given below.

Table 5. 114 Reasons for Inadequate Coverage under Life Insurance

Rank Low income

Lack of sufficient

knowledge

Lack of conviction

/belief

Preference to other

investments

Other reasons

1 189(43.3) 70(16.1) 30(6.9) 128(29.4) 18(4.1) 2 71(16.3) 102(23.4) 106(24.3) 126(28.9) 34(7.8) 3 67(15.4) 149(34.2) 117(26.8) 77(17.7) 24(5.5) 4 86(19.7) 96(22.0) 139(31.9) 95(21.8) 20(4.6) 5 23(5.3) 19(4.3) 44(10.1) 10(2.2) 340(78.0)

Total 436*(100) 436*(100) 436*(100) 436*(100) 436*(100) Source: Primary Data Note: (Out of 530 respondents, 94 respondents believe that they are adequately insured) Note: Figures in parenthesis represent percentage to total in respective rows

The Table makes it clear that the most vital reason for inadequate

coverage among respondents is low income, followed by preference to other

investments, lack of sufficient knowledge, lack of conviction /belief. Only 4.1

per cent of the respondents gave the first rank to others for inadequate

coverage and in total 78 per cent of the respondents had given the lowest rank

for others, for inadequate coverage in life insurance. It means that the listed

factors played a dominant role as the reason for inadequate coverage under

life insurance.

5.3 Analysis of Customer Purchasing Behaviour

The section comprises an analysis of customer attitude and behaviour

towards purchase of life insurance policies. The purchase behaviour of an

Chapter 5

368

investor is influenced by many factors that may be external or internal to an

organisation. The following Tables illustrate the major questions in this regard

as the most dependent source of knowledge on life insurance, the decision- maker

of investment in the family, the most influencing factors to buy life insurance

policy from LIC, the marketing mix element to motivate the purchase decision of

life insurance policy, and the basic motive behind purchasing the life insurance

policy. The perceptions of the sample respondents in this regard will be helpful in

identifying the driving force behind the life insurance policy purchase

decision.

5.3.1 Most Dependent Source of Knowledge on Life Insurance

The media most depended on by customers to have better perception on

the means of investment have a great impact on the formulation of marketing

strategies of any organisation, especially in service industries, as their products

are intangible in nature. One of the major elements of the promotional strategies,

i.e., selection of media through which the idea/product is to be brought to the

notice of prospective investors, is based on the preference of customers. A

prospective investor might be gathering information on financial products,

especially life insurance, through newspaper (NP), magazines and journals

(M/J), TV advertisements (TVA) or radio advertisements (RA), life

insurance agents (LICA), friends and relatives (F/R), internet or websites

(I/WS), tele/mobile media (TMM), Brochures, Diaries and Calendars (BDC),

Bill Boards and Wall Paper (BBWP), etc.

It is very important from the marketing point of view to see if these

responses show any differences between rural and urban populations. Since

the data provides ranks given by respondents, a non- parametric test is used.


369

The results are presented below.

Table 5.115 Descriptive Statistics on source of knowledge on life insurance (n=530)

Source of knowledge on life insurance Mean Std. Deviation Newspaper 3.07 1.631 Magazines/Journals 4.53 1.725 TV Advertisements 3.35 1.836 Radio Advertisements 6.58 2.296 LIC Agents 2.34 1.967 Friends/Relatives 4.87 2.312 Internet/Websites 6.57 2.537 Tele/Mobile Marketing, SMS,MMS Etc 7.92 1.773 Brochures, Diaries, Calendars Etc 7.14 2.241 Bill Boards, Wall Writings Etc 8.81 1.707


Table 5.116 Source of Knowledge on Life Insurance-Mean Ranks

Mean Rank Sum Of Ranks Area Rural Urban Rural Urban N 368 162 368 162 News paper 263.13 270.89 96831.5 43883.5 Magazine/Journals 273.96 246.29 100816.5 39898.5 TV advertisements 252.34 295.4 92861 47854 Radio advertisements 258.67 281.02 95189.5 45525.5 LIC agents 262.88 271.44 96741 43974 Friends/Relatives 266.68 262.82 98138.5 42576.5 Internet/website 276.13 241.36 101614 39101 Tele/mobile marketing SMS,MMS etc 262.88 271.46 96738 43977 Brochures, diaries, calendars 271.32 252.27 99847 40868 Bill boards, wall writings etc 260.05 277.88 95698.5 45016.5


Chapter 5

370

The hypothesis can be stated thus:

H0: There is no difference between rural and urban populations in their

median responses on sources of knowledge on life insurance.

H1: There is difference between rural and urban populations in their median

responses on sources of knowledge on life insurance.

Table 5.117 Mann-Whitney Test NP M/J TV A RA LIC A F/R I/WS TMM BDC BBWP

Mann- Whitney

U 28935.500 26695.500 24965.000 27293.500 28845.000 29373.500 25898.000 28842.000 27665.000 27802.500

Wilcox on W

96831.500 39898.500 92861.000 95189.500 96741.000 42576.500 39101.000 96738.000 40868.000 95698.500

Z -.550 -1.949 -3.028 -1.561 -.650 -.270 -2.426 -.611 -1.336 -1.308

Asymp. Sig.

(2-tailed) .582 .051 .002* .118 .516 .787 .015* .541 .182 .191


Table 5.114 of descriptive statistics shows that LIC agents are the

most depended source of knowledge. The lower the ranks, the higher the

preference. The mean rank Table 5.115 presents that in rural areas, the

highest preference is given to TV advertisements (lowest mean rank value

252.34), while it is Internet/Website (lowest mean rank value 241.36) in the

case of urban areas. While the hypotheses for TV advertisements and

internet/web sites are rejected as the respective p values are 0.002 and

0.015 respectively (p<.05), the hypotheses for other sources are not rejected

as the p values are 0.582, 0.051, 0.118, 0.516, 0.787, 0.541, 0.182, 0.191

and 0.116 (p>0.05). Therefore, it can be concluded that, based on area,

there is significant difference among the preferences of policyholders in

relation to their source of knowledge on life insurance, i.e., TV advertisements

and internet/website.


371

This problem is again considered among the Occupation groups using

the Kruskal-Wallis test, as the category is having more than two groups.


H0: There is no difference in the median responses for sources of knowledge

on life insurance among occupation groups.

H1: There is difference in the median responses for sources of knowledge on

life insurance among occupation groups.

Table 5.118 Mean Ranks of Most Dependent Source of Knowledge on Life Insurance

AC BSE GS PS NRI/FE Others N 25 79 157 87 33 149 News Paper 249.88 269.32 249.12 262.45 323.73 272.23 Magazine/Journals 261.66 279.43 252.95 274.83 288.67 261.41 TV Advertisements 208.2 254.87 283.77 282.53 283.33 247.6 Radio Advertisements 273.78 258.09 265.97 262.88 293.24 262.93 LIC Agents 272.34 267.38 268.64 280.64 237.88 257.32 Friends/Relatives 254.42 254.25 293.59 268.79 246.18 246.08 Internet/Website 280.86 271.06 246.98 258.9 197.03 298.5 Tele/Mobile Marketing SMS, MMS etc

232.58 247.03 271.15 261.14 268.2 276.81

Brochures, Diaries, Calendars

283.24 269.82 250.78 279.44 286.29 263.01

Bill Boards, Wall Writings Etc

206.76 281.46 289.73 253.32 259.26 249.86


Chapter 5

372

Table 5.119 Kruskal Wallis Test NP M/J TVA RA LICA F/R I/WS TMM BDC BBWW Chi-Square

7.544 3.009 9.980 1.435 2.977 8.987 16.588 3.597 3.297 11.927

Df 5 5 5 5 5 5 5 5 5 5 Asymp. Sig.

.183 .699 .076 .921 .704 .110 .005* .609 .654 .036*


The mean rank Table 5.118 indicates that the highest preference is given

to bill boards, wall writings, etc., by agriculturists and private servants;

tele/mobile marketing SMS, MMS, etc; by the business and self- employed

class; internet/websites by government servants and NRI/foreign-employed

people; friends and relatives by others. The hypotheses for internet/web sites

and bill boards, wall writings, etc; are rejected as the p values are 0.005 and

0.036 respectively (p< .05) and other sources are not rejected as the p values

are 0.183, 0.699, 0.076, 0.921, 0.704, 0.110, 0.609 and 0.654 (p>0.05).

Therefore, it can be concluded that, based on occupational groups, there is

significant difference among the preferences of policyholders in relation to their

source of knowledge on life insurance, Internet/Website and Bill Boards, Wall

Writings, etc.

This problem is considered among the family structure groups using the

Kruskal-Wallis test, as the category is having more than two groups.

The hypotheses can be stated for each case as;

H0: There is no difference in the median responses for sources of knowledge

on life insurance among family structure groups.

H1: There is difference in the median responses for sources of knowledge on

life insurance among family structure groups.


373

Table 5.120 Source of Knowledge on Life Insurance Mean Ranks among Family Structure Groups

Nuclear Extended Joint N 430 44 56 Newspaper 266.63 228.53 285.85 Magazine/journals 262.05 275.47 284.13 TV advertisements 267.29 272.97 245.86 Radio advertisements 261.31 261.69 300.69 LIC agents 265.29 257.56 273.32 Friends/relatives 257.47 307.42 294.21 Internet/website 270.55 269.05 223.9 Tele/mobile marketing SMS, MMS etc 266.63 284.11 242.21 Brochures, diaries, calendars 269.22 216.82 275.15 Bill boards, wall writings etc 267.62 268.84 246.59


Table 5.121 Kruskal Wallis test

NP M/J TVA RA LICA F/R I/WE

B TMM BDC BBW

Chi-Square 3.748 1.275 1.119 3.363 .319 6.582 4.697 2.075 5.045 1.075 Df 2 2 2 2 2 2 2 2 2 2 Asymp. Sig.

.154 .529 .572 .186 .853 .037* .095 .354 .080 .584


The mean rank Table 5.120 shows that friends and relatives, brochures,

diaries, calendars and internet/websites are the most dependent sources of

knowledge on life insurance among the three family structure groups. As per

Table 5.121, it is clear that the hypotheses for friends and relatives are

rejected, as the p value is 0.037 (p<0.05) and in the case of other sources, the

hypotheses are not rejected as the p values are seen to be 0.154, 0.529, 0.572,

Chapter 5

374

0.186, 0.853, 0.095, 0.354, 0.080, 0.584 and 0.562 respectively, where

(p>0.05). It points out that there is significant difference among family

structure groups as to the source of knowledge on life insurance to be friends

and relatives.

5.3.2 Decision Making As to Purchase of LIC Policy

The decision for purchasing life insurance policy in a household is

generally made either by the head of the family, self or jointly by all elder

members of the family. However, there may be variations among the observed

responses. It is very important from the marketing point of view to see if these

responses show any differences between rural and urban populations. Since

the data provides ranks given by respondents, a non-parametric test, Man

Whitney test, is used. The results are reported below. (n=530)

Table 5.122 Descriptive Statistics on Decision Makers as to Purchase of Life Insurance Policy

Mean Std. Deviation Head of Family 1.94 0.832 Own Decision 2.1 0.859 Joint Decision 1.96 0.748


Table 5.123 Area -Wise Mean Ranks on Decision Makers as to Purchase of Life Insurance Policy

Area Mean Rank Sum of Ranks

Rural Urban Rural Urban N 368 162 368 162 Head of Family 266.58 263.06 98100 42615 Own Decision 268.47 258.76 98796 41919 Joint Decision 260.91 275.92 96015.5 44699.5



375

The hypotheses can be stated thus:


median responses on decision makers in the family as to purchase of life

insurance policy.


responses on the decision makers in family as to purchase of life

insurance policy.

Table 5.124 Mann-Whitney U test Head of Family Own Decision Joint Decision Mann-Whitney U 29412 28716 28119.5 Wilcoxon W 42615 41919 96015.5 Z -0.259 -0.719 -1.114 Asymp. Sig. (2-tailed) 0.796 0.472 0.265


The mean rank Table 5.123 shows that in rural areas the decision as to

the purchase of life insurance policy is taken jointly (lowest mean score

260.91), but in urban areas the decision is taken individually (lowest mean

score 258.76) by these who commit investment in life insurance. As per

Table 5.124, none of the hypotheses are rejected as the p values are 0.796,

0.472 and 0.265 respectively (p > .05). Therefore, it can be concluded that,

based on area, policyholders have a similar preference as to the decision maker

in the family on life insurance investment.

This problem is again considered among the occupation groups using the

Kruskal-Wallis test, as the category (occupational group) is having more than

two groups.

Chapter 5

376


H0: There is no difference in the median responses for decision makers in the

family as to purchase of life insurance policy among occupation groups.

H1: There is difference in the median responses for decision makers in the

family as to purchase of life insurance policy among occupation groups.

Table 5.125 Occupation- wise Mean Ranks on Decision Makers as to Purchase of Life Insurance Policy


N 25 79 157 87 33 149

Head of Family 241.46 249.75 314.21 270.66 312.14 213.22

Own Decision 283.96 283.03 212.28 275.84 204.32 316.7

Joint Decision 272.72 262.58 272.46 247.66 284.26 264.77 Source: Primary Data

Table 5.126 Kruskal-Wallis Test Head Of Family Own Decision Joint Decision

Chi-Square 42.736 48.789 2.398

Df 5 5 5

Asymp. Sig. 0.000* 0.000* 0.792 Source: Primary Data *Significant at 5 per cent level of significance

As per the mean rank Table 5.125, among agriculturists, business and

self- employed people and others, the decision making as to purchase of life

insurance policy is decided by the head of the family, while in the case of

government servants and NRI/foreign employed people, decision is taken

by themselves, and joint decision is opted among private servants. The

hypotheses except that related to joint decision are rejected as the p value is


377

0.792 (p>0.05). It means that there is difference among different occupational

groups as to the decision making in the family by head of family and by

self.

This problem is considered among the Family structure groups with the

Kruskal-Wallis test, as it is having more than two groups.

The hypotheses can be stated as follows:

H0: There is no difference in the median responses on decision makers in the

family as to purchase of life insurance policy among family structure

groups.

H1: There is difference in the median responses on decision makers in the

family as to purchase of life insurance policy among family structure

groups.

Table 5.127 Family Structure- Wise Mean Ranks

Nuclear Extended Joint N 430 44 56 Head of family 256.26 299.64 309.63 Own Decision 272.15 263.81 215.77 Joint Decision 268.41 228.63 272.12


Table 5.128 Kruskal Wallis Test Head Of Family Own Decision Joint Decision Chi-Square 9.483 7.684 3.229 Df 2 2 2 Asymp. Sig. .009* .021* .199


Chapter 5

378

The family structure- wise mean rank Table shows that among the three

family structure groups, decision by the head of the family, joint decision, and

own decision get prime importance in nuclear, extended and joint family

structure groups respectively, as to taking decision on the purchase of life

insurance policy. All hypotheses except that related to joint decision are rejected

as the p value is seen as 0 .199 (p>0.05). Therefore, it can be concluded that

there is significant difference among the three family structure groups with

regard to decision by head of family and own decision.

5.3.3 Most Influencing/Persuading factors in buying LIC Policy

The selection of a particular institution for investment by customers is

decided by the trust or confidence that the organisation could instill in the

minds of prospective investors. Safety of investment being the primary criteria

on the source which the prospective customers mostly depend upon in

choosing an organisation for investment may be personal or impersonal.

Personal contacts are more powerful than impersonal elements.

From the marketing point of view, it is very interesting to identify which

of the element such as LIC Agents (LICA) or Family Members/Relatives/

Spouse (FMRS), or Friends/Colleagues (FC), or LIC Office Staff (LICO), or

Own Perception/Interest (OPI), or its promotional tools like Advertisement in

Print /Visual Media (APVM) or Informative Brochures/ Pamphlets (IBP)

drives the prospective investor to select a particular institution for financial

commitments. It is relevant to see if these responses show any differences

between rural and urban populations. Since the data provides the ranks given

by respondents, a non-parametric test, Mann-Whitney U test, is used. The

results are reported below.


379

Table 5.129 Area -wise Mean Ranks on Most Influencing Element to Choose LIC

Mean Ranks Sum of Ranks

Rural Urban Rural Urban

N 368 162 368 162

Life Insurance Agents 261.01 275.7 96052 44663

Family Members/Relatives/Spouse 273.14 248.14 100516 40199

Friends/Colleagues 258.87 280.56 95264.5 45450.5

LIC Office Staff 261.79 273.92 96340 44375

Own Perception/Interest 266.84 262.45 98197.5 42517.5

Advertisement In Visual /Print Media 264.45 267.89 97316.5 43398.5

Informative Brochures/Pamphlets 272.83 248.85 100401 40314 Source: Primary Data

Table 5.130 Man -Whitney Test LICA FMRS FC LICO OPI APVM IBP

Mann-Whitney U

28156.000 26996.000 27368.500 28444.000 29314.500 29420.500 27111.000

Wilcox on W 96052.000 40199.000 95264.500 96340.000 42517.500 97316.500 40314.000

Z -1.189 -1.770 -1.528 -.863 -.307 -.244 -1.697

Asymp.Sig. (2-Tailed)

.234 .077 .126 .388 .759 .808 .090


As per Table 5.129 of area- wise mean ranks, Friends / Colleagues in

Rural areas (with the lowest mean rank 258.87) and family Members/

Relatives/Spouse in Urban areas (with the lowest mean rank 248.14) are

found to be mostly influencing the choice of LIC for buying policy. It is also

observed that none of the hypotheses are rejected as the respective p values are

0.234, 0.077, 0.126, 0.388, 0.759, 0.808, 0.090 and 0.991 respectively (p > .05).

Chapter 5

380

It points out that none of the elements significantly differ between rural and

urban areas in influencing policyholders to choose LIC to buy policies. It can

be concluded that all the elements listed above have a similar influence in

driving policyholders to choose LIC in buying policy, irrespective of their area

of residence.


Kruskal-Wallis test, as the category has more than two groups.


H0: There is no difference in the median responses for the most influencing

element to choose LIC while buying policy among occupation groups.

H1: There is difference in the median responses for the most influencing

element to choose LIC while buying policy among occupation groups.

Table 5.131 Occupation- wise Mean Ranks on the Most Influencing Elements to Choose LIC


N 25 79 157 87 33 149

Life Insurance Agents 268.7 292.65 246.69 267.18 285.15 265.06

Family Members/ Relatives/ Spouse

259.9 270.7 280.66 280.28 279.53 235.98

Friends/Colleagues 205.58 261.77 278.33 260.35 287.53 262.14

LIC Office Staff 240.96 219.65 296.6 268.01 304.91 250.97

Own Perception/Interest 256.84 291.03 245.64 256.25 233.23 286.9

Advertisement In Visual /Print Media

274.14 248.43 278.46 270.44 247.88 260.47

Informative Brochures/Pamphlets

313.84 270.04 239.61 271.96 224.24 287.62



381


LICA FMRS FC LICO OPI APVM IBP

Chi-Square 7.408 8.660 6.035 18.746 9.815 2.995 13.269

Df 5 5 5 5 5 5 5

Asymp.Sig. 0.192 0.123 0.303 0.002* 0.081 0.701 0.021* Source: Primary Data *Significant at 5 per cent level of significance

As per the means rank Table given above, the most influencing elements

in choosing LIC, among the occupational groups agriculturists, business and

self- employed people, private servants and others are friends/colleagues, LIC

office staff, own perception/ interest and family members/relatives/spouse

respectively. Informative brochures and pamphlets influence the

government servants, NRI/foreign employed people and others in choosing

LIC to buy policies. The hypotheses for LIC office staff and informative

brochures/ pamphlets are rejected as the p values are 0.002 and 0.021

respectively (p< .05) and for other elements the hypotheses are not rejected

as the respective p values are 0.192, 0.123, 0.303, 0.081, and 0.701

(p>0.05). Therefore, it may be concluded that there is significant difference

among the occupational groups as to the influence of LIC office staff and

informative brochures/ pamphlets in choosing to buy LIC policies.

This problem is considered among the family structure groups using

the Kruskal-Wallis test, as the category (family structure) has more than

two groups.

Chapter 5

382

Table 5.133 Family Structure wise Mean ranks on the Most Influencing Elements to Choose LIC

Nuclear Extended Joint N 430 44 56 Life Insurance Agents 266.57 241.19 276.41 Family Members/Relatives/Spouse 261.77 289.57 275.21 Friends/Colleagues 268.22 244.56 261.04 LIC Office Staff 256.39 321.57 291.41 Own Perception/Interest 274.1 230.34 227.08 Advertisement In Visual /Print Media 259.13 292.2 293.4 Informative Brochures/Pamphlets 268.64 243.86 258.39



H0: There is no difference in the median responses for the most influencing

element to choose LIC while buying policy among family structure

groups.

H1: There is difference in the median responses for the most influencing

element to choose LIC while buying policy among family structure groups.

Table 5.134 Kruskal Wallis Test LICA FMRS FC LICO OPI APVM IBP Chi-Square 1.933 1.638 1.042 9.529 7.355 4.107 1.232

Df 2 2 2 2 2 2 2 Asymp. Sig. 0.380 0.441 0.594 0.009* 0.025* 0.128 0.540

Source: Primary Data *significant at 5 per cent level of significance

The mean rank Table shows that among nuclear, extended and joint

family structure groups, LIC Office staff, Life insurance agents and Own


383

perception/Interest are found to be the most influencing elements. It is also

observed that the hypotheses for LIC office staff and own perception/interest

are rejected as the p values are 0.009 and 0.025 respectively (p< .05) and for

other elements the hypotheses are not rejected as the p values are 0 .380,

0.441, 0.594, 0.128 and 0.540 (p>0.05) respectively. It may be inferred that

there is significant difference among family structure groups as to the

influence of the LIC office staff and own perception/interest in choosing to

buy LIC policies.

5.3.4 Element of Marketing Mix Motivating to Purchase of Policy from LIC India The marketing mix elements play a vital role in formulation of

marketing strategy. Effective monitoring of the impact of strategies in the

seven elements of marketing mix decides the success of the firm. The

preferences among customers as to the criteria for taking purchase decision may

vary. While some may give emphasis to price/premium-related elements, others

give emphasis to the speed and efficiency (process), while a few others might be

valuing the behavioural pattern of the service provider, i.e., Agent, office staff

etc (people). The amenities provided at service counter, “physical evidence”,

the accessibility to the institution providing service ( place), the variety in

product mix offered (product), the power of promotional messages to

influence/ touch the heart of customer (promotion) greatly motivate a

customer to prefer service/products offered by the firm in the competitive

market. It is very important from the marketing point of view to see whether

these responses show any differences between rural and urban populations.

Since the data provides ranks given by respondents, a non- parametric test is

used. The results are reported below.

Chapter 5

384

Table 5.135 Area -wise Mean Rank on Motivating Elements of Marketing Mix

Area Mean Rank Sum of Ranks

Rural Urban Rural Urban N 368 162 368 162 Product 266.63 262.93 98121 42594 Price 265.82 264.77 97822 42893 Promotion 262.78 271.68 96702.5 44012.5 Place 256.42 286.13 94362.5 46352.5 Process 270.07 255.12 99386 41329 People 269.6 256.19 99213 41502 Physical Evidence

262.55 272.2 96618.5 44096.5

Source: Primary Data The hypothesis can be stated thus:


median responses for the elements of marketing mix of service product.


responses for the elements of marketing mix of service product.

Table 5.136 Man Whiteny Test

Product Price Promotion Place Process People Physical Evidence

Mann-Whitney

U 29391.000 29690.000 28806.500 26466.500 28126.000 28299.000 28722.500

Wilcoxon W

42594.000 42893.000 96702.500 94362.500 41329.000 41502.000 96618.500

Z -.272 -.074 -.631 -2.104 -1.050 -.940 -.808 Asymp.

Sig. (2-tailed)

.786 .941 .528 .035* .294 .347 .419



385

The mean rank Table presents that Place and Process elements of

marketing mix, having the lowest mean rank 256.42 and 255.12 in the order,

influenced policyholders in purchasing policy from LIC in rural and urban

areas respectively. The hypothesis related to place is rejected as its p value is

0.035 (p<0.05). None of the hypotheses other than those related to place are

not rejected as the respective p values are 0.786, 0.941, 0.528, 0.294, 0.347,

and 0.419 respectively (p > .05). It clearly states that there is significant difference

between rural and urban areas as to place in motivating policyholders to purchase

policy from LIC. The mean rank for place is the highest in urban areas and the

lowest in rural areas which establishes that the place element has no influence

in urban areas as to the purchase of policies from LIC which is the reverse in

the case of rural areas.


Kruskal-Wallis test, as there are more than two groups.

Table 5.137 Occupation- wise Mean Ranks on Motivating Elements of Marketing Mix

AC BSE GS PS NRI/FE Others N 25 79 157 87 33 149 Product 244.06 290.98 258.31 260.78 255.3 268.18 Price 276.34 260.46 244.18 272.86 270.77 283.36 Promotion 264.34 284.39 260.49 270.32 245.23 262.63 Place 227.68 219.79 278.74 279.93 345.83 255.91 Process 279.98 250.18 281.7 250.48 237.42 269.11 People 297.7 292.14 248.9 263.09 253.39 267.55 Physical Evidence

266.82 255.91 292.97 256.56 277.73 243.93


Chapter 5

386


H0: There is no difference in the median responses for elements of marketing

mix for “product” among occupation groups.

H1: There is difference in the median responses for elements of marketing

mix for “product” among occupation groups.


Product Price Promotion Place Process People Physical

Evidence

Chi-Square 3.695 5.774 2.186 21.095 4.934 5.732 12.901

Df 5 5 5 5 5 5 5

Asymp.Sig 0.594 0.329 0.823 0.001* 0.424 0.333 0.024* Source: Primary Data *significant at 5 per cent level of significance

The mean rank Table points out that “place” is found to be the highly

motivating element of marketing mix for Agriculturists and Business and self-

employed people. While the “process” element motivated private servants and

NRI/ foreign employed people, Price and Physical evidence elements are the

motivating elements for Government servants and others respectively. It is

also found that none of the hypotheses are rejected except those related to

place and physical evidence, as their respective p values are 0.594, 0.329,

0.823, 0.424, 0.333 (p > .05), while the hypotheses as to place and physical

evidence are rejected as the p value are 0.001 and 0.024 (p<0.05) .

Therefore, it may be concluded that there is significant difference among

occupational groups as to “place” and “physical evidence” elements in

motivating customers to buy policies from LIC.


387

This problem is considered among the family structure groups by

Kruskal-Wallis test as the category has more than 2 groups.

Table 5.139 Motivating Elements of Marketing Mix-- Family Structure -wise Mean Ranks

Family Structure N Product Price Promotion Place Process People Physical

Evidence

Nuclear 430 273.25 264.84 263.01 264.12 265.85 267.38 262.2

Extended 44 236.16 293.52 264.69 294.75 234.63 258.01 262.25

Joint 56 229.02 248.58 285.23 253.08 287.04 256.91 293.4 Source: Primary Data


H0: There is no difference in the median responses for elements of marketing

mix for the service “product” among family structure groups.

H1: There is difference in the median responses for elements of marketing

mix for the service “product” among family structure groups.


Product Price Promotion Place Process People Physical

Evidence Chi-Square 6.605 2.264 1.093 2.100 2.979 .355 3.037 Df 2 2 2 2 2 2 2 Asymp.Sig. Sig.

0.037* 0.322 0.579 0.350 0.225 0.837 0.219


As per the mean rank Table, respondents belonging to nuclear, extended

and joint family structures are motivated by the Physical evidence, Process

and Product elements respectively. None of the hypotheses except that related

Chapter 5

388

to product are rejected as the p values are 0.322, 0.579, 0.350, 0.225, 0.837

and 0.219 respectively (p>.05), while the hypothesis as to “product” is

rejected, its p value being 0.037 (p<0.05). It may be concluded that there is

significant difference among family structure groups as to the “product”

element in motivating customers to purchase policy from LIC.

5.3.5 Motive behind Purchasing Life Insurance Policy

The service “product” takes birth from the need of the customer. The

purpose for which a life insurance policy is bought decides the type of policy

purchased and the period of investment. The product strategy calls for an in-

depth understanding of the basic needs and motives of the customers. The purpose

of purchasing policy for specific periods among customers may be based on

their occupational status, income level, future needs, attitude towards the mode

of saving (some people do not prefer to have certain policies that benefit after

they expire), etc. The major motives identified and subjected to analysis are

Risk Coverage (RC), Long-Term Savings (LTS), Income Tax Relief (ITR), Old

Age Protection (OAP), Children’s Marriage/Education (CME), Acquisition of

Home Assets (AHA),Wealth Creation/Additional Income (WCAI), Debts

Payments/Loan Facilities (DPLF), Bequest Motives/ Final Expenses (BMFE)

and Service to Agent (STA). It is important to identify the difference in the

motives of taking out policy, among customers belonging to rural and urban

areas, across different occupational groups and family structure groups. Since

the data provides ranks given by respondents, a non- parametric test, Mann-

Whitney U test , is used. The area-wise results are reported below.


389

Table 5.141 Area -wise Mean Ranks on Motives of Holding Life Insurance Policy

Mean rank Sum of Ranks Rural Urban Rural Urban

Risk Coverage 269.95 255.4 99340 41375

Long- Term Savings 269.55 256.3 99195 41520

Income Tax Relief 276.63 240.23 101798.5 38916.5

Old Age Protection 268.98 257.6 98984 41731

Children’s Marriage/Education 258.2 282.08 95017.5 45697.5

Acquisition Of Home Assets 255.68 287.8 94091 46624

Wealth Creation/Additional Income

270.8 253.46 99655 41060

Debts Payment/Loan Facilities 268.29 259.15 98732 41983

Bequest Motives/Final Expenses 255.4 288.44 93988.5 46726.5

Service To Agent 266.45 263.35 98052.5 42662.5 Source: Primary Data The hypotheses can be stated thus:


median responses on motives for holding life insurance policy.

H1: There is difference between rural and urban populations in their

median responses on motives for holding life insurance policy.

Chapter 5

390


391

The mean rank Table points out that the major motives behind taking

policies are bequest motives and acquisition of home assets in the case of

rural areas and income tax relief in urban areas. None of the hypotheses

except those related to income tax relief, acquisition of home assets and

bequest motives/final expenses are rejected as the respective p values are

0.285, 0.347, 0.426, 0.095, 0.225, 0.520, 0.826 (p>0.05) while the

hypotheses for stated motives are rejected as the p values are 0.011, 0.024

and 0.018 respectively (p< .05). It highlights that there is significant

difference between rural and urban areas as to the motive of holding life

insurance for income tax relief, acquisition of home assets and bequest

motives/final expenses.


Kruskal-Wallis Test as there are more than 2 groups under the category.


H0: There is no difference in the median responses for motives of holding

life insurance policy among occupation groups.

H1: There is difference in the median responses for motives of holding life

insurance policy among occupation groups.

Chapter 5

392

Table 5.143 Occupation-wise Mean Ranks on Motives of Holding Life Insurance Policy

AC BSE GS PS NRI/FE Others N 25 79 157 87 33 149 Risk coverage 304.62 240.6 248.33 276.29 256.17 286 Long- term savings 226.72 262.09 289.18 256.2 259.76 255.56 Income tax relief 331.62 303.42 164.67 272.16 273.8 334.81 Old age protection 286.74 214.32 267.67 280.61 292.88 271.9 Children’s marriage/education 190.98 254.89 297.61 294.25 256.48 235

Acquisition of home assets 269.98 248.86 290.97 259.57 250.15 253.59 Wealth creation/additional income 218.96 308.35 303.02 221.97 223.92 245.68

Debts payment/loan facilities 235.7 230.22 314.49 250.98 256.89 247.98 Bequest motives/final expenses 195.9 260.19 283.4 281.21 306.21 242.95

Service to agents 310.22 277.89 265.39 256.94 292.33 250.6 Source: Primary Data

Table 5.144 Kruskal Wallis Test RC LTS ITR OAP CME AHA WCAI DPLF BMFE STA Chi-Square 10.063 6.718 109.786 11.715 22.676 6.813 30.461 24.786 14.812 5.585

Df 5 5 5 5 5 5 5 5 5 5 Asymp. Sig 0.073 0.242 0.000* 0.039* 0.000* 0.235 0.000* 0.000* 0.011* 0.349


As per the mean rank Table, the motive behind holding life insurance

policy by private servants and NRI/foreign employed people is wealth

creation/additional income, while it is children’s marriage/education for

agriculturists and others. The respondents pertaining to the business and self-

employed class, and government servants have the motives of holding policy

for old age protection and income tax relief. All of the hypotheses, except for

risk coverage, long- term savings, acquisition of home assets and service to


393

agents are rejected as their p values are 0.073, 0.242, 0.235 and 0.349

respectively (p<0.05). It means there is significant difference among

respondents pertaining to different occupation groups as to motives behind

holding policy for income tax relief, old age protection, children’s

marriage/education, wealth creation/additional income, debt payments/loan

facilities and bequest motives and final expenses.

This problem is considered among the Family structure groups with the

Kruskal-Wallis Test, as family structure has more than 2 groups.


H0: There is no difference in the median responses for motives of holding

life insurance policy among family structure groups.

H1: There is difference in the median responses for motives of holding life

insurance policy among family structure groups.

Table 5.145 Family Structure- wise Mean Rank on Motives of Holding Life Insurance Policy

Nuclear Extended Joint N 430 44 56 Risk coverage 272.86 243.92 225.96 Long- term savings 260.25 287.88 288.21 Income tax relief 274.34 245.2 213.56 Old age protection 261.03 299.47 273.16 Children’s marriage/education 256.61 317.83 292.63 Acquisition of home assets 268.13 258.78 250.59 Wealth creation/additional income 266.61 256.77 263.84 Debts payment/loan facilities 260.28 263.94 306.8 Bequest motives/final expenses 265.54 276.93 256.21 Service to agents 267.03 222.61 287.44


Chapter 5

394


RC LTS ITR OAP CME AHA WCAI DPLF BMFE STA Chi-Square

6.319 2.813 8.762 2.722 8.489 .759 .175 4.719 .482 4.851

Df 2 2 2 2 2 2 2 2 2 2 Asymp. Sig.

.042* .245 .013* .256 .014* .684 .916 .094 .786 .088


The mean rank Table shows that the three family structure groups,

nuclear, extended and joint family structure groups, have higher motives

for holding life insurance policies for children’s marriage/education,

service to agents and income tax relief respectively. None of the hypotheses

except those related to risk coverage, income tax relief, children’s

marriage/education are rejected as their respective p values are 0.042,

0.013 and 0.014 (p<0.05). Therefore, it can be concluded that there is

significant difference among family structure groups as to the motive for

life insurance policies in respect of risk coverage, income tax relief, and

children’s marriage/education.

5.4 Customer Perception on Promotional Strategies of LIC

The promotional strategies comprise strategies related to selection of

tools and media, designing content and its mode of presentation. The

measurement of effectiveness is a very complex and tedious job. The

measurement of effectiveness of promotional strategy is multidimensional

as it varies over different market segments. How far the content of the

advertisement is able to influence the customer group, ultimately leading to

purchase of products and services, is the simplest measure of effectiveness.

The influence of promotional strategy over the image of the organisation,


395

creation and enhancement of product/service knowledge, enhanced sales

and gaining customer confidence is vital among other strategies of the

organisation.

The section covers an analysis of mostly used media for information

and entertainment, evaluation of overall promotional strategies through 3

tools, and evaluation of the influence of advertisement through various

media. In order to evaluate the overall promotional strategies, the degree of

compliance with the listed 13 elements by the 3 tools (Advertising, Personal

Selling and Public Relations) is evaluated in terms of scores ranging from 5

for excellent to 1 for very poor. The levels of influence of the advertisements

through the listed 30 media/means are measured at 4 levels. The customers’

responses vary from levels 1 to 4 representing “even not just seen” (ENJS)

the advertisement in the media, to “persuasion of the advertisement” leading

to purchase decision.

5.4.1 Media used for Information and entertainment

The section analyses the various media choices among the selected

policyholders for information (To say, news channels in TV, Newspaper

etc) and for entertainment. The preference index will be useful in designing

promotional mix. It will be economical and result- oriented to promote

products and services through the most popular and depended media. The

message and its mode of delivery are to be designed in tune with the media.

The following Table presents the media choice among the selected

policyholders.

Chapter 5

396

Table 5.147 Frequency Table on Media for Entertainment and Information

Rank TV Radio News Paper Internet Others

1 282(53.2) 21(4.0) 160(30.2) 49(9.2) 19(3.6)

2 151(28.5) 55(10.4) 241(45.5) 62(11.7) 21(4.0)

3 67(12.6) 165(31.1) 99(18.7) 150(28.3) 49(9.2)

4 20(3.8) 170(32.1) 25(4.7) 168(31.7) 146(27.5)

5 10(1.9) 119(22.5) 5(0.9) 101(19.1) 295(55.7) Source: Primary Data

Table 5.146 makes it clear that the most preferred media for entertainment

and informative purpose among sample policyholders are in the order of TV,

newspaper, radio, internet and others such as magazines etc. It also indicates

that most of the respondents prefer any one media listed as the first 3 ranks for

others indicate low frequency.

5.4.2 Evaluation of Promotional Strategies of LIC for Marketing Insurance Products

The promotional tools used prominently by the LIC are Advertising,

Personal selling and Public Relations in promoting their products and services.

The LIC uses the three tools depending upon the purpose and objective of

promotional activity. Every tool has its own pros and cons. A judicious mix of

these tools depending upon the need will serve in attaining the promotional

objectives effectively. It is of vital importance to identify the usefulness of

these tools in creating awareness of products/services, enhancing company

image, changing attitude of customers, promoting the products/services or

enhancing utility of products and services. The sample respondents were

asked to give a score of 5 representing excellent, to 1 representing very poor,

depending upon the degree of usefulness felt by them. The 4 dimensions of


397

usefulness listed above over three tools, Advertising, Personal Selling and

Public Relations, are analysed with MANOVA.

To explain the possible variations in the mean scores of these four

factors across the three tools under study, a MANOVA has been used. Here,

the four variables are taken together, believing that the variables are more

meaningful if taken together than where considered separately.

MANOVA is used here to consider the following hypotheses:

H0: There is no significant variation in the mean scores of set of the

variables describing the usefulness of promotional tools in marketing

insurance.

H1: There is significant variation in the mean scores of set of the variables

describing usefulness of promotional tools in marketing insurance.

The Multivariate Test Table which provides the actual result of the

MANOVA is given below.

Table 5.148 MANOVA -General Linear Model

Multivariate Tests

Effect Value F Hypothesis Df

Error Df Sig.

Intercept Pillai's Trace 0.982 21571.869a 4 1584 0.000* Wilks' Lambda 0.018 21571.869a 4 1584 0.000* Hotelling's Trace 54.474 21571.869a 4 1584 0.000* Roy's Largest Root 54.474 21571.869a 4 1584 0.000*

Group Pillai's Trace 0.147 31.346 8 3170 0.000* Wilks' Lambda 0.854 32.552a 8 3168 0.000* Hotelling's Trace 0.171 33.759 8 3166 0.000* Roy's Largest Root 0.167 66.300b 4 1585 0.000*


Chapter 5

398

Table 5.149 Tests of Between-Subjects Effects

Source Dependent Variable

Type I Sum of Squares Df Mean

Square F Sig.

Intercept Company Image 323396.84 1 323396.84 36985.3 0.000* Personal Attitude 285152.106 1 285152.106 47355.2 0.000* Promotion 156609.057 1 156609.057 27597.6 0.000* Utility 71764.229 1 71764.229 21329 0.000*

Group Company Image 1850.545 2 925.272 105.819 0.000* Personal Attitude 274.681 2 137.34 22.808 0.000* Promotion 427.136 2 213.568 37.635 0.000* Utility 254.092 2 127.046 37.759 0.000*

Error Company Image 13876.615 1587 8.744 Personal Attitude 9556.213 1587 6.022 Promotion 9005.808 1587 5.675 Utility 5339.679 1587 3.365

Total Company Image 339124 1590 Personal Attitude 294983 1590 Promotion 166042 1590 Utility 77358 1590


Table 5.150 Estimated Marginal Means

1. Grand Mean

Dependent Variable Mean Std.

Error


Company Image 14.262 0.074 14.116 14.407 Personal Attitude 13.392 0.062 13.271 13.513 Promotion 9.925 0.06 9.807 10.042 Utility 6.718 0.046 6.628 6.808



399

Table 5.151 Estimated Marginal Means

2. Group

Dependent Variable Group Mean Std.

Error


Lower Bound

Upper Bound

Company Image

Advertisement 15.485 0.128 15.233 15.737

Personal selling

14.44 0.128 14.188 14.692

Public Relations

12.86 0.128 12.608 13.112

Personal Attitude

Advertisement 13.74 0.107 13.531 13.949

Personal selling

13.628 0.107 13.419 13.837

Public Relations

12.808 0.107 12.598 13.017

Promotion Advertisement 9.319 0.103 9.116 9.522

Personal selling

9.87 0.103 9.667 10.073

Public Relations

10.585 0.103 10.382 10.788

Utility Advertisement 7.151 0.08 6.995 7.307

Personal selling

6.817 0.08 6.661 6.973

Public Relations

6.187 0.08 6.031 6.343


Chapter 5

400

Table 5.152 Parameter Estimates

Dependent Variable Parameter B Std.

Error T Sig.

95% Confidence

Interval

Lower Bound

Upper Bound

Company Image

Advertisement 2.625 0.182 14.448 0.000* 2.268 2.981

Personal selling

1.579 0.182 8.694 0.000* 1.223 1.936

Public Relations

0a . . . . .

Personal Attitude

Advertisement 0.932 0.151 6.183 0.000* 0.636 1.228

Personal selling

0.821 0.151 5.445 0.000* 0.525 1.116

Public Relations

0a . . . . .

Promotion Advertisement -1.266 0.146 -8.652 0.000* -1.553 -0.979

Personal selling

-0.715 0.146 -4.887 0.000* -1.002 -0.428

Public Relations

0a . . . . .

Utility Advertisement 0.964 0.113 8.557 0.000* 0.743 1.185

Personal selling

0.63 0.113 5.593 0.000* 0.409 0.851

Public Relations

0a . . . . .


The estimated marginal means and MANOVA Tables 5.148 to 5.152

indicate that the mean score variations of the four variables of usefulness taken

together vary over three promotional tools, and that advertisement is able to

enhance company image, build right personal attitude and create utility to

products and services of LIC, while public relation activities are able to


401

promote products and services as the mean values are high (15.485, 13.740,

7.151 and 10.585). The statistical significance of the variation of the means

confirms this. Moreover, the MANOVA characterised by powerful Pillai’s

Trace test is significant at 5 per cent level of significance (value of f 31.346

with p=0.000<0.05). When the four variables based on 3 tools are taken

independently, differences for variables can be found statistically significant in

the test of between- subjects effects (p<0.05). While considering all the three

tools as a whole, they are found able to enhance company image, build right

personal attitude, promote products and services, and enhance utility in order,

as their mean values are 14.262, 13.392, 9.925 and 6.718 respectively. The

parameter estimates reveal that advertisement and personal selling is able to

enhance the company image 2.625 times and 1.579 times respectively,

compared to public relations. With regard to the ability to build the right

attitude, advertisement and personal selling are 0.932 and 0.821 times better

than public relations. While considering the promotion element, “public

relations” compared to advertisement and personal selling is 1.266 and 0 .715

times better respectively. While considering the utility factor, advertisement

and personal selling, compared to public relations, are 0.964 and 0.630 times

better respectively.

5.4.3 Evaluation of Advertisements of the LIC through Listed Media

The problem relating to promotion through advertisements is studied in

terms of 30 types of promotional efforts used by the LIC in its Promotional

activities in the past. Three options were given to respondents to mark, i.e., Not

Even just seen (ENJS), Observed, Remembering and Persuaded. One entry in the

column “persuaded” means that they have observed and remember the

advertisement found through the media. Respondents were asked to mark the

relevance of each item according to the level of impact, if any, on their life

Chapter 5

402

insurance policy purchase decision, using an ordered scale. If the distribution of

responses shows equal frequencies in each case, then it will imply that there is no

relevance of that item. This is equivalent to seeing if the distribution is uniform.

The One-Sample Kolmogorov-Smirnov Test procedure compares the

observed cumulative distribution function for a variable with a specified

theoretical distribution, in this case, Uniform distribution. The Kolmogorov-

Smirnov Z is computed from the largest difference (in absolute value) between

the observed and theoretical cumulative distribution functions. This goodness-

of-fit test tests whether the observations could reasonably have come from the

Uniform distribution.

The promotional tools subject to evaluation by respondents are Newspaper

advertisements (NPA), Business publications (BP), TV Advertisements (TVA),

Radio Advertisements (RA), TV/Radio interviews (TVRI), Film advertisements

(FA), Bill boards (BB), Electric displays (ED), Sponsoring academic activities

(SAA), Sponsoring contests/sports events (SCSE), Sponsoring social

responsible programmes (SSRP), Sending marketing material with customer

communication (SMMWCC), Customer review websites (CRW), Online banner

advertisements (OBA), Advertisements in insurance websites (AIW), Email/

newsletters (EMNL), Social networks (SNW), Hoarding/welcome boards at

prominent places (HWBAPP), Brochures/leaflets/booklets/press releases

(BLBPR), Bulletins/banners at branch premises (BBBP), Calendars/diaries/

business cards of agents (CDBCA), Maintaining public parks at important

places (MPIP), Transit advertising (TA), Sending holiday/birthday cards/

messages (SHBC), Tele-marketing (TM), Posters/ banner/sign board etc

(PBSB), Newspaper inserts/bound inserts in books (NPI/BI), Information kiosks

(IK), Wall paintings (WP) and Publicity vans (PV).


403

Table 5.153 Evaluation of Advertisements of LIC Through Listed Media

SN

Promotional tools / means K

S Z

val

ue

Asy

mp.

Sig

(2

taile

d)

Frequency

Enj

s (0

)

Obs

erve

d (1

)

Rem

embe

ring

(2

)

Purs

uade

d (3

)

Tot

al

1 NPA 6.458 0.000* 28(5) 257(48) 167(32) 78(15) 530(100) 2 BP 7.659 0.000* 120(23) 233(44) 144(27) 33(6) 530(100) 3 TVA 6.834 0.000* 40(8) 212(40) 156(29) 122(23) 530(100) 4 RA 6.776 0.000* 156(29) 171(32) 170(32) 33(6) 530(100) 5 TVRI 9.209 0.000* 212(40) 167(32) 118(22) 33(6) 530(100) 6 FA 9.701 0.000* 192(36) 208(39) 91(17) 39(7) 530(100) 7 BB 10.005 0.000* 204(38) 203(38) 100(19) 23(4) 530(100) 8 ED 9.614 0.000* 205(39) 193(36) 118(22) 14(3) 530(100) 9 SAA 9.831 0.000* 217(41) 186(35) 94(18) 33(6) 530(100) 10 SCSE 11.033 0.000* 254(48) 170(32) 81(15) 25(5) 530(100) 11 SSRP 10.83 0.000* 243(46) 183(35) 83(16) 21(4) 530(100) 12 SMMWCC 11.728 0.000* 270(51) 158(30) 84(16) 18(3) 530(100) 13 CRW 13.031 0.000* 300(57) 154(29) 60(11) 16(3) 530(100) 14 OBA 11.728 0.000* 270(51) 148(28) 100(19) 12(2) 530(100) 15 AIW 10.526 0.000* 237(45) 182(34) 96(18) 15(3) 530(100) 16 EMNL 13.422 0.000* 309(58) 134(25) 62(12) 25(5) 530(100) 17 SNW 12.988 0.000* 299(56) 156(29) 57(11) 18(3) 530(100) 18 HWBAPP 9.093 0.000* 139(26) 247(47) 116(22) 28(5) 530(100) 19 BLBPR 8.528 0.000* 95(18) 278(52) 123(23) 34(6) 530(100) 20 BBBP 8.354 0.000* 107(20) 262(49) 134(25) 27(5) 530(100) 21 CDBCA 5.85 0.000* 74(14) 221(42) 193(36) 42(8) 530(100) 22 MPIP 9.527 0.000* 172(32) 224(42) 120(23) 14(3) 530(100) 23 TA 11.134 0.000* 208(39) 225(42) 84(16) 13(2) 530(100) 24 SHBC 10.121 0.000* 233(44) 166(31) 109(21) 22(4) 530(100) 25 TM 10.83 0.000* 211(40) 215(41) 92(17) 12(2) 530(100) 26 PBSB 8.094 0.000* 105(20) 258(49) 133(25) 34(6) 530(100) 27 NPI/BI 8.615 0.000* 164(31) 211(40) 123(23) 32(6) 530(100) 28 IK 13.639 0.000* 314(59) 128(24) 73(14) 15(3) 530(100) 29 WP 6.964 0.000* 133(25) 204(38) 166(31) 27(5) 530(100) 30 PV 10.222 0.000* 191(36) 221(42) 90(17) 28(5) 530(100) *Source: Primary Data Note : Figures in brackets show percentages to 530 respondents *Significant at 5 per cent level of significance

Chapter 5

404

In each case the hypothesis will be:

H0: The distribution is not different from uniform distribution as frequencies in

each class are equal.

H1: The distribution is different from uniform distribution as frequencies in

each class are not equal.

In either of the cases above, the Kolmogorov Smirnov Z is seen to be

significant at 5 per cent level of significance, (p < 0.05). There seem to be

differences in respondents’ views. The result also shows that newspaper

advertisements (5 per cent), TV advertisements (8 per cent) and promotion

through calendars, diaries and business cards of agents (14 per cent) have the

least values as to the criterion Even Not Just Seen (ENJS), indicating that

advertisements of the LIC through these listed means/media are mostly seen

by policyholders. Considering the other 3 criteria too, except in the case of

observed criteria (where Brochures /Leaflets/Booklets/Press releases and

Bulletins/Banners at Branch Premises outshine ), the above- listed means of

advertisements outshine other 27 means in influencing policyholders towards

the purchase of life insurance policies. While taking into account the most

powerful criterion “persuaded” TV advertisements (23 per cent), newspaper

advertisements (15 per cent), and promotion through calendars, diaries and

Business cards of agents (8 per cent) are found to be the most influencing

means in the promotion mix of LIC.

5.5 Customer Satisfaction on the Products and Services of the LIC

The ultimate measure of business performance is customer satisfaction.

The ultimate stakeholder in any form of organisation is the customer. Products

and services are born out of them and delivered to them. The existence of the

organisation itself depends on the satisfaction of the customers in all respects.


405

Measuring customer satisfaction in a service firm is more complex than in

other firms. It is because the marketing service product requires much more

expertise and caution than tangible products. The customer satisfaction on the

products and services of the LIC is measured in terms of 7 dimensions of the

marketing mix of service firms.

The seven elements of service marketing mix are:-

1) Product- What is offered to the general public for purchase.

2) Price/Premium-the charge for purchase of product, paid periodically

or in a lump.

3) Place/Distribution-the function that bridges the gap between the

service provider and the organisation.

4) People-the backbone of the organisation who deliver the products

and services.

5) Process-sum total of activities involved in the delivery of services

and satisfaction.

6) Promotion-the activity oriented towards educating customers/

potential customers about the service/ product of a firm.

7) Physical evidence-the facilities and environment organisation

arranged to service customers at their premises.

5.5.1 Customer Satisfaction on the Products of the LIC

The insurance product is a bundle of satisfaction. Apart from just a

product, it encompasses terms and conditions (as period, age of entry and exit,

features and benefits (like riders, loan facility, surrender, transfer and

assignment, convertibility, etc). The service or product must satisfy the

needs/expectations of customers at its 5 levels. The customer should be able to

identify, compare and evaluate products in terms of his needs. The Product

Chapter 5

406

strategies are to be designed taking into account these factors so that lapsation

of policy and evils in marketing, such as misselling, can be eliminated to a

great extent, leading to enhanced customer satisfaction. The product-related

satisfaction by area and among occupational groups and family structure

groups is analysed with Two-Way ANOVA and the output is presented below.

5.5.1.1 Two-Way ANOVA on Product- Related Satisfaction of Policy holders (PDTRS) by Area and Occupation

The variations of product -related satisfaction of policy holders (PDTRS)

are analysed with Two-Way ANOVA by two categories, area and occupation,

and the output is presented in the following Tables.

Table 5.154 Area -wise Estimated Marginal Means-PDTRS 1. Area

Dependent Variable: Product -Related Satisfaction



Rural 57.196 .505 56.204 58.188 Urban 57.615 .726 56.190 59.041


Table 5.155 Occupation- wise Estimated Marginal Means-PDTRS

2. Occupation Dependent Variable: Product -Related Satisfaction



Agriculture 56.873 1.696 53.541 60.205 Business & self- employed 57.039 .943 55.186 58.892 Govt service 56.882 .675 55.556 58.209 Private service 58.885 .892 57.132 60.637 NRI/FE 57.421 1.446 54.581 60.261 Others 57.335 .704 55.951 58.718



407

Table 5.156 Two-Way ANOVA - PDTRS




Square F Sig.

Area 32.283 1 32.283 .471 .493

Occupation 250.239 5 50.048 .730 .601

Error 35875.486 523 68.596


The test of mean variation of the scores for product related satisfaction

between rural and urban areas and among different occupational groups by

Two-Way ANOVA reveals that area and occupation- wise variations of the

mean scores are not statistically significant at 5 per cent level of significance

(value of F 0.471 and 0.730 Df 1 and 5 with p=0.493 and 0.601>0.05). As

per Tables 5.154, 5.155 and 5.156, there is no significant difference between

rural and urban areas and among different occupational groups as to product-

related satisfaction. Therefore, it may be concluded that, based on area and

occupational group, the selected policyholders are having the same level of

satisfaction as to products of the LIC. The high mean score for private service

(58.885) in the occupational group implies high level of satisfaction in

comparison with other elements in the group.

5.5.1.2 Two-Way ANOVA on Product -Related Satisfaction of Policy holders (PDTRS) by Area and Family Structure

The variations in product-related satisfaction of policy holders (PDTRS)

are analysed with Two-Way ANOVA by area and family structure, and the


Chapter 5

408

Table 5.157 Area -wise Estimated Marginal Means-PDTRS

1. Area Dependent Variable: Product -Related Satisfaction



Rural 56.931 .615 55.723 58.139


Table 5.158 Family Structure- wise Estimated Marginal Means-PDTRS

2. Family Structure Dependent Variable: Product -Related Satisfaction





Table 5.159 Two-Way ANOVA - PDTRS

Tests Of Between – Subjects Effects



Square F Sig.

Area 32.283 1 32.283 .472 .493

Family structure

117.479 2 58.739 .858 .425

Error 36008.246 526 68.457



409

The mean variation of the scores for product-related satisfaction between

rural and urban areas and among different family structure groups is tested

using Two-Way ANOVA which shows that area-wise and family structure -

wise variations of the mean scores are not statistically significant at 5 per

cent level of significance (value of F 0.472 and 0.858 Df 1 and 2 with

p=0.493 and 0.425 >0.05). As per Tables 5.157, 5.158 and 5.159, there is no

significant difference between rural and urban areas and among different

groups of family structures as to satisfaction on products and services offered

by the LIC. Therefore, it may be concluded that rural and urban respondents

and respondents pertaining to different groups of family structures have the

same level of satisfaction as to products services offered by the LIC.

5.5.1.3 Two-Way ANOVA on Product -Related Satisfaction of Policy holders (PDTRS) by Family Structure and Occupation

The variations in product- related satisfaction of policy holders (PDTRS)

are analysed with Two-Way ANOVA by Family structure and occupation


Table 5.160 Family Structure -wise Estimated Marginal Means-PDTRS

1. Family Structure

Dependent Variable: Product- Related Satisfaction




Nuclear 57.441 .485 56.489 58.394

Extended 57.816 1.288 55.287 60.346

Joint 55.962 1.155 53.692 58.232 Source: Primary Data

Chapter 5

410

Table 5.161 Occupation -wise Estimated Marginal Means-PDTRS

2. Occupation Dependent Variable: Product -Related Satisfaction



Agriculture 56.534 1.719 53.158 59.910

Business & self - employed

56.687 1.062 54.600 58.774

Govt service 56.652 .755 55.169 58.136

Private service 58.624 .984 56.690 60.557

NRI/FE 57.040 1.511 54.071 60.008

Others 56.904 .862 55.211 58.596 Source: Primary Data

Table 5.162 Two-Way ANOVA – PDTRS

Tests of Between-Subjects Effects Dependent Variable: Product -Related Satisfaction


Square F Sig.

Family structure 120.517 2 60.258 .879 .416

Occupation 260.248 5 52.050 .759 .579

Error 35777.243 522 68.539


The test of mean variation of the scores for product related satisfaction

among different family structures and different occupational categories using

Two-Way ANOVA indicates that family structure and occupation- wise

variations of the mean scores are not statistically significant at 5 per cent level

of significance (value of F 0.879 and 0.759 Df 2 and 5 with p=0.416 and

0.579 >0.05). As per Tables 5.160, 5.161 and 5.162, there is no significant


411

difference among different categories of family structures and among different

occupational categories as to satisfaction on products and services of the LIC.

Therefore, it may be concluded that the same level of satisfaction persists

among different categories of family structure and occupation on products and

services of the LIC.

5.5.2 Price/Premium- Related Satisfaction

Premium is the monetary consideration paid by the insured to the

insurers for the insurance granted by the policy. The main consideration to be

kept in mind in this regard is that it must be acceptable to the target customers

and must reflect the other components of the mix accurately. To have an

effective pricing strategy, it is to be set as an intrinsic element of market

positioning strategy and not as independent of other elements in marketing

mix. The level of satisfaction as to price is decided by the rate of premium on

policies, penalties in case of delay or default in payment and service charges,

availability of flexible premium payment schedule (i.e. Monthly, quarterly etc)

and different modes of premium(i.e. individual agents, office, electronic

means, premium collection points etc) and the discounts and rebates as to

period of premium payment and sum assured of policy.

5.5.2.1 Two-Way ANOVA on Price/Premium - Related Satisfaction of Policyholders (PPRS) by Area and Occupation

The variations in price/premium-related satisfaction of policy holders

(PPRS) by area and occupation are analysed with Two-Way ANOVA and the


Chapter 5

412

Table 5.163 Area -wise Estimated Marginal Means-PPRS

1. Area Dependent Variable: Price/Premium- Related Satisfaction




Table 5.164 Occupation -wise Estimated Marginal Means-PPRS

2. Occupation Dependent Variable: Price/Premium- Related Satisfaction

Occupation Mean Std. Error 95% Confidence Interval

Lower BoundUpper Bound Agriculture 28.754 .910 26.965 30.542 Business & self- employed 28.527 .506 27.533 29.521 Govt service 28.862 .362 28.150 29.574 Private service 29.973 .479 29.032 30.914 NRI/FE 28.808 .776 27.284 30.333 Others 29.363 .378 28.621 30.106 Source: Primary Data

Table 5.165 Two-Way ANOVA – PPRS

Tests of Between-Subjects Effects Dependent Variable: Price/Premium -Related Satisfaction


Square F Sig.

Area 46.300 1 46.300 2.343 .126 Occupation 116.631 5 23.326 1.181 .317 Error 10333.001 523 19.757 Total 10495.932 529 Source: Primary Data


413

The test of the mean variation of the scores for price/premium- related

satisfaction by Two-Way ANOVA between rural and urban areas and among

different occupational groups shows that area-wise and occupation- wise



0.317>0.05). Tables 5.163, 5.164 and 5.165 exhibit that there is no significant


groups, as to price related satisfaction. Therefore, it may be concluded that

respondents belonging to either area and different occupational group have the

same level of satisfaction as to pricing products of the LIC. The high mean

scores of 29.973 in the private service category and 29.345 in the urban area

indicate the high level of satisfaction in comparison to other elements in the

group.

5.5.2.2 Two-Way ANOVA on Price/Premium -Related Satisfaction of Policyholders (PPRS) by Area and Family Structure

The variations in price/premium-related satisfaction of policy holders

(PPRS) are analysed by two categories, area and family structure, with Two-

Way ANOVA and the output is presented in the following Tables.

Table 5.166 Area -wise Estimated Marginal Means-PPRS

1. Area Dependent Variable: Price/Premium -Related Satisfaction




Chapter 5

414

Table 5.167 Family Structure -wise Estimated Marginal Means-PPRS

2. Family Structure Dependent Variable: Price/Premium -Related Satisfaction



Nuclear 29.112 .229 28.662 29.562 Extended 29.087 .677 27.756 30.417 Joint 29.364 .603 28.179 30.550 Source: Primary Data


Tests of Between-Subjects Effects Dependent Variable: Price /Premium -Related Satisfaction


Square F Sig.

Area 46.300 1 46.300 2.331 .127 Family structure 3.261 2 1.631 .082 .921 Error 10446.371 526 19.860 Total 10495.932 529 Source: Primary Data

The mean variation of the scores for price/premium-related satisfaction

between rural and urban areas and different family structures as per Two-Way

ANOVA shows that area and family structure-wise variations of the mean


of F 2.331 and 0.082 Df 1 and 2 with p=0.127 and 0.921 >0.05). As per

Tables 5.166, 5.167 and 5.168, there is no significant difference between rural

and urban areas and among different groups of family structures as to

satisfaction on product and services offered by the LIC. Therefore, it may be

concluded that respondents pertaining to rural and urban areas and different

groups of family structure have the same level of satisfaction as to price

charged for products and services by the LIC. The high mean scores of 29.511


415

in urban area and 29.112 for the nuclear family structure group show higher

level of satisfaction in the respective groups.

5.5.2.3 Two-Way ANOVA on Price/Premium -Related Satisfaction of Policyholders (PPRS) by Family Structure and Occupation

The variations in price/premium-related satisfaction of policy holders (PPRS)

are analysed with Two-Way ANOVA by two categories, family structure and

occupation, and the output is presented in the following Tables.

Table 5.169 Family Structure -wise Estimated Marginal Means-PPRS

1. Family Structure Dependent Variable: Price/Premium - Related Satisfaction





Table 5.170 Occupation -wise Estimated Marginal Means-PPRS

2. Occupation Dependent Variable: Price/Premium - Related Satisfaction



Agriculture 28.515 .925 26.698 30.333 Business & Self - Employed 28.476 .572 27.353 29.600 Govt Service 28.793 .406 27.995 29.592 Private Service 29.955 .530 28.914 30.996 NRI/FE 28.793 .813 27.195 30.391 Others 29.290 .464 28.379 30.201


Chapter 5

416


Tests of Between-Subjects Effects Dependent Variable: Price/Premium - Related Satisfaction


Square F Sig.

Family structure 2.631 2 1.316 .066 .936 Occupation 125.269 5 25.054 1.261 .279 Error 10368.032 522 19.862 Total 10495.932 529


The Two-Way ANOVA test of the mean variation of the scores for

price/premium-related satisfaction among different family structure groups

and different occupational categories presents that family structure and

occupation- wise variations of the mean scores are not statistically significant

at 5 per cent level of significance (value of F 0.066 and 1.261 Df 2 and 5

with p=0.936 and 0.279 >0.05). As per Tables 5.168, 5.169 and 5.170, there is

no significant difference among different categories of family structure and

among different occupational categories as to satisfaction on price charged

on products and services of the LIC. Therefore, it may be concluded that the

satisfaction on products and services of the LIC among different categories of

family structure and occupation is at the same level.

5.5.3 Customer Satisfaction on Place/ Distribution Services Individual agents play a vital role in the distribution of life insurance

products and services of the LIC. Even though multiple channels are available

for distribution of insurance products and services, the element of personal

touch, easy accessibility and confidence distinguish the channel from others.

The nearness/ convenient location of office facilitates easy approach and

accessibility for seeking products and services. Use of internet and technology


417

in the delivery of product and services helps to reduce the distance between

the consumer and the LIC to a great extent in accessing its services.

5.5.3.1 Two-Way ANOVA on Place /Distribution Related Satisfaction of Policyholders (PDRS) by Area and Occupation

The variations on place/distribution-related satisfaction of policy holders

(PDRS) are analysed with Two-Way ANOVA by area and occupation, and the


Table 5.172 Area -wise Estimated Marginal Means-PDRS

1. Area Dependent Variable: Place/ Distribution -Related Satisfaction



Rural 18.274 .213 17.857 18.692


Table 5.173 Occupation- wise Estimated Marginal Means-PDRS

2. Occupation Dependent Variable: Place /Distribution -Related Satisfaction



Upper Bound

Agriculture 18.105 .714 16.702 19.507 Business &Self - Employed

17.978 .397 17.198 18.759

Govt Service 18.498 .284 17.940 19.057 Private Service 19.128 .376 18.390 19.866 NRI/FE 17.698 .609 16.502 18.894 Others 18.920 .297 18.337 19.502


Chapter 5

418

Table 5.174 Two-Way ANOVA - PDRS

Tests of Between-Subjects Effects Dependent Variable: Place/ Distribution -Related Satisfaction


Square F Sig.

Area 7.025 1 7.025 .578 .448 Occupation 104.271 5 20.854 1.715 .129 Error 6360.721 523 12.162 Total 6472.017 529


The test of the mean variations of the scores for place /distribution-

related satisfaction between rural and urban areas and different occupational

groups by Two-Way ANOVA presents that area and occupation- wise



0.129>0.05). As per Tables 5.172, 5.173 and 5.174, there is no significant


groups as to place/distribution-related satisfaction. Therefore, it may be

concluded that respondents belonging to rural and urban areas and different

occupational groups have the same level of satisfaction as to place/distribution

elements of the marketing mix of the LIC. The high mean scores of 19.128 and

18.501 indicate high levels of satisfaction among respondents in private service

and urban areas compared to other elements in their respective groups.

5.5.3.2 Two-Way ANOVA on Place/ Distribution Related Satisfaction of Policyholders (PDRS) by Area and Family Structure

The variations in place/distribution-related satisfaction of policy holders

(PDRS) are analysed with Two-Way ANOVA by area and family structure



419

Table 5.175 Area -wise Estimated Marginal Means-PDRS

1. Area Dependent Variable: Place/ Distribution- Related Satisfaction



Rural 18.401 .261 17.889 18.913


Table 5.176 Occupation- wise Estimated Marginal Means-PDRS

2. Family Structure Dependent Variable: Place /Distribution -Related Satisfaction

Family structure Mean

Std. Error


Nuclear 18.598 .180 18.244 18.952

Extended 18.437 .533 17.390 19.484


Table 5.177 Two-Way ANOVA – PDRS

Tests of Between-Subjects Effects Dependent Variable: Place/ Distribution- Related Satisfaction


Square F Sig.

Area 7.025 1 7.025 .572 .450

Family structure 1.125 2 .563 .046 .955

Error 6463.867 526 12.289


Chapter 5

420

The Two-Way ANOVA test to identify the mean variations of the scores

for place/distribution-related satisfaction among rural and urban areas and

different family structures shows that area and family structure-wise variations

of the mean scores are not statistically significant at 5 per cent level of

significance (value of F 0.572 and 0.046 Df 1 and 2 with p=0.450 and 0.955

>0.05). As per Tables 5.175, 5.176 and 5.177, there is no significant difference

among rural and urban areas and among different groups of family structures

as to satisfaction on place/distribution-related elements of the marketing mix

of the LIC. Therefore, it may be concluded that rural and urban respondents

and respondents pertaining to different groups of family structures have the

same level of satisfaction as to place/distribution services offered by the LIC.

5.5.3.3 Two-Way ANOVA on Place /Distribution Related Satisfaction of Policyholders (PDRS) by Family Structure and Occupation

The variations in place/distribution-related satisfaction of policyholders

(PDRS) by family structure and occupation are analysed with Two-Way

ANOVA and the output is presented in the following Tables.

Table 5.178 Family Structure- wise Estimated Marginal Means-PDRS

1. Family Structure Dependent Variable: Place /Distribution- Related Satisfaction

Family structure Mean Std.


Lower Bound Upper Bound Nuclear 18.357 .204 17.955 18.759

Extended 18.193 .543 17.126 19.260



421

Table 5.179 Occupation -wise Estimated Marginal Means-PDRS

2. Occupation Dependent Variable: Place/ Distribution -Related Satisfaction



Agriculture 17.943 .725 16.519 19.367

Business & Self- employed 17.877 .448 16.997 18.757

Govt service 18.414 .319 17.789 19.040

Private service 19.053 .415 18.237 19.869

NRI/FE 17.618 .637 16.366 18.870


Table 5.180 Two-Way ANOVA – PDRS

Tests of Between-Subjects Effects Dependent Variable: Place/ Distribution -Related Satisfaction


Square F Sig.

Family structure 1.229 2 .614 .050 .951

Occupation 105.525 5 21.105 1.731 .126

Error 6365.263 522 12.194


The test of the mean variations of the scores for place /distribution -related

services of the LIC among different family structure groups and different

occupational categories with Two-Way ANOVA presents that family structure

and occupation-wise variations of the mean scores are not statistically

significant at 5 per cent level of significance (value of F 0.050 and 1.731 Df 2

Chapter 5

422

and 5 with p=0.951 and 0.126 >0.05). Tables 5.178, 5.179 and 5.180 prove that

there is no significant difference among different categories of family structures

and among different occupational categories as to satisfaction on place/

distribution of services of the LIC. Therefore, it may be concluded that

satisfaction with regard to the place/distribution-related services of the LIC

among different categories of family structure and occupation are similar.

5.5.4 People -Related Satisfaction of Policyholders

The term “people” comprises all the participants involved in the service

delivery. It includes employees providing services (direct employees and individual

agents), and customers and the co-customers in the service environments. People as

service performers are important because a customer sees a company through its

employees. Well-informed, trained and knowledgeable service personnel with

professional approach maintaining personal relationship with customers can

contribute to customer delight and win customer approval. The friendly , honest

and trustworthy approach in rendering the service, and immediate availability in

case of service requirements instill confidence in the minds of customers to

continue the relationship. The level of customer satisfaction on “people”

depends on the quality of service, care and caution in the dealings, courteous

and polite interactions, timely, precise, accurate and updated information on

service requirements, dependable, expertise and advice in servicing policy.

Apart from these, people should be able to understand customer- specific

requirements and render solution at the earliest.

5.5.4.1 Two-Way ANOVA on People Related Satisfaction of Policyholders (PERS) by Area and Occupation

The variations in people-related satisfaction of policyholders (PERS)

are analysed with Two-Way ANOVA by two categories, area and occupation,



423

Table 5.181 Area- wise Estimated Marginal Means-PERS

1. Area Dependent Variable: People - Related Satisfaction




Table 5.182 Occupation -wise Estimated Marginal Means-PERS

2. Occupation Dependent Variable: People- Related Satisfaction



Upper Bound

Agriculture 70.286 2.214 65.936 74.636 Business & Self- employed 68.175 1.231 65.756 70.594 Govt service 68.107 .882 66.375 69.840 Private service 69.387 1.165 67.099 71.676 NRI/FE 66.138 1.888 62.429 69.846 Others 69.085 .919 67.279 70.892


Table 5.183 Two-Way ANOVA – PERS

Tests of Between-Subjects Effects Dependent Variable: People - Related Satisfaction


Square F Sig.

Area 135.310 1 135.310 1.157 .283 Occupation 410.362 5 82.072 .702 .622 Error 61161.830 523 116.944 Total 61707.502 529


Chapter 5

424

The test of mean variations of the scores for people-related satisfaction

among rural and urban areas and different occupational groups using Two-

Way ANOVA shows that the area-wise and occupation-wise variation of the

mean scores are not statistically significant at 5 per cent level of significance.

(Value of F 1.157 and 0.702 Df 1 and 5 with p=0.283 and 0.622>0.05).

Tables 5.181, 5.182 and 5.183 exhibit that there is no significant difference

among rural and urban areas and among different occupational groups as to

people-related satisfaction. Therefore, based on areas of residence and

occupational groups, respondents have the same level of satisfaction as to the

people of the LIC. The high mean scores of 69.145 and 70.286 for urban and

agriculture groups respectively indicate their high level of satisfaction on the

service of people compared to other elements in the group.

5.5.4.2 Two-Way ANOVA on People- Related Satisfaction of Policy holders (PERS) by Area and Family Structure

The variations in people-related satisfaction of policy holders (PERS) by

area and family structure are analysed with Two-Way ANOVA and the output


Table 5.184 Area -wise Estimated Marginal Means-PERS

1. Area Dependent Variable: People - Related Satisfaction


Lower Bound Upper Bound Rural 68.307 .804 66.728 69.886

Urban 69.407 1.039 67.366 71.448 Source: Primary Data


425

Table 5.185 Family Structure -wise Estimated Marginal Means-PERS

2. Family Structure Dependent Variable: People - Related Satisfaction



Nuclear 68.461 .556 67.369 69.552 Extended 69.748 1.643 66.519 72.976



Tests of Between-Subjects Effects Dependent Variable: People -Related Satisfaction


Square F Sig.

Area 135.310 1 135.310 1.157 .283 Family structure 68.490 2 34.245 .293 .746 Error 61503.702 526 116.927 Total 61707.502 529


To test the mean variations of the scores for people- related satisfaction

between rural and urban areas and among different family structures, Two-

Way ANOVA is used and it is found that area-wise and family structure- wise


(value of F 1.157 and 0.293 Df 1 and 2 with p=0.283 and 0.746 >0.05).

Tables 5.184, 5.185 and 5.186 show that there is no significant difference

between areas and different groups of family structures as to satisfaction on

people of the LIC. Therefore, it may be concluded that based on area and

different groups of family structures, respondents are having the same level of

satisfaction as to services of people of the LIC.

Chapter 5

426

5.5.4.3 Two-Way ANOVA on People -Related Satisfaction of Policyholders (PERS) by Family Structure and Occupation

The variations in people-related satisfaction of policy holders (PERS)

are analysed with Two-Way ANOVA by family structure and occupation, and

the output is presented in the following Tables.

Table 5.187 Family Structure -wise Estimated Marginal Means-PERS

1. Family Structure Dependent Variable: People - Related Satisfaction



Upper Bound



Table 5.188 Occupation -wise Estimated Marginal Means-PERS

2. Occupation Dependent Variable: People - Related Satisfaction

Occupation Mean Std. Error 95% Confidence Interval

Lower Bound

Upper Bound

Agriculture 70.129 2.248 65.712 74.546 Business & Self -employed 68.335 1.390 65.604 71.065 Govt service 68.194 .988 66.253 70.134 Private service 69.583 1.288 67.053 72.113 NRI/FE 66.251 1.977 62.368 70.135 Others 69.160 1.127 66.945 71.374



427


Tests of Between-Subjects Effects Dependent Variable: People -Related Satisfaction


Square F Sig.

Family structure 67.798 2 33.899 .289 .749 Occupation 397.605 5 79.521 .678 .640 Error 61242.099 522 117.322 Total 61707.502 529


As per the Two-Way ANOVA on the mean variations of the scores for

people-related satisfaction among different family structures and different

occupational categories, it is found that family structure and occupation- wise


of significance (value of F 0.289 and 0.678 Df 2 and 5 with p=0.749 and 0.640

>0.05). As per Tables 5.187, 5.188 and 5.189, there is no significant difference

among different categories of family structures and among different occupational

categories as to satisfaction on people of the LIC. Therefore, it may be concluded

that the levels of satisfaction on services of people of the LIC among different

categories of family structure and occupation are similar.

5.5.5 Process-Related Satisfaction

The term “process” refers to the system by which the consumer

receives delivery of service in a service organisation. It relates to the systems

and procedures involved in the interactions of employees in an organisation

with its customers and other stakeholders. The level of satisfaction on the

process element is determined by speed and efficiency in handling

transactions with minimum/no error, fast settlement of claims and other

payments(loan, surrender, annuity installments etc) without tedious formalities

Chapter 5

428

and procedures, optimum use of technology in delivering services, quick

solution to customer grievances and complaints, online contact/toll free services,

convenient working hours of the office, proper and timely communication on

policy/service- related aspects, etc.

5.5.5.1 Two-Way ANOVA on Process -Related Satisfaction of Policyholders (PSRS) by Area and Occupation

The variations in process-related satisfaction of policy holders (PSRS)

by area and occupation are analysed with Two-Way ANOVA and the output is

presented in the following Tables.

Table 5.190 Area -wise Estimated Marginal Means-PSRS

1. Area Dependent Variable: Process- Related Satisfaction




Table 5.191 Occupation - wise Estimated Marginal Means-PSRS

2. Occupation Dependent Variable: Process -Related Satisfaction



Agriculture 61.336 1.781 57.837 64.835 Business & Self -employed 61.449 .991 59.503 63.395 Govt service 61.531 .709 60.138 62.925 Private service 63.414 .937 61.573 65.255 NRI/FE 60.503 1.519 57.520 63.486 Others 61.912 .740 60.459 63.365



429

Table 5.192 Two-Way ANOVA – PSRS

Tests of Between-Subjects Effects Dependent Variable: Process -Related Satisfaction


Square F Sig.

Area 357.383 1 357.383 4.723 .030* Occupation 307.482 5 61.496 .813 .541

Error 39575.024 523 75.669 Total 40239.889 529


Two-Way ANOVA is used to test the mean variations of the scores for

process- related satisfaction between rural and urban areas and among

different occupational groups and it is found that the area- wise variation of

the mean scores is statistically significant at 5 per cent level (value of F 4.723 Df

1 and 5 with p=0.030<0.05). With regard to different occupational groups,

variation of the mean scores is not statistically significant at 5 per cent level

(value of F 0.813 Df 5 with p=0.541>0.05). Tables 5.190, 5.191 and 5.192 show

that while there is significant difference in the level of satisfaction as to area with

regard to process- related satisfaction, the difference is not significant in case of

occupational groups. Therefore, it may be concluded that respondents belonging

to urban areas have more satisfaction as to process (having high mean of

62.534) compared to rural respondents while the process- related satisfaction

among different occupational groups is of the same level.

5.5.5.2 Two-Way ANOVA on Process-Related Satisfaction of Policyholders (PSRS) by Area and Family Structure

The variations in process-related satisfaction of policyholders (PSRS) by



Chapter 5

430

Table 5.193 Area- wise Estimated Marginal Means-PSRS

1. Area Dependent Variable: Process -Related Satisfaction




Table 5.194 Family Structure -wise Estimated Marginal Means-PSRS

2. Family Structure Dependent Variable: Process -Related Satisfaction



Upper Bound

Nuclear 61.781 .447 60.902 62.660

Extended 61.868 1.323 59.270 64.467


Table 5.195 Two-Way ANOVA – PSRS



Square F Sig.

Area 357.383 1 357.383 4.718 .030*


Error 39841.968 526 75.745

Total 40239.889 529 Source: Primary Data * Significant at 5 per cent level of significance


431

The mean variation of the scores for process -related satisfaction

between rural and urban areas and different family structure groups by Two-

Way ANOVA shows that the area -wise variation of the mean scores is

statistically significant at 5 per cent level (value of F 4.718 and Df 1 with

p=0.030<0.05), while, among different occupational groups, the variation of

the mean scores is not statistically significant at 5 per cent level (value of F

0.268 and Df 2 with p=0.765>0.05 ). As per Tables 5.193, 5.194 and 5.195,

there is significant difference between rural and urban areas as to process -

related satisfaction, but among different groups of family structures, there is

no significant difference as to process-related satisfaction. Therefore, it may

be concluded that urban respondents have a high level of satisfaction as to

“process” with a mean score of 63.012 and respondents pertaining to different

groups of family structures have the same level of satisfaction as to “process”.

5.5.5.3 Two-Way ANOVA on Process- Related Satisfaction of Policyholders (PSRS) by Family Structure and Occupation

The variations in process- related satisfaction of policy holders (PSRS)

are analysed with Two-Way ANOVA by family structure and occupation, and


Table 5.196 Family Structure- wise Estimated Marginal Means-PSRS

1. Family Structure Dependent Variable: Process -Related Satisfaction



Upper Bound

Nuclear 61.233 .512 60.228 62.238

Extended 61.292 1.359 58.622 63.962


Chapter 5

432

Table 5.197 Occupation -wise Estimated Marginal Means-PSRS

2. Occupation Dependent Variable: Process- Related Satisfaction



Upper Bound

Agriculture 60.719 1.814 57.156 64.283

Business & self -employed 61.367 1.121 59.164 63.570

Govt service 61.381 .797 59.815 62.946

Private service 63.414 1.039 61.372 65.455

NRI/FE 60.509 1.595 57.376 63.642


Table 5.198 TWO-WAY ANOVA – PSRS



Square F Sig.


Occupation 351.122 5 70.224 .920 .468

Error 39854.907 522 76.350


To test the mean variations of the scores for process-related satisfaction

among different family structure groups and different occupational categories,

Two-Way ANOVA is used, and it is found that family structure and occupation

-wise variations of the mean scores are statistically significant at 5 per cent



433

and 0.468 >0.05). As per Tables 5.196, 5.197 and 5.198, there is no significant

difference among different categories of family structures and among different

occupational categories as to satisfaction on “process” of the LIC. Therefore,

it may be concluded that the satisfaction on process of the LIC among

different categories of family structure and occupation are similar in nature.

5.5.6 Customer Satisfaction with Regard to “Promotion”

The promotional efforts are intended to create awareness and

understanding on the products and services and ultimately to persuade them to

purchase them. The promotional efforts should be ethical in all respects

without concealing cost factors influencing customer decision. Misselling on the

part of individual agents, and incomplete disclosure of terms and conditions in the

case of other promotional efforts create negative attitudes in the minds of

policyholders.

5.5.6.1 Two-Way ANOVA on Satisfaction with Regard to Promotion (SWRP) by Area and Family Structure

The variations in satisfaction with regard to “promotion” (SWRP) are

analysed with Two-Way ANOVA by two categories, area and family

structure, and the output is presented in the following Tables.

Table 5.199 Area -wise Estimated Marginal Means-SWRP

1. Area Dependent Variable: Satisfaction with Regard to Promotion




Chapter 5

434

Table 5.200 Family Structure -wise Estimated Marginal Means-SWRP

2. Family Structure Dependent Variable: Satisfaction with Regard to Promotion



Upper Bound

Nuclear 17.527 .170 17.193 17.861

Extended 17.324 .503 16.337 18.312


Table 5.201 Two-Way ANOVA - SWRP

Tests of Between-Subjects Effects Dependent Variable: Satisfaction with Regard to Promotion


Square F Sig.

Area 26.171 1 26.171 2.392 .123


Error 5755.884 526 10.943


The mean variation of the scores for satisfaction on “promotion”

among different areas and family structures, based on Two-Way ANOVA,

shows that area and family structure-wise variations of the mean scores are

not statistically significant at 5 per cent level of significance (value of F 2.392

and 0.318 Df 1 and 25 with p=0.123 and 0.728 >0.05). Tables 5.199, 5.200

and 5.201 show that there is no significant difference in the satisfaction of

policyholders towards promotional elements between rural and urban areas

and among three groups of family structure. Therefore, it may be concluded

that the satisfaction on promotion-related elements of the LIC between among


435

rural and urban areas and among different categories of family structure are

similar in nature.

5.5.6.2 Two-Way ANOVA on Satisfaction with Regard to Promotion (SWRP) by Area and Occupation

The variations in promotion- related satisfaction of policy holders

(SWRP) by area and occupation are analysed with Two-Way ANOVA and the


Table 5.202 Area -wise Estimated Marginal Means-SWRP

1. Area Dependent Variable: Satisfaction with Regard to Promotion




Table 5.203 Occupation- wise Estimated Marginal Means-SWRP

2. Occupation Dependent Variable: Satisfaction with Regard to Promotion



Upper Bound

Agriculture 17.068 0.676 15.739 18.396

Business & self- employed 17.926 0.376 17.187 18.665

Govt service 17.163 0.269 16.634 17.692

Private service 17.376 0.356 16.677 18.075

NRI/FE 17.067 0.577 15.935 18.2

Others 17.789 0.281 17.237 18.341 Source: Primary Data

Chapter 5

436

Table 5.204 Two-Way ANOVA – SWRP



Square F Sig.

Area 26.171 1 26.171 2.399 0.122

Occupation 56.518 5 11.304 1.036 0.396

Error 5706.322 523 10.911


To test the mean variation of the scores for satisfaction on promotion

between rural and urban areas and among occupational groups, Two-Way

ANOVA is used and it is found that area and occupational-group wise



and 0.396 >0.05). Tables 5.202, 5.203 and 5.204 reveal that there is no

significant difference as to satisfaction on “promotion” of the LIC in rural

and urban areas and among different occupational groups. Therefore, it may

be concluded that the sample respondents in rural and urban areas and

different occupational groups have the same level of satisfaction on promotion

of the LIC.

5.5.6.3 Two-Way ANOVA on Satisfaction with Regard to Promotion (SWRP) by Family Structure and Occupation

The variations in promotion-related satisfaction of policy holders

(SWRP) by family structure and occupation are analysed with Two-Way

ANOVA, and the output is presented in the following Tables.


437

Table 5.205 Family Structure- wise Estimated Marginal Means-SWRP

1. Family Structure Dependent Variable: Satisfaction with Regard to Promotion



Upper Bound



Table 5.206 Occupation- wise Estimated Marginal Means-SWRP

2. Occupation Dependent Variable: Satisfaction with Regard to Promotion



Upper Bound

Agriculture 16.771 .688 15.420 18.122 Business & Self- employed 17.749 .425 16.914 18.584 Govt service 17.018 .302 16.424 17.611 Private service 17.251 .394 16.477 18.025 NRI/FE 16.918 .605 15.730 18.106 Others 17.578 .345 16.900 18.255


Table 5.207 Two-Way ANOVA – SWRP



Square F Sig.



Chapter 5

438

Based on the Two-Way ANOVA, the mean variation of the scores for

promotion- related satisfaction among different family structures and different

occupational categories is tested and it is found that family structure and

occupation-wise variations of the mean scores are not statistically significant at

5 per cent level of significance (value of F 0.354 and 0.922 Df 2 and 5 with

p=0.702 and 0.466 >0.05). Tables 5.205, 5.206 and 5.207 depict that there is no

significant difference among different categories of family structures and different

occupational categories as to satisfaction on promotion of the LIC. Therefore, it

may be concluded that the satisfaction with regard to promotion of the LIC among

different categories of family structure and occupation is similar.

5.5.7 Customer Satisfaction with Regard to Physical Evidence

Physical evidence refers to the environment in which the service is to be

delivered and the place where the customer interacts with the firm, and any

tangible parts that facilitate performance or communication of the service. The

facilities and amenities provided at office premises for basic requirements of

visiting customers may create a positive image in their minds towards the

organisation. Even though it has nothing to do directly with service quality,

the elements create a positive image of the organisation. It may be in the form

of Brochures, Furnishings, Signage, Business Cards, the building itself (such

as prestigious offices or scenic headquarters), colorful interior design and

layout, drinking water, sanitation facilities, etc.

5.5.7.1 Two-Way ANOVA on Satisfaction with Regard to Physical Evidence (SWRPE) by Area and Occupation

The variations in satisfaction with regard to physical evidence (SWRPE)

by area and occupation are analysed with Two-Way ANOVA and the output is


Chapter 5

440

Table 5.208 Area - wise Estimated Marginal Means-SWRPE

1. Area Dependent Variable: Satisfaction with Regard to Physical Evidence


Lower Bound Upper Bound Rural 34.319 0.374 33.584 35.054 Urban 35.186 0.538 34.13 36.243


Table 5.209 Occupation - wise Estimated Marginal Means-SWRPE

2. Occupation Dependent Variable: Satisfaction with Regard to Physical Evidence



Upper Bound

Agriculture 35.079 1.257 32.61 37.548 Business & self- employed 34.957 0.699 33.584 36.33 Govt service 34.069 0.501 33.086 35.052 Private service 35.244 0.661 33.945 36.543 NRI/FE 33.724 1.072 31.619 35.829 Others 35.442 0.522 34.416 36.467


Table 5.210 Two-Way ANOVA - SWRPE

Tests of Between-Subjects Effects Dependent Variable: Satisfaction with Regard to Physical Evidence


Square F Sig.

Area 73.114 1 73.114 1.941 0.164 Occupation 203.980 5 40.796 1.083 0.369 Error 19705.351 523 37.678 Total 19982.445 529



441

Two-Way ANOVA is used to test the mean variations of the scores for

satisfaction on physical evidence among different areas and occupational groups

and it is found that area and occupational group-wise variations of the mean

scores are not statistically significant at 5 per cent level of significance (value of

F 1.941 and 1.083 Df 1 and 5 with p=0.164 and 0.369 >0.05). Tables 5.208,

5.209 and 5.210 reveal that there is no significant difference as to satisfaction

on physical evidence -related elements of the LIC in rural and urban areas and

among different occupational groups. Therefore, it may be concluded that

sample respondents in rural and urban areas and belonging to different

occupational groups have the same level of satisfaction towards physical

evidence of the LIC.

5.5.7.2 Two-Way ANOVA on Satisfaction with Regard to Physical Evidence (SWRPE) by Area and Family Structure

The variations in satisfaction with regard to physical evidence (SWRPE)

by area and family structure are analysed with Two-Way ANOVA and the


Table 5.211 Area -wise Estimated Marginal Means-SWRPE

1. Area

Dependent Variable: Satisfaction with Regard to Physical Evidence



Rural 34.035 .457 33.138 34.933


Chapter 5

442

Table 5.212 Family Structure -wise Estimated Marginal Means-SWRPE

2. Family Structure Dependent Variable: Satisfaction with Regard to Physical Evidence



Upper Bound



Table 5.213 Two-Way ANOVA – SWRPE



Square F Sig.

Area 73.114 1 73.114 1.936 .165 Family structure 45.159 2 22.580 .598 .550 Error 19864.172 526 37.765 Total 19982.445 529


The mean variation of the scores for satisfaction on physical evidence

between rural and urban areas and among different family structure groups is

tested using Two-Way ANOVA and it is found that area and family structure

- wise variations of the mean scores are not statistically significant at 5 per

cent level of significance (value of F 1.936 and 0.598 Df 1 and2 with

p=0.165 and 0.550 >0.05). Tables 5.211, 5.212 and 5.213 show that there is

no significant difference in the satisfaction of policyholders towards physical

evidence between rural and urban areas and among three groups of family

structure. Therefore, it may be concluded that the satisfaction on physical

evidence of the LIC among respondents in rural and urban area and different

categories of family structure is similar.


443

5.5.7.3 Two-Way ANOVA on Satisfaction with Regard to Physical Evidence (SWRPE) by Family Structure and Occupation

The variations in promotion-related satisfaction of policy holders

(SWRPE) by two categories, family structure and occupation, are analysed

with Two-Way ANOVA and the output is presented in the following Tables.

Table 5.214 Family Structure - wise Estimated Marginal Means-SWRPE

1. Family Structure Dependent Variable: Satisfaction with Regard to Physical Evidence



Upper Bound

Nuclear 34.667 .360 33.959 35.375

Extended 33.979 .957 32.099 35.860


Table 5.215 Occupation -wise Estimated Marginal Means-SWRPE

2. Occupation Dependent Variable: Satisfaction with Regard to Physical Evidence



Upper Bound

Agriculture 34.399 1.277 31.890 36.908 Business & Self employed 34.486 .790 32.935 36.037

Govt service 33.693 .561 32.591 34.796

Private service 34.889 .732 33.452 36.326

NRI/FE 33.336 1.123 31.130 35.542


Chapter 5

444

Table 5.216 Two-Way ANOVA – SWRPE



Square F Sig.


Occupation 172.326 5 34.465 .910 .474

Error 19762.118 522 37.858


In order to test the mean variations of the scores for physical evidence

- related satisfaction among different family structure groups and different

occupational categories, Two-Way ANOVA is used and it is found that

family structure and occupation-wise variations of the mean scores are not


and 0.910 Df 2 and 5 with p=0.531 and 0.474 >0.05). Tables 5.214, 5.215

and 5.216 reveal that there is no significant difference among different

categories of family structure and different occupational categories as to

satisfaction on physical evidence of the LIC. Therefore, it may be concluded

that the satisfaction on physical evidence of the LIC is similar among

respondents in different categories of family structure and occupation.

5.6 Customer Satisfaction on the Services of LIC Agents

Life insurance agents play a dominant role in the distribution of

insurance products. The quality of the service rendered by the agents has an

equal place with product quality in satisfying customer needs. It is the agents

who educate the customers in person and the basic medium through the

confidence can be built on the products and services of the firm. The quality of


445

service decides the satisfaction level of customers. The level of satisfaction

among customers is measured at two stages of marketing life insurance, viz,

before issue of policy and after issue of policy.

5.6.1 Customer Satisfaction on Service of Agents before Issue of Policy

Agents render many services even before issue of life policy. The

services may include educating the potential customer as to the need for

having life insurance policy and successfully linking his need to suitable

products available in the market. The application forms to be filled in and

preparing necessary documents in connection with it before submission to

office is usually done by agents. The services include those upto the timely

handover of the policy document to the customer.

5.6.1.1 Two-Way ANOVA on Satisfaction on Service of Agents before Issue of Policy (SSBIP) By Area and Occupation

The variations in the levels of satisfaction on service of agents before

issue of policy (SSBIP) by area and occupation are analysed with Two-Way


Table 5.217 Area- wise Estimated Marginal Means-SSBIP

1. Area Dependent Variable: Service Satisfaction before Issue of Policy




Chapter 5

446

Table 5.218 Occupation -wise Estimated Marginal Means-SSBIP

2. Occupation Dependent Variable: Service Satisfaction before Issue of Policy



Upper Bound

Agriculture 26.217 1.245 23.771 28.663 Business & Self- employed 27.969 0.692 26.609 29.329 Govt service 28.602 0.496 27.628 29.576 Private service 28.352 0.655 27.066 29.639 NRI/FE 27.992 1.061 25.907 30.077 Others 27.816 0.517 26.801 28.832


Table 5.219 Two-Way ANOVA – SSBIP

Tests of Between-Subjects Effects Dependent Variable: Service Satisfaction before Issue of Policy


Square F Sig.

Area 32.689 1 32.689 0.884 0.347 Occupation 144.836 5 28.967 0.784 0.562 Error 19333.247 523 36.966 Total 19510.772 529


To test the mean variations of the scores for level of satisfaction on the

services of agents before issue of policy between rural and urban areas and

among different occupational groups, Two-Way ANOVA is used and it is

found that the area-wise and occupation-wise variations of the mean scores are


and 0.784 Df 1 and 5 with p=0.347 and 0.562>0.05). Tables 5.217,5.218 and

5.219 reveal that there is no significant difference as to the level of satisfaction

on the services of agents before issue of policy in rural and urban areas and


447

among 5 occupational groups. The mean value of the level of satisfaction in

urban area and government service in occupational group is found to be high,

which reveals higher level of satisfaction as to this, compared to others in the

group. Therefore, it may be concluded that rural and urban policyholders and

policyholders belonging to 5 occupational groups have the same level of

satisfaction as to services of agents before issue of policy.

5.6.1.2 Two-Way ANOVA on Satisfaction on Service of Agents before Issue of Policy (SSBIP) by Area and Family Structure

The variations in the level of satisfaction on service of agents before

issue of policy (SSBIP) by area and family structure are analysed with Two-

Way ANOVA and the output is presented in the following Tables.

Table 5.220 Area -wise Estimated Marginal Means –SSBIP

1. Area Dependent Variable: Service Satisfaction before Issue of Policy




Table 5.221 Family Structure- wise Estimated Marginal Means-SSBIP

2. Family Structure Dependent Variable: Service Satisfaction before Issue of Policy



Upper Bound



Chapter 5

448




Square F Sig.

Area 32.689 1 32.689 .883 .348 Family structure 3.106 2 1.553 .042 .959 Error 19474.977 526 37.025 Total 19510.772 529


To test the mean variations of the scores for the level of satisfaction on

the services of agents before issue of policy between rural and urban areas and

among different occupational groups, Two-Way ANOVA is used and it is

found that the area-wise and family structure -wise variations of the mean


of F 0.883 and 0.042 Df 1 and 2 with p=0.348 and 0.959>0.05). Tables 5.220,

5.221 and 5.222 reveal that there is no significant difference as to the level of

satisfaction on the services of agents before issue of policy in rural and urban

area and among three family structure groups. Therefore, it may be concluded

that rural and urban policyholders and policyholders belonging to different

family structure groups have the same level of satisfaction as to services of

agents before the issue of policy.

5.6.1.3 Two-Way ANOVA on Satisfaction on Service of Agents before Issue of Policy (SSBIP) by Family Structure and Occupation

The variations in the levels of satisfaction on service of agents before

issue of policy (SSBIP) by family structure and occupation are analysed

with Two-Way ANOVA and the output is presented in the following

Tables.


449

Table 5.223 Family Structure- wise Estimated Marginal Means-SSBIP

1. Family Structure Dependent Variable: Service Satisfaction before Issue of Policy



Upper Bound



Table 5.224 Occupation-wise Estimated Marginal Means-SSBIP

2. Occupation Dependent Variable: Service Satisfaction Before Issue Of Policy



Upper Bound

Agriculture 25.893 1.264 23.411 28.376 Business & Self employed 27.748 .781 26.214 29.283 Govt service 28.423 .555 27.332 29.513 Private service 28.183 .724 26.761 29.605 NRI/FE 27.819 1.111 25.636 30.001 Others 27.575 .634 26.331 28.820





Square F Sig.



Chapter 5

450


services of agents before issue of policy among rural and urban areas and

different occupational groups, Two-Way ANOVA is used and it is found that

family structure-wise and occupation-wise variations of the mean scores are


and 0.483 Df 2 and 5 with p=0.959 and 0.483>0.05). Tables 5.223, 5.224

and 5.225 reveal that there is no significant difference as to the level of

satisfaction on the services of agents before issue of policy among policyholders

belonging to different family structure and among 5 occupational groups. The

mean value of the level of satisfaction in nuclear family structure (27.787) and

government service (28.423) among occupational groups is found to be high,

which reveals higher level of satisfaction compared to other elements in the

group. Therefore, it may be concluded that policyholders belonging to different

occupational groups and family structure groups have the same level of

satisfaction as to services of agents before issue of policy.

5.6.2 Customer Satisfaction on Service of Agents after Issue of Policy

The services rendered by agents after issue of policy are: reminding

customers of premium dates, collection of premium (authorized one only),

renewal of lapsed policies, assistance to take loans on policies, settlement of

claims, settlement of surrenders, etc. The quality of service rendered after

issue of policy ensures customer retention and enhanced customer base due to

the word-of-mouth effect of satisfied policyholders.

5.6.2.1 Two-Way ANOVA on Satisfaction on the Service of Agents after Issue of Policy (SSAIP) by Area and Occupation

The variations in the levels of satisfaction on the service of agents after

issue of policy (SSAIP) by area and occupation are analysed with Two-Way



451

Table 5.226 Area -wise Estimated Marginal Means-SSAIP

1. Area Dependent Variable: Service Satisfaction after Issue of Policy Mean Std. Error


Rural 46.098 .656 44.810 47.385 Urban 48.160 .942 46.309 50.010


Table 5.227 Occupation- wise Estimated Marginal Means-SSAIP

2. Occupation Dependent Variable: Service Satisfaction after Issue of Policy



Upper Bound

Agriculture 44.989 2.202 40.663 49.314 Business & self -employed 47.201 1.224 44.796 49.606 Govt service 47.546 .877 45.823 49.268 Private service 48.846 1.158 46.571 51.121 NRI/FE 45.705 1.877 42.018 49.393 Others 48.485 .914 46.689 50.281


Table 5.228 Two-Way ANOVA Tests of Between-Subjects Effects

Tests of Between-Subjects Effects Dependent Variable: Service Satisfaction after Issue of Policy


Square F Sig.

Area 549.400 1 549.400 4.752 .030* Occupation 541.240 5 108.248 .936 .457 Error 60465.579 523 115.613 Total 61556.219 529


Chapter 5

452


services of agents after issue of policy among rural and urban areas and different

occupational groups, Two-Way ANOVA is used and it is found that the area -

wise variation of the mean scores is statistically significant at 5 per cent level of

significance (value of F 4.752 Df 1 and 5 with p=0.030<0.05) and in the case of

occupational groups, the variation of mean scores is not statistically significant at

5 per cent level of significance (value of F 0.936 Df 5 with p=0.457>0.05).

Tables 5.226, 5.227 and 5.228 reveal that there is significant difference as to the

level of satisfaction on the services of agents before issue of policy in rural and

urban areas and no significant difference among the 5 occupational groups. The

mean value of the level of satisfaction in urban area and private service among

occupational groups (48.160, 48.846) is found to be high, which highlights the

higher level of service satisfaction compared to others in the group. To conclude,

while policyholders belonging to different occupational groups are having the same

level of satisfaction on the services of agents after issue of policy, respondents in

urban areas have a higher level of satisfaction than those in rural areas.

5.6.2.2 Two-Way ANOVA on Satisfaction on the Service of Agents after Issue of Policy (SSAIP) by Area and Family Structure

The variations in the levels of satisfaction after issue of policy (SSAIP)

by area and family structure are analysed with Two-Way ANOVA and the


Table 5. 229 Area -wise Estimated Marginal Means-SSAIP 1. Area

Dependent Variable: Service Satisfaction after Issue of Policy



Rural 46.592 0.8 45.02 48.164 Urban 48.79 1.034 46.757 50.822



453

Table 5.230 Family Structure - wise Estimated Marginal Means-SSAIP

2. Family Structure Dependent Variable: Service Satisfaction after Issue of Policy



Nuclear 47.809 0.553 46.722 48.896 Extended 48.2 1.636 44.985 51.414 Joint 47.064 1.458 44.2 49.928


Table 5.231 Two-Way ANOVA – SSAIP



Square F Sig.

Area 549.400 1 549.4 4.740 0.030* Family structure 36.672 2 18.336 0.158 0.854 Error 60970.147 526 115.913 Total 61556.219 529


To test the mean variation of the scores for the level of satisfaction on

the services of agents before issue of policy among rural and urban areas and

different occupational groups, Two-Way ANOVA is used and it is found that the

area- wise variation of the mean scores is statistically significant at 5 per cent

level of significance (value of F 4.740 Df 1 with p=0.030<0.05). Tables 5.229,

5.230 and 5.231 show that there is significant difference in the level of satisfaction

on the services of agents after issue of policy in rural and urban areas, while there

is no significant difference among family structure groups. In the case of different

occupational groups the mean variation is not significant at 5 per cent level of

significance (value of F 0.158 with p=0.854>0.05). Therefore, it may be

Chapter 5

454

concluded that policyholders belonging to different family structure groups are

having same level of satisfaction and policyholders in urban areas are highly

satisfied (having mean score of 48.790 compared to mean score of rural areas

46.592) as to the services of agents after issue of policy.

5.6.2.3 Two-Way ANOVA on Satisfaction on the Service of Agents after Issue of Policy (SSAIP) by Family Structure and Occupation

The variations in the level of satisfaction after issue of policy (SSAIP)

by family structure and occupation are analysed with Two-Way ANOVA and


Table 5.232 Family Structure -wise Estimated Marginal Means-SSAIP

1. Family Structure Dependent Variable: Service Satisfaction after Issue of Policy





Table 5.233 Occupation -wise Estimated Marginal Means-SSAIP

2. Occupation Dependent Variable: Service Satisfaction after Issue of Policy



Agriculture 44.014 2.242 39.609 48.418 Business & self- employed 46.731 1.386 44.008 49.454 Govt service 47.141 .985 45.206 49.077 Private service 48.552 1.284 46.028 51.075 NRI/FE 45.296 1.971 41.423 49.169 Others 47.871 1.124 45.663 50.080



455

TABLE 5.234 Two-Way ANOVA – SSAIP



Square F Sig.



To test the mean variation of the scores for level of satisfaction on the

services of agents before issue of policy among rural and urban areas and

different occupational groups, Two-Way ANOVA is used and it is found that

family structure-wise and occupation-wise variations of the mean scores

are not statistically significant at 5 per cent level of significance (value of

F 0.186 and 1.055 Df 2 and 5 with p=0.831 and 0.385>0.05). Tables

5.232, 5.233 and 5.234 present that there is no significant difference as to

the level of satisfaction on the services of agents after issue of policy

among policyholders belonging to different family structures and among

different occupational groups. The mean value of the level of satisfaction

in extended among family structure groups and private service among

occupational groups is found to be high, which reveals higher level of

satisfaction in this respect compared to other groups. Therefore, it may be

concluded that policyholders belonging to occupational groups and family

structure groups have the same level of satisfaction as to the services of

agents after issue of policy.

Chapter 5

456

5.7 Knowledge and Behavioural Pattern of Agents in Marketing Life Insurance Products The agents are presupposed to have sufficient knowledge of customer

needs and attitudes, the products and services offered by their organisation, the

status and state of the firm or industry. The customers’ perceptions on the

level of knowledge of agents on the above 3 dimensions depend on the

attitude and behavioural pattern of agents in marketing life insurance

products, and direct their approach and attitude towards investment in life

insurance and success of marketing strategy. The policyholders expect the

agents to be knowledgeable on the firm, its products and services, issues

related to the functioning of the firm and industry, and policy matters of

regulatory authorities and governments affecting the industry. Confidence

is built up among customers on the firm, depending upon these factors, i.e.,

how far they are satisfied on these aspects as expected from their service

providers. Only those agents who can substantiate the clarifications of the

customers with visible evidence sustain in the field which, in turn,

necessitates multifaceted knowledge on the part of agents- the ultimate

service community.

The levels of knowledge of agents as perceived by policyholders are

analysed from 4 perspectives.

1. Level of knowledge on needs and attitudes of policyholders

The delivery of right product and services requires a good knowledge

of customer needs and attitudes. Understanding the personal

requirements of an individual is a difficult task as it is related to many

factors.


457

2. Level of knowledge on LIC

A better understanding on the organisation served, its functioning and

policy framework, authorities, rules and procedures are prerequisites to

serve the customer group.

3. Level of knowledge on products and services

The agents should have thorough knowledge on the products and

services that they market. They should be in a position to clarify any

query on its features, benefits or cost element. The agents should be

able to tailor the needs of the customer to the product feature so that

the customer will get attracted to the product, which requires

expertise on the part of agents in understanding the product and

services and effectively conveying the need of the products to the

customer’s mind.

4. Attitudes and behavioural patterns of agents

Agents should build and keep up a right attitude towards the profession.

Enhancing policy sale through misselling will make loose his customer

base in the long run.

5.7.1 Knowledge of Agents on Customer Needs and Attitudes (KACNA)

The delivery of right products and services demands a good knowledge

of customer needs and attitudes. Understanding the personal requirements of

an individual as to investment and suggesting suitable plans requires patience

and attentive listening to customer queries. The lapsation rate of policies can

be reduced to a great extent if they are sold understanding his capabilities

(including financial) and expectations.

Chapter 5

458

5.7.1.1 Two-Way ANOVA on Knowledge of Agents on Customer Needs and Attitudes (KACNA) by Area and Occupation

The variations in the knowledge of agents on customer needs and attitudes

(KACNA) by area and occupation are analysed with Two-Way ANOVA and the


Table 5.235 Area -wise Estimated Marginal Means-KACNA

1. Area Dependent Variable: Knowledge on Customer Needs and Attitudes




Table 5.236 Occupation -wise Estimated Marginal Means-KACNA

2. Occupation Dependent Variable: Knowledge on Customer Needs and Attitudes



Upper Bound

Agriculture 24.512 .989 22.569 26.455 Business & self- employed 24.016 .550 22.936 25.097 Govt service 23.996 .394 23.222 24.769 Private service 24.534 .520 23.512 25.557 NRI/FE 21.779 .843 20.122 23.435 Others 24.131 .411 23.324 24.938



459

Table 5.237 Two-Way ANOVA – KACNA

Tests of Between-Subjects Effects Dependent Variable: Knowledge on Customer Needs and Attitudes


Square F Sig.



To test the mean variations of the scores for the knowledge of agents on

customer needs and attitudes among rural and urban areas and different

occupational groups, Two-Way ANOVA is used and it is found that area- wise

and occupation- wise variations of the mean scores are not statistically


and 5 with p=0.799 and 0.137>0.05). Tables 5.235, 5.236 and 5.237 show that

there is no significant difference in the perceptions of policyholders on the

knowledge of agents on the customer needs and attitudes, between rural and

urban areas, and among different groups of occupation. Therefore, it may be

concluded that policyholders in rural and urban areas and those belonging to

different occupational groups have similar perceptions on the knowledge of

agents on customer needs and attitudes.

5.7.1.2 Two-Way ANOVA on Knowledge of Agents on Customer Needs and Attitudes (KACNA) by Area and Family Structure

The variations in the knowledge of agents on customer needs and

attitudes (KACNA) by area and family structure are analysed with Two-Way


Chapter 5

460

Table 5.238 Area -wise Estimated Marginal Means-KACNA

1. Area Dependent Variable: Knowledge on Customer Needs and Attitudes




Table 5.239 Occupation- wise Estimated Marginal Means-KACNA

2. Family Structure Dependent Variable: Knowledge on Customer Needs and Attitudes



Upper Bound



Table 5.240 Two-Way ANOVA – KACNA



Square F Sig.




461


customer needs and attitudes, among rural an urban areas and different family

structure groups, Two-Way ANOVA is used and it is found that area- wise

and family structure- wise variations of the mean scores are not statistically

significant at 5 per cent level of significance (value of f 0.065 and 0.976 Df 1

and 2 with p=0.800 and 0.337>0.05). Tables 5.238, 5.239 and 5.240 show

that there is no significant difference in the perceptions of policyholders on the

knowledge of agents on the customer needs and attitudes, between rural and

urban areas and among different groups of family structures. Therefore, it may

be concluded that policyholders belonging to rural and urban areas and

different occupational groups have similar perceptions on the knowledge of

agents of the customer needs and attitudes.

5.7.1.3 Two-Way Anova on Knowledge of Agents on Customer Needs and Attitudes (KACNA) by Family Structure and Occupation

The variations on knowledge of agents on customer needs and attitudes

(KACNA) by two categories, family structure and occupation, are analysed


Table 5.241 Family Structure -wise Estimated Margins-KACNA

1. Family Structure Dependent Variable: Knowledge on Customer Needs and Attitudes






Chapter 5

462

Table 5.242 Occupation -wise Estimated Marginal Means-KACNA

2. Occupation Dependent Variable: Knowledge on Customer Needs and Attitudes



Upper Bound

Agriculture 24.506 1.002 22.537 26.474 Business & self –employed 24.125 0.62 22.908 25.342 Govt service 24.044 0.44 23.178 24.909 Private service 24.624 0.574 23.496 25.752 NRI/FE 21.927 0.881 20.195 23.658 Others 24.264 0.502 23.277 25.251


Table 5.243 Two-Way ANOVA -KACNA



Square F Sig.




customer needs and attitudes, among different family structure groups and

different groups of occupation, Two-Way ANOVA is used and it is found that

family structure-wise and occupation-wise variation of the mean scores are not


and 1.613 Df 2 and 5 with p=0.378 and 0.155>0.05). Tables 5.241, 5.242 and


463

5.243 show that there is no significant difference in the perception of

policyholders on the knowledge of agents on customer needs and attitudes,

among different family structure groups and different groups of occupation.

Therefore, it may be concluded that policyholders pertaining to different

occupations and family structures have similar perceptions on knowledge of

agents on the customer needs and attitudes.

5.7.2 Perception of Policyholders on the Level of Knowledge of Agents on LIC The opening up of the life sector made the insurance industry competitive.

In the competitive market, survival requires updated and perfect knowledge on

systems and procedures in the functional area. An agent should possess industry

and organisation level knowledge of his profession. The knowledge of higher

authorities is prerequisite to handling customer grievances.

5.7.2.1 Two-Way ANOVA on Knowledge of Agents on LIC (KALIC) by Area and Occupation

The variations in the level of knowledge of agents on LIC (KALIC) by

area and occupation are analysed with Two-Way ANOVA and the output is


Table 5.244 Area- wise Estimated Marginal Means-KALIC

1. Area Dependent Variable: Knowledge of Agents on LIC




Chapter 5

464

Table 5.245 Occupation -wise Estimated Marginal Means-KALIC

2. Occupation Dependent Variable: Knowledge of Agents on LIC



Upper Bound

Agriculture 19.535 .705 18.150 20.920 Business & self -employed 18.613 .392 17.843 19.384 Govt service 18.553 .281 18.002 19.105 Private service 18.544 .371 17.815 19.272 NRI/FE 18.762 .601 17.581 19.943 Others 18.659 .293 18.084 19.234


Table 5.246 Two-Way ANOVA – KALIC

Tests of Between-Subjects Effects Dependent Variable: Knowledge of Agents on LIC


Square F Sig.

Area .093 1 .093 .008 .930 Occupation 22.132 5 4.426 .373 .867 Error 6199.377 523 11.853 Total 6221.602 529



LIC among rural and urban areas and different occupational groups, Two-Way

ANOVA is used and it is found that area-wise and occupation-wise variation

of the mean scores are not statistically significant at 5 per cent level of

significance (value of F 0.008 and .373 Df 1 and 5 with p=0.930 and

0.867>0.05). Tables 5.244, 5.245 and 5.246 show that there is no significant

difference in the perceptions of policyholders on the knowledge of agents on


465

LIC between rural and urban areas and among different groups of occupation.

Therefore, it may be concluded that policyholders in rural and urban areas and

policyholders belonging to different occupational groups have similar perceptions

on the knowledge of agents on LIC.

5.7.2.2 Two-Way ANOVA on Knowledge of Agents on LIC (KALIC) by Area and Family Structure

The variations of the level of knowledge of agents on LIC (KALIC) by



Table 5.247 Area -wise Estimated Marginal Means-KALIC

1. Area Dependent Variable: Knowledge of Agents on LIC




Table 5.248 Family Structure- wise Estimated Marginal Means-KALIC

2. Family Structure Dependent Variable: Knowledge of Agents on LIC



Upper Bound



Chapter 5

466




Square F Sig.

Area .093 1 .093 .008 .929 Family structure

.500 2 .250 .021 .979

Error 6221.009 526 11.827 Total 6221.602 529



LIC, among rural and urban areas and different family structure groups, Two-

Way ANOVA is used and it is found that area-wise and occupation-wise


of significance (value of F 0.008 and .021 Df 1 and 2 with p=0.929 and

0.979 >0.05). Tables 5.247, 5.248 and 5.249 show that there is no significant

difference in the perceptions of policyholders on the knowledge of agents on

LIC, between rural and urban areas and among different groups of family

structure groups. Therefore, it may be concluded that policyholders in rural

and urban areas and policyholders belonging to different family structure

groups have similar perceptions on the knowledge of agents on LIC.

5.7.2.3 Two-Way ANOVA on Knowledge of Agents on LIC (KALIC) by Family Structure and Occupation

The variations in the level of knowledge of agents on LIC (KALIC) by

family structure and occupation are analysed with Two-Way ANOVA and the



467

Table 5.250 Area -wise Estimated Marginal Means-KALIC

1. Family Structure Dependent Variable: Knowledge of Agents on LIC



Upper Bound



Table 5.251 Occupation- wise Estimated Marginal Means-KALIC 2. Occupation

Dependent Variable: Knowledge of Agents on LIC



Upper Bound






Square F Sig.

Family structure .511 2 .256 .022 .979 Occupation 22.149 5 4.430 .373 .867 Error 6198.942 522 11.875 Total 6221.602 529


Chapter 5

468


LIC among different family structure groups and different occupational

groups, Two-Way ANOVA is used and it is found that family structure-wise

and occupation-wise variations of the mean scores are not statistically

significant at 5 per cent level of significance (value of F 0.022 and .373 Df 2



knowledge of agents on LIC, among different family structure groups and

among different categories of occupation. Therefore, it may be concluded that

policyholders in different family structure groups and policyholders belonging

to different occupational categories have similar perceptions on the knowledge

of agents on LIC.

5.7.3 Knowledge of Agents on Products and Services (KAPS)

The knowledge on products and services is essential in marketing. Apart

from making the customers understand the product, its features and benefits,

making them believe in it and leading them to purchase decision requires

perfect knowledge on the comparative features and benefits of products. At the

same time, exaggeration of product features and misselling should be avoided,

as it will be detrimental in long- term perspective. An agent should keep in

mind the financial goals of the customer and establish creditworthiness of

products to successfully market the products and services.

5.7.3.1 Two-Way ANOVA on Knowledge of Agents on Products and Services (KAPS) by Area and Occupation

The variations in the perceptions of policyholders on knowledge of

agents on products and services (KAPS) by area and occupation are analysed



469

Table 5.253 Area -wise Estimated Marginal Means-KAPS

1. Area Dependent Variable: Knowledge on Products and Services




Table 5.254 Occupation -wise Estimated Marginal Means-KAPS

2. Occupation Dependent Variable: Knowledge on Products and Services



Upper Bound



Table 5.255 Two-Way ANOVA – KAPS

Tests of Between-Subjects Effects Dependent Variable: Knowledge on Products and Services


Square F Sig.



Chapter 5

470

To test the mean variations of the scores for the knowledge of agents

on the products and services of LIC, among rural and urban areas and

different occupational groups, Two-Way ANOVA is used and it is found

that area and occupation-wise variations of the mean scores are not


and 0.943 Df 1 and 5 with p=0.743 and 0.452>0.05). Tables 5.253, 5.254

and 5.255 present that there is no significant difference among rural and

urban areas and different occupational groups as to perception of

policyholders on the knowledge of agents on the products and services of

the LIC. Therefore, it may be concluded that policyholders in rural and

urban areas and policyholders belonging to different occupational groups

have similar perceptions on the knowledge of agents on the products and

services of the LIC.

5.7.3.2 Two-Way Anova on Knowledge of Agents on Products and Services (KAPS) by Area and Family Structure

The variations in the perceptions of policyholders on the knowledge of

agents on products and services of LIC (KAPS) by area and family structure


following Tables.

Table 5.256 Area -wise Estimated Marginal Means-KAPS

1. Area Dependent Variable: Knowledge on Products and Services





471


2. Family Structure Dependent Variable: Knowledge on Products and Services



Upper Bound






Square F Sig.

Area 1.373 1 1.373 .108 .742 Family structure 49.566 2 24.783 1.959 .142 Error 6655.742 526 12.654 Total 6706.681 529



the products and services of the LIC, among rural and urban areas and different

family structure groups, Two-Way ANOVA is used and it is found that area -

wise and family structure-wise variations of the mean scores are not statistically




knowledge of agents on the products and services of the LIC, between rural and

urban areas and among different groups of family structures. Therefore, it may

be concluded that policyholders belonging to rural and urban areas and different

Chapter 5

472

occupational groups have similar perceptions on the knowledge of agents on the

products and services of the LIC.

5.7.3.3 Two-Way ANOVA on Knowledge of Agents on Products and Services (KAPS) by Family Structure and Occupation

The variations in the perceptions of policyholders on the knowledge of

agents on products and services of LIC (KAPS), by family structure and

occupation, are analysed with Two-Way ANOVA and the output is presented


Table 5.259 Family Structure -wise Estimated Marginal Means-KAPS

1. Family Structure Dependent Variable: Knowledge on Products and Services



Lower Bound Upper Bound Nuclear 23.735 .208 23.326 24.144 Extended 23.082 .553 21.995 24.169 Joint 24.409 .496 23.434 25.385



2. Occupation Dependent Variable: Knowledge on Products and Services



Upper Bound




473




Square F Sig.




the products and services of the LIC, among different family structure groups

and different groups of occupation, Two-Way ANOVA is used and it is found

that family structure-wise and occupation-wise variations of the mean scores

are not statistically significant at 5 per cent level of significance (value of F

1.947 and 0.819 Df 2 and 5 with p=0.144 and 0.536>0.05). Tables 5.259,

5.260 and 5.261 show that there is no significant difference in the perceptions

of policyholders on the knowledge of agents on the products and services of

the LIC, among different family structure groups and different groups of

occupation. Therefore, it may be concluded that policyholders pertaining to

different occupations and family structures have similar perceptions on the

knowledge of agents on the products and services of the LIC.

5.7.4 Attitude and Behavioural Patterns of LIC Agents (ABPA)

The approach and attitude of agents in marketing products should be

professional. The ability to listen to customer queries and listening to his needs

and requirements with patience is very essential. The negative behavioural

characteristics like whining about poor business and creating excessive pressure

on customers to buy products may create a negative customer attitude. The

Chapter 5

474

agent should be able to win the confidence of customers by discharging the

service requirements in tune with their expectations.

5.7.4.1 Two-Way ANOVA on Attitude and Behavioural Patterns of LIC Agents (ABPA) by Area and Occupation

The variations in the perceptions of policyholders on the attitude and

behavioural patterns of LIC agents (ABPA) by area and occupation are


Tables.

Table 5.262 Area- wise Estimated Marginal Means-ABPA

1. Area Dependent Variable: Attitude and Behavioural Pattern of Agents




Table 5.263 Occupation -wise Estimated Marginal Means-ABPA

2. Occupation Dependent Variable: Attitude and Behavioural Pattern of Agents



Upper Bound

Agriculture 23.030 1.014 21.038 25.022 Business & self -employed 22.943 .564 21.835 24.050 Govt service 22.398 .404 21.605 23.192 Private service 20.877 .533 19.829 21.924 NRI/FE 21.063 .864 19.365 22.761 Others 22.184 .421 21.357 23.011



475

Table 5.264 Two-Way ANOVA – ABPA

Tests of Between-Subjects Effects Dependent Variable: Attitude and Behavioural Pattern of Agents


Square F Sig.



To test the mean variations of the scores for the perceptions of

policyholders on the attitude and behavioural pattern of agents, among rural

and urban areas and different occupational groups, Two-Way ANOVA is

used and it is found that area-wise and occupation-wise variations of the mean


of F 0.041 and 2.088 Df 1 and 5 with p=0.839 and 0.065>0.05).Tables 5.262,


of policyholders on the attitude and behavioural pattern of agents, between

rural and urban area and among different groups of occupation. Therefore, it

may be concluded that policyholders in rural and urban areas and policyholders

belonging to different occupational groups have similar perceptions on the

attitude and behavioural pattern of agents.

5.7.4.2 Two-Way Anova on Attitude and Behavioural Patterns of LIC Agents (ABPA) by Area and Family Structure


behavioural patterns of agents of LIC (ABPA) by area and family structure are


Tables.

Chapter 5

476

Table 5.265 Area -wise Estimated Marginal Means-ABPA

1. Area Dependent Variable: Attitude and Behavioural Pattern of Agents



Rural 22.462 .369 21.737 23.188


Table 5.266 Family Structure- wise Estimated Marginal Means-ABPA

2. Family Structure Dependent Variable: Attitude and Behavioural Pattern of Agents



Upper Bound

Nuclear 21.911 .255 21.410 22.413

Extended 22.434 .755 20.950 23.918





Square F Sig.

Area 1.016 1 1.016 .041 .839


Error 12988.449 526 24.693



477

To test the mean variations of the scores for the knowledge of customers

on the attitude and behavioural pattern of agents among rural and urban areas

and different family structure groups, Two-Way ANOVA is used and it is

found that area-wise and family structure-wise variations of the mean scores

are not statistically significant at 5 per cent level of significance (value of F

0.041 and 01.842 Df 1 and 2 with p=0.0.839 and 0.160 >0.05). Tables 5.265,


of policyholders on the attitude and behavioural pattern of agents, between

rural and urban areas and among different groups of family structures.

Therefore, it may be concluded that policyholders belonging to rural and urban

areas and different occupational groups have similar perceptions on the


5.7.4.3 Two-Way ANOVA on Attitude and Behavioural Patterns of LIC Agents (ABPA) by Family Structure and Occupation


behavioural patterns of agents of LIC (ABPA) by family structure and



Table 5.268 Family Structure -wise Estimated Marginal Means-ABPA

1. Family Structure Dependent Variable: Attitude and Behavioural Pattern of Agents



Upper Bound



Chapter 5

478

Table 5.269 Occupation- wise Estimated Marginal Means-ABPA

2. Occupation Dependent Variable: Attitude and Behavioural Pattern of Agents



Upper Bound

Agriculture 23.345 1.025 21.331 25.359

Business & self- employed 23.43 0.634 22.185 24.675

Govt service 22.717 0.45 21.832 23.602

Private service 21.287 0.587 20.133 22.441

NRI/FE 21.536 0.901 19.766 23.307

Others 22.701 0.514 21.691 23.711 Source: Primary Data




Square F Sig.

Family structure 90.299 2 45.15 1.851 0.158

Occupation 256.665 5 51.333 2.104 0.064

Error 12733.453 522 24.394



the attitude and behavioural pattern of agents, among different family structure

groups and different groups of occupation, Two-Way ANOVA is used and it is

found that family structure-wise and occupation-wise variations of the mean



479

of F 1.851 and 2.104 Df 2 and 5 with p=0.158 and 0.064>0.05). Tables 5.268,


of policyholders on the knowledge of agents on the attitude and behavioural

pattern of agents, among different family structure groups and different groups

of occupation. Therefore, it may be concluded that policyholders belonging to

different occupations and family structures have similar perceptions on the


5.7.5 Motive behind Choosing a Particular Agent in Servicing Policy

Policyholders’ preference of certain agents for servicing their policy is

influenced by many factors. It might be because an agent may be a Family

Relative or Friend (FRF), Knowledge and Expertise (KE), Offering Gifts with

Policies (OGP), Proper Financial Advice (PFA), Caring Policyholders’

Welfare (CPW), Easy Accessibility (EA), Prompt and Quick Service (PQS),

Good Salesmanship Qualities (GSQ), Exerting Excessive Pressure (EEP) or

Good Personality (GP) etc. The following Table depicts the frequency

distribution of ranks obtained for the motives behind choosing a particular

agent for servicing policy.

Chapter 5

480

Table 5.271 Distribution of Ranks Obtained for the Selection of Agents in Policy Servicing

Rank FRF KE OGP PFA CPW EA PQS GSQ EEP GP Others

1 219

(41.3) 140

(26.4) 11

(2.1) 49

(9.2) 14

(2.6) 33

(6.2) 15

(2.8) 10

(1.9) 24

(4.5) 15

(2.8) 0

(0)

2 69

(13) 85

(16) 31

(5.8) 96

(18.1) 60

(11.3) 59

(11.1) 46

(8.7) 24

(4.5) 34

(6.4) 34

(6.4) 3

(0.6)

3 36

(6.8) 82

(15.5) 22

(4.2) 76

(14.3) 91

(17.2) 77

(14.5) 66

(12.5) 32 (6)

21 (4)

24 (4.5)

7 (1.3)

4 25

(4.7) 46

(8.7) 30

(5.7) 76

(14.3) 76

(14.3) 66

(12.5) 119

(22.5) 37 (7)

21 (4)

31 (5.8)

7 (1.3)

5 16 (3)

62 (11.7)

39 (7.4)

63 (11.9)

69 (13)

80 (15.1)

65 (12.3)

61 (11.5)

26 (4.9)

48 (9.1)

7 (1.3)

6 42

(7.9) 33

(6.2) 37 (7)

49 (9.2)

42 (7.9)

70 (13.2)

71 (13.4)

53 (10)

35 (6.6)

85 (16)

10 (1.9)

7 43

(8.1) 33

(6.2) 36

(6.8) 37 (7)

52 (9.8)

59 (11.1)

37 (7)

97 (18.3)

45 (8.5)

77 (14.5)

13 (2.5)

8 30

(5.7) 20

(3.8) 41

(7.7) 39

(7.4) 49

(9.2) 42

(7.9) 38

(7.2) 113

(21.3) 81

(15.3) 60

(11.3) 13

(2.5)

9 25

(4.7) 19

(3.6) 81

(15.3) 26

(4.9) 32 (6)

23 (4.3)

48 (9.1)

60 (11.3)

124 (23.4)

55 (10.4)

33 (6.2)

10 17

(3.2) 10

(1.9) 177

(33.4) 17

(3.2) 28

(5.3) 10

(1.9) 19

(3.6) 34

(6.4) 94

(17.7) 80

(15.1) 40

(7.5)

11 8

(1.5) 0( 0)

25 (4.7)

2 (0.4)

17 (3.2)

11 (2.1)

6 (1.1)

9 (1.7)

25 (4.7)

21 (4)

397 (74.9)

Total 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) 530

(100) Source: Primary Data

From the Table, it is clear that the majority of the first ranks are attributed

to the element of “family relative/friend” and “knowledge and expertise”, and


481

the majority of the second ranks are given to “proper financial advice”. The

third rank is mostly attributed to “ policyholders welfare”

To have a clear assessment of the preferences, Friedman test is used to

test the following hypotheses.

H0: There is no difference in the preference of policyholders as to choosing

an agent for servicing policy.

H1: There is difference in the preference of policyholders as to choosing an

agent for servicing policy.

The test results are presented in the following Table.

Table 5.272 Mean Ranks Obtained for the Motive behind Choosing Particular Agent/s in Policy Servicing

Mean rank Rank Relative /friend 3.64 2 Knowledgeable and expertise 3.62 1 Offer gifts 7.64 10 Render proper financial advice 4.52 3 Care policyholders welfare 5.35 6 Easily accessible 5.01 4 Render prompt and quick service 5.27 5 Have good salesmanship qualities 6.60 7 Exert excessive selling pressure 7.33 9 Good personality 6.78 8 Other reasons 10.24 11


The mean ranks obtained for the ten motives behind selection of agents

are stated above. The lower the ranks, the higher will be the preference. As

per Table 5.271 given above, the highest preference is given to Knowledge

Chapter 5

482

and Expertise (mean rank 3.62), followed by Relative /friend (mean rank

3.64) and Proper Financial Advice (mean rank 4.52).

Table 5.273 Friedman Test

N 530 Chi-Square 1872.456

Df 10 Asymp. Sig. 0.000*


The χ2 statistic provides a value of 1872.456, which is significant at 5

per cent level of significance (p=0.000<0.05). Therefore, the null hypothesis

of “no difference in the preferences among the selected policyholders” is

rejected. This indicates the variation in the preferences of policyholders in the

selection of agents for servicing policy.

5.8 Customer Perception on Brand Image and evaluation of Brand Trust, Brand Loyalty, Customer Satisfaction and its impact on Brand Equity A “brand” is a symbolic embodiment of all the information connected to

the product and serves to create associations and expectations around it. In the

simplest form, “brand” is nothing more than and nothing less than the

promises of value an organisation or its product makes. Brand image is the

consumers’ perception as reflected by the associations they hold in their minds

when they think of a brand. Brand equity is the sum total of all the different

values people attach to the brand or the holistic value of the brand to its owner

as a corporate asset. Brand equity is decided by multiple factors including the

consumers’ brand image, brand trust, and brand loyalty.


483

The section is divided into 4 sections.

1) Customer perception on Brand Image, Brand Trust, Brand Loyalty

and Brand Equity.

2) Impact of Brand Image, Brand Trust, Brand Loyalty on Brand Equity.

3) Impact of Service satisfaction of agents before and after issue of

policy on Brand Trust.

4) Impact of customer satisfaction on the products and services of the

LIC on Brand Equity.

5.8.1 Customer Perception on Brand Image, Brand Trust, Brand Loyalty and Brand Equity

The analysis of the points of view of the customers on the Brand Image,

Brand Trust, Brand Loyalty and Brand Equity provides a comprehensive

assessment of customer perception on the organisation’s position in the minds

of customers. These concepts are interrelated and the ultimate measure of

customer evaluation will be in the form of brand equity. A Two-Way ANOVA

is used to analyse the differences in the customer perceptions on the 4

dimensions of brand enlisted here, based on the area, occupation and family


5.8.1.1 Customer Perception on Brand Image

Brand Image is the perception or association consumers form as a result of

their memory concerning a product or simply the emotional perception or reason

that consumers assign to a particular brand. The peculiar feature is that it does not

exist in the features, technology or the actual product itself; it is sometimes

brought out by advertisement, promotion or users. Brand image enables a

consumer to recognize a product, lower purchase risks, evaluate the quality, and

obtain certain experience and satisfaction out of product differentiation.

Chapter 5

484

5.8.1.1.1 Two-Way ANOVA on LIC Brand Image (LICBI) by Area and Occupation

The variations of LIC Brand Image (LICBI) by area and occupation are


Tables.

Table 5.274 Area- wise Estimated Marginal Means-LICBI

1. Area Dependent Variable :LIC Brand Image Factors




Table 5.275 Occupation -wise Estimated Marginal Means-LICBI

2. Occupation Dependent Variable :LIC Brand Image Factors



Upper Bound




485

Table 5.276 Two-Way ANOVA – LICBI

Tests of Between-Subjects Effects Dependent Variable :LIC Brand Image Factors


Square F Sig.



To test the mean variations of the scores for customer perception on brand

image of the LIC among rural and urban areas and different occupational groups,

Two-Way ANOVA is used and it is found that area wise variation of the mean

scores is statistically significant at 5 per cent level of significance (value of F

11.524 Df 1 with p=0.001<0.05). In the case of occupational groups, the

variation of mean scores is not statistically significant at 5 per cent level of

significance (value of F .343 Df 5 with p=0.887>0.5). Tables 5.274, 5.275 and

5.276 reveal that there is significant difference between rural and urban areas

as to customer perception on the brand image of the LIC, while among different

occupational groups, there is no significant difference as to customer perception

on brand image of the LIC. It can be inferred that customer perception on brand

image of the LIC is better in urban area (mean score 28.590) than in rural areas.

Such a difference in perception is not significant among different occupational

groups of respondents in respect of Brand Image of the LIC.

5.8.1.1.2 Two-Way ANOVA on LIC Brand Image (LICBI) by Area and Family Structure

The variations of LIC brand image (LICBI) by area and family structure are


Tables.

Chapter 5

486

Table 5.277 Area- wise Estimated Marginal Means-LICBI

1. Area Dependent Variable :LIC Brand Image Factors




Table 5.278 Family Structure- wise Estimated Marginal Means-LICBI

2. Family Structure Dependent Variable :LIC Brand Image Factors



Upper Bound






Square F Sig.

Area 242.103 1 242.103 11.571 .001* Family structure 17.841 2 8.920 .426 .653 Error 11006.026 526 20.924 Total 11265.970 529


To test the mean variations of the scores for customer perception on

brand image of the LIC among rural and urban areas and different family

structure groups, Two-Way ANOVA is used and it is found that area- wise


487

variation of the mean scores is statistically significant at 5 per cent level of

significance (value of F 11.571 Df 1 with p=0.001<0.05). In the case of

family structure, the variation of mean scores is not statistically significant at 5

per cent level of significance (value of F .426 Df 2 with p=0.653>0.5). Tables

5.277, 5.278 and 5.279 reveal that while there is significant difference among

rural and urban areas as to customer perception on brand image of the LIC, it

is not significant among different family structure groups. It can be inferred

that customer perception on brand image of the LIC is better in urban areas

than rural areas, its mean value being 28.560. Such a difference in perception

is not significant among different family structure groups of respondents as to

brand image of the LIC.

5.8.1.1.3 Two-Way ANOVA on LIC Brand Image (LICBI) by Family Structure and Occupation

The variations in customer perception on brand image of the LIC (LICBI)

by family structure and occupation are analysed with Two-Way ANOVA and


Table 5.280 Family Structure-- wise Estimated Marginal Means-LICBI

1. Family Structure Dependent Variable :LIC Brand Image Factors



Upper Bound



Chapter 5

488

Table 5.281 Occupation -wise Estimated Marginal Means-LICBI

2. Occupation Dependent Variable :LIC Brand Image Factors



Upper Bound






Square F Sig.




brand image of the LIC, among 2 areas and different occupational groups,

Two-Way ANOVA is used and it is found that area and occupation -wise

variation of the mean scores is not statistically significant at 5 per cent

level of significance (value of F .389 and .622 Df 2 and 5 with p=0.678 and

0.683 >0.05). Tables 5.280, 5.281 and 5.282 reveal that there is no significant

difference among different family structure groups and occupational groups as

to customer perception on brand image of the LIC. It can be inferred that


489

customer perception on brand image of the LIC is similar among different

family structure groups and occupational categories.

5.8.1.2 Customer Perception on Brand Trust

“Trust” is the most critical component in building and maintaining a strong,

emotionally driven and enduring brand. Building Trust is the only thing holding

the relationship with the customer together. Trust is the most difficult component

to establish at the beginning of any relationship. Relationships with trusted brands

are built and maintained in this same fashion. People naturally will measure, with

great care and consideration, how the brand is likely to behave in a given situation

depending on the rewards for being trustworthy and the deterrents against

untrustworthy behaviour. When trust is established at its highest level between a

brand and the customer, there is always an emotional “investment” made between

the two parties. Trust is a fundamental building block of any brand. Marketing

strategy should be built under the assumption that stories create an emotional

bond between a customer and a brand, a client and a service.

5.8.1.2.1 Two-Way ANOVA on Brand Trust on LIC (LICBT) by Area and Occupation

The variations on Brand Trust on the LIC (LICBT) by area and



Table 5.283 Area -wise Estimated Marginal Means-LICBT

1. Area Dependent Variable :LIC Brand Trust Factors

Area Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound

Rural 15.079 .168 14.750 15.409 Urban 15.676 .241 15.202 16.150


Chapter 5

490

Table 5.284 Occupation -wise Estimated Marginal Means-LICBT

2. Occupation Dependent Variable :LIC Brand Trust Factors



Upper Bound



Table 5.285 Two-Way ANOVA – LICBT

Tests of Between-Subjects Effects Dependent Variable :LIC Brand Trust Factors


Square F Sig.




brand trust of the LIC among rural and urban areas and different occupational

groups, Two-Way ANOVA is used and it is found that the area -wise variation

of the mean scores is statistically significant at 5 per cent level of significance

(value of F 5.319 Df 1 with p=0.021<0.05). In the case of occupational

groups, the variation of mean scores is not statistically significant at 5 per cent


491

level of significance (value of F .480 Df 5 with p=0.791>0.05). Tables 5.283,

5.284 and 5.285 reveal that there is significant difference between rural and

urban areas as to customer perception on brand trust of the LIC but there is no

significant difference as to customer perception on brand trust of the LIC

among different occupational groups. It can be inferred that customer perception

on brand trust of the LIC is better in urban areas than in rural areas, its mean

value being 15.676. Such a difference in perception is not significant among

different occupational groups of respondents as to brand trust of the LIC.

5.8.1.2.2 Two-Way ANOVA on Brand Trust on LIC (LICBT) by Area and Family Structure

The variations on brand trust of the LIC (LICBT) by area and family


the following Tables.

Table 5.286 Area- wise Estimated Marginal Means-LICBT

1. Area Dependent Variable :LIC Brand Trust Factors




Table 5.287 Family Structure- wise Estimated Marginal Means-LICBT

2. Family Structure Dependent Variable :LIC Brand Trust Factors





Chapter 5

492




Square F Sig.

Area 40.318 1 40.318 5.330 .021* Family structure 4.200 2 2.100 .278 .758 Error 3978.593 526 7.564 Total 4023.111 529


To test the mean variations of the scores for customer perception on brand

trust of the LIC among rural and urban areas and different family structure

groups, Two-Way ANOVA is used and it is found that the area- wise variation of

the mean scores is statistically significant at 5 per cent level of significance (value

of F 5.330 Df 1 with p=0.021<0.05). In the case of occupational groups, the

variation of mean scores is not statistically significant at 5 per cent level of

significance (value of F .278 Df 2 with p=0.758>0.05). Tables 5.286, 5.287 and

5.288 reveal that there is significant difference among rural and urban areas as

to customer perceptions on brand trust of the LIC but the difference is not

significant among different family structure groups for customer perception on

brand trust of the LIC. It can be inferred that customer perception on brand trust

of the LIC is better in urban areas than in rural areas as it has the higher mean

value of 15.794. Such a difference in perception is not significant among different

family structure groups of respondents, on brand trust of LIC.

5.8.1.2.3 Two-Way ANOVA on Brand Trust on LIC (LICBT) by Family Structure and Occupation

The variations on the perceptions of customers on brand trust of LIC

(LICBT) by family structure and occupation are analysed with Two-Way



493

Table 5.289 Family Structure -wise Estimated Marginal Means-LICBT

1. Family Structure Dependent Variable :LIC Brand Trust Factors



Upper Bound



Table 5.290 Occupation- wise Estimated Marginal Means-LICBT

2. Occupation Dependent Variable :LIC Brand Trust Factors



Upper Bound






Square F Sig.



Chapter 5

494


brand trust of the LIC among rural and urban areas and different occupational

groups, Two-Way ANOVA is used and it is found that area and occupation-

wise variation of the mean scores is not statistically significant at 5 per cent

level of significance (value of F .230 and .471 Df 2 and 5 with p=0.794 and

0.798 >0.05). Tables 5.289, 5.290 and 5.291 reveal that there is no significant

difference among different family structures and occupational groups as to

customer perception on brand trust of the LIC. It can be inferred that customer

perception on brand trust of the LIC is similar among different family

structure groups and occupational categories.

5.8.1.3 Customer Perception on Brand Loyalty

Brand Loyalty refers to deeply held commitment to re-buy a preferred

product/service consistently in the future, thereby causing repetition of same-

brand or same brand set purchasing, despite situational influence and

marketing efforts having the potential to cause switching behaviours. Brand

loyalty may be understood in terms of repeated purchases of the brand or

extent of dispositional promises with respect to some particular advantages

connected with the brand. The concept has much significance in marketing

performance as it serves as a measure of customer retention.

5.8.1.3.1 Two-Way ANOVA on LIC Brand Loyalty (LICBL) by Area and Occupation

The variations of LIC Brand Loyalty (LICBL) by area and occupation


following Tables.


495

Table 5.292 Area -wise Estimated Marginal Means-LICBL

1. Area Dependent Variable :LIC Brand Loyalty Factors




Table 5.293 Occupation- wise Estimated Marginal Means-LICBL

2. Occupation Dependent Variable :LIC Brand Loyalty Factors



Upper Bound

Agriculture 32.041 1.197 29.689 34.392 Business & self- employed 32.198 .666 30.890 33.505 Govt service 32.395 .477 31.458 33.331 Private service 33.360 .630 32.123 34.596 NRI/FE 32.967 1.020 30.963 34.972 Others 32.422 .497 31.446 33.399


Table 5.294 Two-Way ANOVA – LICBL

Tests of Between-Subjects Effects Dependent Variable :LIC Brand Loyalty Factors


Square F Sig.

Area 104.189 1 104.189 3.049 .081 Occupation 84.316 5 16.863 .494 .781 Error 17870.506 523 34.169 Total 18059.011 529


Chapter 5

496

The Two-Way Anova used to test the mean variation of the scores for

customer perception on brand loyalty of the LIC, among rural and urban areas

and different occupational groups, shows that area and occupation- wise

variation of the mean scores is statistically significant at 5 per cent level of

significance (value of F 3.049 and 0.494 Df 1 and 5 with p=0.081 and 0.781

>0.05). As per Tables 5.292, 5.293 and 5.294, the difference between rural

and urban areas and across different occupational groups as to customer

perception on brand image of the LIC is not significant. This implies similarity

in the perceptions of respondents as to brand loyalty of the LIC.

5.8.1.3.2 Two-Way ANOVA on LIC Brand Loyalty (LICBL) by Area and Family Structure

The variations of LIC Brand Loyalty (LICBL) by area and family


the following Tables.

Table 5.295 Area -wise Estimated Marginal Means-LICBL

1. Area Dependent Variable :LIC Brand Loyalty Factors


Rural 32.014 .433 31.162 32.865 Urban 32.987 .560 31.887 34.087


Table 5.296 Occupation -wise Estimated Marginal Means-LICBL

2. Family Structure Dependent Variable :LIC Brand Loyalty Factors






497

Table 5.297 Two-Way ANOVA - LICBL



Square F Sig.

Area 104.189 1 104.189 3.066 .081 Family structure 80.362 2 40.181 1.182 .307 Error 17874.460 526 33.982 Total 18059.011 529



brand loyalty of the LIC among rural and urban areas and different family

structure groups, Two-Way ANOVA is used and it is found that area and

family structure-wise variation of the mean scores is not statistically

significant at 5 per cent level of significance (value of F 3.066 and 1.182

Df 1 and 2 with p=0.081 and 0.307 >0.05). Tables 5.295, 5.296 and 5.297

reveal that there is no significant difference among rural and urban area and

different family structure groups as to customer perception on brand loyalty

of the LIC. It can be inferred that the perception of respondents as to brand

loyalty of the LIC is similar as to area and among different occupational

groups.

5.8.1.3.3 Two-Way ANOVA on LIC Brand Loyalty (LICBL) by Family Structure and Occupation

The variations in Customer Perception on Brand Loyalty of the LIC

(LICBL) by family structure and occupation are analysed with Two-Way


Chapter 5

498

Table 5.298 Family Structure- wise Estimated Marginal Means-LICBL

1. Family Structure Dependent Variable :LIC Brand Loyalty Factors



Upper Bound

Nuclear 32.383 .343 31.710 33.056 Extended 31.288 .910 29.500 33.076


Table 5.299 Occupation -wise Estimated Marginal Means-LICBL

2. Occupation Dependent Variable :LIC Brand Loyalty Factors



Upper Bound

Agriculture 31.448 1.215 29.062 33.834 Business & self -employed 31.920 .751 30.445 33.395 Govt service 32.123 .534 31.074 33.171 Private service 33.154 .696 31.787 34.521 NRI/FE 32.819 1.068 30.721 34.917 Others 32.134 .609 30.938 33.330


Table 5.300 Two-Way ANOVA – LICBL



Square F Sig.




499


brand loyalty of the LIC among rural and urban areas and different


occupation-wise variation of the mean scores are not statistically significant at

5 per cent level of significance (value of F 1.140 and .645 Df 2 and 5 with

p=0.321 and 0.665 >0.05). Tables 5.298, 5.299 and 5.300 reveal that there is

no significant difference among different family structure groups and

occupational groups as to customer perception on brand loyalty of the LIC. It

can be inferred that customer perception on brand loyalty of the LIC is similar

among different family structure groups and occupational categories.

5.8.1.4 Customer Perception on Brand Equity

Brand equity, from the perspective of the customer, is based on consumer

knowledge, familiarity and associations with respect to the features of life

insurance products and services. It is the value added to product by the customer.

The analysis of customer perception on the brand equity of the LIC is a

prerequisite of successful formulation and implementation of marketing strategies.

5.8.1.4.1 Two-Way ANOVA on LIC Brand Equity (LICBE) by Area and Occupation

The variations of LIC brand equity (LICBE) by area and occupation are


Tables.

Table 5.301 Area- wise Estimated Marginal Means-LICBE 1. Area

Dependent Variable :LIC Brand Equity Factors


Rural 21.145 .254 20.646 21.645 Urban 21.324 .366 20.605 22.042


Chapter 5

500

Table 5.302 Occupation -wise Estimated Marginal Means-LICBE

2. Occupation Dependent Variable :LIC Brand Equity Factors



Upper Bound



Table 5.303 Two-Way ANOVA – LICBE

Tests of Between-Subjects Effects Dependent Variable :LIC Brand Equity Factors


Square F Sig.




brand equity of the LIC among rural and urban areas and different


occupation- wise variation of the mean scores is statistically significant at 5

per cent level of significance (value of F 0.317 and 0.670 Df 1 and 5 with

p=0.574 and 0.646>0.05). Tables 5.301, 5.302 and 5.303 reveal that there is


501

no significant difference among rural and urban areas and among different

occupational groups as to customer perception on the brand equity of the

LIC. It can be inferred that sample respondents are having similar perceptions

on brand equity of the LIC.

5.8.1.4.2 Two-Way ANOVA on LIC Brand Equity (LICBE) by Area and Family Structure

The variations of LIC brand equity (LICBE) by area and family structure


following Tables.

Table 5.304 Area -wise Estimated Marginal Means-LICBE

1. Area Dependent Variable :LIC Brand Equity Factors




Table 5.305 Family Structure -wise Estimated Marginal Means-LICBE

2. Family Structure Dependent Variable :LIC Brand Equity Factors



Upper Bound

Nuclear 21.475 .214 21.053 21.896 Extended 21.205 .634 19.959 22.451


Chapter 5

502

Table 5.306 Two-Way ANOVA – LICBE



Square F Sig.



To test the mean variation of the scores for customer perception on

brand equity of the LIC among rural and urban areas and different family

structure groups, Two-Way ANOVA is used and it is found that area and

family structure-wise variation of the mean scores is not statistically

significant at 5 per cent level of significance (value of F 0.317 and 0.1362

Df 1 and 2 with p=0.574 and 0.873 >0.05). Tables 5.304, 5.305 and 5.306

reveal that there is no significant difference among rural and urban areas

and different family structure groups as to customer perception on brand

equity of the LIC. It can be inferred that the perception of respondents as

to brand equity of LIC is similar as to area and different occupational

groups.

5.8.1.4.3 Two-Way ANOVA on LIC Brand Equity (LICBE) by Family Structure and Occupation

The variations in Customer Perception on Brand Loyalty of LIC

(LICBE) by family structure and occupation are analysed with Two-Way



503

Table 5.307 Family Structure- wise Estimated Marginal Means-LICBE

1. Family Structure Dependent Variable :LIC Brand Equity Factors



Upper Bound



Table 5.308 Occupation- wise Estimated Marginal Means-LICBE

2. Occupation Dependent Variable :LIC Brand Equity Factors



Upper Bound



Table 5.309 Two-Way ANOVA –LICBE



Square F Sig.



Chapter 5

504

To test the mean variation of the scores for customer perception on

brand equity of the LIC among rural and urban areas and different


occupation- wise variation of the mean scores is not statistically significant at

5 per cent level of significance (value of F 0.133 and 0.702 Df 2 and 5 with

p=0.875 and 0.622 >0.05). Tables 5.307, 5.308 and 5.309 reveal that there is

no significant difference among different family structure groups and

occupational groups as to customer perception on brand equity of the LIC. It

can be inferred that customer perception on brand equity of the LIC is similar

among different family structure groups and occupational categories.

5.8.2 Impact of Brand Image, Brand Trust, and Brand Loyalty on Brand Equity

The concept of “brand equity” has much relevance and utility in the

formulation and implementation of marketing strategies of any organisation,

especially service firms. The brand equity of a firm reflects the overall

perception of its customers on its practices and performance as to its products

and services. The analysis intends to consider the problem of Brand Equity. In

order to arrive at an explanation of Brand equity, the three variables

considered are Brand Image, Brand Trust and Brand Loyalty. Since it is

observed that Brand Trust, Brand Image and Brand Loyalty are related ( as

provided by the Correlation Table given below), it is difficult to consider all

the variables in the regression of Brand Equity on these variables due to

multicollinearity. Hence Brand Trust is regressed on Brand Loyalty and brand

Image and the output is given below.


505

Table 5.310 Correlations

LICBI LICBT LICBL LICBE N 530 530 530 530 LICBI Pearson Correlation 1 .722* .667* .581*

Sig. (2-tailed) .000* .000* .000* LICBT

Pearson Correlation .722* 1 .746* .597* Sig. (2-tailed) .000* .000* .000*

LICBL

Pearson Correlation .667* .746* 1 .726* Sig. (2-tailed) .000* .000* .000*

LICBE

Pearson Correlation .581* .597* .726* 1 Sig. (2-tailed) .000* .000* .000*

* Correlation is significant at the 0.01 level (2-tailed).

Table 5.311 Model Summary

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .804a .646 .645 1.64329 a. Predictors: (Constant), LIC Brand Loyalty Factors, LIC Brand Image Factors Source: Primary Data

Table 5.312 ANOVAb

ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 2600.007 2 1300.003 481.414 .000a

Residual 1423.104 527 2.700

Total 4023.111 529 a. Predictors: (Constant), LIC Brand Loyalty Factors, LIC Brand Image Factors

b. Dependent Variable: LIC Brand Trust Factors Source: Primary Data

Chapter 5

506

Table 5.313 Coefficients a

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std.

Error Beta

1 (Constant) 1.385 .458 3.024 0.003*

LIC Brand Image Factors

.241 .021 .404 11.610 0.000*

LIC Brand Loyalty Factors

.225 .016 .476 13.679 0.000*

a. Dependent Variable: LIC Brand Trust Factors Source: Primary Data *Significant at 5 per cent level of significance

From the above Table, it may be observed that the regression is fairly

good with 64.6 per cent explanation of the variation in brand trust and that

this explanation is statistically valid, as the associated F value (481.414) is

statistically significant (p< 0.05). All the Beta coefficients are significant, as

the associated t-values are significant (observed p< 0.05). Therefore, it can be

inferred that brand image and brand loyalty factors influence brand trust.

Now, considering Brand Trust and Brand Equity, the regressions of

Brand Trust on Brand equity are given below.



1 .597a .357 .355 3.34218

a. Predictors: (Constant), LIC Brand Trust Factors Source: Primary Data


507

Table 5.315 ANOVAb

Model Sum of Squares df Mean

Square F Sig.

1 Regression 3269.774 1 3269.774 292.723 0.000a*

Residual 5897.856 528 11.170

Total 9167.630 529 a. Predictors: (Constant), LIC Brand Trust Factors b. Dependent Variable: LIC Brand Equity Factors Source: Primary Data

Table 5.316 Coefficientsa

Model



t Sig. B Std.

Error Beta

1 (Constant) 7.616 .820 9.286 0.000*

LIC Brand Trust Factors

.902 .053 .597 17.109 0.000*

a. Dependent Variable: LIC Brand Equity Factors Source: Primary Data *significant at 5 per cent level of significance

From the above Table, it may be observed that the regression is

fairly good with 35.7 per cent explanation of the variation in brand equity

and that this explanation is statistically valid, as the associated F value

(292.723) is statistically significant (p < 0.05). The Beta coefficient is

significant, as the associated t-value is significant (observed p < 0.05).

Therefore, it can be inferred that brand trust influences brand equity. As

such, it can be inferred that brand equity is influenced by brand image,

brand loyalty and brand trust.

Chapter 5

508

5.8.3 Impact of SSBIP and SSAIP on Brand Trust

The satisfaction of the customer on the services rendered by agents

before issue of policy and after issue of policy determines his attitude and

approach towards future interactions with the orgnisation. Here, the issue

considered is the impact of satisfaction of selected policyholders on the

services rendered by agents before and after issue of policy (SSBIP and

SSAIP). The output of the linear regression analysis is presented in the

following Tables.



1 .423a .179 .176 2.50379 a. Predictors: (Constant), Service Satisfaction After Issue Of Policy, Service Satisfaction

Before Issue Of Policy


Table 5.318 ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 719.362 2 359.681 57.375 0.000a *

Residual 3303.749 527 6.269

Total 4023.111 529 a. Predictors: (Constant), Service Satisfaction After Issue Of Policy, Service Satisfaction

Before Issue Of Policy

b. Dependent Variable: LIC Brand Trust Factors



509

Table 5.319 Coefficientsa

Model Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 9.676 .549 17.613 0.000*

SSBIP .068 .024 .151 2.864 0.004*

SSAIP .079 .013 .308 5.854 0.000*

a. Dependent Variable: LIC Brand Trust Factors Source: Primary Data

From the above Table, it may be observed that the regression is fairly

good with 17.9 per cent explanation of the variation in brand trust and that

this explanation is statistically valid, as the associated F value (57.375) is

statistically significant (p < 0.05). Both the Beta coefficients are significant, as

the associated t-values are significant (observed p < 0.05). Therefore, it means

that service satisfaction before issue of policy and service satisfaction after

issue of policy influence brand trust.

5.8.4 Impact of Satisfaction of Policyholders on Products and services of LIC on Brand Equity

The satisfaction of customers on the products and services of the LIC

directly influences its Brand Equity. The satisfaction of customers on the

products and services of the LIC are analysed via its Marketing Mix elements

via Product, Price, Place, Promotion, People, Process and Physical Evidence.

Each element has a prominent role in deciding the level of Brand equity.

Based on the perceptions of selected policyholders, the levels of influence of

these 7 marketing mix elements on Brand Equity are tested using Linear

Regression analysis. The following Tables illustrate the impact of the seven

service marketing mix elements in deciding the brand equity of the LIC.

Chapter 5

510


Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .556 .309 .300 3.48356 Source: Primary Data

Table 5.321 ANOVA

Model Sum of Squares Df Mean

Square F Sig.

1 Regression 2833.072 7 404.725 33.351 0.000a* Residual 6334.558 522 12.135 Total 9167.630 529


Table 5.322 Coefficients a

Model



t Sig. B Std.

Error Beta

1 (Constant) 3.669 1.267 2.895 .004* Product related satisfaction

.056 .024 .111 2.338 .020*

Price related satisfaction -.017 .049 -.019 -.354 .724 Place/ distribution related satisfaction

.144 .056 .121 2.595 .010*

People service related satisfaction

.068 .022 .176 3.060 .002*

Process related satisfaction

.037 .028 .077 1.308 .192

Promotion related satisfaction

.008 .065 .006 .124 .901

Physical evidence related satisfaction

.154 .036 .227 4.222 .000*

a. Dependent Variable: LIC Brand Equity Factors Source: Primary Data * Significant at 5 per cent level of significance


511

From the above Table, it may be observed that the regression is

satisfactorily good with 30.9 per cent explanation of the variation in Brand

Equity and that this explanation is statistically valid, as the associated F value

(33.351) is statistically significant (p < 0.05). All the Beta coefficients except

price- related satisfaction, process- related satisfaction, and satisfaction with

regard to promotion- related elements are significant, as the associated t-values

are significant (Observed P < 0.05). Therefore, it means that while product-

related satisfaction, place/distribution- related satisfaction, people service -

related satisfaction and physical evidence- related satisfaction factors influence

brand equity, other elements do not influence in deciding the brand equity of

the organisation.

5.9 Evaluation on the Relative Importance of Features and Benefits of Policies of LIC The analysis intends to identify the feature/benefit of life insurance

policy that the policyholders give prime importance to their purchase decision.

While some prefer safety of investment with good return, policyholders like

government servants and tax payers prefer tax benefit on investment. Low and

unstable income group may prefer policies with lower premium. The identified

9 features of life insurance policies are Safety, Liquidity, Tax Benefit,

Surrender value, Loan facility and amount of loan, Loyalty additions/Riders/

Bonus, Maturity Benefit, Death Benefit and premium.

Conjoint (trade off ) analysis is one of the most widely used statistical

techniques applied in marketing research to determine how people value

different features that make up an individual product or service. The objective

of the analysis is to identify what combination of a limited number of

attributes is most influential on the respondent’s choice or decision making. A

controlled set of potential products or services is shown to respondents and by

Chapter 5

512

analyzing how they make preference between these products, the implicit

valuation of the individual elements making up the product or service can be

determined. The technique is used to measure the perceived values of specific

product features, to learn how demand for a particular product or service is

related to price, and to forecast what the likely acceptance of the product

would be if introduced in market. Conjoint analysis is used to study the factors

that influence the customer’s purchasing decisions. A financial service

product, life insurance products especially possess attributes such as safety,

return, liquidity, transferability, etc. The analysis is based on a main effects

analysis of variance model. Subjects provide data about their preferences for

hypothetical products defined by combinations of attributes. Conjoint analysis

decomposes the judgment data into components, based on qualitative

attributes of the products.

A numerical part-worth utility value is computed for each level of each

attribute. Large part-worth utilities are assigned to the most preferred levels

and small part- worth utilities are assigned to the least preferred levels. The

attributes with the largest part- worth utility range are considered the most

important in predicting preferences.

Conjoint analysis is applied to evaluate the relative importance of

features and benefits of policies of the LIC in 10 possible combinations upon

which the customers are asked to point out their most preferred combinations

for which a maximum score of 10 and a minimum score of 1 should be

assigned. The level of each element in each possible combination is H (High),

M (Medium) and L (Low). The possible combinations are given below.


513

Table 5.323 Combination of The Features And Benefits of LIC Policies

Seri

al N

o

Safe

ty

Liq

uidi

ty

Tax

ben

efit

Surr

ende

r va

lue

Loa

n Fa

cilit

y /a

mou

nt

Loy

alty

add

ition

s /b

onus

Mat

urity

ben

efit

Dea

th b

enef

it

prem

ium

1 H H H H H H H H L 2 H H H H H H H H M 3 H H H H H H M H M 4 H M H H M H M H H 5 H M M H M H H M H 6 M H M H H L H H M 7 M L H H L M H H M 8 H H M M H L H H L 9 H M L H M L H H M 10 H M M H M M M H M

Source:

The output of conjoint analysis is given below

Table 5.324 Summary Output of Conjoint Analysis

Summary Output Regression Statistics

Multiple R 0.755942 R Square 0.571448 Adjusted R Square 0.569774 Standard Error 1.881609 Observations 5300


Chapter 5

514

Table 5.325 ANOVA of Conjoint analysis Df SS MS F Significance

Regression 14 24973.95 1783.854 783.7657 0.000* Residual 5290 18728.98 3.540451 Total 5304 43702.94


Table 5.326 Conjoint Co-Efficient of the Level of Responses of the Customers

Coefficients(B) Standard Error t Stat P-value Intercept -2.75472 0.294688 -9.3479 0.000* X Variable 1 4.54717 0.200201 22.71298 0.000* X Variable 2 .000 .000 65535 0.000* X Variable 3 5.084906 0.163464 31.10725 0.000* X Variable 4 .000 .000 65535 0.000* X Variable 5 3.516981 0.305813 11.50045 0.000* X Variable 6 4.167925 0.283127 14.72102 0.000* X Variable 7 .000 .000 65535 0.000* X Variable 8 .000 .000 65535 0.000* X Variable 9 1.011321 0.163464 6.186821 0.000* X Variable 10 -4.60189 0.516918 -8.90255 0.000* X Variable 11 1.460377 0.115586 12.63452 0.000* X Variable 12 -1.74151 0.432484 -4.02676 0.000* X Variable 13 -1.31321 0.115586 -11.3613 0.000* X Variable 14 .000 .000 65535 0.000*

.000 indicates a very small value near zero *significant at 5 per cent level of significance

X1= Dummy for safety X2 and X3 = Dummy for Liquidity X4 and X5 = Dummy for Tax Benefit X6= Dummy for Surrender Value X7 and X8= Dummy for Loan facility/Amount X9 and X10= Dummy for Loyalty Additions/Bonus X11= Dummy for Maturity Benefit X12= Dummy for Death Benefit X13 and X14 = Dummy for Premium


515

Equation U= b0 + b1x1 +b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7 +b8x8 + b9x9 + b10x10 + b11x11 + b12x12 + b13x13 + b14x14

b0= -2.75 b1=4.54 b2=0.000 b3=5.08 b4=0.000 b5= 3.52 b6= 4.17 b7=0.000 b8= 0.000 b9=1.01 b10= -4.6 b11= 1.46 b12= -1.74 b13= -1.31 b14= 0.00

Table 5.327 Policyholders’ preference on features/benefits of LIC policy

Relative importance for Value Rank Safety 0.16539 3 Liquidity 0.18543 2 Tax Benefit 0.12823 5 Surrender Value 0.15228 4 Loan facility/Amount 0 9 Loyalty Additions/Bonus 0.20437 1 Maturity Benefit 0.05319 7 Death Benefit 0.06339 6 Premium 0.04772 8


Chapter 5

516

The output highlights that respondents give utmost preference to the

factors (features /benefits of Life insurance policy) having higher values in

order. As such Loyalty additions/Bonus is assigned top preference among the

listed features/benefits of life insurance policies by respondents, followed by

liquidity and safety.

5.10 Conclusion

This chapter examines the marketing practices and policies followed by

LIC as perceived by its policyholders. The demographic profile of

policyholders shows that majority are males and married residing in rural

areas. Most of them are holding 3 or less policies with a sums assured upto 5

lakh. The level of awareness of product and distribution channel of the LIC

among policyholders belonging to nuclear family structure is found higher.

The main source of knowledge on life insurance products among

policyholders is TV Advertisements and Internet/Websites in Rural and Urban

areas respectively. Considering the basic motive behind holding life insurance

policy, customers in rural areas hold policies with bequest motives/final

expenses and acquisition of home assets. At the same time customers in urban

areas hold with motive of income tax relief. The TV advertisements,

Newspaper advertisements and calendars, diaries and business cards are found

to be most effective in persuading customers to buy life insurance policies.

The satisfaction on the products and services of the LIC analysed with Two-

Way Anova shows that urban customers are highly satisfied as to process

element of marketing mix of the LIC. The most preferred criterion depended

by customers in choosing an agent to service insurance policies is expert and

knowledge of agent. The multiple linear regression model indicates that the

product, place or distribution, people and physical evidence elements of

marketing mix greatly decide the Brand equity of the LIC. Finally the conjoint


517

analysis reveals that policyholders of the LIC prefer loyalty additions and

bonus as most preferred feature/benefit of policy while purchasing it.

References

[1]. Field, Andy (2010), Discovering Statistics Using SPSS Software, Sage publications.

[2]. Hinton, Perry R. Hinton, Charlotte Brownlow, Isabella McMurray and Bob Cozens (2004), SPSS Explained, First Edition, Routledge, Taylor and Francis Group Publications.

[3]. Gaur Ajay S., Sanjaya S. Gaur (2010), Statistical Methods for Practice and Research, Response Books, Sage Publications.

[4]. Everitt, Brian S (2010), Applied Multivariate Data Analysis, Wiley Publications.

[5]. Somekh, Bridget (2010), Theory and Methods of Social Research, 2nd Edition, Sage Publications

[6]. Silverman, David (2010), Qualitative Research, 3rd Edition, Sage Publications

[7]. Argyrous, George (2009), Statistics for Research- with a Guide to SPSS, Sage Publications.

[8]. Guthrie, Gerard (2010), Basic Research Methods- An Entry to Social Science Research, Sage Publications.

[9]. Hair, Joseph F. (2010), Multivariate Data Analysis, Pearson Publications.

[10]. Bernard Russell, H. (2011), Social Research Methods- Qualitative and Quantitative Approaches, Sage Publications.

[11]. Cunningham, James B, James O. Aldrich (2012), Using SPSS – An Interactive Hands on Approach, Sage Publications

[12]. Coakes Sheridan J, Lyndall steed and Peta Dzidic (2007), SPSS for windows – analysis without anguish, Wiley Publications.

Chapter 5

518

[13]. George, Darren, Paul Mallery (2012], SPSS for windows- step by step, 10th Edition, Pearson Education

[14]. Julie Pallant (2007), SPSS Survival Manual, Mc Graw Hill Education

[15]. Ho Robert (2006), Handbook of Univariate and Multivariate data analysis and interpretation with SPSS, Taylor and Francis Group

[16]. Carver, Robert H, Jane Gradwohl Nash (2012), Doing Data Analysis with SPSS, Cengage Learning

[17]. Pandya, Kiran. Smruti Bulsari and Sanjay Sinha (2012), SPSS in Simple Steps, Dreamtech Press

[18]. Goddard Wayne and Stuart Melville (2006), Research Methodology – An introduction, JUTA

[19]. Elliot Alan C. and Wayne A. Woodward (2010), Statistical Analysis- Quick Reference Guidebook with SPSS Examples, Sage publications

[20]. Mayers, Andrews (2013), Introduction to Statistics and SPSS in Psychology, Pearson Education.

….. …..

immpact of mmaarrkkeettiinngg...

Documents