This chapter deals with the description of the sample of 1000 policyholders focused

CHAPTER -5 DATA ANALYSIS, INTERPRETATION AND FINDINGS This chapter deals with the description of the sample of 1000 policyholders focused on demographic factors (gender, age, religion, residence) data analysis and its interpretation. The chapter has been divided into three major sections. Section 5.1 deals with important descriptive statistics, demographic profile of the policyholders, and frequency distribution. The section 5.2 deals with designing of the constructs with respect to objective and hypotheses of the study, analyzing the validity of various constructs related to the study. The construct validity includes convergent, discriminant as well as face validity. After analyzing the constructs validity the structural model was tested and explained. Section 5.3 provides associations with the variables with respect to various demographic characteristics of the policyholders. In this section comparison of mean values, p-values, z-statistics and t-statistics is also provided for the purpose of testing various hypotheses. 5.1 DEMOGRAPHIC PROFILE OF THE POLICYHOLDERS The marketing concept was born out of the awareness that marketing starts with the determination of policyholder wants and ends with satisfaction of those wants. The entire business environment operates in a dynamic scenario where it is not easy to solve the puzzle of buyer decision making. Policyholders vary tremendously in terms of age, income and educational levels. Marketers also find it useful to distinguish different policyholder groups and segments and to develop product and services tailored to these needs. Thus presentation of sample profile would provide a clearer understanding of the marketing environment in which policyholders are placed. Policyholders purchase decisions are significantly influenced by their cultural, social and geographical factors that are uncontrollable by marketers. Therefore this section elaborates profile of the policyholders. 211

TABLE 5.1: Characteristics of the policyholders on the basis of location or residence (N=1000) Region Frequency Percent Rural 500 50.0% Urban 500 50.0% Total 1000 100.0% As policyholder behaviour of Indian policyholder forms a part of this study, an attempt has been made to draw a broad sketch of rural and urban policyholders. As the country is vast geographically, market by great diversity in climate, religion, region, language, life style, educational level and economic status, Indian policyholder present a varied view. Hence the sample comprised of rural and urban areas, from different regions and religions. The total numbers of policyholders were 1000, where 500 policyholders were those from rural background and 500 policyholders were from urban areas as shown in table 5.1. The graphical representation is also provided herewith. TABLE 5.2: Characteristics of the policyholders on the basis of Gender (N=1000) Gender Frequency Percent Male 770 77.0% Female 230 23.0% Total 1000 100.0% 212

The second important demographic variable was gender which is an important variable for marketers in more than one aspect. In this chapter it is proposed to discuss demographic and socio-economic profile of the policyholders. Demographic characteristics deal with vital statistics about the policyholder such as their age, sex, religion, location, marital status and education whereas socio-economic characteristics deal with financial position, occupation, income, wealth and other such attributes. The total number of policyholders was 1000 where 230 (23%) policyholders were females and 770 (77%) policyholders were males as shown in table 5.2. The graph also represents that there was a fair percentage of male policyholders. TABLE 5.3: Characteristics of the policyholders on the basis of Age groups (N=1000) Age Group Frequency Percent Below 25 years 195 19.5% 25-34 years 309 30.9% 35-44 years 192 19.2% 45-54 years 96 9.6% 55-64 years 176 17.6% 64 or Above 32 3.2% Total 1000 100.0% 213

People buy goods and services during their life time. Segmenting the market by age provides useful insight into the potential size of markets. As shown in the table 5.3 the policyholders were grouped in six categories and 195 (19.5%) policyholders were below 25 years of age, 309 (30.9%) policyholders were 25-34 years old, 192 policyholders (19.2%) were 35-44 years old, 96 (9.6%) policyholders were 45-54 years old, 176 (17.6%) policyholders were 55-64 years old and only 32 (3.2%) policyholders were those above 64 years of age. The graph also indicates there was a fair representation of young policyholders who purchased life insurance. TABLE 5.4: Characteristics of the policyholders on the basis of Income groups (N=1000) Income Group (Annual) Frequency Percent Less than 1 Lakh 88 8.8% 1 to 1.5 Lakh 109 10.9% 1.5 to 2.5 Lakh 184 18.4% 2.5 to 5 Lakh 275 27.5% 5 to 10 Lakh 264 26.4% 10 Lakh and above 80 8.0% Total 1000 100.0% 214

It is obvious that unless people have money or assurance of acquiring it, they cannot be regarded as potential policyholders. The amount of money they can spend will also affect the types of goods they are likely to buy. For this reason most of the analyst study income data. On the social scene the emergence of a large middleclass perhaps the most significant of all developments from the marketing point of view. The middle class in now emerging as the Consumption Community in the country are recognized as educated and rational policyholders. On the basis of income groups of the policyholders it was observed that 88 (8.8%) policyholders were those earning less than `1 lakh, 109 (10.9%) policyholders were those earning between ` 1-1.5 lakh, 184 (18.4%) policyholders were those earning between ` 1.5-2.5 lakh, 257 (27.5%) policyholders were those earning between ` 2.5-5 lakh, 264 (26.4%) were those earning between 5-10 lakh and only 80 (8%) were earning `10 lakh and above as shown in the table 5.4. The graph also represents that there was a fair percentage of policyholders falls in the middle class income group of ` 2.5-5 lakh followed by 5-10 lakh. TABLE 5.5: Characteristics of the policyholders on the basis of owner s wealth (N =1000) Owner s Wealth Frequency Percent Below 10 Lakh 283 28.3% 10-50 Lakh 325 32.5% 50 Lakh-1 Crore 192 19.2% 1-5 Crore 72 7.2% 5-10 Crore 104 10.4% More Than 10 Crore 24 2.4% 215

Owner s Wealth Frequency Percent Below 10 Lakh 283 28.3% 10-50 Lakh 325 32.5% 50 Lakh-1 Crore 192 19.2% 1-5 Crore 72 7.2% 5-10 Crore 104 10.4% More Than 10 Crore 24 2.4% Total 1000 100.0% Consumption is also shaped by family wealth and expenditure pattern therefore it is important to consider owners wealth for analysis. As shown in the table above the policyholders grouped in six groups on the basis of owner s wealth. The table 5.5 shows that 283 (28.3%) policyholders had wealth below ` 10 lakh, 325 (32.5%) policyholders had wealth of ` 10-50 lakh, 192 (19.2%) policyholders had wealth of ` 50 lakh-1 crore, 72 (7.2%) policyholders had wealth of ` 1-5 crore, 104 (10.4%) policyholders had wealth of ` 5-10 crore and only 24 (2.4%) policyholders had wealth of more than ` 10 crore. The graph also represents that there was a fair percentage of policyholders acquired wealth between ` 10-50 lakh. 216

TABLE 5.6: Characteristics of the policyholders on the basis of family head (N =1000) Head of family Frequency Percent Grand Father 69 6.9% Father 539 53.9% Brother/Sister 56 5.6% Mother 40 4.0% You 200 20.0% Spouse 96 9.6% Total 1000 100.0% As shown in the table 5.6 the policyholders showed mixed responses related to heads in their families, in 69 (6.9%) cases grandfather was head of the family, in 539 (53.9%) cases father was the head of the family, in 56 (5.6%) cases sibling (brother or sister) was heads of the family, in 40 (4%) cases mother was head of the family, in 200 (20%) cases policyholders themselves were head of their family and only in 96 (9.6%) cases spouse of the policyholder was head of the family. 217

Table 5.7: Characteristics of the policyholders on the basis of occupations of the policyholders (N =1000) Occupation Frequency Percent Agriculture 72 7.2% Self Employed-Shop 109 10.9% Self Employed-Other 80 8.0% Business Owner 125 12.5% Service Professionals Pvt. 302 30.2% Govt. Employees 208 20.8% Dependent 56 5.6% Retired from Pvt. Job 8.8% Retired from Govt. Job 40 4.0% Total 1000 100.0% The rapid social and economic development taking place in the country is more apparent in the economic activities of policyholder in insurance. With growth in urbanization large number of policyholders entering in the job market. As shown in the table 5.7 policyholders were surveyed from different occupational backgrounds. Seventy two (7.2%) policyholders were farmers, 109 (10.9%) policyholders were shop owners, 80 (8.0%) policyholders were self employed, 125 (12.5%) policyholders were business owner, 302 (30.2%) policyholders were serving private sector, 208 (20.8%) were government employees, 50 (5.6%) policyholders were dependent, 8 (0.8%) were retired from private jobs and 40 (4.0%) policyholders were retired from government jobs. The graph also represents that there was a fair percentage of service professionals followed by 218

government employees in the sample size. TABLE 5.8: Characteristics of the policyholders on the basis of educational qualifications of the policyholders (N =1000) Educational Qualifications Frequency Percent Below 10 th 64 6.4% 10th Pass 61 6.1% 12th Pass 152 15.2% Graduate 301 30.1% Diploma Holder 128 12.8% Post Graduate 230 23.0% Professional 64 6.4% Total 1000 100.0% Education is a means to provide systematic instruction to make the policyholders intellectually superior and rational. Spread of education certainly leads to liberal attitude, information sharing, social and legal reforms and a desire to acquire high standard of living. Education therefore is determining a factor which is likely to bring about a change for the better in the society and to enhance the status of policyholder awareness. As shown in the table 5.8 policyholders were from different educational backgrounds. In the sample surveyed 64 (6.4%) policyholders were studied up to below tenth standard, 61 (6.1%) policyholders were studied up to tenth standard, 152 (15.2%) policyholders were studied up to twelfth standard. It was also observed that out of 1000 only 301 (30.1%) policyholders were graduates, 128 (12.8%) 219

policyholders were diploma holders, 230 (23%) policyholders were post graduates and 64 (6.4%) policyholders were professionally qualified. The graph also represents that there was a fair percentage of graduate policyholders. TABLE 5.9: Characteristics of the policyholders on the basis of Life cycle stage (N =1000) Personal Status (Life-cycle- stage) Frequency Percent Single(Unmarried) 270 27.0% Married, no child 96 9.6% Married, child/children below 5 years 112 11.2% Married, children 5-18 years 266 26.6% Married, Children in College 72 7.2% Married, living with working children 112 11.2% Separated, without children 8.8% Married, child/children separated 24 2.4% Separated, living with children 8.8% Widow/widower and Single 24 2.4% Remarried 8.8% Total 1000 100.0% With the tremendous economic and social changes, transformation in attitude and beliefs, increased geographical mobility in search of income, wealth, occupation, increased standard of living the extended families will becoming less popular. Nuclear family has become the vogue of family life styles in India. In the present study a family which has two adults and one to three children is treated as small or nuclear 220

family. Big family or extended family is one which has more than two adults and more than three children. As shown in the table 5.9 in the sample surveyed 270 (27%) policyholders were unmarried, 96 (9.6%) policyholders were married and had no child, 112 (11.2%) policyholders were married and had child/children below 5 years of age, 266 (26.6%) policyholders were married and had child/children between 5-18 years of age, 72 (7.2%) policyholders were married and had college going child/children, 112 (11.2%) policyholders had working child/children, 8 (0.8%) policyholders were not living with their child/children, 24 (2.4%) policyholders were widow or widower and only 8 (0.8%) policyholders were remarried. The graph presented above also represents that there was a sound number of singles and married policyholders who had children between 5-18 years. TABLE 5.10: Number of children in the family of policyholder (N =1000) No of children Frequency Percent Nil 381 38.1% 1 219 21.9% 2 240 24.0% 3 128 12.8% 4 or more 32 3.2% Total 1000 100.0% The family is defined as two or more persons related by blood, marriage or adoption who resides together. In a more dynamic sense individual who constitute a family might be described as members of the most basis social group who live together and interact to satisfy their personal and mutual needs. Family is a primary group 221

exercising considerable influence on policyholder behaviour. The table 5.10 shows that 381(38.1%) policyholders had no child, 219 (21.9%) policyholders had single child, 240 (24%) policyholders had two children, 128 (12.8%) policyholders had three children and only 32 (3.2%) policyholders had four or more children. The graph also represents that there was a fair percentage of policyholders who had no child in their family followed by number of policyholders who had two children in their family. TABLE 5.11: Earning members in family of policyholder (N =1000) Earning Members Frequency Percent 1 269 26.9% 2 571 57.1% 3 120 12.0% 4 or more 40 4.0% Total 1000 100.0% Family may be extended, joint or nuclear. Policyholder behaviour researches have revealed that in every family there is role specialisation for example Karta in joint family decides the household products to be bought, in extended family the decider may be one of the grand parent and in nuclear it is the housewife who has a more decisive role to play. The table 5.11 shows that 269 (26.9%) policyholders had only one earning member in their family, 571 (57.1%) policyholders had two earning members in their family, 120 (12%) policyholders had three earning members in their 222

family and only 40 (4.0%) policyholders had four or more earning members in their family. The graph also represents that there was a fair percentage of policyholders who had 2 earning members in their family. TABLE 5.12: Religion of policyholders (N =1000) Religion Frequency Percent Hindu 896 89.6% Muslim 24 2.4% Sikh 48 4.8% Christian 16 1.6% Others 16 1.6% Total 1000 100.0% The table 5.12 shows that the policyholders were surveyed from different religions, 896 (89.6%) policyholders were Hindus, 24 (2.4%) policyholders were Muslims, 48 (4.8%) policyholders were Sikhs, 16 (1.6%) were Christians and 16 (1.6%) were from other religions not listed in the questionnaire. The graph also represents that there was a fair percentage of Hindu policyholders followed by Sikhs and Muslims. 223

TABLE 5.13: Home ownership of policyholders (N =1000) Home ownership Frequency Percent Yes 771 77.1% No 229 22.9% Total 1000 100.0% The table 5.13 shows that 771 (77.1%) policyholders had home ownership whereas 229 (22.9%) policyholders had no home ownership. The graph also represents that there was a fair percentage of home owners in the sample size. TABLE 5.14: Type of vehicle policyholders posses (N =1000) Type of Vehicle Frequency Percent Heavy Motor Vehicle 117 11.7% Light Motor Vehicle 547 54.7% Motor Cycle/Scooter geared 192 19.2% Scooter non-geared 40 4.0% None 104 10.4% Total 1000 100.0% 224

The table 5.14 shows that in the sample surveyed 117 (11.7%) policyholders had heavy motor vehicle, 547 (54.7%) had light motor vehicle, 192 (19.2%) policyholders had geared motor cycle/scooter, 40 (4%) had non-geared scooter whereas only 104 (10.4%) policyholders had no vehicle. The graph also represents that there was a fair percentage of policyholders those who posses light motor vehicle. TABLE 5.15: Type of back accounts policyholders operates (N =1000) Type of Bank Account Frequency Percent Personal 643 64.3% Joint 96 9.6% Both 261 26.1% Total 1000 100.0% The table 5.15 shows that 643 (64.3%) policyholders were operating personal bank account, 96 (9.6%) had joint account whereas 261 (26.1%) were operating both joint as well as personal accounts. The graph also represents that there was a fair percentage of policyholders who were operating personal account. 225

TABLE 5.16: Property of policyholders (N =1000) Own Property (Agriculture/ Commercial land) Frequency Percent Yes 640 64.0% No 360 36.0% Total 1000 100.0% The table 5.16 shows that 640 (64%) policyholders were property owner (agriculture or commercial) whereas 360 (36%) policyholders were not owner of any kind of property. TABLE 5.17: Card policyholders operates (N =1000) Type of credit/debit card Frequency Percent Credit Card 189 18.9% Debit Card/ATM 739 73.9% Kisan Credit Card 24 2.4% Master/Visa card 48 4.8% Total 1000 100.0% The table 5.17 shows that 189 (18.9%) policyholders were credit card holders, 739 (73.9%) were debit card holders, 24 (2.4%) were kisan credit card holders and only 48 226

(4.8%) were master/visa card holders. DEMOGRAPHIC PROFILE OF RURAL AND URBAN POLICYHOLDERS TABLE 5.18: Gender and Region (N =1000) Gender Gender Regionality Rural Urban Total Male 407 363 770 Female 93 137 230 Total 500 500 1000 The table 5.18 shows that the number of male policyholders was more in rural (407) as well as urban areas (363) as compare to female policyholders in rural (93) and urban areas (137). TABLE 5.19: Age Group and Region (N =1000) Age Group Age Group Regionality Rural Urban Total Below 25 years 146 49 195 25-34 years 162 147 309 35-44 years 92 100 192 45-54 years 12 84 96 55-64 years 69 107 176 64 or Above 19 13 32 Total 500 500 1000 227

The table-5.19 shows that number of young policyholders was more in rural (below 25 years 146, below 35 years was 162) as well as in urban area (below 25 years was 49 and below 35 was 147), followed by the policyholders of 55 to 64 years of age (69 in rural and 107 in urban area)there was a fare participation of each age group in the sample. TABLE 5.20: Income Group and Region (N =1000) Income Group (Annual) Regionality Rural Urban Total Less than 1 Lakh 59 29 88 1 to 1.5 Lakh 78 31 109 1.5 to 2.5 Lakh 91 93 184 2.5 to 5 Lakh 176 99 275 5 to 10 Lakh 74 190 264 10 Lakh and above 22 58 80 Total 500 500 1000 Income group of an individual plays a vital role in buying insurance policy. The above table 5.20 provide the details about the income groups of the policyholders. In 228

the sample surveyed 59 rural and 29 urban policyholders were earning less than ` 1 lakh, 78 rural and 31 urban policyholders were earning between ` 1 1.5 lakh, 91 rural and 93 urban policyholders were earning between ` 1.5 to 2.5 lakh, 176 rural and 99 urban policyholders were earning between ` 2.5 to 5 lakh, 74 rural and 190 urban policyholders were earning between ` 5 to 10 lakh and 22 rural and 58 urban policyholders were earning above ` 10 lakh per annum. TABLE 5.21: Owner s Wealth and Region (N =1000) Owner's Wealth in family Regionality Rural Urban Total Below 10 Lakh 194 89 283 10-50 Lakh 191 134 325 50 Lakh-1 Crore 58 134 192 1-5 Crore 13 59 72 5-10 Crore 27 77 104 More Than 10 Crore 17 7 24 Total 500 500 1000 It was observed on the basis of owners wealth of a household that in the sample surveyed owners of 194 rural and 89 urban policyholders had a wealth below 10 lakhs,191 rural and 31 urban policyholders had wealth between ` 10 50 lakhs 229

followed by 58 rural policyholders and 134 urban policyholders had the wealth between ` 50 to 1 Crore as shown in table 5.21. TABLE 5.22: Head of Family and Region (N =1000) Owner/head of your family Rural Regionality Urban Total Grand Father 57 12 69 Father 255 284 539 Brother/Sister 22 34 56 Mother 24 16 40 You 114 86 200 Spouse 28 68 96 Total 500 500 1000 The table 5.22 shows that in case of 57 rural and 12 urban policyholders head of the family was Grand Father where as in case of 255 rural policyholders and 284 urban policyholders father was the head of family. It was also observed in case of 114 rural and 86 urban policyholders were the head in their families. 230

TABLE 5.23: Occupation and Region (N =1000) Occupation Regionality Total Rural Urban Agriculture 55 17 72 Self Employed-Shop 77 32 109 Self Employed-Other 44 36 80 Business Owner 42 83 125 Service Professionals Pvt. 121 181 302 Govt. Employees 93 115 208 Dependent 41 15 56 Retired from Pvt. Job 1 7 8 Retired from Govt. Job 26 14 40 Total 500 500 1000 The table 5.23 shows that there were a fair percentage of service professionals (121 in rural and 181 urban areas), government employees (93 rural and 115 urban policyholders) followed by self-employed and farmers in the sample surveyed. 231

TABLE 5.24: Educational Qualifications and Region (N =1000) Educational Qualifications Regionality Total Rural Urban Below 10th 48 16 64 10th Pass 49 12 61 12th Pass 114 38 152 Graduate 131 170 301 Diploma Holder 48 80 128 Post Graduate 90 140 230 Professional 20 44 64 Total 500 500 1000 The information in the table 5.24 reveals that 48 rural and 16 urban policyholders were not educated up to 10 th standard, 49 rural and 12 urban policyholders were studied up to 10 th standard, 114 rural and 38 urban policyholders were educated up to 12 th standard. It was also observed that 131 rural and 170 urban policyholders were graduates, 48 rural and 80 urban policyholders were diploma holders, 90 rural and 140 urban policyholders were post graduates whereas 20 rural and 44 urban policyholders were professionally qualified. 232

TABLE 5.25: Personal Status (Life-stage) and Region (N =1000) Personal Status of the policyholders Rural Regionality Urban Total Single (Unmarried) 181 89 270 Married, no child 33 63 96 Married, child/children below 5 years 68 44 112 Married, children 5-18 years 119 147 266 Married, Children in College 33 39 72 Married, living with working children 28 84 112 Separated, without children 1 7 8 Married, child/children separated 11 13 24 Separated, living with children 8 0 8 Widow/widower and Single 18 6 24 Remarried 0 8 8 Total 500 500 1000 The table 5.25 make clear that there was 181 rural and 89 urban policyholders were unmarried, 33 rural and 63 policyholders were married without children, 68 rural and 44 urban policyholders were married and living with children. It was also observed that 1 rural and 7 urban policyholders were separated and have no issues, 11 rural and 233

13 urban policyholders were married and their children were not living with them, 8 rural policyholders were separated and living with their children, 18 rural and 6 urban policyholders were widow/widowers. There were a fair percentage of married policyholders in urban 147 and rural 119 areas that had children aged between 5-18 years of age. TABLE 5.26: Earning Members in Family and Region (N =1000) Earning Members in family Rural Regionality Urban Total 1 122 147 269 2 323 248 571 3 29 91 120 4 or more 26 14 40 Total 500 500 1000 The information in the table 5.26 reveals that 121 rural and 147 urban policyholders had only one earning member in their family, 323 rural and 248 urban policyholders had two earning members in their family, 29 rural and 91urban policyholders had 234

three earning members in their family followed by 26 rural and 14 urban policyholders had four or more earning members in their family. TABLE 5.27: Number of Children and Region (N =1000) Number of Children Regionality Total Rural Urban Nil 212 169 381 1 122 97 219 2 74 166 240 3 68 60 128 4 or more 24 8 32 Total 500 500 1000 The information in the table 5.27 reveals that 212 rural and 169 urban policyholders had no child, 122 rural and 97 urban policyholders had one child, 74 rural and 166 urban policyholders has two children, 68 rural and 60 urban policyholders had three children and 24 rural and 8 urban policyholders had four or more children in their family. 235

TABLE 5.28: Religion and Region (N =1000) Religion of the policyholders Regionality Total Rural Urban Hindu 465 431 896 Muslim 9 15 24 Sikh 9 39 48 Christian 9 7 16 Others 8 8 16 Total 500 500 1000 The table 5.28 shows that there were 465 rural and 431 urban policyholders were Hindus, 9 rural and 15 urban policyholders were Muslims, 9 rural and 39 policyholders were Sikhs, 9 rural and 7 urban policyholders were Christians and 8 rural and 8 urban policyholders were from other religion. 236

TABLE 5.29: Home Ownership and Region (N =1000) Home Ownership Regionality Rural Urban Total Yes 372 399 771 No 128 101 229 Total 500 500 1000 The table 5.29 shows 372 rural and 399 urban policyholders had home ownership whereas 128 rural and 101 urban policyholders do not have their own homes. TABLE 5.30: Type of Vehicle and Region (N =1000) Type of Vehicle Rural Regionality Urban Total Heavy Motor Vehicle 48 69 117 Light Motor Vehicle 215 332 547 Motor Cycle/Scooter geared 141 51 192 Scooter non-geared 24 16 40 None 72 32 104 Total 500 500 1000 237

The information in the table 5.30 reveals that 48 rural and 69 urban policyholders were owner of heavy motor vehicle, 215 rural and 332 urban policyholders were owner of light motor vehicle, 141 rural and 51 urban policyholders had geared two wheeler, 24 rural and 16 urban policyholders had non-geared two wheeler and only 72 rural and 32 urban policyholders were not owner of any vehicle. TABLE 5.31: Type of Bank Account and Region (N =1000) Type of bank account Regionality Rural Urban Total Personal 371 272 643 Joint 36 60 96 Both 93 168 261 Total 500 500 1000 238

The information in the table 5.31 reveals that 48 rural and 69 urban policyholders were owner of heavy motor vehicle, 215 rural and 332 urban policyholders were owner of light motor vehicle, 141 rural and 51 urban policyholders had geared two wheeler, 24 rural and 16 urban policyholders had non-geared two wheeler and only 72 rural and 32 urban policyholders were not owner of any vehicle. TABLE 5.32: Property Ownership and Region (N =1000) Own property(agriculture/commercial/land) and Regionality Regionality Total Rural Urban Yes 316 324 640 No 184 176 360 Total 500 500 1000 The table 5.32 shows 316 rural and 324 urban policyholders were owner of property whereas 184 rural and 174 urban policyholders do not own property. 239

TABLE 5.33: Type of Card and Region (N =1000) Type of credit/debit Rural Regionality Urban Total Credit Card 77 112 189 Debit Card/ATM 383 356 739 Kisan Credit Card 16 8 24 Master/Visa card 24 24 48 Total 500 500 1000 The information in the table 5.33 reveals that 77 rural and 112 urban policyholders were owner of credit cards, 383 rural and 356 urban policyholders were owner of debit cards/atms, 16 rural and 8 urban policyholders were owner of kisan credit cards whereas 24 rural and 24 urban policyholders were owner of master visa cards. TABLE 5.34: Insured Amount and Region (N =1000) Approximate amount insured by the policyholders in life insurance policy/policies Rural Urban Frequency Percent Frequency Percent 1-3 Lakh 416 83.2 363 72.6 4-7Lakh 65 13.0 100 20.0 More than 7Lakh 19 3.8 37 7.4 Total 500 100.0 500 100.0 240

The information in the table 5.34 reveals that 83.2 percent rural and 72.6 percent urban policyholders insured approximately ` 1 to 3 laks of, 13 percent rural and 20 percent urban policyholders insured ` 4 to 7 laks and 3.8 rural and 8.4 percent urban policyholders insured more than ` 7 laks. PREFERENCES OF INSURANCE POLICIES AND REGION The information collected from the respondent revealed that policyholders posses more than one policy of same company of different companies. Therefore, the data related to types of insurance plan chosen by policy holders and detail of the insurer provided below: TABLE 5.35: Whole Life Scheme Whole Life Scheme Rural Urban Frequency Percent Frequency Percent Yes 70 14.0 82 16.4 No 430 86.0 418 83.6 Total 500 100.0 500 100.0 The information in the table 5.35 reveals that only 14 percent rural and 16.4 percent urban policyholders had whole life insurance policy. The whole life policies were not popular among rural and urban policyholders. 241

TABLE 5.36: Endowment Scheme Endowment Scheme Rural Urban Frequency Percent Frequency Percent Yes 339 67.8 269 53.8 No 161 32.2 231 46.2 Total 500 100.0 500 100.0 The information in the table 5.36 reveals that 67.8 percent rural and 53.8 percent urban policyholders had endowment life insurance scheme. Therefore it is stated that endowment schemes were quite popular among rural and urban policyholders. TABLE 5.37: Term Insurance Plan Term Insurance Plan Rural Urban Frequency Percent Frequency Percent Yes 60 12.0 52 10.4 No 440 88.0 448 89.6 Total 500 100.0 500 100.0 242

The information in the table 5.37 reveals that only 12 percent rural and 10.4 percent urban policyholders had Term insurance plans. Term insurance plans were not popular among rural and urban policyholders. TABLE 5.38: Periodic Money Back Plan Periodic Money Back Plan Rural Urban Frequency Percent Frequency Percent Yes 34 6.8 14 2.8 No 466 93.2 486 97.2 Total 500 100.0 500 100.0 243

The information in the table 5.38 reveals that only 6.8 percent rural and 2.8 percent urban policyholders had periodic money back plan. Periodic money bank plans were least preferred by the sample policyholders. TABLE 5.39: Medical Benefits Linked Insurance Medical Benefits Linked Insurance Rural Urban Frequency Percent Frequency Percent Yes 38 7.6 82 16.4 No 462 92.4 418 83.6 Total 500 100.0 500 100.0 The information in the table 5.39 reveals that only 7.6 percent rural and 16.4 percent urban policyholders had medical benefit linked insurance. Medical benefit linked insurance plans were least preferred by the sample policyholders. 244

TABLE 5.40: Children Plan Children Plan Rural Urban Frequency Percent Frequency Percent Yes 105 21.0 84 16.8 No 395 79.0 416 83.2 Total 500 100.0 500 100.0 The information in the table 5.40 reveals that only 21 percent rural and 16.8 percent urban policyholders had children plan. Children plan were opted by rural and urban policyholders but were not popular among sample policyholders. TABLE 5.41: Joint Life Plan Joint Life Plan Rural Urban Frequency Percent Frequency Percent Yes 61 12.2 91 18.2 No 439 87.8 409 81.8 Total 500 100.0 500 100.0 245

The information in the table 5.41 reveals that only 12.2 percent rural and 18.2 percent urban policyholders had whole life insurance policy. The joint life plan were least preferred by the sample policyholders. TABLE 5.42: Capital Market Limited Plan Capital Market Limited Plan Rural Urban Frequency Percent Frequency Percent Yes 18 3.6 6 1.2 No 482 96.4 494 98.8 Total 500 100.0 500 100.0 246

The information in the table 5.42 reveals that only 3.6 percent rural and 1.2 percent urban policyholders had capital market linked plan. Capital market linked plans were least preferred by the sample policyholders. TABLE 5.43: Group Schemes Group Schemes Rural Urban Frequency Percent Frequency Percent Yes 24 4.8 - - No 476 95.2 500 100.0 Total 500 100.0 500 100.0 The information in the table 5.43 reveals that only 4.8 percent rural policyholders had group life insurance schemes. Group schemes were least preferred by the sample policyholders in rural as well as urban segment. TABLE 5.44: Social Security Social Security Rural Urban Frequency Percent Frequency Percent Yes 16 3.2 - - No 484 96.8 500 100.0 Total 500 100.0 500 100.0 247

The information in the table 5.44 reveals that only 3.2 percent rural policyholders had social security schemes. Social security plans were least preferred by the sample policyholders. TABLE 5.45: Education Plan Education Plan Rural Urban Frequency Percent Frequency Percent Yes 17 3.4 7 1.4 No 483 96.6 493 98.6 Total 500 100.0 500 100.0 248

The information in the table 5.45 reveals that only 3.6 percent rural and 1.2 percent urban policyholders had educational plan. Educational plans were least preferred by the sample policyholders. TABLE 5.46: Pension Plan Pension Plan Rural Urban Frequency Percent Frequency Percent Yes 26 5.2 38 7.6 No 474 94.8 462 92.4 Total 500 100.0 500 100.0 The information in the table 5.46 reveals that only 5.2 percent rural and 7.6 percent urban policyholders had pension plan. Pension plans were least preferred by the sample policyholders in rural and urban segments. 249

TABLE 5.47: Growth Plan Growth Plan Rural Urban Frequency Percent Frequency Percent Yes 37 7.4 35 7.0 No 463 92.6 465 93.0 Total 500 100.0 500 100.0 The information in the table 5.47 reveals that only 7.4 percent rural and 7 percent urban policyholders had capital market linked plan. Growth plans were least preferred by the sample policyholders. TABLE 5.48: Unit Linked Plan Unit Linked Plan Rural Urban Frequency Percent Frequency Percent Yes 68 13.6 100 20.0 No 432 86.4 400 80.0 Total 500 100.0 500 100.0 250

The information in the table 5.48 reveals that only 13.6 percent rural and 20 percent urban policyholders had unit linked plan. Unit linked plans were less poplar among the sample policyholders. TABLE 5.49: Systematic Investment Plan Systematic Investment Plan Rural Urban Frequency Percent Frequency Percent Yes 24 4.8 8 1.6 No 476 95.2 492 98.4 Total 500 100.0 500 100.0 251

The information in the table 5.49 reveals that only 4.8 percent rural and 1.6 percent urban policyholders had systematic investment plan. Systematic investment plans were least preferred by the sample policyholders. TABLE 5.50: Individual Plan Individual Plan Rural Urban Frequency Percent Frequency Percent Yes 306 61.2 142 28.4 No 194 38.8 358 71.6 Total 500 100.0 500 100.0 The information in the table 5.50 reveals that only 61.2 percent rural and 28.4 percent urban policyholders had individual plan. Individual plans were quite popular among the rural policyholders. TABLE 5.51: Money Back Plan Money Back Plan Rural Urban Frequency Percent Frequency Percent Yes 144 28.8 56 11.2 No 356 71.2 444 88.8 Total 500 100.0 500 100.0 252

The information in the table 5.51 reveals that only 28.8 percent rural and 11.2 percent urban policyholders had capital money back plan. Money back plans were also less preferred by the sample policyholders in both the segments. TABLE 5.52: Special Plan Special Plan Rural Urban Frequency Percent Frequency Percent Yes 16 3.2 - - No 484 96.8 500 100.0 Total 500 100.0 500 100.0 The information in the table 5.52 reveals that only 3.2 percent rural policyholders had special plan. Special plans were least preferred by the sample policyholders. 253

TABLE 5.53: Health Plan Health Plan Rural Urban Frequency Percent Frequency Percent Yes 25 5.0 23 4.6 No 475 95.0 477 95.4 Total 500 100.0 500 100.0 The information in the table 5.53 reveals that only 5 percent rural and 4.6 percent urban policyholders had health plan. Health plans were least preferred by the sample policyholders in rural and urban segments. TABLE 5.54: Multiplier Plan Multiplier Plan Rural Urban Frequency Percent Frequency Percent Yes 16 3.2 8 1.6 No 484 96.8 492 98.4 Total 500 100.0 500 100.0 254

The information in the table 5.54 reveals that only 3.2 percent rural and 1.6 percent urban policyholders had Multiplier plan. Multiplier plans were least preferred by the sample policyholders. TABLE 5.55: Plan with Flexible Investment Option Plan with Flexible Investment Option Rural Urban Frequency Percent Frequency Percent Yes 16 3.2 8 1.6 No 484 96.8 492 98.4 Total 500 100.0 500 100.0 255

The information in the table 5.55 reveals that only 3.2 percent rural and 1.6 percent urban policyholders had plan with flexible investment option. Plans with flexible investment option were least preferred by the sample policyholders in both the segments. TABLE 5.56: Security Security Rural Urban Frequency Percent Frequency Percent Yes 384 76.8 304 60.8 No 116 23.2 196 39.2 Total 500 100.0 500 100.0 The information in the table 5.56 reveals that only 76.8 percent rural and 60.8 percent urban policyholder s posses a policy with security. Social security is one of the criteria thee policyholders expect to be part of most of the policies. 256

TABLE 5.57: Security and Critical Pension Security and critical pension Rural Urban Frequency Percent Frequency Percent Yes 60 12.0 100 20.0 No 440 88.0 400 80.0 Total 500 100.0 500 100.0 The information in the table 5.57 reveals that only 12 percent rural and 20 percent urban policyholders had a policy with security and critical pension plan features. TABLE 5.58: Systematic Investment Plan Systematic Investment Plan Rural Urban Frequency Percent Frequency Percent Yes 15 3.0 65 13.0 No 485 97.0 435 87.0 Total 500 100.0 500 100.0 257

The information in the table 5.58 reveals that only 3 percent rural and 13 percent urban policyholders opted for a policy with systematic investment plan. TABLE 5.59: Saving Plan Saving Plan Rural Urban Frequency Percent Frequency Percent Yes 366 73.2 242 48.4 No 134 26.8 258 51.6 Total 500 100.0 500 100.0 The information in the table 5.59 reveals that only 73.2 percent rural and 48.4 percent urban policyholders preferred a policy offering saving plan. 258

TABLE 5.60: Risk Disability Risk Disability Rural Urban Frequency Percent Frequency Percent Yes 16 3.2 8 1.6 No 484 96.8 492 98.4 Total 500 100.0 500 100.0 The information in the table 5.60 reveals that only 3.2 percent rural and 1.6 percent urban policyholders preferred a policy with risk disability plan. TABLE 5.61: Critical Plan Critical Pension Rural Urban Frequency Percent Frequency Percent Yes 15 3.0 49 9.8 No 485 97.0 451 90.2 Total 500 100.0 500 100.0 259

The information in the table 5.61 reveals that only 3 percent rural and 9.8 percent urban policyholders chosen life insurance with critical pension plan. TABLE 5.62: Security Illness Security Illness Rural Urban Frequency Percent Frequency Percent Yes 9 1.8 31 6.2 No 491 98.2 469 93.8 Total 500 100.0 500 100.0 The information in the table 5.62 reveals that only 1.8 percent rural and 6.2 percent urban policyholders preferred a policy with security illness plan. TABLE 5.63: Annuity Insurance Annuity Insurance Rural Urban Frequency Percent Frequency Percent Yes 8 1.6 8 1.6 No 492 98.4 492 98.4 Total 500 100.0 500 100.0 260

The information in the table 5.63 reveals that only 1.6 percent rural and 1.6 percent urban policyholders chosen a policy with annuity schemes. TABLE 5.64: Flexibility Investment Portfolio Flexible Investment Portfolio Rural Urban Frequency Percent Frequency Percent Yes 8 1.6 32 6.4 No 492 98.4 468 93.6 Total 500 100.0 500 100.0 261

The information in the table 5.64 reveals that only 1.6 percent rural and 6.4 percent urban policyholders opted for life insurance with flexible investment portfolio plan. TABLE 5.65: Payer s Benefit Payer's Benefit Frequency Percent Frequency Percent Yes 8 1.6 8 1.6 No 492 98.4 492 98.4 Total 500 100.0 500 100.0 The information in the table 5.65 reveals that only 1.6 percent rural and 1.6 percent urban policyholders opted for life insurance with payer s benefit plan. TABLE 5.66: Risk Coverage Risk Coverage Frequency Percent Frequency Percent Yes 341 68.2 251 50.2 No 159 31.8 249 49.8 Total 500 100.0 500 100.0 262

The information in the table 5.66 reveals that only 68.2 percent rural and 50.2 percent urban policyholders favored life insurance with maximum risk coverage plan. TABLE 5.67: Investment in Equity Funds Investment in equity funds Frequency Percent Frequency Percent Yes 8 1.6 40 8.0 No 492 98.4 460 92.0 Total 500 100.0 500 100.0 The information in the table 5.67 reveals that only 1.6 percent rural and 8 percent urban buyer preferred a policy where investment in equity funds is offered to the policyholders. 263

TABLE 5.68: Investment in Growth Funds ` Frequency Percent Frequency Percent Yes 8 1.6 16 3.2 No 492 98.4 484 96.8 Total 500 100.0 500 100.0 The information in the table 5.68 reveals that only 1.6 percent rural and 3.2 percent urban policyholders had preferred a policy with growth funds. TABLE 5.69: Investment in Debts Funds Investment in debts funds Frequency Percent Frequency Percent Yes 13 2.6 27 5.4 No 487 97.4 473 94.6 Total 500 100.0 500 100.0 264

The information in the table 5.69 reveals that only 2.6 percent rural and 5.4 percent urban policyholders opted for a policy with debt funds. TABLE 5.70: Investment in Liquid Funds Investment in liquid funds Frequency Percent Frequency Percent Yes 8 1.6 - - No 492 98.4 500 100.0 Total 500 100.0 500 100.0 The information in the table 5.70 reveals that only 1.6 percent rural policyholders preferred policy where money is invested in liquid funds. 265

TABLE 5.71: Maturity Safety Switch Options Maturity Safety Switch Options Frequency Percent Frequency Percent Yes 218 43.6 123 24.6 No 282 56.4 377 75.4 Total 500 100.0 500 100.0 The information in the table 5.71 reveals that only 43.6 percent rural and 24.6 percent urban policyholders opted for life insurance with maturity safety switch options. TABLE 5.72: Auto Fund Rebalancing Auto Fund Rebalancing Frequency Percent Frequency Percent Yes 8 1.6 - - No 492 98.4 500 100.0 Total 500 100.0 500 100.0 266

The information in the table 5.72 reveals that only 1.6 percent rural policyholders opted for a life insurance policy where the money is invested in auto fund rebalancing scheme. TABLE 5.73: Milestone Withdrawals Milestone Withdrawals Frequency Percent Frequency Percent Yes 8 1.6 - - No 492 98.4 500 100.0 Total 500 100.0 500 100.0 The information in the table 5.73 reveals that only 1.6 percent rural policyholders 267

preferred a plan where milestone withdrawals are possible. TABLE 5.74: Partial Withdrawals Partial Withdrawals Frequency Percent Frequency Percent Yes 80 16.0 48 9.6 No 420 84.0 452 90.4 Total 500 100.0 500 100.0 The information in the table 5.74 reveals that only 16.0 percent rural and 9.6 percent urban policyholders preferred a policy where partial withdrawals are possible. TABLE 5.75: Settlement Options Settlement Options Frequency Percent Yes 56 11.2 64 12.8 No 444 88.8 436 87.2 Total 500 100.0 500 100.0 The information in the table 5.75 reveals that only 11.2 percent rural and 12.8 percent urban policyholders preferred a life insurance where settlement options is provided to them. 268

TABLE 5.76: Revival Policy Revival of Policy Frequency Percent Frequency Percent Yes 302 60.4 194 38.8 No 198 39.6 306 61.2 Total 500 100.0 500 100.0 The information in the table 5.76 reveals that only 60.4 percent rural and 38.8 percent urban policyholders preferred a life insurance where revival of policy is easier. 269

TABLE 5.77: Policyholders of Different Life Insurance Companies Insurance Company No. of Policyholders Bajaj 189 HDFC 112 SBI Life 355 Aviva 16 Canara Bank HSBC 24 AMP Sanmar 0 ICICI 200 ING Vysya 24 Birla Sunlife 24 Sahara 16 Max New York 149 Shriram Life 8 LIC 920 Tata AIG 235 Reliance Life 32 Kotak Mahindra 40 Metlife India 0 Others 16 Total 2360 The table values indicated that the approached policyholders were holding different life insurance policies from different companies and there were many policyholders who had more than one policy from the same or different companies. The majority of policyholders bought LIC policy and they preferred to continue the association with the company. SBI life, Tata AIG and ICICI are also holding good position in the minds of policyholder. 270

The graph 5.77 indicates that there was a fair representation of LIC policyholders in the sample size (920) followed by SBI life insurance and TATAAIG. Therefore it is inferred that LIC is holding major market share in the insurance sector and winning policyholders faith. 271

CORRELATIONS ANALYSIS TABLE 5.78: Correlation between Region and Type of Insurance Policy Rationality Type of Insurance Policy Rural Urban Whole Life Scheme.451** -.056 Endowment Scheme.125**.118** Term Insurance Plan.492** -.043 Periodic Money Back Plan.673** -.122* Medical Benefits Linked Insurance.634** -.156* Children Plan.353** -.057 Joint Life Plan.488** -.060 Capital Market Limited Plan.041* -.014 Group Schemes.810**.00 Social Security 1.000** 1.000** Education Plan.969** -.015 Pension Plan.776**.445** Growth Plan.643** -.135* Unit Linked Plan.458** -.164* Systematic Investment Plan.010 -.016 Individual Plan.145** -.080 Money Back Plan.286** -.145* Special Plan 0.034.00 Health Plan.793** -.028 Multiplier Plan 0.001.00 Plan with Flexible Investment Option.000* -.016 The above table reveals the information that Endowment Scheme, Periodic Money Back Plan, Medical Benefits Linked Insurance, ULIPS, Social Security, Pension Plan are closely linked with the urban region as there is a significant correlation between urban region and above plans. Therefore because of information search and awareness of urban respondents these plans were popular among urban policyholders. Periodic, Money Back Plan, Medical Benefits Linked Insurance, ULIPS and Growth Plan show 272

negative correlation with rural region due to poor awareness of rural policyholders. TABLE 5.79: Correlation between Gender and Type of Insurance Policy Gender Type of Insurance Policy Rural Urban Whole Life Scheme -.074 -.091* Endowment Scheme -.043 -.156** Term Insurance Plan.034 -.129** Periodic Money Back Plan.129**.104* Medical Benefits Linked Insurance -.037.090* Children Plan.146**.084 Joint Life Plan.162** -.070 Capital Market Limited Plan.092*.068 Group Schemes.107* 0.00 Social Security.087 0.00 Education Plan.090*.073 Pension Plan.112*.041 Growth Plan.135**.169** Unit Linked Plan.175**.049 Systematic Investment Plan.107*.078 Individual Plan.094* -.150** Money Back Plan.304** -.123** Special Plan.087 0.00 Health Plan -.079.135** Multiplier Plan.087.078 Plan with Flexible Investment Option.087.078 The above table reveals the information that Periodic Money Back Plan, Children Plan, Joint Life Plan, Capital Market Limited Plan, Pension Plan, ULIPS, Systematic Investment Plan, Individual Plan, Money Back Plan are closely linked with the gender in urban region as there is a significant positive correlation. Whereas Endowment, 273

Term Insurance Plan, Individual, Money back plans shows negative correlation with gender in rural region. TABLE 5.80: Correlation between Occupation and Type of Insurance Plan Occupation Type of Insurance Policy Rural Urban Whole Life Scheme -.191** -.065 Endowment Scheme.078.076 Term Insurance Plan -.046 -.159** Periodic Money Back Plan.017.033 Medical Benefits Linked Insurance -.161** -.086 Children Plan -.243**.081 Joint Life Plan -.067.190** Capital Market Limited Plan -.214** -.295** Group Schemes -.168** 0.00 Social Security -.178** 0.00 Education Plan -.176** -.015 Pension Plan -.179** -.290** Growth Plan -.172**.056 Unit Linked Plan -.135**.171** Systematic Investment Plan -.202** -.016 Individual Plan.251**.222** Money Back Plan -.062.182** Special Plan -.178** 0.00 Health Plan -.227**.107* Multiplier Plan -.178** -.016 Plan with Flexible Investment Option -.178** -.016 The above table reveals that occupation has negative correlation with several plans rural and urban segments such as Capital market plan, Pension plan, ULIPs etc. 274

TABLE 5.81: Correlation between Age and Type of Insurance Plan Age Type of Insurance Policy Rural Urban Whole Life Scheme -.202** -.281** Endowment Scheme.214**.312** Term Insurance Plan -.375**.026 Periodic Money Back Plan -.366** -.162** Medical Benefits Linked Insurance -.204** -.109* Children Plan -.223** -.172** Joint Life Plan -.312**.262** Capital Market Limited Plan -.400** -.225** Group Schemes -.279** 0.00 Social Security -.370** 0.00 Education Plan -.370** -.070 Pension Plan -.244** -.076 Growth Plan -.168** -.066 Unit Linked Plan.367**.193** Systematic Investment Plan -.330** -.168** Individual Plan.331**.040 Money Back Plan -.286**.194** Special Plan -.370** 0.00 Health Plan -.270**.078 Multiplier Plan -.370**.109* Plan with Flexible Investment Option -.370**.017 The above table reveals that age is also associated with type of Insurance plans. Endowment schemes and ULIPs had a positive correlation with age whereas Whole life, Money back, Children and Capital market linked plans had negative correlation with age of the policyholders. 275

TABLE 5.82: Correlation between educational level and type of insurance Education Type of Insurance Policy Rural Urban Whole Life Scheme -.202** -.281** Endowment Scheme.214**.312** Term Insurance Plan -.375**.026 Periodic Money Back Plan -.366** -.162** Medical Benefits Linked Insurance -.204**.262** Children Plan -.223**.168** Joint Life Plan -.312**.262** Capital Market Limited Plan -.400** -.177** Group Schemes -.279**.085 Social Security -.370**.065 Education Plan -.370**.470** Pension Plan -.244**.114* Growth Plan.168**.145** Unit Linked Plan -.367**.193** Systematic Investment Plan -.330** -.168** Individual Plan.331**.613** Money Back Plan -.286**.194** Special Plan -.370**.008 Health Plan -.270**.078 Multiplier Plan -.370**.109* Plan with Flexible Investment Option -.370**.017 The above table reveals that education is also linked with type of insurance plan selected by the policyholders. Education level have negative correlation with different types of insurance plans such as Systematic investment plan, Capital market plan, Whole life and periodic money back plans in both the region. Education was positively correlated with Endowment and Growth plans in both the regions. 276

TABLE 5.83: Correlation between income group and type of insurance Income Type of Insurance Policy Rural Urban Whole Life Scheme.260**.208** Endowment Scheme.132**.275** Term Insurance Plan -.149** -.183** Periodic Money Back Plan.187**.174** Medical Benefits Linked Insurance.169**.253** Children Plan.252** -.161** Joint Life Plan.205**.356** Capital Market Limited Plan -.311** -.154** Group Schemes.268**.098 Social Security -.285**.078 Education Plan.298**.167** Pension Plan.166**.033 Growth Plan.264** -.068 Unit Linked Plan.184** -.008 Systematic Investment Plan.213** -.083 Individual Plan.249**.582** Money Back Plan -.027 -.004 Special Plan.285** 0.009 Health Plan.281** 136** Multiplier Plan -.285**.203** Plan with Flexible Investment Option -.285** -.179** The above table reveals that income of respondents had positive correlation with type of Insurance plans. Whole Life Scheme, Endowment Scheme, Periodic Money Back Scheme, Medical Benefit Linked Scheme, Joint Life Plan, Individual Plan and Health Plan have positive correlation with income group whereas Capital Market Plan and Term Insurance had negative correlation with income group. 277

TABLE 5.84: Correlation between Personal status and type of insurance Personal Status Type of Insurance Policy Rural Urban Whole Life Scheme -.159** -.164** Endowment Scheme.073.291** Term Insurance Plan -.186**.073 Periodic Money Back Plan -.252** -.285** Medical Benefits Linked Insurance -.201** -.086 Children Plan -.223** -.148** Joint Life Plan -.308**.118** Capital Market Limited Plan -.306** -.108* Group Schemes -.173**.073 Social Security -.296**.063 Education Plan -.292** -.007 Pension Plan -.192** -.111* Growth Plan -.079.052 Unit Linked Plan -.275**.229** Systematic Investment Plan -.269** -.125** Individual Plan.123**.544** Money Back Plan -.246**.237** Special Plan -.296**.023 Health Plan -.200**.018 Multiplier Plan -.296**.051 Plan with Flexible Investment Option -.296** -.008 The above table reveals that personal status of respondents also shown negative correlation with type of Insurance plans in rural and urban segment in case of Whole Life Scheme, Periodic Money Back Plan, Children Plan, Capital Market Linked Plan, Pension Plan and Systematic Plan. 278

TABLE 5.85: Correlation between amount insured and other variables Approximate amount insured by you in life insurance policy/policies Gender -.096* Age Group.302** Income Group (Annual).303** Owner's Wealth in family.461** Owner/head of your family.257** Your Occupation.358** Educational Qualifications -.058 Personal Status.366** Earning Members in family residing with you.152** Number of Children you have.237** Regionality.258* Religion.273** Home Ownership.184** Type of Which Vehicle you posses -.111* Type of bank account you have.458** Own property(agriculture/commercial/land) -.033 Type of credit/debit card used.191** The above table reveals that amount of life insurance is positively correlated with age, income, wealth, occupation, head in family, personal status of respondents, earning members in family, region, religion, home ownership, type of bank account etc. 279

5.2 CONFIRMATORY FACTOR ANALYSIS AND DESIGNING OF THE CONSTRUCTS Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables. CFA allows the researcher to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists. The researcher uses knowledge of the theory, empirical research, or both, postulates the relationship pattern a priori and then tests the hypothesis statistically CFA allows the researcher to test the hypothesis that a relationship underlying latent construct(s) exists. The researcher uses knowledge of the theory, empirical research, or both, postulates the relationship pattern a priori and then tests the hypothesis statistically. The use of CFA could be impacted by: The research hypothesis being testing The requirement of sufficient sample size (e.g., 5-20 cases per parameter estimate) Measurement instruments Multivariate normality Parameter identification Outliers Missing data Interpretation of model fit indices A suggested approach to CFA proceeds through the following process: Review the relevant theory and research literature to support model specification Specify a model (e.g., diagram, equations) Determine model identification (e.g., if unique values can be found for parameter estimation; the number of degrees of freedom, (df), for model testing is positive) collect data Conduct preliminary descriptive statistical analysis (e.g., scaling, missing data, 280

collinearity issues, outlier detection) estimate parameters in the model assess model fit present and interpret the results. Confirmatory factor analysis (CFA) provides enhanced control for assessing unidimensionality (i.e., the extent to which items on a factor measure one single construct) than exploratory factor analysis (EFA) and is more in line with the overall process of construct validation. In this study, confirmatory factor analysis model is run through AMOS software. Confirmatory Factor Analysis is a statistical technique used to verify the factor structure of a set of observed variables. Confirmatory Factor Analysis (CFA) allows the researcher to test the hypothesis that a relationship between observed variable and the underlying latent construct exists. The researcher uses the knowledge of the theory, empirical research or both, postulates the relationship patter a priori and than tests the hypothesis statistically. Confirmatory Factor Analysis could occur with the development of measurement instruments such as satisfaction scales, attitude or policyholder service questionnaires. In this research a blueprint is developed, questions written, appropriate scales were determined. The research instrument was used after conducting spade work and pilot survey, data collected and Confirmatory Factor Analysis completed. Confirmatory Factor Analysis allows the researcher to test the hypothesis that a relationship between the observed variables and their underlying latent construct (s) exists. Various dimensions of Confirmatory Factor Analysis are defined below: Validity Analysis The validity of scale may be defined as the extant to which differences in observed scale reflect true differences among objects on the characteristics being measured, rather than systematic or random errors. Some of the important validity tests generally considered includes content, construct, discriminant and criterion related validity. Content validity Content validity also called face validity which consists of a subjective but systematic evaluation of the repetitiveness of the contents of a scale. The content validity of a 281

construct can be defined as the degree to which the measure spans the domain of the constructs. For the present study, the content validity of the instrument was ensured as the service quality dimensions and items were identified from the literature and were thoroughly reviewed by professionals and academicians. Construct Validity Construct validity is a type of validity that addresses the construct or characteristic of the defined measuring scale. Construct validity require the a sound theory of the nature the construct being measured and how it is related to other construct. It involves the assessment of the degree to which an operationalization correctly measures its targeted variables. Establishing construct validity involves the empirical assessment of unidimensionality, reliability and validity (convergent and discriminant validity). In the present study, in order to check for unidimensionality, a measurement model was specified for each construct and CFA was run for all the constructs. Individual items in the model were examined to see how closely they represent the same construct. A comparative fit index (CFI) of 0.90 or above for the model implies that there is a strong evidence of unidimensionality. The CFI values obtained for all the six dimensions in the scale are equal to or above 0.90 as shown in the respective constructs. This indicates a strong evidence of unidimensionality for the scale. Once unidimensionality and reliability of a scale is established, it is further subjected to validation analysis. Convergent Validity It is a measure of construct validity that measures the extant to which the scale correlates positively with other measures of the same construct. It is the degree to which multiple methods of measuring a variable provide the same results. Convergent validity can be established using a coefficient called Bentler-Bonett coefficient. Scale with values of 0.90 or above shows strong evidence of convergent validity (Bentler and Bonett, 1980). The values for the Bentler- Bonett coefficient are summarized for all the six dimensions. All the dimensions have a value of more than 0.90, thereby demonstrating strong convergent validity. 282

Discriminant Validity Discrminant validity assesses the extant to which a measure does not correlate with other construct from which it is suppose to differ. It involves demonstrating a lack of correlation among differing a construct. It is the degree to which the measures of different latent variables are unique. Discriminant validity is ensured if a measure does not correlate very highly with other measures from which it is supposed to differ. For assessing discriminant validity, two chi-square comparison models were considered. The two comparison models are referred as Model 1 and Model 2. The comparison of chi-square statistic for Model 1 and Model 2 provides support for discriminant validity. Criterion-related Validity It is a type of validity that examines whether a scale performs as expected in to other variables selected as meaningful criteria.it is established when a criterion, external to the measurement instrument is correlated with the factor structure. In the present study, criterion validity is established by correlating the policyholder perceived service quality scale scores with overall service quality, which is considered to be the outcome construct. The correlations values also supports that all the dimensions have significant positive correlations with overall service quality. Thus, criterion related validity is established for all the dimensions. A construct can be defined as the latent variable which cannot or difficult to be measured directly from the policyholders. Hence a set of variables is to be included in the construct for its measurement. Before finalizing the set of variables in the construct the content validity is to be assured. The best practice to ensure the content validity is to show the set of possible variables in the construct to five academicians as well as five industry experts. After analyzing the advice received from these experts the constructs along with the set of variables is finalized. In this way the issue of content validity is resolved. After ascertaining the content validity the next issue was to analyze the validity of each individual construct. The construct validity consists of convergent validity, discriminant validity and face validity. The 283

convergent validity can be tested with help of factor loadings of each individual variable to the construct. The high Factor loadings indicate convergent validity and since high factor loadings indicate that the variable is highly explained by the construct, hence it will not be explained by any other construct which indicates the presence of discriminant validity. The description of various constructs, the set of variables in each construct and their factor loadings are shown as below:- Table 5.86: Possible Construct Name of Construct First Construct: Second Construct: Third Construct: Fourth Construct: Fifth Construct: Sixth Construct: Seventh Construct: Eight Construct: Ninth Construct: Parameter Selection Criteria (recommendation) Source of Information Purpose of Buying Feeling and Attitude Service Attributes Product Attributes Service Attributes Agents Attributes Other Attributes Structural Equation Modeling Traditional statistical methods normally utilize one statistical test to determine the significance of the analysis. However, Structural Equation Modeling (SEM), CFA specifically, relies on several statistical tests to determine the adequacy of model fit to the data. The chi-square test indicates the amount of difference between expected and observed covariance matrices. A chi-square value close to zero indicates little difference between the expected and observed covariance matrices. In addition, the probability level must be greater than 0.05 when chi-square is close to zero. The Comparative Fit Index (CFI) is equal to the discrepancy function adjusted for sample size. CFI ranges from 0 to 1 with a larger value indicating better model fit. Acceptable model fit is indicated by a CFI value of 0.90 or greater (Hu &Bentler, 1999). 284

Policyholder decision making process an analysis The policyholder decision to purchase or reject a product is the moment of final truth for the marketer. It signifies whether the marketing strategy has been wise, insightful and effective or whether it was poorly planned and missed the mark. Thus the marketers are particularly interested in policyholder decision making process. Therefore various aspects of decision making process were considered and constructs were designed accordingly. Root Mean Square Error of Approximation (RMSEA) is related to residual in the model. RMSEA values range from 0 to 1 with a smaller RMSEA value indicating better model fit. Acceptable model fit is indicated by an RMSEA value of 0.06 or less (Hu & Bentler, 1999). If model fit is acceptable, the parameter estimates are examined. The ratio of each parameter estimate to its standard error is distributed as a z statistic and is significant at the 0.05 level if its value exceeds 1.96 and at the 0.01 level it its value exceeds 2.56 (Hoyle, 1995). Unstandardized parameter estimates retain scaling information of variables and can only be interpreted with reference to the scales of the variables. Standardized parameter estimates are transformations of unstandardized estimates that remove scaling and can be used for informal comparisons of parameters throughout the model. Standardized estimates correspond to effect-size estimates. 285

Table 5.87: Construct Selection criteria on the basis of Recommendations (buying decision) Source of Information Purpose of buying Feelings and attitude Service attribute Product attributes Agents attributes Other factors My own decision. My employer s suggestion. Recommended by family member My Friend s suggestion Insurance agent s suggestion. My spouse s suggestion. Recommended during advertisement News paper /magazines Television Internet /E-mails Agent Office/Workplace Circular/Notices Spouse/children Friends Insurance Experts/advisors Extra money at the time of my retirement. Extra money at the time of my retirement. Extra money in case of emergency (illness, accident). To avoid incurring unnecessary costs of insurance in future To maintain same life style over years Death protection for family members To provide financial support to spouse To save tax Premium amount gives me adequate coverage Feel secure after buying adequate insurance Insurance is better than investment in stock market Premium instalments are affordable for me I will receive guaranteed fund value Insurance policy will grant loan facility Flexible investment option plans are risky Reputation and loyalty Ambience and experience Comfort and promptness Quality of services offered Hassel free paper work and documentation Presentation, appearance and surroundings Clarity of contract and terms in document SMS/Reminders about premium payment Type of insurance plan Risk coverage Premium or cost of coverage Variety and associated range of products Tax benefits Payment option (mode of payment) Product flexibility (surrender, loan, revival) Maturity period and grace period Agent provides error free services Committed to fulfill promises timely Perform the service right in first instance Provides accuracy (such as payment record) Providing satisfactory services. Prompt, responsive and reliable. Cooperative and friendly. Known and trustworthy. The State financial policy and interest rates Novelty products on the insurance market. Details of insurance terms and conditions. Legal aspects of the policy I consider. 286

Selection criteria on the basis of Recommendations (buying decision) Source of Information Purpose of buying Feelings and attitude Service attribute Product attributes Agents attributes Other factors Word of mouth SMS/Reminder alerts about new products Growth and benefits Properly remind about the due premium. Bankers Information brochures, leaflets and letters Explain features, advantages and benefits of the policy Promotional telephone call/sms Application of latest technology in providing services Memorable advertisement Thoroughness of follow up on questions/ enquiries/ requests prior to purchase decision Attire of the agent is acceptable Attitude of agent towards policyholders is good Behaviour of agent is good with policyholders Agent have enough past experience in the field Attention focused on your priorities Awareness about terms and conditions of policy. 287

5.2.1 Analyzing the Construct Validity After ascertaining the content validity the next issue is to analyze the validity of each individual construct. The construct validity consists of convergent validity, discriminant validity and face validity. The convergent validity can be tested with help of factor loadings of each individual variable to the construct. The high Factor loadings indicate convergent validity and since high factor loadings indicate that the variable is highly explained by the construct, hence it will not be explained by any other construct which indicates the presence of discriminant validity. In describing a construct three types of variables were used in this structural modeling. Manifest variable (Observed behavior, usually dependent) Latent Variable (Unobserved behavior, explanatory) Residual Variable (Unobserved behavior, unexplained) The description of various constructs, the set of variables in each construct and their factor loadings are shown as below:- 5.2.2 Factors Influencing Policyholders in Selecting the Insurance Policy (Selection Criteria) The first construct defined as the factors influencing policyholders in selecting the insurance policy along with the set of variables are shown below in figure 5.1. The first construct consists of seven manifest, seven residual and one latent variable. The regression weights of each variable as result of the construct are shown in table 5.88. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.88. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the variations in the construct. The results as shown in table 5.88 indicate that the agent of the insurance company is the most influencing 288

criteria for the policyholder of the insurance policy followed by the friends and family members. The agents being the most informed source has maximum influence on the policyholders as compared to other sources especially in rural segment. The advertisements of the insurance companies also influence the policyholders in deciding the insurance policy. The squared multiple correlation of insurance agent indicates that the 67 percent of the variance of the impact of insurance agent that can be explained with the help of the selection criteria. The fit of the model is shown in table 5.89. The results indicate that the model is fit. TABLE 5.88: Regression Weights Selection Criteria Suggestion of Buying the Insurance Nobody influenced me, it was my own decision. My employer s suggestion. Recommended by family member My Friend s suggestion Insurance agent s suggestion. My spouse s suggestion. Recommended during advertisement Estimate Standardised Regression Weight Squared Multiple Correlation 1.000.221.049 S.E. C.R. P 1.051.312.098.194 5.41 *** 2.632.755.570.409 6.44 *** 2.585.742.551.402 6.43 *** 3.289.819.672.508 6.47 *** 1.950.411.169.332 5.87 *** 2.586.583.339.413 6.26 *** TABLE 5.89: Model Fit Selection Criteria Model Fit Statistic Chi-square 632.485 CFI.725 NFI.722 RFI.583 RMSEA.210 LO 90.196 HI 90.224 289

The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.89. FIGURE: 5.1 5.2.3 Sources of Information Influencing Policyholders in Selecting the Insurance Policy In order to increase the awareness about the importance of insurance policies among the investors, the insurance companies uses different sources to pass on the necessary 290

information to their prospective policyholders. The various source of information may be TV, newspapers, agents, phone calls, internet, emails, mobile SMS, print media etc. The second construct represents the impact of various sources of information in influencing the policyholders in selecting the insurance policy along with the set of variables. The construct consists of eleven manifest, eleven residual and one latent variables are shown below in fig. 5.2. People have easy access to news papers and variety of other sources of communication due to which policyholders are exposed to new products, opinions and advertisements. In the present study the importance of various sources of communication was analysed. The regression weights of each variable as result of the construct are shown in table 5.90. As shown in the table all the regression weights are high (more than 0.5) and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.90.The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. It is found from the results that the most influential source of information for policyholders was insurance agent and friends. This is due to the fact that still today the policyholders from the rural background do not have the enough awareness about the websites and internet. The insurance agents in most of the rural areas are actually the persons who commands good position in the society and can influence the policyholders in deciding and buying the insurance policies. The office workplace notifications/ circulars also influence the policyholders in selecting the insurance policy especially for the service class policyholders. The squared multiple correlations of insurance agent and friends indicate that the 81 percent of the variance of the impact of insurance agent and friends that can be explained with the help of the selection criteria. The fit of the model is shown in table 5.91. The results indicate that the construct is fit. 291

TABLE 5.90: Regression Weights Sources of Information Sources of Information News paper /magazines Estimate Standardised Regression Weight Squared Multiple Correlation 1.000.291.085 S.E. C.R. P Television 1.575.426.181.202 7.804 *** Internet /E-mails -.218 -.073.005.100-2.175.030 Agent 5.149.903.816.560 9.195 *** Office/Workplace Circular/Notices 3.137.706.498.353 8.890 *** Spouse/children 1.159.276.076.182 6.378 *** Friends 4.860.903.816.529 9.195 *** Insurance Experts/advisors 2.633.570.325.309 8.512 *** Word of mouth 5.955.862.743.651 9.148 *** Bankers.089.026.001.114.777.437 Promotional telephone call/sms 1.898.580.336.222 8.546 *** TABLE 5.91: Model Fit Sources of Information Model Fit Statistic Chi-square 3372.77 CFI.538 NFI.535 RFI.419 RMSEA.275 LO 90.267 HI 90.283 The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.91. 292

FIGURE: 5.2 5.2.4 Purpose of Buying the Insurance Policy The policyholder buy the product or a service in order to satisfy some need or wants. The insurance policy is a service which actually covers the risk of loss due to some unwanted happenings with the person insured. The insurance policies also help the persons in saving their income tax and provide them lump sum money at the time of maturity of the policy so that the long term liabilities can be fulfilled with that money. The third construct is defined as the purpose of buying the insurance policy consists of eight manifest, eight residual and one latent variable. The third construct represents the perception of the policyholders about the different purpose of buying the insurance policy. The construct along with the set of variables are shown below in fig. 5.3. The regression weights of each variable as result of the construct are shown in table 5.92. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.92.The standardizes regression weights indicates comparative influence of the construct to its variables. The high 293

value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. The results indicate that the most important purpose of buying insurance policy is to provide death protection for family members in case of any untoward incident as well as the saving of the income tax. The results also indicate that another important purpose of buying insurance to provided once self some extra money in case of emergency (illness, accident). It can be concluded from the results that the purpose to cover the risk of life and to save the family members from the financial loss due to unwanted events in the life is the main purpose to buy the insurance policies. For service class policyholders the saving of income tax is another main reason to buy the insurance policy. TABLE 5.92: Regression Weights Purpose of Buying Purpose of Buying Estimate Standardised Regression Weight Squared Multiple Correlation S.E. C.R. P To provide myself with some extra money at the time of my retirement. To provide my dear ones with some extra money at the time of my retirement. To provide myself with some extra money in case of emergency (illness, accident). To avoid incurring unnecessary costs of insurance in future To invest/save money to maintain same life style over years To provide death protection for family members in case of any untoward incident To provide financial support to spouse 1.000.619.383.796.406.165.069 11.557 *** 1.883.814.663.093 20.263 ***.464.239.057.066 7.021 ***.377.138.019.092 4.106 *** 2.265.884.781.107 21.235 ***.710.363.132.068 10.430 *** To save tax 1.998.837.701.097 20.624 *** 294

TABLE 5.93: Model Fit Purpose of Buying Model Fit Statistic Chi-square 1601.101 CFI.602 NFI.600 RFI.439 RMSEA.281 LO 90.270 HI 90.292 The squared multiple correlation of death protection, indicates that the 78 percent of the variance of the impact of insurance agent and friends can be explained with the help of the selection criteria. The statistics for goodness of fit of the model is shown in table 5.93. The results indicate that the model is fit. The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.93. FIGURE: 5.3 295

5.2.5 Buying Experience of the Policyholders Every policyholder is having the experience (good or bad) with the product or a service after buying it. To analyse the buying experience of the policyholder after the purchase of product is one of the purpose of the companies. Hence in the study the policyholders were asked to provide their perceptions regarding various aspects of their experience related with the insurance policy after buying it. The fourth construct is defined as the buying experience consists of 15 variable; seven manifest, seven residual and one latent variable. The fourth construct represents the factors related with various aspects of the buying experience of policyholders consists of seven manifest, seven residual and one latent variable. The construct along with the set of variables are shown below in figure 5.4. The regression weights (unstandardised and standardized) of each variable as result of the construct are shown in table 5.94. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.94.The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. The results indicate that the policyholder found that insurance is better than investing in stock market and also consider flexible investment plans to be risky. The rural policyholders with most of the urban policyholders may be risk averse and avoids investments in stocks and instruments related to stocks. When they compare the insurance policies with these instruments, they found it safe and feel better with the insurance policies. It can be concluded that although most of the insurance policies are not the investment products (except the ULIP or stock market related policies) but the policyholders have a tendency to compare then with the other investment plans and found it safe to put their money in insurance policies and feel safe. The squared multiple correlation of insurance is better than investment in stock market and flexible investment plans indicates that the 61 percent of the variance can be explained with 296

the help of buying experience. The fit of the model is shown in table 5.95. The results indicate that the model is fit. TABLE 5.94: Regression Weights Buying Experience Buying Experience Premium amount gives me adequate coverage I feel secure after buying adequate insurance Insurance is better than investment in stock market Premium installments affordable for me I will receive guaranteed fund value Insurance policy will grant loan facility Flexible investment option plans are risky Estimate Standardise d Regression Weight Squared Multiple Correlation 1.000.657.431 S.E. C.R. P 1.043.779.607.051 20.51 *** 1.219.781.610.059 20.54 ***.731.484.235.053 13.678 *** -.488 -.251.063.067-7.314 *** 1.309.728.530.067 19.460 *** 1.449.784.615.070 20.612 *** TABLE 5.95: Model Fit Buying Experience Model Fit Statistic Chi-square 662.805 CFI.787 NFI.784 RFI.676 RMSEA.215 LO 90.202 HI 90.230 The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.95. 297

FIGURE: 5.4 5.2.6 Service Attributes Influencing Policyholders in Selecting the Insurance Policy The services associated with the products increases the perceptive quality in the mind of policyholders. Their buying behavior and level of satisfaction also depends with the various attributes of services rendered by the company, agents, regulatory bodies etc. The fifth construct named as the service attributes influencing policyholders in selecting the insurance policy along with the set of variables are shown below in figure 5.5. The regression weights of each variable as result of the construct are shown in table 5.96 The fifth construct represents the various aspects of the service attributes and consists of twelve manifest, twelve residual and one latent variable. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple 298

squared correlations are shown in table 5.96.The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. The results indicate that the quality of services offered to the policyholders and reputation of the insurance company is the most influencing criteria for the policyholder of the insurance policy followed comfort and promptness provided to the policyholder. Ambience and experience of service provider also influence the policyholders in deciding the insurance policy. The squared multiple correlation of quality of services of insurance company indicates that the 90 percent of the variance of the impact quality of services can be explained with the help of the services attributes. The fit of the model is shown in table 5.97. The results indicate that the model is fit. Service attributes TABLE 5.96 Regression Weights Service Attributes Estimate Standardised Regression Weight Squared Multiple Correlation Reputation and loyalty 1.000.907.823 S.E C.R. P Ambience and experience.677.869.755.016 41.972 *** Comfort and promptness.833.894.799.018 45.044 *** Quality of services offered.897.952.907.017 54.066 *** Hassel free paper work and documentation Presentation, appearance and surroundings Clarity of contract and terms in document SMS/Reminders about premium payment SMS/Reminder alerts about new products Information brochures, leaflets and letters Application of latest technology in providing services Company is having memorable advertisement.601.792.627.018 34.333 ***.241.022 10.934 ***.669.787.620.020 33.959 ***.705.683.466.027 26.537 ***.400.466.218.025 15.882 ***.297.375.141.024 12.345 ***.391.484.235.023 16.619 ***.175.240.058.023 7.631 *** 299

TABLE 5.97: Model Fit Service Attributes Model Fit Statistic Chi-square 3708.754 CFI.664 NFI.661 RFI.585 RMSEA.260 LO 90.253 HI 90.257 The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.97. FIGURE: 5.5 300

5.2.7 Product Attributes Influencing Policyholders in Selecting the Insurance Policy Insurance companies are offering various products with tremendous benefits to satisfy the policyholders. These insurance products are developed after immense efforts, research, financial analysis and policyholder involvement. Therefore before conducting any parallel study it is important to describe product attributes. The sixth construct named as the product attributes influencing policyholders in selecting the insurance policy along with the set of variables are shown below in figure 5.7. The sixth construct represents the various aspects of the product attributes and consists of nine manifest, nine residual and one latent variable. The regression weights of each variable as result of the construct are shown in table 5.98. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.98. The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. The results indicate that the tax benefits given to the policy holders is the most influencing criteria for the policyholder of the insurance policy. The squared multiple correlation of tax benefits given to the policy holders indicates that the 73 percent of the variance of the impact of tax benefits that can be explained with the help of the product attribute. The fit of the model is shown in table 5.99. The results indicate that the model is fit. The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.99. 301

Table 5.98: Regression Weights Product Attributes Product Attributes Estimate Standardised Regression Weight Squared Multiple Correlation S.E. C.R. P Type of insurance plan (pension, growth, term) 1.000.018.493 Risk coverage 47.944.837.360 87.639.547.584 Premium or cost of coverage 35.941.663.271 65.708.547.584 Variety and associated range of products 28.167.469.645 51.515.547.585 Tax benefits 46.691.857.735 85.348.547.584 Payment option (mode of payment) 43.430.803.220 79.390.547.584 Product flexibility (surrender, loan, revival) 29.191.521.440 53.380.547.584 Maturity period and grace period 35.948.600.701 65.725.547.584 Growth and benefits 40.653.702.000 74.319.547.584 TABLE 5.99 Model Fit Product Attributes Model Fit Statistic Chi-square 1677.688 CFI.677 NFI.674 RFI.565 RMSEA.247 LO 90.237 HI 90.258 302

FIGURE: 5.6 5.2.8 The Agents Attributes Influencing Policyholders in Selecting the Insurance Policy The operations in insurance industry are influenced by strong agent s network. Most of the insurance companies are running on the shoulders of insurance agents and marketing of insurance, increasing base, providing policyholder satisfaction, retaining policyholders and customer relationship management is the whole sole responsibility of the agents. The strong network of agents and insurance advisors are significantly contributing for the development and marketing of insurance products. The seventh construct named as the factors influencing policyholders in selecting the insurance policy along with the set of variables are shown below in figure 5.7. The fifth construct represents the various aspects of the agents attributes and consists of seventeen manifest, seventeen residual and one latent variable. The regression weights of each variable as result of the construct are shown in table 5.100. As shown in the table all the regression weights are high and significant. Hence 303

the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.100. The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. Agent s Attributed Agent provides error free services Committed to fulfill promises timely Perform the service right in first instance Provides accuracy (such as payment record) TABLE 5.100 Regression Weights Agents Attributes Estimate 1.000 1.396 1.268 1.753 Standardised Regression Weight Squared Multiple Correlation.723.523.829.687.745.555.918.842 S.E. C.R. P.052 26.792 ***.053 23.929 ***.059 29.878 *** Providing satisfactory services. 1.712.890.792.059 28.898 *** Prompt, responsive and reliable. 1.659.885.784.058 28.741 *** Cooperative and friendly. 1.901.923.852.063 30.060 *** Known and trustworthy. 1.857.922.850.062 30.018 *** Properly remind about the due premium. Explain features, advantages and benefits of policy Thoroughness of follow up on questions/ enquiries/ requests prior to purchase decision 1.791 1.843 1.734.872.761.910.827.937.878.063 28.297 ***.062 29.593 ***.057 30.555 *** Attire of the agent is acceptable 1.087.712.507.048 22.839 *** Attitude of agent towards policyholders is good Behaviour of agent is good with policyholders Agent have enough past experience in the field Attention focused on your priorities Awareness about terms and conditions of policy. 2.009 1.927 1.444 1.548 1.648.927.860.926.857.797.636.797.635.855.731.066 30.217 ***.064 30.169 ***.056 25.714 ***.060 25.701 ***.060 27.685 *** 304

TABLE 5.101: Model Fit Statistics Agents Attributes Model Fit Statistic Chi-square 3220.386 CFI.869 NFI.841 RFI.864 RMSEA.162 LO 90.157 HI 90.166 The results indicate that tthoroughness of follow up on questions/ enquiries/ requests prior to purchase decision of the agent is the most influencing criteria for the policyholder of the insurance policy followed by the positive attitude of agent towards policyholders. Several other attributes also influence the policyholders of insurance policy such as good behaviour of agent, his extended cooperation and help to the policyholders, trustworthiness, accuracy in record keeping and explanation given to the policyholder. The squared multiple correlation of tthoroughness of agent and follow up on questions/ enquiries/ requests prior to purchase decision indicates that the 87 percent of the variance of the impact of insurance agent on buying that can be explained with the help of the agents attributes. The fit of the model is shown in table 5.101. The results indicate that the model is fit. The Chi-square value is presented in the matrices. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.101. 305

FIGURE: 5.7 5.2.9 Other Attributes Influencing Policyholders in Selecting the Insurance Policy Financial practices, insurance regulations, saving pattern, tax benefits and certain other factors also influence a policyholder while finalizing an insurance plan. Therefore it is important to understand insurance market and various other factors that may influence a policyholder. The eight construct named as the other factors influencing policyholders in selecting the insurance policy along with the set of variables are shown below in figure 5.8. The fifth construct represents the other attributes influencing buying decisions consists of four manifest, four residual and one latent variable. The regression weights of each variable as result of the construct are shown in table 5.102. As shown in the table all the regression weights are high and significant. Hence the construct validity is ensured and can be concluded that the construct significantly explains the variables. The standardized regression weights as well as the multiple squared correlations are shown in table 5.102.The standardizes regression weights indicates comparative influence of the construct to its variables. The high value of the 306

standardized weights indicates the higher influence of the construct to the variable. The squared multiple correlations indicate the percentage of variance of the measured variable that can be explained with the help of the construct. The results indicate that the agent of the insurance company is the most influencing criteria for the policyholder of the insurance policy is the State Financial Policy and interest rates followed by novelty of products in the insurance market. The squared multiple correlation of State Financial Policy and interest rates indicates that the 70 percent of the variance of the impact of State Financial Policy and interest rates that can be explained with the help of the other influential factors. The fit of the model is shown in table 5.103. The results indicate that the model is fit. TABLE 5.102 Regression Weights Other Attributes Other Attributes Estimate Standardised Regression Weight Squared Multiple Correlation S.E. C.R. P The State financial policy and interest rates Novelty products on the insurance market. Details of insurance terms and conditions. Legal aspects of the policy I consider. 1.000.837.700 1.022.832.692.040 25.269 ***.907.740.547.038 23.556 ***.374.233.054.054 6.884 *** TABLE 5.103 Model Fit Other Attributes Model Fit Statistic Chi-square 222.613 CFI.855 NFI.854 RFI.563 RMSEA.322 LO 90.296 HI 90.370 The Chi-square value is presented in the table 5.103. The RMSEA value indicates the 307

amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown. FIGURE: 5.8 CONSTRUCT MODEL - 1 The policyholders until and unless realize the need or a want, cannot go for actual buying the product. The policyholder if aware tries to get the information about the product as much as possible. For a policyholder there are various sources of getting information. These are friends, family members, employers, agents, advisors, relatives etc. These persons not only provide the information to the policyholder but also influence the buying behavior of the policyholder. In addition to this the insurance companies also take the help of many media for passing the information to the policyholders. These sources include TV, internet, websites, phone calls, emails, 308

agents etc. Hence it can be said that the policyholder is having influence from his possible contacts as well as the sources of information used by the companies. Both will increase the level of awareness in the mind of policyholders and make him felt the need of having the insurance policy. The policyholder becomes aware about the nature, variety and the advantage of the product. Hence the policyholder at this stage has the purpose of buying the insurance policies. After buying the policy the policyholder have after sales experience with the product which influences from the purpose of buying the product. The above explained theory was tested using structural equation modeling using the software AMOS. The degree of freedom was 100 and probability level was less than 0.05. The model along with the required out is shown in table 5.104. The number of the variables in this model is provided below: TABLE 5.104: Summary of Model 1 Number of variables in model: 38 Number of observed variables: 16 Number of unobserved variables: 22 Number of exogenous variables: 20 Number of endogenous variables: 18 The results indicates that the suggestions from agents, friends etc have significant impact on the purpose of buying. Although the different sources of information used by the company is having negative impact of the purpose of buying. The purpose of buying significantly influences the buying experiences of the policyholder with the product. The squared multiple correlation of 0.804 indicates that 80 percent of the variations in the buying experience can be explained by the variations in the purpose of buying. The results also indicate the positive correlations of 0.591 between the suggestions and the different sources of information. The goodness of fit indices Are sown in table indicates that the tested structural model is fit. 309

TABLE 5.105: Regression Weights Model- 1 Name of the Variable Estimate S.E. C.R. P Purpose of Buying Suggestions.795.053 14.953 *** Purpose of Buying Buying Experience Source of Information Purpose of Buying -.823.036-22.820 ***.481.019 25.490 *** TABLE 5.106: Standardized Regression Weights Model- 1 Name of the Variable Standardised Regression Weight Squared Multiple Correlation Purpose of Buying Suggestions.623 -- Purpose of Buying Source of Information -.911 -- Buying Experience Purpose of Buying.896.804 TABLE 5.107: Correlations Model- 1 Name of the Variable Estimate Sourse of Information <--> Suggestions.591 TABLE: 5.108 Model Fit Statistics Model- 1 Model Fit Statistic Chi-square 2532.800 CFI.722 NFI.795 RFI.754 RMSEA.156 LO 90.151 HI 90.161 310

The Chi-square value is presented in the table 5.116. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table. The goodness of fit indices (CFI, NFI and RFI are greater than 0.7) indicates the fitness of the model also the badness of fit the RMSEA, LO 90 and HI 90 found to be very low hence it can be concluded that the product and the agents attributes significantly influences the purpose of buying which ultimately influence the buying behavior of the policyholder. FIGURE: 5.9 CONSTRUCT MODEL-1 311

CONSTRUCT MODEL-2 The product is a combination of different associated attributes related to the service, agent and other factors. These all attributes are supposed to have significant influence on the purpose of buying the insurance policy. As studied in the theory of marketing the intangible services related to the product are the marketing tools in the hands of companies influence the buying behavior of the policyholders. The efficient use of these tools which comes under 3Ps of marketing of services can increase the demand and hence the sales of insurance products in the market. After buying the insurance product the experience of the policyholder with the insurance policy is supposed to be influence the purpose of buying. When the performance of the product matches with the expectations of the policyholders, while buying the product the policyholder will feel satisfied otherwise dissatisfaction cause in the mind of policyholders. The above explained theory was tested applying structural equation modeling using the software AMOS. The degree of freedom was 241 and probability level was less than 0.05. The model along with the required out is shown in table 5.109. The number of the variables in this model is provided below: TABLE 5.109: Summary of Model 2 Number of variables in your model: 56 Number of observed variables: 24 Number of unobserved variables: 32 Number of exogenous variables: 30 Number of endogenous variables: 26 The above proposed theory was tested by applying the structural equation modeling assuming that the different attributes associated in the buying process with the product influences the buying behavior of the policyholder which also results into the structural equation model is shown form table no. 5.109 to 5.114. The structural model is also shown in figure 5.10. The results indicate that the product attributes having the most significant impact in explaining the purpose of buying the insurance 312

policy followed by the agents attributes. The results also indicate that service attributes as well as other attributes are not significantly influenced the purpose of buying the insurance policy. The purpose of buying has a significant impact on the buying experience as shown by the standardized regression weight 0.896. The goodness of fit indices (CFI, NFI and RFI are greater than 0.7) indicates the fitness of the model also the badness of fit the RMSEA, LO 90 and HI 90 found to be very low hence it can be concluded that the product and the agents attributes significantly influences the purpose of buying which ultimately influence the buying behavior of the policyholder. TABLE 5.110: Regression Weights Model- 2 Name of the Variable Estimate S.E. C.R. P Purpose Service.094.059 1.591.112 Purpose Product 1.240.161 7.716 *** Purpose Agent.246.059 4.192 *** Purpose Others.072.034 2.150.032 Buying Experience Purpose.848.032 26.136 *** TABLE 5.111: Standardized Regression Weights Model- 2 Endogenous variable Exogenous variable Estimate Purpose Service.130 Purpose Product.457 Purpose Agent.276 Purpose Others.056.653 Buying Experience Purpose.896.803 313

TABLE 5.112: Correlations Model- 2 Name of the variable Estimate Agent Product.681 Agent Others.249 Service Product.762 Agent Service.912 Service Others.276 Product Others.156 TABLE 5.113: Model Fit Statistics Model- 2 Model Fit Statistic Chi-square 5714.785 CFI.773 NFI.766 RFI.732 RMSEA.151 LO 90.147 HI 90.154 The Chi-square value is presented in the table 5.113. The RMSEA value indicates the amount of unexplained variance or residual is large than 0.06 or less critical. CFI and NFI value are not in complete agreement but are very close to the criteria (0.90 or larger) for acceptable model. The model fit statistics from AMOS output is shown in the table 5.113. TABLE 5.114: Comparisons of Models Model Fit Model 1 Model 2 Chi-square 2532.800 5714.785 P.000.000 CFI.722.773 NFI.795.766 RFI.754.732 RMSEA.156.151 LO 90.151.147 HI 90.161.154 314

Since the CFI, NFI, RFI and RMSEA values are more closed to acceptable criteria in case of Model 2. It is concluded that Model 2 is explaining the constructs (higher CFI value and low RMSEA, LO 90 and HI90 as compared to model -1) in best possible manner as compared to model-1. FIGURE: 5.10 CONSTRUCT MODEL-2 5.3 MEAN SCORE AND STANDARD DEVIATIONS OF DIFFERENT ASPECTS OF BUYING BEHAVIOR OF POLICYHOLDERS IN CASE OF INSURANCE PRODUCTS TABLE 5.115: Mean Values Selection Criteria Selection Criteria Mean Std. Deviation Nobody Influenced me, it was my own decision 3.97 1.146 My Employer's suggestion 3.09 1.017 Recommended by family member 3.75 0.884 My Friend's Suggestion 3.53 0.882 Insurance agent's Suggestion 4.12 0.852 My Spouse's Suggestion 3.69 1.204 Recommended during advertisement 2.75 1.125 315

The table 5.115 shows mean values of the factors related to selection criteria used by the sample policyholders for buying insurance plan. Insurance agent plays crucial role while suggesting insurance policy to the policyholders with mean value 4.12. TABLE 5.116 Mean Values Source of Information Source of information Mean Std. Deviation Television 3.74 0.895 Internet/E-mails 3.44 1.118 Agent 4.44 0.843 Office/Workplace Circular/Notices 3.475 1.0754 Spouse/Children 3.86 1.017 Friends 3.68 0.792 Insurance Experts/Advice 4.45 0.721 Word of Mouth 2.92 1.672 Bankers 2.67 1.379 Promotional telephone call/sms 2.47 1.302 316

The table 5.116 shows mean values of the sources of information used by the sample policyholders for buying insurance plan. The role of insurance agent and insurance expert was found to be significant while suggesting insurance policy to the sample policyholders with their respective mean values 4.44 and 4.45. TABLE 5.117: Mean Values Purpose of Buying Source of information Mean Std. Deviation Extra money at the time of retirement 3.97 1.2 Some extra money at the time of my retirement to my dear ones 4.09 1.118 Extra money in case of emergency, illness, accident 4.11 1.083 To avoid incurring un necessary cost of insurance in future 2.9 0.911 To invest/ save money to maintain same life style over years 3.18 1.281 To provide death protection for family 4.62 0.757 To provide financial support to spouse 4.22 0.916 To save tax 4.21 0.918 317

The table 5.117 shows mean values of the basic purpose of buying insurance plan. It was observed that the policyholders buy insurance for various purposes such as to provide death protection for family, to provide financial support to spouse, to save tax, to provide themselves with extra money in case of emergency or illness and to provide extra money at the time of retirement. With mean value 4.62 death protections was considered to as the most basic purpose of buying insurance plan. TABLE 5.118 Mean Values Buying Experience Source of information Mean Std. Deviation Premium amount gives adequate coverage 3.96 0.873 Feel secure after buying adequate insurance 4.29 0.768 Insurance is better than investment in stock market 4.18 0.895 Premium installments are affordable for me 4.12 0.865 I will receive guaranteed fund value 2.87 1.116 Insurance policy will grant loan facility 3.9 1.031 Flexible investment option plans are risky 3.7 1.059 318

The table 5.118 shows mean values of the buying experience related to insurance policy. It was observed that the policyholders shown differed responses related to their buying experiences. With mean value 4.29 policyholders felt secure after buying insurance policy. With mean value 4.18 policyholders also felt that insurance is better than investment in stock market. TABLE 5119: Mean Value Service attributes Source of information Mean Std. Deviation Reputation and loyalty 3.8 1.357 Ambience and experience 3.8 1.16 Comfort and promptness 3.67 1.147 Quality of services offered 4.18 0.959 Hassle free paperwork and documentation 3.98 0.935 Presentation appearance and surroundings 3.44 0.883 Clarity of contract and terms in documents 3.94 1.046 SMS/Reminders about the premium installments 3.18 1.272 SMS/Reminders about new products 2.47 1.056 Information brochures, leaflets and letters 2.27 0.974 Application of latest technology in their services 2.53 0.993 Company is having memorable advertisement 2.65 0.897 319

The table 5.119 shows mean values of the influential service attributes while buying an insurance policy. With mean value 4.18 policyholders felt that quality of services offered was the most influential parameter followed hassle free paper work and documentation, clarity of contract, reputation and ambience. TABLE 5.120: Mean Value Product Attributes Product Attributes Mean Std. Deviation Type of insurance plan 3.46 0.711 Risk coverage 4.44 0.774 Premium or cost of coverage 4.49 0.7 Variety and associated range of products 4.06 0.776 Tax benefits 4.33 0.724 Payment option 4.46 0.699 Product flexibility 4.6 0.705 Maturity period and grace period 4.42 0.741 Growth benefits 4.34 0.748 320

The table 5.120 shows mean values of the influential product attributes related to insurance policy. With mean value 4.6 policyholders felt product flexibility was the most influential criteria followed by premium amount with mean value 4.49. TABLE 5.121: Mean Values Agents Attributes Agent Attribute Mean Std. Deviation Error free services 4.34 0.821 Committed to fulfil promises timely 3.98 1 Perform the service right in the first instance 3.75 1.011 Provides accuracy 3.94 1.134 Providing satisfactory services 3.98 1.142 Prompt responsive and reliable 3.77 1.113 Cooperative and friendly 3.96 1.223 Known and trustworthy 3.86 1.196 Properly remind about the due premium 3.83 1.219 Explain features advantages and benefits of the policy 3.86 1.203 Thoroughness of follow up of questions/ inquiries/request 3.86 1.099 Attire of agent 3.28 0.906 Attitude of agent towards policyholder 3.79 1.286 Behaviour of agent is good 3.86 1.236 Past experience 3.7 1.076 Attention focused on priorities 3.82 1.153 Awareness about terms and conditions of policy 3.9 1.145 321

The table 5.121 shows mean values of the influential agents attributes at the time of buying insurance policy. With mean value 4.34 policyholders felt error free services, agent s awareness about terms and conditions of policy (mean value.9) was the most influential parameters followed by agent s commitment. TABLE 5.122 Mean Values Other Attributes Other Factors Mean Std. Deviation Novelty products in insurance market 2.73 0.879 Details of insurance terms and conditions 3.77 1.147 Legal aspects of the policy 2.81 0.878 322

The table 5.122 shows mean values of the other influential factors while buying insurance policy. With mean value 3.77 the details of terms and conditions provided to the policyholder was the most influential parameters followed by legal aspects of the insurance contract. 5.4 POLICYHOLDER BUYING BEHAVIOR FOR INSURANCE POLICIES WITH RESPECT TO URBAN AND RURAL BACKGROUND Insurance as a product now becomes a necessarily for every policyholder irrespective of their different demographic, psychographic and geographic profiles. In the study the responses of different policyholders/buyer from different regions were collected through personal contact method with the help of self-designed questionnaire. The background of the policyholder in terms of region (urban and rural) supposed to be influences the buying behavior of the policy holder. In order to analyze the difference in the buying pattern and decision making process of the policyholder in case of insurance policy independent sample t-tests were applied. The inferences were drawn between urban and rural policyholders with respect to their buying pattern and decision process in different aspects of selection of insurance policies and buying experience. Here, the independent sample t-test was applied in order to test the significance of difference between policyholders from different regional background (Rural and Urban) in terms of various aspects of their attitude, information search and buying behavior with respect to insurance policies offered by different companies. The results of independent sample t-test are shown below from table no. 5.123 to 5.130. Hypothesis-1 H O : There is no significant difference between urban and rural policyholders in terms of suggestions received by policyholders for buying Insurance policies. H A : There is a significant difference between urban and rural policyholders in terms of suggestions received by policyholders for buying Insurance policies. 323

The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and decision of selecting insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and decision of selecting insurance policy The above mentioned hypothesis was tested and independent sample t-test was applied to draw the inferences between rural and urban policyholders. The independent sample t-test, in general, is used to analyze the difference between two independent samples in terms of interval or ratio variable. The rule of thumb in case of independent sample t-test is that, with 95% confidence level if the p-value is less than 0.05 or t statistic is more than 2, the null hypothesis was rejected in the favour of alternate hypothesis. The results of independent sample t-test to analyze the difference between rural and urban policyholder in terms of different factors influencing their decision for selecting the insurance policy is shown in table no 5.123.The result indicates that there exists a significant difference in the behavior of rural and urban policyholders since the p value in most of the cases was less than 0.05 (except in case of employer suggestion and recommendation by the family members). The results also indicate that rural policyholders take their decisions at their own consciousness followed by the suggestion from the insurances agents. Whereas in case of urban policyholders the most influential source of information were suggestions received from the agents, suggestion from spouse, family members and the friends. Hence it can be concluded from the results that urban policyholders select the insurance policy by consulting the decision with agent, friends, family members and spouse etc. the result also indicated that advertisement and suggestion from the employer were the least significant factors influencing the buying decisions. 324

TABLE 5.123: Independent Sample T-Test W.R.T. Different Factors Influencing The Policyholder s Decision Of Selecting Of The Insurance Policy. Rural Urban T- Statistic (P value) Remark Chisquare Remark Nobody Influenced me, it was my own decision 4.21 3.72 6.89 102.175 a Difference My Employer's suggestion 2.85 3.33-1.299 (0.194) No Difference 4.650 a Recommended by family member 3.70 3.80-1.899 (0.058) Border line difference 10.765 a My Friend's Suggestion 3.40 3.65-4.636 (0.000) Difference 34.984 a Insurance agent's Suggestion 4.08 4.15-7.676 (0.000) 102.962 a Difference My Spouse's Suggestion 3.50 3.87-4.889 (0.000) Difference 36.440 a Recommended during advertisement 2.58 2.92-4.717 (0.000) Difference 47.689 a Hypothesis-2 H O : The source of information has a significance influence on selection of policy and post-purchase behavior among rural and urban policyholders. H A : The source of information has a significance influence on selection of policy and post-purchase behavior among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and source of information for buying insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and source of information in selecting life insurance policy 325

The above mentioned hypothesis was tested and independent sample t-test was applied to draw the inferences between rural and urban policyholders. The independent sample t-test, in general used to analyze the difference between two independent samples in terms of interval or ratio variable. The rule of thumb in case of independent sample t-test is that, with 95% confidence level if the p-value is less than 0.05 or t statistic is more than 2, the null hypothesis was rejected in the favour of alternate hypothesis. The results of independent sample t-test to analyze the difference between rural and urban policyholder in terms of different factors influencing their decision of selecting the insurance policy is shown in table no 5.124. The result indicates that there exists a significant difference in the behavior of rural and urban policyholders since the p value in most of the cases was less than 0.05. The different sources of information about insurance policies add the level of awareness and importance to the policyholders. The necessity of insurance policies influences the buying decision of the policyholders. The companies in insurance sector take the help of different sources to provide the information about their insurance policies through different sources. The various sources of providing the information are the newspaper, magazine, advertisement through television, insurance experts, bankers, workplace circulars, spouse, promotional telephone calls, internet, word of mouth, insurance agent and friends. Therefore independent sample t-test was applied to analyze the difference of the importance of these sources between urban and rural policyholders. The results indicated that there was significant difference between urban and rural policyholders in terms of the role of different sources of information except in case of insurance agents and insurance experts and advisor. Hence the null hypothesis is rejected in the favour of alternate hypothesis. However in case of the role of insurance experts and advisors is found to be same for both urban and rural policyholders. The results indicated that the mean score of urban policyholders was significantly higher than rural policyholders and the most influencing sources of information for urban policyholders was insurance agents, experts and advisor. In case of rural policyholders the major sources of information were insurance agent and advisors. 326

TABLE 5.124 : T-Statistics Sources of Information Region Rural Urban T-Statistic Remark Chisquare Value Remark News paper /magazines 3.41 3.78-7.059 Difference 52.892 a Signific ant Television 3.60 3.88-5.114 Difference 42.614 a Signific ant Internet /E-mails 3.10 3.78-9.993 Difference.139 a Signific ant Agent 4.34 4.54-3.698 Difference 105.653 a Signific ant Office/Workplace Circular/Notices 3.16 3.78-9.482 Difference 96.826 a Signific ant Spouse/children 3.59 4.13-8.809 Difference 153.074 a Signific ant Friends 3.47 3.89-8.821 Difference 81.039 a Signific ant Insurance Experts/advisors 4.45 4.44.263 (.793) Border line difference 104.718 a Signific ant Word of mouth 2.41 3.43-10.078 Difference 101.586 a Signific ant Bankers 2.34 3.00-7.789 Difference 34.903 a Signific ant Promotional telephone call/sms 2.14 2.80-8.283 Difference 92.250 a Signific ant 327

Hypothesis-3 H O : The purpose of buying insurance policy is not different among rural and urban policyholders. H A: The purpose of buying insurance policy is different among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and purpose of buying insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and purpose of buying life insurance policy Every product or a service has a purpose of satisfying some need or want of the policyholder. If a policyholder buys the insurance policy, this is because he wants to fulfill his need or some requirement. It is also possible that the purposes of buying insurance policy for different policyholders are different. The efforts were made in the study to analyze the importance of various identified purpose of buying insurance policy for rural and urban policyholders. The independent sample t-test was applied to test the null hypothesis of no difference between rural and urban policyholders in terms of their purpose of buying the insurance policy. The mean score and t-statistics of independent sample t-test is shown in the table 5.125. Hence the null hypothesis is rejected in the favour of alternate hypothesis. The results indicates that there exists significant difference between rural and urban policyholder except in case of providing extra money at the time of their retirement 328

(p value = 0.462). In case of rural policyholders the basic purpose of buying insurance policy is to provide death protection to their family members, availability of extra money in case of emergency and to have financial support to family members in future whereas in case of urban policyholders the purpose is slightly different. The urban policyholders buy insurance policy to save tax, to provide extra money against unwanted happenings and to provide financial support to family members in future. TABLE 5.125 : T-Statistics Purpose of Buying the Insurance Policy. Purpose of buying the Insurance policy. To provide myself with some extra money at the time of my retirement. To provide my dear ones with some extra money at the time of my retirement. To provide myself with some extra money in case of emergency (illness, accident). To avoid incurring unnecessary costs of insurance in future To invest/save money to maintain same life style over years To provide death protection for family members in case of any untoward incident To provide financial support to spouse Rural Region Urban 4.08 3.86 4.11 4.06 4.27 3.95 2.72 3.07 2.83 3.54 4.56 4.67 4.11 4.33 To save tax 3.99 4.44 T- Statistic 2.856 (.004).735 (.462) 4.722-6.077-9.092-2.260 (.024) -3.752-7.951 P value Border line difference Border line difference Difference Difference Difference Border line difference Difference Difference Chisquare Value 61.549 a 61.549 a 26.040 a 47.354 a 102.042 a 39.517 a 67.654 a 21.806 a Remark 329

Hypothesis-4 H O : The buying experience of insurance policy is not different among rural and urban policyholders. H A: The buying experience of insurance policy is different among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and buying experience is rejected. Hence it is inferred that there is a significant association between rural and urban region and buying experience. After buying the insurance policy different policyholders have different buying experiences from their insurance policies. The independent sample t- test was applied to test the null hypothesis between rural and urban and policyholders in term of their buying experience after buying their insurance policies. The results of independent sample t-test along with their individual mean scores are shown in table 330

5.126. The result indicates that there exists no significant difference in terms of adequate coverage by the premium, feeling of security and affordable premium installments for both rural and urban policyholders. Hence the alternate hypothesis is rejected in the favour of null hypothesis. The results also indicates that there exists significant difference between rural and urban policyholders in terms of their perceptions of insurance policies as an investment, receiving guaranteed fund values, loan facility available with insurance policy and the comparison of insurance policies with other investment plans. The urban policyholder feel adequately secured after buying insurance policy and consider insurance policy better than other investment plans whereas the rural policyholder is not aware of other investments plans and consider it much safer and feel adequately safe with affordable premium installments. TABLE 5.126 : T-Statistics Region Rural Urban T- Statistic P value Chisquare Value Remark Premium amount gives me adequate coverage 3.95 3.98 -.543 (.587) Border line difference 4.482 a (.345) Not significant I feel secure after buying adequate insurance 4.3 4.28.494 (.621) Border line difference 8.922 a (.063) Not significant Insurance is better than investment in stock market 4.23 4.12 2.052 (.040) Border line difference 30.036 a Premium instalments are affordable for me 4.09 4.16-1.280 (.201) Border line difference 10.618 a (.031) I will receive guaranteed fund value 2.75 2.98-3.215 (.001) Border line difference 45.733 a Insurance policy will grant loan facility 4.12 3.69 6.704 Difference 63.644 a Flexible investment option plans are risky 3.93 3.48 6.840 Difference 57.940 a 331

Hypothesis-5 H O : The service attributes influencing selection of insurance policy are not different among rural and urban policyholders. H A: The service attributes influencing selection of insurance policy are different among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and service attributes influencing selection of insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and service attributes influencing selection of insurance policy. The service attributes of insurance agent, insurance company also play very important role in selection process of insurance product by the policyholder in order to analyze 332

the relative importance of these service attributes with respect to the rural and urban policyholders. Independent sample t-test was applied the results of independent sample t-tests were shown in the table 5.127. The result indicates there exists significant difference between rural and urban policyholders in term of their perception about various service attributes between rural and urban policyholders the results indicate that for a rural policyholder the most influencing service attributes are the quality of service offered, clarity of contract and term in documents, hassle free paper work and proper documentation, reputation and loyalty and ambience and experience whereas urban policyholders is more concerned about the quality of services offered, reputation and loyalty and ambience and experience. Hence the null hypothesis is rejected in the favour of alternate hypothesis. 333

TABLE 5.127: T-Statistics Service Attributes Region Rural Urban T- Statistic P value Chisquare Value Remark Reputation and loyalty 4.05 3.55 5.973 Difference 85.688 a Ambience and experience 4.02 3.58 6.164 Difference 66.925 a Comfort and promptness 3.84 3.49 4.794 Difference 115.518 a Quality of services offered 4.36 4 6.042 Difference 131.005 a Hassel free paper work and documentation 4.07 3.88 3.262 (.001) Border line difference 60.036 a Presentation, appearance and surroundings 3.42 3.47 -.788 (.431) Border line difference 42.644 a Clarity of contract and terms in document 4.13 3.76 5.712 Difference 64.532 a SMS/Reminders about premium payment 3.4 2.97 5.370 Difference 52.813 a SMS/Reminder alerts about new products 2.59 2.35 3.525 Difference 31.048 a Information brochures, leaflets and letters 2.39 2.15 3.823 Difference 29.866 a Application of latest technology in providing services 2.66 2.4 4.141 Difference 29.764 a Company is having memorable advertisement 2.73 2.57 2.938 (.003) Border line difference 23.628 a 334

Hypothesis-6 H O : The products attributes influencing selection of insurance policy are not different among rural and urban policyholders. H A: The products attributes influencing selection of insurance policy are different among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. 335

As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and products attributes influencing selection of insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and products attributes influencing selection of insurance policy. It was found that there the association between rural or urban region and growth and benefit is insignificant. The product attributes of types of insurance policy, benefits provided by insurance company also play very important role in selection process of insurance product by the policyholder in order to analyse the relative importance of these product attributes with respect to the rural and urban policyholders. Independent sample t-test was applied the results of independent sample t-tests were shown in the table 5.128. The results indicate that the product attributes play most important role in selection of insurance policy for both rural and urban policyholders as the mean score of different aspect of product attribute is more than 4 in the scale of 1-5.No statistical difference is found in between rural and urban policyholders in case of type of insurance plan, premium, product flexibility, grace period since these factors are same for both the type of policyholders, whereas significant difference between rural and urban policyholder is found in case of risk coverage, variety in the products, tax benefits, payment options and growth and benefits. Hence the alternate hypothesis is rejected in the favour of null hypothesis. The result indicates that the behavior of rural and urban is different in case of the qualitative parameters associated with the insurance policies such as risk coverage, variety of products and growth and benefits from insurance policy in case of tax benefits there is also significant difference is found between rural and urban policyholders. For a rural policyholder the most influencing product attribute is found to be flexibility in the product, risk coverage, payment option and cost of payment whereas for urban policyholders the most important policyholder attributes is found to be product flexibility, premium payment option and tax benefits. 336

TABLE 5.128: T-Statistics Product Attributes Region Rural Urban T- Statistic P value Chisquare Value Remark Type of insurance plan (pension, growth, term) 3.44 3.48-1.024 (.306) Border line difference 20.155 a Risk coverage 4.55 4.33 4.538 Difference 33.062 a Premium or cost of coverage 4.51 4.46 1.084 (.279) Border line difference 15.660 a (.001) Variety and associated range of products 3.95 4.17-4.569 Difference 25.378 a Tax benefits 4.24 4.41-3.559 Difference 10.225 a (.037) Payment option (mode of payment) 4.51 4.41 2.265 (.024) Border line difference 25.934 a Product flexibility (surrender, loan, revival) 4.59 4.61 -.628 (.0530) Border line difference 22.180 a Maturity period and grace period 4.47 4.38 1.966 (.050) Border line difference 59.153 a Growth and benefits 4.38 4.29 2.032 (.042) Border line difference 5.877 a (.118) Not significant 337

Hypothesis-6 H O : The agent s attributes influencing selection of insurance policy are not different among rural and urban policyholders. H A: The agent s attributes influencing selection of insurance policy are different among rural and urban policyholders. The above mentioned hypothesis was tested and chi-square test was applied for understanding association between variables. As shown in the table the p value of the chi-square statistics is less than.05 hence the null hypothesis that there is no association between rural and urban region and agent s attributes influencing selection of insurance policy is rejected. Hence it is inferred that there is a significant association between rural and urban region and agent s attributes influencing selection of insurance policy. In order to analyse the relative importance of these agent attributes with respect to the rural and urban policyholders. Independent sample t-test was applied the results of independent sample t-tests were shown in the table 5.129 The result indicates that the exists significant difference in the perception of rural and urban policyholders in terms of various attributes of insurance agents. For a rural policyholder the most influencing attribute of the agent is error free services from the agent, awareness about terms and conditions about the policy, behavior of the agent, friendly nature of the agent and commitment of agent to fulfill the promises. For urban policyholders in addition to error free services by the agent which is the most influencing attribute of insurance agent are satisfactory services, friendly behavior, responsiveness to the queries and explanation about the police are the most influencing attributes. 338

TABLE 5.129: T-Statistics Agent Attributes Rural Region Urban T- Statistic P value Chisquare Value Remark Agent provides error free services 4.48 4.21 5.148 Difference 45.215 a Committed to fulfill promises timely 4.11 3.85 4.143 Difference 36.924 a Perform the service right in first instance 3.87 3.64 3.586 Difference 19.284 a (.001) Provides accuracy (such as payment record) 4.07 3.82 3.590 Difference 52.627 a Providing satisfactory services. 4.1 3.85 3.564 Difference 54.173 a Prompt, responsive and reliable. 3.96 3.58 5.477 Difference 48.510 a Cooperative and friendly. 4.11 3.81 3.853 Difference 34.503 a Known and trustworthy. 4.09 3.64 5.970 Difference 61.643 a Properly remind about the due premium. 4.02 3.65 4.881 Difference 47.028 a Explain features, advantages and benefits of the policy 4.05 3.68 4.892 Difference 43.975 a Thoroughness of follow up on questions/ enquiries/ requests prior to purchase decision 4.04 3.69 5.011 Difference 84.768 a Attire of the agent is acceptable 3.41 3.15 4.440 Difference 25.121 a Attitude of agent towards policyholders is good 4.1 3.49 7.688 Difference 78.217 a Behaviour of agent is good with policyholders 4.12 3.6 6.803 Difference 74.728 a Agent have enough past experience in the field 3.93 3.46 7.109 Difference 85.183 a Attention focused on your priorities 4.08 3.57 7.254 Difference 79.160 a Awareness about terms and conditions of policy. 4.13 3.66 6.718 Difference 72.660 a 339

340

Other factors The other factors also play important role in buying therefore in order to analyse the relative importance of these factors with respect to the rural and urban policyholders. Independent sample t-test was applied the results of independent sample t-tests were shown in the table 5.130. TABLE 5.130: T-Statistics Other Factors Rural Region Urban T- Statistic P value Chisquare Value Remark The State Financial Policy and Interest rates 2.65 2.7 -.850 (.396) Border line difference 33.558 a Novelty products in insurance market 2.67 2.8-2.343 (.019) Border line difference 19.579 a (.001) Details of insurance terms and conditions 4.03 3.51 7.357 Difference 38.972 a Legal aspects of the policy 2.92 2.7 3.955 Difference 105.170 a 341