Life insurance has a long history in India. The Bombay Mutual Life Insurance Society was the first in business when it started insuring both Indian and non Indian lives for the same amount of premium. It has been recorded that the business of insurance was with overseas companies till the end of the nineteenth century. Insurance was regulated in the country with the passing of the Life Insurance Companies Act in the year 1912. The comprehensive legislation on insurance was passed in the year 1938 and was called the Insurance Act of 1938. This was necessitated by the occurrence of frauds in insurance and mushrooming of insurance companies. With this, insurance spread in India but was restricted to the major cities. The next big change in life insurance came in 1956 when the government brought over the private life insurance companies and the provident societies under one company called the Life Insurance Corporation (LIC), which enjoyed monopoly for the next four decades. The nationalization of insurance business was also necessitated by the needs of funds for the rapid industrialization envisaged at that time.
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In 1993, with the view of liberalizing the insurance sector, the government appointed the Malhotra committee to recommend the road map needed for opening up the insurance sector in the country. This was done to induce the much needed competitiveness in the industry which had become stagnated. The insurance market which was underdeveloped in the country slowly started to open up and increase its cover with the entry of the private players in 1999 when the government opened up the sector. Prior to this, life insurance was seen more as a tax saving instrument than a life insurance, even though LIC had been steadily covering some ground. The pace of coverage extension was found to be awfully inadequate. The private players customized the products and introduced innovations in annuity or pension products. The market is currently growing at a good pace. The life insurance market generated total revenues of $41.36 billion in 2007, thus representing a compound annual growth rate (CAGR) of 11.84% for the period spanning 2000-2007. Life insurance market had a growth of $22.46 billion within a period of 7 years with a growth rate of 118.24%. Estimated life premiums rose from INR 1,301,540 million ($32.54 billion) in 2005 to INR 1,470,800 million ($36.77 billion) in 2006. It is expected that life premiums in 2011 will be $65.96 billion, a growth larger than they were in 2007. The performance of the market is forecast to accelerate, with an anticipated CAGR of 9.78% for the four-year period 2007-2011 and expected to drive the market to a value of $65.96 billion by the end of 2011. There would be a growth of $24.6 billion i.e., 59.48% in the next 4 years. The penetration of life insurance is around 3% of GDP as of now and this is aimed to be increased to the 8% level. Vast sections of society are yet to be brought into the life insurance market. There are vast rural areas that are yet to learn about the life insurance and even in urban areas, the poor are still out of the purview of the life insurance. New companies are entering into the market and they are needed for penetrating the market further. There is also another dimension to this growth. People also see life insurance as an instrument to invest as the growth in Unit Linked Insurance Plans (ULIP) would establish. The investments in life insurance also cater to the increased savings from the households thus increasing the much needed domestic savings.
Statement of the Problem
Service quality has been defined as a gap between the customer's expectations of a service and the customer's perceptions of the service received (Parasuraman et al., 1985). The consumer satisfaction literature views these expectations as predictions about what is likely to happen during an impending transaction, whereas the service quality literature views them as desires or wants expressed by the consumer (Kandampully,2002). To date, "there is no universal, parsimonious, or all-encompassing definition or model of service quality" (Reeves & Bednard, 1994, p. 436). Grönroos (1984) defines service quality as "the outcome of an evaluation process where the consumer compares his expectations with the service he perceived he has received" (p. 37). Definitions of quality have included: a) satisfying or delighting the customer or exceeding expectations; b) product of service features that satisfy stated or implied needs; c) conformance to clearly specified requirements; and d) fitness for use, whereby the product meets the customers' needs and is free of deficiencies (Chelladurai & Chang, 2000).Life insurance companies strive to improve the quality of their services and the level of customer satisfaction in the belief that this effort will create loyal customers. Loyal customers will purchase the product and recommend it to others (Tian-Cole & Cromption, 2003). Sparks and Westgate (2002) suggest that service failure can have devastating effects on an organization because customers frequently switch to a different provider when they experience a service failure. However, among customers who experience service problems, those who receive satisfactory resolution are more likely to remain loyal to the service provider (p. 214).It has been observed that insurance agents should constantly monitor the level of satisfaction among his/her customers to keep themselves close to the customers for fulfilling their needs(Joseph et al.,2003). Ennew et al.(1993) indicated that a comparison of mean scores on the importance of service attributes provides a very effective method of measuring the ability of services to meet the needs of customers. Perceived service quality has a significant effect on the attitude towards obtaining insurance (Arora and stoner, 1996). Moreover, the degree of success in the implementation of enterprise in the life insurance industry is positively correlated to the management performance of external aspects like providing increased customer satisfaction (Luran et al.,2003).customer satisfaction and salespersons relation orientation significantly influences the future business opportunities and as the salespersons are able to enhance their relationship with the clients, clients are more satisfied and are more willing to trust and thus secures the long term demand for the services (tam and wong,2001). Hellier et al.(2003) found that in insurance purchase brand preference is an intervening factor between customer satisfaction repurchase intention and the main factor influencing the brand preference is the perceived value and customer satisfaction. Both the company and agents service quality as well as recommendations of friends are factors that significantly affect decision of purchasing life insurance policies (chow - chua and Lim,2000).Stafford et al.(1998) in a study on auto casualty industry proved that reliability is consistently the most important determinant of both perceived service quality and feelings of satisfaction among customers engaged in auto industry claims. No such study has been carried out in the area of life insurance. Given the importance of the life insurance industry in India in terms of increasing market size, growing competition and the share of the total insurance Premium market, this research attempts to identify the service quality dimensions which contribute to the maximum customer satisfaction in the life insurance industry India.
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Following are the objectives of the research:
To examine the Demographic and socio economic environments influence the different dimensions of service quality.
To find out the relationship between the dimensions of service quality on Life insurance and its influence on the Policy holder Satisfaction as the mediating factor.
To suggest suitable model for service quality on Life insurance.
Conceptualized Research Model
There are 9 dimensions were framed for this study. Those are; i) Policy,, ii) Procedure and formalities, iii) charges, iv) Premium and payment, v) communication, vi) settlement of claims, vii) care on customers, viii) Policy holder Satisfaction and ix) Customer Loyalty. Here Demographic variables, Policy, Procedure and formalities, charges, Premium and payment, communication, settlement of claims, care on customers, Policy holder Satisfaction are independent variables and Customer Loyalty is the dependent variable. It is studied that how and what extent the independent variables make changes in the dependent variable. The proposed conceptual research model shows the process of research as follows:
Fig: 1: Conceptual Model for Studying the Mediating Effects on Service Quality on life insurance
The research was basically a survey on the mediating effects of service quality on Life insurance in Tamilnadu. For this research, the data was collected through personal contacts, the sample frames were the individuals who are investing in life insurance policies.
The sampling procedure used for the study was stratified random sampling. The stratification has been done based on the divisional offices in southern Zonal of Tamilnadu and from each divisional office, non-probabilistic convenience and judgmental sampling technique was used. At the initial stage, a pilot study was held at limited scale in order to know the scope and possibilities of the research. On the basis of experiences and findings of such study, the objectives were framed. Structured Questionnaire was used to collect primary data, consists of 58 questions with 7 points scale the response varied from Highly Dissatisfied to Highly Satisfied. 800 samples were collected throughout Tamilnadu by adopting the method of personal interview method. The questionnaire put under pre-testing among 50 sample respondents, and some corrections and modifications were made on the basis of pre-testing.
Procedure for Data Analysis
The data collected were analyzed for the entire sample. Data analyses were performed with Statistical Package for Social Sciences (SPSS) using techniques that included descriptive statistics, Correlation analysis and AMOS package for Structural Equation Modeling (SEM) testing.
Structural Equation Modeling
The main study used Structural Equation Modeling (SEM) because of two advantages: "(1) estimation of multiple and interrelated dependence relationships, and (2) the ability to represent unobserved concepts in these relationships and account for measurement error in the estimation process" (Hair et al., 1998, p. 584). In other words, a series of split but independent multiple regressions were simultaneously estimated by SEM. Therefore, the direct and indirect effects were identified (Tate, 1998). However, a series of separate multiple regressions had to be established based on "theory, prior experience and the research objectives to distinguish which independent variables predict each dependent variable" (Hair et al., 1998, p. 584). In addition, because SEM considers a measurement error, the reliability of the predictor variable was improved. AMOS 16.0, a computer program for formulating, fitting and testing Structural Equation Models (SEM) to observed data was used for SEM and the data preparation was conducted with SPSS 15.0. Linear Structural Equation Models (SEMs) are widely used in sociology, econometrics, management, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the "error" or "disturbance" terms) and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is nothing more than a heuristic device for illustrating the assumptions of the model. However, in this research, the researcher will show how path diagrams can be used to solve a number of important problems in Structural Equation Modeling. Structural Equation Models (SEM) with latent variables are more and more often used to analyze relationships among variables in marketing and consumer research (see for instance Bollen, 1989; Schumacker & Lomax, 1996, or Batista-Foguet & Coenders, 2000, for an introduction and Bagozzi, 1994 for applications to marketing research). Some reasons for the widespread use of these models are their parsimony (they belong to the family of linear models) their ability to model complex systems (where simultaneous and reciprocal relationships may be present, such as the relationship between quality and satisfaction) and their ability to model relationships among non-observable variables while taking measurement errors into account (which are usually sizeable in questionnaire data and can result in biased estimates if ignored).
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Demographic and socio economic environments influence the different dimensions of service quality.
The dimensions of service quality influence the Policy holder Satisfaction as the mediating factor.
Dimensions of service quality positively influence the Customer Loyalty towards the Life insurance policy.
H1: The service quality dimensions (Policy, Procedure and formalities, charges, Premium and payment, communication, settlement of claims, care on customers) are mediated by Policy holder Satisfaction towards attainment of Customer Loyalty towards the life insurance policies.
H2: The service quality dimensions (Policy, Procedure and formalities, charges, Premium and payment, communication, settlement of claims, care on customers) are positively influences the Customer Loyalty towards the life insurance policies.
H3: The service quality mediating dimensions (Policy holder Satisfaction) positively influence the Customer Loyalty towards the life insurance policies.
H4: The service quality dimensions are positively correlated with each other.
H5: The interaction between dimensions of the service quality and Policy holder Satisfaction will explain more of the variance in overall service quality than the direct influence of dimensions of service quality or Policy holder Satisfaction on their own.
Results and Discussion
Evaluation of Model Fit
According to the usual procedures, the goodness of fit is assessed by checking the statistical and substantive validity of estimates, the convergence of the estimation procedure, the empirical identification of the model, the statistical significance of the parameters, and the goodness of fit to the covariance matrix. Since complex models are inevitably mis specified to a certain extent, the standard Ï‡2 test of the hypothesis of perfect fit to the population covariance matrix is given less importance than measures of the degree of approximation between the model and the population covariance matrix. The Root Mean Squared Error of Approximation (RMSEA) is selected as such a measure. Values equal to 0.05 or lower are generally considered to be acceptable (Browne & Cudeck, 1993). The sampling distribution for the RMSEA can be derived, which makes it possible to compute confidence intervals. These intervals allow researchers to test for close fit and not only for exact fit, as the Ï‡2 statistic does. If both extremes of the confidence interval are below 0.05, then the hypothesis of close fit is rejected in favor of the hypothesis of better than close fit. If both extremes of the confidence interval are above 0.05, then the hypothesis of close fit is rejected in favor of the hypothesis of bad fit. Several well-known goodness-of-fit indices were used to evaluate model fit: the chi-square Ï‡2, the Comparative Fit Index (CFI), the unadjusted Goodness-of-Fit Indices (GFI), the Normal Fit Index (NFI), the Tucker-Lewis Index (TLI), The Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean square error Residual (SRMR).
FIT Indexes for the INSURQUAL Structural equation model
Recommended level of GOF measure
Ï‡2 / Degree of Freedom
1 to 2
<0.05 indicates very good fit(Threshold level=0.10)
P-Value for hypothesis test of RMSEA<0.05
0 to 1
Comparative fit Index(CFI)
0 to 1
Normal fit index (NFI)
0 to 1
Finally the researcher empirically tested and proved the proposed conceptual model (Fig. 1.2, Page. 40) with structural equation modeling by using SPSS 18 and AMOS 18 packages and various goodness of fit indices. The researcher identify that the Policyholder Satisfaction is the mediating factor for the Life Insurance Loyalty in the study area. Hence Life Insurers would be concentrated on Policyholder Satisfaction to improve the Customer loyalty for the growth of Indian insurance sector. The major findings were;
Policyholder satisfaction is the mediating factor for service quality in life insurance.
Policyholder satisfaction mediates the customer loyalty towards life insurance policies in Tamilnadu.
Procedure and formalities and communication are the most influencing factors to the mediating factor.
Charges and settlement of claims are also an influencing factor for quality of service in life insurance service quality.
Care on customer also has an influence in the quality of service in life insurance service quality.
All the dimensions of insurance service quality have positively influenced the mediating factor for life insurance.
The researcher confines only to the policyholders of LIC of India and excludes other life insurance policyholders. The research is restricted to only in Tamilnadu.The research conducted only in the places of divisional zonal offices in Tamilnadu, which had considerable policy holders in the insurance market. While collecting the data, some of the respondents were hesitant to disclose certain details about the insurance agents.
Conclusions and Future area of research
It may be concluded that the responsiveness of service quality provides maximum customer satisfaction to the life insurance industry in India. With the increase in overall market size of the industry as well as increasing competition since 2000, different players of the industry should invest to improve the customer relationship and quality of services. Still some actions are needed for developing insurance market. Insurance industry has to go ahed. A lot of opportunities are still waiting. This research will help in developing the market share, Loyalty and further development in insurance sector. The study can be further extended to investigate the causal relationship between service quality, customer satisfaction, loyalty and retention. Such a study would enhance the level of understanding for managers and academicians.