A Study on Customers Perception Towards Lic Health Insurance Plans in Salem City

This is analyzing the customers' perception towards LIC health insurance plans in Salem city. The main objec- tives are to study the effect of customers' perception upon health insurance products; to identify the differ- ences perceived by the customers among the policies of health insurance; and to assess the customer overall satisfaction level with health insurance products. Data were collected using questionnaire, the most common tool to evaluate the customers' perception. The sample unit of the study is existing customers' of LIC health insurance policies living in the city of Salem. The total sample of the study is 352. Primary research data is collected in the form of structured survey results from various respondents in the city of Salem. Secondary research data is collected in the form of reference literature on the research topic. Discriminant analysis is used for data analysis in this study. The customers must get clear information about the policy, he/she purchased or going to purchase. Larger group of people have not bought health insurance plans since they do not get correct information relating to product of LIC. The actual perception of the customers' is that LIC never lags behind compare to private players' product in health insurance. Majority of the respondents are satisfied after investing in health insurance plans of LIC. This study also discloses few areas of product attribute improvement. Custom- ers rated its product as average since they do not recognize product differentiation in terms of benefits among the plans marketed by LIC.


INTRODUCTION
The term Health Insurance is used to describe a form of insurance that pays for medical expenses. It is used more broadly to include insurance that covers disability or long-term nursing or custodial care needs. In simple words, if you are covered under Health Insurance, you pay some amount of premium every year to an insurance company and if you have an accident or if you have to undergo an operation or a surgery, the insurance company will pay for the medical expenses. It is a tough ordeal if you are diagnosed with an illness and need to be hospitalized, no matter if you are rich or poor, male or female, young or old.
The list of lifestyle diseases like heart problems, diabetes, stroke, renal failure, some cancers just seems to get longer and more common these days. Thankfully there are more specialty hospitals and specialist doctors -but all that comes at a cost. The super-rich can afford such costs, but what about an average middle class person? For an illness that requires hospitalization / surgery, costs can easily run into 5 figures. A Health Insurance Policy can cover such expenses to a large extent. There are mainly 3 types of Health Insurance covers which are Individual Mediclaim, Family Floater Policy, and Unit Linked Health Plans.

REVIEW OF LITERATURE
Clifford Paul S. Joseph Anbarasu D., and Annette Barnabas (2010) conducted a "Study on Socio Economic Status and Awareness of Indian Investors of Insurance" The study uncover customer awareness and customers perception about the true value of life insurance.
Dr. Binod Kumar Singh (2009) conducted a study with the title "An Empirical Study on Perception of Consumer in Insurance Sector". This study has concentrated on the factor influencing buying decision and model of consumer decisions making process and the reasons for investing in life insurance have been studied. Ms. BabitaYadav (2011) this study is conducted in Jabalpur city of Madhya Pradesh. This research aims to measure customer satisfaction level on various Insurance Policies mar-keted by LIC and examine the common reasons for customer dissatisfaction.

NEED FOR THE STUDY
Life insurance industry of this country has so many players now. The industry was opened up for private players ten years ago. We now have a plethora of products in the market. Innovative services are also on offer for the customers. Insurers are visible everywhere through catchy advertisements placed by them in all the print and electronic media. The industry is also growing.

OBJECTIVES OF THE STUDY
· To study the effect of customers perception upon health insurance products. · To identify the differences perceived by the customers among the policies of health insurance. · To assess the customer overall satisfaction level with health insurance products.

SCOPE OF THE STUDY
This study is very useful for understanding the customers' perception towards health insurance products and it may helpful to frame new health insurance policies.
LIMITATIONS OF THE STUDY · The research was conducted only in Salem city therefore to generalize the results for the entire health insurance sector may not be possible. · The assessment of the pre test and post test was conducted it is unavoidable that in this study, certain degree of subjectivity can be found. In fact, it had been decided by two or three examiners.

RESEARCH METHODOLOGY
Data were collected using questionnaire, the most common tool to evaluate the customers' perception. The sample unit of the study is existing customers' of LIC health insurance policies living in the city of Salem. The total sample of the study is 352. Primary research data is collected in the form of structured survey results from various respondents in the city of Salem. Secondary research data is collected in the form of reference literature on the research topic. The collected data were analyzed using statistical method. Statistical tools (SPSS) were used for data input and analysis. Discriminant analysis is used for data analysis in this study.  Table No.1provides strong statistical evidence of significant differences between means of Reason to join -to earn livelihood, to save for children, to get loan, to meet over emergency need, and to provide employment for other groups for all II's with age and number of children producing very high value F's. The ranks and natural logarithms of determinants printed are those of the group covariance matrices. In ANOVA, an assumption is that the variances were equivalent for each group but in DA the basic assumption is that the varianceco-variance matrices are equivalent. Box's M tests the null hypothesis that the covariance matrices do not differ between groups formed by the dependent. The researcher wants this test not to be significant so that the null hypothesis that the groups do not differ can be retained. Tests null hypothesis of equal population covariance matrices. For this assumption to hold, the log determinants should be equal. When tested by Box's M, we are looking for a nonsignificant M to show similarity and lack of significant differences. In this case the log determinants appear similar and Box's M is 414.556 with F = 1.029 which is significant at p < 0.000 (Tables No.2 and 3). However, with large samples, a significant result is not regarded as too important. Where three or more groups exist, and M is significant, groups with very small log determinants should be deleted from the analysis.   The interpretation of the discriminant coefficients (or weights) is like that in multiple regressions. Table No.6 provides an index of the importance of each predictor like the standardized regression coefficients (beta's) did in multiple regression. The sign indicates the direction of the relationship.

ReseaRch PaPeR
In the function 1, Type of Policy score (0.611) was the strongest predictor while less information sources score (-0.147) was next in importance as a predictor. These two variables with large coefficients stand out as those that strongly predict allocation to very good or very poor group. Other predictor scores were less successful as predictors.
In the function 2, good plans of LIC score (0.588) was the strongest predictor while low feel LIC lags behind compare to private players in insurance industry score (-0.430) was next in importance as a predictor. These two variables with large coefficients stand out as those that strongly predict allocation to very good or very poor group. Other predictor scores were less successful as predictors.
In the function 3, information source score (0.467) was the strongest predictor while less good plans of LIC score (-0.448) was next in importance as a predictor. These two variables with large coefficients stand out as those that strongly predict allocation to very good or very poor group. Other predictor scores were less successful as predictors.
In the function 4, Type of Policy score (0.494) was the strongest predictor while lessAdvertisements of LIC health insurance plans more often score (-0.555) was next in importance as a predictor. These two variables with large coefficients stand out as those that strongly predict allocation to very good or very poor group. Other predictor scores were less successful as predictors. Variables ordered by absolute size of correlation within function.
*. Largest absolute correlation between each variable and any discriminant function Table No.7 provides another way of indicating the relative importance of the predictors and it can be seen below that the same pattern holds. Many researchers use the structure matrix correlations because they are considered more accurate than the Standardized Canonical Discriminant Function Coefficients. The structure matrix table (Table No.7) shows the correlations of each variable with each discriminate function. These Pearson coefficients are structure coefficients or discriminant loadings. They serve like factor loadings in factor analysis. By identifying the largest loadings for each discriminate function the researcher gains insight into how to name each function. Here the researchers have type of policy (high score) and information sources (low score) in the function 1; LIC Products (high score) and Mode of Premium (low score) in the function 2; Information sources (high score) and LIC Products (low score) in the function 3; Type of Policy and Advertisements of LIC health insurance plans more often (low scores) in the function 4 which suggest a label of personal confidence and effectiveness as the function that discriminates between very good and very poor. Generally, just like factor loadings, 0.30 is seen as the cut-off between important and less important variables. Absence is clearly not loaded on the discriminant function, i.e. is the weakest predictor and suggests that work absence is not associated with rate of the health insurance policies of LIC with other companies but a function of other un assessed factors.    x Rate LIC health insurance policies against similar companies that respondents have dealt with in the past) + (-0.037 x Feel LIC lags behind compare to private players in insurance industry) + (-0.026 x Feel after investing in health insurance plans of LIC) + (-0.543 x Advertisements of LIC health insurance plans more often) − -2.412.
The discriminant function coefficients b or standardized form beta both indicate the partial contribution of each variable to the discriminate function controlling for all other variables in the equation. They can be used to assess each IV's unique contribution to the discriminate function and therefore provide information on the relative importance of each variable. If there are any dummy variables, as in regression, individual beta weights cannot be used and dummy variables must be assessed as a group through hierarchical DA running the analysis, first without the dummy variables then with them. The difference in squared canonical correlation indicates the explanatory effect of the set of dummy variables. A further way of interpreting discriminant analysis results is to describe each group in terms of its profile, using the group means of the predictor variables. These group means are called centroids. These are displayed in the Group Centroids table (Table No.9). Cases with scores near to a centroid are predicted as belonging to that group.

ReseaRch PaPeR
Finally, there is the classification phase. The classification table, also called a confusion table, is simply a table in which the rows are the observed categories of the dependent and the columns are the predicted categories. When prediction is perfect all cases will lie on the diagonal. The percentage of cases on the diagonal is the percentage of correct classifications. The cross validated set of data is a more honest presentation of the power of the discriminant function than that provided by the original classifications and often produces a poorer outcome. The cross validation is often termed a 'jackknife' classification, in that it successively classifies all cases but one to develop a discriminant function and then categorizes the case that was left out. This process is repeated with each case left out in turn. This cross validation produces a more reliable function. The argument behind it is that one should not use the case you are trying to predict as part of the categorization process.
The classification results (Table No.10) reveal that 73.9% of respondents were classified correctly into 'Very Good' or 'Good' or 'Better' or 'Poor' or 'Very Poor' groups. This overall predictive accuracy of the discriminant function is called the 'hit ratio'. Very Good groups were classified with slightly better accuracy (45.6%) than Good (24.4%), Better (16.3), Poor (23.7), Very Poor (37.8). The researcher hascompared the calculated hit ratio with what he could achieve by chance. If two samples are equal in size then you have a 50/50 chance anyway. Most researchers would accept a hit ratio that is 25% larger than that due to chance.

FINDINGS
A canonical correlation of 0.193 of Function 2 suggests that the model explains 3.72% of the variation in the grouping variable, i.e. whether a respondent feel good or very poor. A canonical correlation of 0.157 of Function 3 suggests that the model explains 2.46% of the variation in the grouping variable, i.e. whether a respondent feel average or very poor. A canonical correlation of 0.115 of Function 4 suggests that the model explains 1.32% of the variation in the grouping variable, i.e. whether a respondent feel poor or very poor.

SUGGESTIONS
The customers must get clear information about the policy, he/she purchased or going to purchase. Larger group of people have not bought health insurance plans since they do not get correct information relating to product of LIC. In case of traditional plan minimum knowledge regarding Sum assured, premium and the duration of policy along with the mode of payment is expected from the insurer.

CONCLUSION
The actual perception of the customers' is that LIC never lags behind compare to private players' product in health insurance. Majority of the respondents are satisfied after investing in health insurance plans of LIC. This study also discloses few areas of product attribute improvement.Customers rated its product as average since they do not recognize product differentiation in terms of benefits among the plans marketed by LIC.