Changing Dimensions of Customers Behaviour for A Life Insurance Product Amid COVID-19 Pandemic
Dr. Pramod Kumar Pandey
Associate Professor,
Presidency University,
Itgalpur Rajanakunte,
Yelahanka, Bengaluru,
Karnataka
Renisha Rana
Research Scholar,
Jesus and Mary College,
University of Delhi,
Chanakyapuri, New Delhi
Abstract
COVID-19 pandemic can be considered as an economic and health crisis of uncertain magnitude and duration. Frequent lockdowns, salary cuts, and losses of jobs and lives have spurred changes in human behaviour. Today customers are experiencing a transformation in their perceptions towards a life insurance product. The common attitude to treat life insurance as an optional investment instrument has been shifted towards a mandatory risk protection instrument. Thus the demand for a life insurance product is increasing by leaps and bounds and undoubtedly competition among insurance providers at the same time. This article seeks to examine the features that should form part of a life insurance product to attract customers in Indiaduring the COVID-19 pandemic. Data for the study were collected for 159 respondents from October 2020 to December 2020 from Delhi and NCR region. The data were analyzed using Principal Component Analysisafter walking through a maze of articles relating to normal times and crisis times. There is testable evidence to show a paradigm shift in the outlook of customers regarding a life insurance product. Thus, the article also paves the path for future research in the direction of customers behaviour for a life insurance product.
Key Words:Life insurance, surrender value, maturity value, revival policy, policy tenure, premium and LIC
JEL classification: G20, G22, G41 and H2
Introduction
COVID-19 has posed unforeseen challenges to the day-to-day economic life of people. As compared to the financial crisis in the year 1930 followed by in the year 2008, the adverse impacts of the COVID-19 pandemic are more severe and of uncertain duration. Due to consecutive lockdowns, business houses faced unprecedented crises and as a result, several job losses, salary cuts, and employment on a large scale have been experienced. The disposable income of common people has been severely eroded. As per the report of PWC (2020), India ranks among the top fifteen economies severally affected due to the COVID-19 pandemic.
The task of life insurance is to protect the livelihoods of people and their future financial security, and thus, it directly correlates with people's earnings, business performance, and net worth. From the onset of the pandemic, there has been a rush to increase one's cover. As per the report (Adams, et al., 2020), 'pure life covers' should see renewed interest and should see a boost in demand. As long-term guarantees will look attractive, insurers will still face constraints as the interest rates plummet, making consumers more cautious about their investments and leading to a shaky stock market. It is believed that as the pandemic's restrictions are coming to ease with declining new covid cases, the month of June shows a better sequential trend. Adding to this, the first quarter of the fiscal year is generally the smallest for life insurers; ergo, the year-long impact from lockdowns should be limited. Thus, there is enough opportunity to grow as the restrictions are lifted.The life insurance sector has seen a surge in people buying such policies to safeguard themselves from the ill effects of the pandemic; even the people who never thought of buying one have started reconsidering and purchasing it. The predictions of the 3rd wave are only increasing the fear among the public and is another reason behind the surge seen in the sector.
The role of life insurance cannot be ignored for human civilization. It is a contract of indemnification wherein the risk of the insurer is transferred to the insured for payment. The history of life insurance in India can be traced back to 1818 when oriental life insurance opened firstly set up its firm. However, real business growth started only from the year 1983-84 (Rao, 1999). Initially, in the post-independence era, the Life insurance Corporation (LIC) enjoyed monopolies since nationalization in 1956 till 1999 when Insurance regulatory and development act (IRDA) allowed private players to enter the industry and the insurance market turned out into a competitive market. This competition among insurance companies leads to growth in the insurance sector because whenever a monopolistic market is created, there occurs a negative growth of the insurance sector (Outreville, 1996). But till now LIC is a strong competitor with the highest market share. Due to having 40 years of experience, it is occupying the most advantageous position (Ranade & Ahuja, 1999).
.If the growth rate in life insurance density and life insurance penetration is seen over the last eighteen years (Figure-1), it can be inferred that the growth rate of both was highest in the year 2006 and thereafter both are showing declining trend and situation of the growth rate of insurance penetration is poorer as compared to the growth rate of life insurance density.
Figure-1: Growth rate in Life insurance density and penetration in last 18 years in India
Source: Author’s computation from IRDAI annual report 2019-20
As per the annual report, 2019-20 of IRDAI, the life insurance business of India has ranked 10 among 88 countries in the world, and the share of the life insurance market stood at 2.73% during the year 2019. Due to the impact of COVID-19, the overall business of life insurance companies reflected a declining trend in the year 2019-20 as compared to the year 2018-19. The report of IRDAI also justifies the same as per which growth in renewal premium decreased from 10.76 % in the year 2018-19 to 7.00 % in the year 2019-20 and again are expected to fall in the year 2020-21. Again life insurance density which denotes the ratio of premium to total population is showing very marginal growth in the last nine years. It was USD 55.7 in the year 2010 and reached only USD 58 in the year 2019. Again insurance penetration which denotes the ratio of premium to GDP is showing negative growth in the last nine years. It was 4.4% in the year 2010 and declined to 2.82% in the year 2019.
The life insurance companies in India faced twin shocks, first decline in sales across different life insurance policies and second huge financial stress in claim settlement due to escalation of deaths in the country. As per the working paper of the Indian Council for Research on International Economic Relations (2020), the Insurance sector plays a pivotal role in the economic growth of the country however, the share of the insurance sector as compared to advanced countries is still very low. Amidst this fact, the COVID-19 pandemic has further weakened the performance of the sector. According to a report by the National Insurance Academy on the impact of COVID-19 on the Indian insurance industry, due to an increase in the number of deaths, the outgo of claims is expected to jump very high. The new business is expected to be flat due and existing policies may encounter more surrenders due to the poor liquidity position of the people. Union budget 2021 has taken several initiatives to boost the insurance industry in India by increasing the FDI limit from 49% to 74% in this sector and relaxing in certain cases from taxation of ULIP.
As suggested by a report (Acharya, et al., April 2020), the national GDP reverting to pre-COVID levels can occur as early as the nearest quarter of the year or as late as the Q-3 of 2022. The outbreak has decreased the global insurance index by 22.6%, leading to a decline in share prices by 25.9%, clearly implying the hit seen by the sector.
As per the report (Ibef, May 2021), The life insurance sector saw a hit and was weak in the first quarter as the pandemic began, but they have been experiencing a swift recovery recently as the restrictions are lifting and the conditions are getting better. Additionally, with about 53% of the new business market share in FY20, LIC has been dominating the sector and the only public sector life insurer that has continued to be the market leader (Figure-2).
Figure-2: Premiums Market Sharein First Year Life Insurance (FY20)
Source:Extracted from reports publishlied by IBEF
The pandemic has led to some notable trends in the life insurance sector, i.e., increasing market share of private players in the life insurance industry and launching apps to increase convenience and be at par with the increasing digitalization the world is facing. In November 2020, Life Insurance Corporation of India, the most significant player in the life insurance sector in India, launched its first software application, ANANDA, an acronym for 'Atmanirbhar Agents New Business Digital App'. There have been some notable strategies adopted by the private players as well. The one that has come to light is product launches. In May 2021, Max Life Insurance Co. Ltd. launched 'Max Life Saral Pension', Bajaj Allianz General Insurance launched Criti-Care, in April 2021, and Bharti AXA General Insurance launched 'Health AdvantEDGE' in February 2021.
A sectoral release (Oswal, May 2021) reported that the drastic increase in COVID-19 infections and fatality rates had raised major concerns about the mortality claims that the industry may witness in the following months. Additionally, HDFC Life, SBI Life, and ICICI Prudential Life, the top private life insurance players, would need to shore up provisioning buffers. Furthermore, most insurers have reported a higher claim in FY21 and have made provisions to incorporate the potential claims that may occur if a more devastating version of Covid 19 occurs. This gives the general public a sense of comfort that the industry is stepping up to safeguard them even if another pandemic hits.
Literature review
About life insurance industry in India
The pandemic that started as a health crisis has become a financial crisis. As the global economy is crashing and multiple sectors taking a significant financial hit, the insurance industry has become an important segment of the new reality of the economy. The task of life insurance is to protect the livelihoods of people and their future financial security, and thus, it directly correlates with peoples earnings, business performance, and net worth. The ongoing pandemic has badly hit the sector, but the industry is coming back with positive signs. Srivastava & Tripathi (2012) observed where almost all the industries in the world are trying hard to survive due to the significant economic meltdown; the Indian life insurance industry is one of the sectors still observing good growth. The authors concluded that this sector requires more insurance density and insurance penetration, especially in rural areas. India will soon become the new insurance giant by adopting an appropriate strategy, proper government support, and IRDA guidance. The industry has seen a tremendous growth of the private sector, with some companies doing well enough to come into the light and share headlines with LIC, the public sector giant leading the sector as Ashraf & Singhal (2015) evaluated the investment performance of the private life insurance industry's investment performance. The authors observed that the deepening of the insurance market makes a positive contribution to economic growth. They highlighted that the least number of companies were efficient during 2011-2012 mainly due to the decline in income from investment. It reflected the condition prevailing in the stock market and a decline in the unit-linked business for the life insurance industry. However, the industry sure has a long way to go and has to cover the setbacks; Murugesh (2015) stated that life insurance could not afford to lose sight of its social relevance and shy away from its social responsibilities. The author concluded that the insurance industry has still to go a long way ahead by covering and solving the negatives, as pointed out by Kumar &Selvamani (2016), who researched to find the claim settlement ratio of the various insurance sectors in India and to know about new life insurance policies of LIC. The authors concluded that human beings suffer many health problems in the modern era and the life insurance industry plays a vital role in improving the national economy. Yadav & Suryavanshi (2021) evaluated the effect of the pandemic on the business of the life insurance sector and analyzed the sector's performance pre and post-pandemic.
The author revealed that the pandemic severely affected the life insurance business in terms of fall in sales of new policy, premium income and crises of claim settlement. Harris, Yelowitz, & Courtemanche (2021) analyzed how life insurance companies changed pricing and offerings due to COVID‐19 by studying monthly data about term life insurance policies. They found premiums differentially increased for individuals with very high risk and that some policies were removed for the oldest of the old. They suggest minimal observable adjustments through February 2021. Harris R. L. (2021) explained that practitioners should be aware of the procedural changes of 2020 to deal with the COVID-19 pandemic and the effect of low interest rates on the performance of life insurance. Prahasti (2021) evaluated the performance of life insurance companies with indicators such as net premium income, claims, investment yield and net profit in Indonesia before and during the covid-19 pandemic. The author revealed differences in net premium income and investment yield with no difference between claims and Net Profit received by life insurance companies.
About customer’s behaviour towards life insurance product
There are many confusing facts and a severe lack of awareness about life insurance policies in India. Many tend to assume that this is a matter of investment instead of protection from risk coverage. Ergo the fundamental motto for taking a life insurance policy has been lost. It is considered optional instead of mandatory. Consumers are strongly affected by the pandemic and have had several reactions in the industry sector as well. Studying their behaviour has revealed many facts. Sahu, Jaisawal & Pandey (2009) conducted a preliminary study to study the behaviour of customers for a life insurance policy. They used correlation and Z test. They found that customer loyalty, service quality, straightforward processes, brand image, and client relationship with the customers are the most important factors. The curent situation makes it imperetive to study what affects the consumer behaviour. Wang (2010) used primary data obtained through a survey in China and applied the probit model for analyzing the factors responsible for taking out an insurance policy. They found that knowledge and trust, consumer profile and investment preferences, product attributes, and socio-demographics affect the decision of investors in taking an insurance policy. Siddiqui, Ghosh & Sharma (2010) conducted a study to determine the most important factor for life insurance investors. They applied Factor analysis on primary data and concluded that service quality is the most crucial determinant for taking out a life insurance policy. Finding the factors that play are more important role in the consumer’s behaviour during the pandemic would greatly help in taking the next steps. Some argue servise quality and other simirlar factors are important but some argue tax rebate and other financial benefits remain improtant. Negi & Singh (2012) conducted a survey and collected a sample size of 800 respondents of Uttarakhand. Through factor analysis, they explored that product quality, brand image, service quality, customer friendliness and brand loyalty are essential factors influencing the purchase of life insurance products in India.Priyan & Selva (2012) evaluated through factor analysis that tax rebate, claim settlement time, ease of procedure and premium were included among the most important factors.
Similarly, it is also important to study the extent of lack of imformation and confsuion amoung consumers to protect them from making wrong decesions. Buzatu (2013) adopted a general approach to study the investor's behaviour in Romania. He emphasized that risk perception, false information or lack of information, and biases create irrational investors. Mathur & Tripathi (2014) worked on 120 samples from Ajmer City in Rajasthan. They applied factor analysis to determine important factors for the choice of an insurance company and found that Accessibility, the reputation of the company, efficiency and quality services are important factors determining the choice of the insurance company. Rajkumar & Kannan (2014) did a primary study on a sample size of 135 respondents. They used ANOVA and concluded that innovative product features, Accessibility, low premium amount, advertising, proper redressal of complaints and better claim settlement are important factors for selecting a particular company.
In addition, it is also imperetive to know that are the factors that affect the consumer’s decision of purchase of life insurrace products, whether it is timely service or customer convenience, and they value these factors to what extent. Chaudhary (2016) worked on deciding important factors that affect the purchase of life insurance products. Through factor analysis, he found that timely and effective Services, better Company Reputation, customer convenience, tangible benefits and healthy customer-client relationships are important factors that affect the purchase of life insurance products. Subashini & Kishori (2016) conducted a study to determine the basic factors for taking a life insurance policy. They discussed that life insurance could be seen as an instrument for risk coverage. However, returns are also important. Tati & Baltazar (2018), with a sample size of 75 people, used the Chi-square test to evaluate the most important reasons for purchasing a life insurance product. They mentioned that there is no association between annual income and the choice of an insurance company. An insurance product is not only for tax saving purposes but also for risk protection and investment instrument.
Customer’s knowledge of products makes all the difference in making a rational choice and this factor still hold its ultimate importance while selecting a product in life insurance. Luvisia & Nzulwa (2018) conducted a study in Kenya to find the features that decide the choice of a particular insurance company. They selected 47 insurance companies in Kenya and used Cross-section analysis to find the features. They found that convenience, customer's knowledge of products and associated benefits of policy were important factors affecting the choice of the particular insurance company. Giri (2018) worked on primary data to find factors that play a prominent role in selecting a life insurance policy. He used logistics regression and found that belief and subjective norms play an important role in taking up an insurance policy.
Financial losses of the insurance companies arenot hidden, and it is imperitive to uncover the reasons behind them, it can range from fradulent behaviour to the pandemic’s unavoidable effects. Islam et al. (2020) researched to find what causes financial loss to insurance companies. They used primary data for the study and applied the ARLAS model. They emphasized that adverse behaviour of policyholders causes financial loss to insurance companies due to fraudulent insurance claims. Agarwal & Azmi (2020) conducted survey analysis through charts and diagrams to evaluate the impact of COVID-19 on investors' perception of a life insurance product. They concluded that the COVID-19 pandemic has substantially changed the outlook of investors to look into an insurance policy. The shift is from treating insurance as an investment and risk protection instrument during the crisis. Spirtitual factors are very important in the current and privious generations lives, Ergo it is essential to know to what extent do these affect the consumer behaviour. Mehta, Saxena & Purohit (2020) used both primary and secondary data to find the impact of spiritual forces. They applied the consumer behaviour model. They found the significant role of spiritual forces in deciding consumer behaviour, resource mobilization and creating new products. Halonen, Rizan, & Suhud (2020) examined service quality and brand trust in life insurance companies. They applied Structured equation modelling and concluded that both these factors positively and significantly affected customer satisfaction and trust. Tyagi & Pabalkar (2021) conducted a study through a review of various reports. They emphasized that consumer behaviour is rapidly changing, and behaviour before pandemic can not be equated with behaviour during the pandemic. Patrick (2021) worked to find deciding factors for attracting customers for insurance. They argued that it is necessary to create a vibrant insurance market where the focus should be balancing the protection of policyholders with portfolio productivity.
Research gap and need for the study
Twin attacks of the COVID-19 pandemic have forced people to think about family and shifted attitudes towards life insurance products (Tyagi & Pabalkar,2021). The shift is from a life insurance instrument as an investment opportunity to a risk protection instrument (Agarwal & Azmi,2020). Due to the rising demand for life insurance products, there is huge competition between insurance companies. However, inside of all the facts, the share of LIC is highest in India, and due to having 40 years of experience, it is occupying the most advantageous position (Ranade & Ahuja, 1999). At times inadequate or false information is provided to customers, particularly by insurance agents, which creates irrational behaviour among investors (Buzatu, 2013). A customer shows different behaviour in normal times and crisis times. Due to the economy suffering from economic instability, there has been a great change in customers' behaviour (Mehta, Saxena, and Purohit, 2020). Therefore, it is very much necessary to know precisely how customer's behaviour changed during the COVID-19 pandemic, what features ought to be included in a life insurance product for building and enhancing the trust of customers(Babuna et al.,2020).
Material and methods
The data were collected from 159 respondents from the north India region. The participants were asked to rate their preferences for purchasing a new life insurance policy during the COVID-19 pandemic. They were asked the question, “what factors would you like to consider while buying a new life insurance policy?”. They were given the twelve factors(Table-1) to rate on a scale of 1 to 5, 1 being not important and 5 being most important.
Table-1: Factor development
|
Item |
Factors included in questionnaire |
References |
Factors |
|
1 |
Maturity value |
Luvisia & Nzulwa (2018), Subashini & Kishori (2016), Russell, Fier,Carson & Dumm (2013), Subashini & Ramaswamy &Velmurugan (2015), Subashini & Kishori (2016), Luvisia & Nzulwa (2018) and Chaudhary (2016) |
Assurance |
|
2 |
Surrender value |
||
|
3 |
Revival policy option |
||
|
4 |
Survival benefits |
||
|
5 |
Service quality |
Chaudhary (2016), Halonen, Rizan, & Suhud, (2020), Sahu, Jaisawal & Pandey (2009), Siddiqui ,Ghosh & Sharma (2010), Rajkumar & Kannan (2014) and Luvisia & Nzulwa (2018) |
Comfort |
|
6 |
Convenience |
||
|
7 |
Grievance settlement |
Subashini & Kishori (2016), Rajkumar & Kannan (2014) and Priyan & Selva (2012) |
Resolution |
|
8 |
Claim settlement |
||
|
9 |
Brand image |
Chaudhary (2016), (Halonen, Rizan, & Suhud, 2020), Sahu, Jaisawal & Pandey (2009), Negi & Singh (2012), Luvisia & Nzulwa (2018) and Wang (2010) |
Trust |
|
10 |
Customer’s knowledge of product |
||
|
11 |
Premium |
Rajkumar & Kannan (2014), Priyan & Selva (2012) and Rajkumar & Kannan (2014) |
Outlay |
|
12 |
Charges |
For deciding the important factors deciding the purchase of insurance policy we conduct Principal component analysis using as data reduction technique (Fricker, Kulzy & Appleget, 2012) and for testing our three hypothesis (H1, H2 and H3) we apply logistics regression.
Results and dicsussions
Table-2 shows sample structure of the data collected which were collected from 159 respondents from the north India region during COVID-19 pandemic belonging to different groups based on age, gender, income level and marital status. Respondents included both existing and potential customers.
Table-2: Sample structure
|
Marital status |
Married |
94% |
|
Unmarried |
6% |
|
|
Gender |
Male |
90% |
|
Female |
10% |
|
|
Annual income |
Below 1 Lakh |
2% |
|
1 Lakh - 5 Lakhs |
13% |
|
|
5 Lakhs - 10 Lakhs |
16% |
|
|
Above 10 Lakhs |
69% |
|
|
Age |
Below 30 years |
6% |
|
31 years - 40 years |
14% |
|
|
41 years - 50 years |
63% |
|
|
51 years - 60 years |
14% |
|
|
Above 60 years |
3% |
|
|
Whether Life insurance taken? |
Yes |
94% |
|
No |
6% |
|
|
How did you buy policy? |
On-line |
40% |
|
Through insurance agents |
60% |
|
|
Which company's policy taken? |
LIC |
55% |
|
Others |
45% |
|
|
Type of policy taken |
Endowment plan |
22% |
|
Term plan |
22% |
|
|
ULIP |
8% |
|
|
Whole life plan |
18% |
|
|
Money back |
28% |
|
|
Others |
2% |
Firstly reliability of data was tested using Cronbach’s alpha. The value of Cronbach’s alpha obtained was 0.782(Table-3) which is considered good as far as the reliability of data is concerned.
Table-3: Reliability Statistics using cronbach’s Alpha
|
Case processing summary |
Reliability Statistics
|
||||
|
|
N |
% |
Cronbach's Alpha |
N of Items |
|
|
Cases |
Valid |
159 |
100.0 |
.782 |
12 |
|
Excluded |
0 |
0 |
|||
|
Total |
159 |
100.0 |
|||
For testing the adequacy of sample, the study used KMO and Bartlett's Test. The sample is supposed to be adequate if value of KMO is greater than 0.6. The value of KMO close to 1 is supposed to be better. In this the value of KMO obtained was 0.739 (Table-4) which reflected that the sample is adequate. Further Bartlett's Test of Sphericity also showed a significance level of 0.000. The significance level of Bartlett's Test of Sphericity should be lesser than 0.05 to prove that variables in the correlation matrix are not related.
Table-4: KMO and Bartlett's Test Results
|
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. |
.739 |
|
|
Bartlett's Test of Sphericity |
Approx. Chi-Square |
597.372 |
|
df |
66 |
|
|
Sig. |
.000 |
|
Communalities which shows that what proportion of each variable’s variance is explained by the factors. The value closed to 1 is supposed to be better. All communalities obtained showed acceptable results (Table-5)
Table-5: Value of Communalities
|
|
Initial |
Extraction |
|
Premium |
1.000 |
.779 |
|
Charges |
1.000 |
.790 |
|
Convenience |
1.000 |
.738 |
|
Service quality |
1.000 |
.694 |
|
Brand image |
1.000 |
.749 |
|
Grievance settlement |
1.000 |
.798 |
|
Claim settlement |
1.000 |
.784 |
|
Maturity value |
1.000 |
.785 |
|
Surrender value |
1.000 |
.753 |
|
Revival policy |
1.000 |
.740 |
|
Customer’s knowledge of the product |
1.000 |
.637 |
|
Survival benefit |
1.000 |
.664 |
|
Extraction Method: Principal Component Analysis. |
||
Extraction explains how many factors best explains the covariation of variables. The purpose is to sort out fewer number of factors that could explain the largest amount of variation. All components having igen value greater than 1 should be retained. In our case five components obtainted igen value of greater than 1 and able to explain 74.257 % of the variations (Table-6).
|
Table-6: Total Variance Explained in extraction using Principal Component Analysis. |
||||||||||
|
Component |
Initial Eigenvalues |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
|||||||
|
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
|
|
1 |
3.804 |
31.696 |
31.696 |
3.804 |
31.696 |
31.696 |
2.516 |
20.964 |
20.964 |
|
|
2 |
1.655 |
13.789 |
45.485 |
1.655 |
13.789 |
45.485 |
1.671 |
13.925 |
34.889 |
|
|
3 |
1.284 |
10.702 |
56.187 |
1.284 |
10.702 |
56.187 |
1.648 |
13.737 |
48.626 |
|
|
4 |
1.151 |
9.590 |
65.777 |
1.151 |
9.590 |
65.777 |
1.632 |
13.599 |
62.224 |
|
|
5 |
1.018 |
8.480 |
74.257 |
1.018 |
8.480 |
74.257 |
1.444 |
12.033 |
74.257 |
|
|
6 |
.673 |
5.607 |
79.864 |
|
|
|
|
|
|
|
|
7 |
.595 |
4.957 |
84.820 |
|
|
|
|
|
|
|
|
8 |
.517 |
4.309 |
89.129 |
|
|
|
|
|
|
|
|
9 |
.406 |
3.386 |
92.515 |
|
|
|
|
|
|
|
|
10 |
.361 |
3.006 |
95.521 |
|
|
|
|
|
|
|
|
11 |
.310 |
2.584 |
98.105 |
|
|
|
|
|
|
|
|
12 |
.227 |
1.895 |
100.000 |
|
|
|
|
|
|
|
|
Extraction Method: Principal Component Analysis. |
||||||||||
Further the screen plot also reflected that the study should reduce the factors to five components (Figure-3).
Figure-3: Screen plot of extraction using principal component analysis
The rotation creates a simple structure to describe the best measures of respective factors and maximizes factor loading. Two popular methods for rotation are Varimax and Direct oblimin. It is advised to use the direct oblimin method in case factors are correlated. However, Varimax should be used if factors are not supposed to be correlated. Since the correlation matrix did not show any significant correlation in the present study, the study has used the Varimax method for rotation by suppressing the absolute values lesser than 0.60. The rotated component matrix computes which variable may be included in which factor. The rotated component matrix is given in table-7. Here surrender value, maturity value, survival benefits and revival policy are included in factor 1, claim settlement and grievance redressal are included in factor 2, convenience and services are included in factor 3, brand image and customer’s knowledge about the product are included in factor 4 and premium and charges are included in factor 5.
Table-7: Rotated component matrix using Varimax
|
|
Component |
||||
|
1 |
2 |
3 |
4 |
5 |
|
|
Premium |
|
|
|
|
.843 |
|
Charges |
|
|
|
|
.800 |
|
Convenience |
|
|
.772 |
|
|
|
Service quality |
|
|
.782 |
|
|
|
Brand image |
|
|
|
.805 |
|
|
Grievance redressal |
|
.858 |
|
|
|
|
Claim settlement |
|
.850 |
|
|
|
|
Maturity value |
.792 |
|
|
|
|
|
Surrender value |
.827 |
|
|
|
|
|
Revival policy |
.782 |
|
|
|
|
|
Customer’s product knowledge |
|
|
|
.733 |
|
|
Survival benefit |
.684 |
|
|
|
|
|
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. |
|||||
Even though value of Cronbach’s alpha more than 0.70 is considered reliable but Cronbach’s alpha coefficient ranging in between 0.45 and 0.60 can be considered acceptable having regard to limited number of items (Berger & Hanze, 2015).Now we present hereunder the five factors found after data reduction.
Factor-1: Assurance
Here all factor loadings are significantly higher with 0.792, 0.827, 0.782 and 0.684 respectively with total factor score of 3.085. Four variables namely surrender value, maturity value, survival benefits and revival policy are included in this first factor. All four variables taken together are able to explain 31.696% of the variance and having Eigen value of 3.804 with Cronbach’s alpha of 0.778 (Table-8)
Table-8: Factor one (Assurance)
|
Variables |
Maturity value |
Surrender value |
Revival policy |
Survival benefit |
|
Factor loading |
0.792 |
0.827 |
0.782 |
0.684 |
|
% of variance explained |
31.696 % |
|||
|
Factor score |
3.085 |
|||
|
Eigen value |
3.804 |
|||
|
Cronbach’s alpha |
0.778 |
|||
|
Rank |
I |
|||
Factor-2: Resolution
Under this, all factor loadings are significantly higher with 0.858 and 0.850, respectively, with a total factor score of 1.708. Two variables, namely claim settment and grievance redressal are included in this second factor. All two variables taken together are able to explain 13.789% of the variance and having Eigen value of 1.655 with Cronbach’s alpha of 0.749 (Table-9)
Table-9: Factor two (Resolution)
|
Variables |
Claim settlement |
Grievance redressal |
|
Factor loading |
0.858 |
0.850 |
|
% of variance explained |
13.789% |
|
|
Factor score |
1.708 |
|
|
Eigen value |
1.655 |
|
|
Cronbach’s alpha |
0.749 |
|
|
Rank |
II |
|
Factor-3: Comfort
Under this, all factor loadings are significantly higher with 0.772 and 0.782, respectively,with a total factor score of 1.554. Two variables, namely convenience and service quality are included in factor are included in this second factor. All two variables taken together are able to explain 10.702% of the variance and having Eigen value of 1.284 with Cronbach’s alpha of 0.749 (Table-10)
Table-10: Factor three (Comfort)
|
Variables |
Convenience |
Service quality |
|
Factor loading |
0.772 |
0.782 |
|
% of variance explained |
10.702% |
|
|
Factor score |
1.554 |
|
|
Eigen value |
1.284 |
|
|
Cronbach’s alpha |
0.693 |
|
|
Rank |
IV |
|
Factor-4: Trust
Under this, all factor loadings are significantly higher with 0.805 and 0.733 ,respectively ,with a total factor load of 1.538. Two variables, namely brand image and customer’s knowledge about the product are included in this second factor. All two variables taken together explain 9.590% of the variance and having an Eigen value of 1.151 with Cronbach’s alpha of 0.603 (Table-11)
Table-11: Factor four (Trust)
|
Variables |
Convenience |
Service quality |
|
Factor loading |
0.805 |
0.733 |
|
% of variance explained |
9.590% |
|
|
Factor score |
1.538 |
|
|
Eigen value |
1.151 |
|
|
Cronbach’s alpha |
0.603 |
|
|
Rank |
V |
|
Factor-5: Outlay
Under this, all factor loadings are significantly higher with 0.843 and 0.800, respectively, with a total factor score of 1.643. Two variables, premium and charges are included in factor are included in this second factor. All two variables taken together explain 8.480% of the variance and having an Eigen value of 1.018.(Table-12)
Table-12: Five (Outlay)
|
Variables |
Premium |
Charges |
|
Factor loading |
0.843 |
0.800 |
|
% of variance explained |
8.480% |
|
|
Factor score |
1.643 |
|
|
Eigen value |
1.018 |
|
|
Cronbach’s alpha |
0.584 |
|
|
Rank |
III |
|
After identifying the five important factors, we present here several other preferences which participants exhibited for different features while buying a new life insurance policy.
Participants were asked to give their preferences for different premium payment options. 57% of the participants given their preference in favor of annual premium payment options followed by 24% in favor of monthly premium options (figure-3)
Figure-3: Prefernce towards different premium payment option
The participants were asked to exercise their preferences for different policy options. It can be observed that the money-back plan is the topmost priority followed by the endowment plan and term plan (Figure-4). 28 percent of the participants showed their preferences for money back, 22% for a term plan and endowment plan.
Figure-4: Preference for different policy options
Participants were given the choice to select a company of their choice for taking a policy. 55 percent of respondents voted for LIC followed by 12 percent for ICICI (Figure-5). Thus, it can be argued that LIC occupies the topmost position among insurance companies according to preferences given by participants.
Figure-5: Preference for different insurance companies
When asked to show their motives for taking a new policy, 55% of participants voted for risk coverage, followed by 21% for tax benefits. Only 16% of the participants voted for return and only 8% for child welfare (Figure-6)
Figure-6: Motives behing taking a policy
Nowadays different rider benefits are being offered by different insurance for attracting customers. When participants were asked to give their preference for different rider benefits in a new policy, the highest preference of 31% was given in favor of accidental death benefit followed by 28% for critical illness (Figure-7)
Figure-7: Preference for different rider benefits in a policy
Discussion and findings
The task of life insurance is to protect the livelihoods of people and their future financial security, and thus, it directly correlates with people's earnings, business performance, and net worth. COVID-19 has drastically changed the way people think about a life insurance policy (Tyagi & Pabalkar,2021). The shift is from treating a life insurance policy as an investment instrument to a risk protection instrument (Agarwal & Azmi,2020). There has been greater awareness and enthusiasm among common people towards taking a policy. Due to financial pressure owing to job losses and salary cuts, many customers perceived policy lapses and surrenders. Companies also faced huge financial pressure due to a drastic jump in claims (PWC,2020). After the second wave, companies have started launching new products to attract customers (Adams et al., 2020). However, it is high time when companies should understand the changed behaviour and expectation of people towards a life insurance product. We here emphasize that surrender value, maturity value, survival benefits, and revival policy are the most demanding features, followed by claim settlement and grievance redressal (Patrick,2021, Sahu, Jaisawal & Pandey,2009). At the same time, emphasize providing better services for attracting customers (Chaudhary,2016, Halonen, Rizan, & Suhud, 2020). For creating awareness towards a particular insurance product, sizable investment in advertisement is desirable (Negi & Singh,2012). The role of an insurance agent can not be ignored for creating new customers, particularly in rural areas which are still out of internet connectivity (Srivastava & Tripathi, 2012). Compared to other insurers, Life Insurance Corporation enjoys a better reputation among investors in the life insurance sector.
Conclusion
The main purpose of this study was to evaluate changes in customer’s behaviour due to the COVID-19 pandemic and highlight the features that should form part of a life insurance product to attract customers. After the pandemic, we have found that people have become more serious about life insurance and started treating it as an instrument to cover risks. However, at the same time, many had a bad experience with claim and grievance settlement and service concerns during the COVID-19 pandemic. We emphasize that assurance (maturity value, surrender value, survival benefits and revival policy) and resolution ( claim and grievance settlement) are the most important factors that insurance companies should improve to attract customers during the COVID-19 pandemic. Further, proper disclosure of information to the buyers creates satisfaction among buyers of a life insurance policy because many investors claimed that all information were not disclosed at the time of taking policy.
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