Tudying the factors affecting the decision to buy life insurance products in Vietnam

ed and the relevance of the models in considering the decision to buy life insurance from the perspective of buying behavior in order to build a suitable research model for the thesis. 2.2.1. Theory of Reasonable Action (TRA) The departure from conventional academic models paved the way for the work of Fishbein and Ajzen (1980) ; Ajzen (1985) by creating a theoretical model called the "Theory of Reasonable Action Model”. Fishbein has identified many flaws of previous models that were lack of predictability and difficult to measure. On that basis, he and his colleagues created a theoretical model to use for businesses in predicting and explaining behavior. He did this by studying one of the main foundations of psychological theory of the relationship between attitude and behavior and drawing some conclusions. 2.2.2. The theory of planned behavior (TPB) The theory of planned behavior (TPB) Ajzen (1991) is the development and improvement of rational action theory. The Ajzen TPB (1991) has expanded the model of the TRA to overcome the limitations in explaining out-of-control behaviors. Basically, TPB is an extension of TRA (Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980). The theory of planned behavior (TPB) states that human behavior is guided by three types of considerations: beliefs about the probable consequences or other attributes of behavior (behavior beliefs), beliefs about other people's standard expectations (standard beliefs) and beliefs about the presence of factors that can support or hinder the behavior of behavior (belief control). 2.2.3. Theoretical basis for the relationship between attitudes, intentions and behaviors ( Ajzen and Fishbein, 2005 ) The study was developed by Ajzen and Fishbein to look for the effect of attitude on behavior that previous studies have concluded conflicting results . With such conflicting conclusions in their research, Ajzen and Fishbein (2005) have tried to explain the relationship between attitudes, intentions and behaviors with some basic assumptions including: Intention is direct basic of actual behavior; Intention, in turn, is determined by attitudes toward behavior, subjective standards, and cognitive control behaviors. These determinants are a corresponding function of basic behavior, norms and control of beliefs; Beliefs about behavior, norms, and control can vary according to the function of a range of background factors. 2.3. Factors affecting the decision to buy life insurance products 2.3.1. Factors belong to attitude The behavior or decision to buy life insurance is directed towards the individual's belief that what results (positive or negative) in buying insurance, such as: buying life insurance is useful or buying life insurance is a waste of money ( Ogenyi Ejye and Owusu-Frimpong, 2007 ; Omar, 2007). Attitudes towards the purchase of life insurance are constituted including the following factors: perceive benefits; risk awareness and risk attitude. 2.3.2. Subjective norm factors The structure of subjective norms is the perceived social pressure to engage or not engage in a behavior ( Ajzen, 2008 ). It is assumed that subjective norms are determined by the total number of normative beliefs that can be achieved in relation to the expectations of an important allusion. Normative allusions can be invoked through questions about which groups of people will approve or disapprove; support or not support an individual's conduct of specific acts. 2.3.3. Cognitive controls Behavior Cognitive controls behavior shows how well an individual feels about their ability to perform a behavior, related to how easy or difficult it is to perform a behavior. Accordingly, in the field of insurance, behavioral control elements are classified into 2 groups including: - The group of internal control factors related to product knowledge (knowledge, skills on life insurance in particular and the financial sector in general) is called financial knowledge; - The group of external factors related to the availability or accessibility or performance of life insurance purchase (distribution channel system, product availability, etc.) is called product accessibility. 2.3.4. Intention to act Decision Attitude towards risks Subjective norms Attitude towards life insurance Financial knowledge Intention Cognitive of benefits Product accessibility Cognitive of Risks Intention to buy life insurance means its planned behaviors in the future of purchasing life insurance in a specific period of time and in the thesis using a time range of the following 5 years. 2.3.5. Consumer behavior or decisions (buying decisions) The act or decision to buy life insurance products is the personal process of approaching, selecting and using life insurance services. This behavior may be done independently of the influence of internal factors (risk attitude; product attitude; attitude towards purchasing action) but may also be due to the influence of external factors such as subjective norms or control of perceived behavior or intention. 2.3.6. The relationship between intention and behavior As analyzed above, behavior is driven by the intention to perform it. The higher an individual's intention to perform the act, the greater the likelihood that he or she will commit it. In this study, the relationship between intent to behavior reflected by the behavioral control awareness factor (financial knowledge and product accessibility) may exert a strong or weakening effect of the intention to purchase life insurance products. CHAPTER 3 MODELS AND RESEARCH METHODS 3.1. Theoretical model and hypothesis are used to examin the factors affecting the decision to buy life insurance products Based on an integrated analysis of research results and theoretical models, as well as a review of the research results in Vietnam within the framework of its research, with suggestions and insights from researches, the author desires to solve research issues by proposing a model to study the factors affecting the decision to buy life insurance as follows: Figure 3.1: Model of factors influencing product purchase decisions in life insurance Source: Synthesis of the author Accordingly, the research hypothesis of the model includes: Hypothesis H1: The attitude towards risk negatively affects the attitude towards the purchase of life insurance products Hypothesis H2: Cognitive of benefits of life insurance products positively affect attitudes towards the purchase of life insurance products Hypothesis H3: Cognitive of risk positively affects attitude towards the purchase of life insurance products Hypothesis H4: Attitude towards life insurance positively affects the intention to buy life insurance Hypothesis H5: Subjective norms positively influence the intention to buy life insurance Hypothesis H6: Cognitive of risk positively affects the intention to buy life insurance products Hypothesis H7: Financial knowledge positively affects the intention to buy life insurance Hypothesis H8: Product accessibility positively affects the intention to buy life insurance Hypothesis H9: intention to buy life insurance positively influences the decision to buy life insurance Hypothesis H10: financial knowledge positively affects the decision to buy life insurance. Hypothesis H11: product accessibility positively influences life insurance purchasing decisions Hypothesis H12: Product accessibility positively influences the impulse process from intent to purchasing behavior. Hypothesis H13: financial knowledge positively influences the impulse process from intention to purchasing behavior. 3.2. Research Methods The thesis uses 2 data sources: secondary data sources (data that has been previously collected and published) and primary data sources (data collected by graduate students). The metrics used for this study were adapted from the same metrics used in previous studies. On that basis, the PhD student carried out the inheritance and adopted the modifications after the survey and in-depth interviews with experts. In addition, the scales have been adjusted to suit the Vietnamese context. Accordingly, the specific scale of the model is shown in detail in the following table: Table 3.3: Encode the scale and observed variables of the research model The scale Encode Observation variable Source Attitude towards risks TDRR1 I am willing to accept financial risks. ( Brahmana et al, 2018 ; Ogenyi Ejye and Owusu-Frimpong, 2007 ) ; ( Dohmen et al., 2011 ) TDRR2 I like the investments that are more profitable even though they are more risky. TDRR3 I do not like risky investments even though the probability of risk is low Cognitive of product benefits CNLI1 Life insurance is a form of savings for the future ( Brahmana et al, 2018 ; Ogenyi Ejye and Owusu-Frimpong, 2007 ) CNLI2 Life insurance provides financial support for life events (giving birth, retiring, etc.) CNLI3 Life insurance provides the assurance to dependents Risk awareness NTRR1 I always listen to others' advice about the future financial plan ( Jacobs-Lawson and Hershey, 2005 ; Jacoby and B. Kaplan, 1972 ) ( Eisenhauer and Halek, 1999 ; Nguyen Tien Dung et al., 2015 ) ; ( Brahmana et al, 2018 ) NTRR2 I like to think about how I will live in the future. NTRR3 The future is too vague and uncertain NTRR4 The future is too far away to plan Attitude towards life insurance TDBH1 I think buying life insurance is a necessary option ( Brahmana et al, 2018 ; Ogenyi Ejye and Owusu-Frimpong, 2007 ; Omar, 2007 ; Fletcher and Hastings, 1984 ; Ajzen and Fishbein, 2005 ) TDBH2 Buying life insurance helps me plan my financial future TDBH3 Buying life insurance helps me feel secure at work and enjoy my life TDBH4 Buying life insurance is a way of savings Subjective norms on life insurance CMCQ1 Everyone who influences me thinks I should buy life insurance ( Ajzen and Fishbein, 2000 ; Ajzen and Fishbein, 2005 ; Ogenyi Ejye and Owusu- Frimpong, 2007 ) CMCQ2 People in my environment think I should buy insurance life CMCQ3 My friends all have life insurance CMCQ4 My relatives support me to buy life insurance Financial Knowledge HBTC1 I have enough knowledge to buy life insurance ( Brahmana et al, 2018 ; Ajzen and Fishbein, HBTC2 Life insurance products are very The scale Encode Observation variable Source simple and easy to understand 2005 ; Jacobs-Lawson and Hershey, 2005 ; Jacoby and B. Kaplan, 1972 ) HBTC3 I am knowledgeable about the mechanism of operation of life insurance HBTC4 I am very confident about my ability in relation to future financial plans. Product accessibility TCSP1 I know where life insurance products are sold ( Jacobs-Lawson and Hershey, 2005 ; Jacoby and B. Kaplan, 1972 ; Nguyen Vu Hung and Hoang Thi Bao Thoa, 2016 ; Brahmana et al, 2018 ; Ogenyi Ejye and Owusu-Frimpong, 2007 ) TCSP2 I know there are many life insurance product distribution channels TCSP3 When I need to search for information on insurance financial services I know exactly where to get information and where to get it Intention to buy life insurance YD1 I will buy life insurance in the future ( Nguyen Tien Dung et al, 2015 ; Ogenyi Ejye and Owusu- Frimpong, 2007 ; Omar, 2007 ; Brahmana et al, 2018 ) YD2 I plan to buy life insurance in the near future (next 5 years) YD3 I am not sure if I will buy life insurance in the future Decide to buy life insurance QD1 I bought life insurance through an insurance consultant / broker / bank ( Nguyen Vu Hung and Hoang Thi Bao Thoa, 2016 ; Ajzen, 2006 ; Ajzen and Fishbein, 2005 ) QD2 I was convinced to buy life insurance without any intention from the beginning. QD3 I actively seek and buy life insurance. QD4 I recommend the product of life insurance to relatives and friends. Source: Summary of the author 3.3. Data analysis method Data collected from the survey will be analyzed and processed in the following order: - Data entry; Data cleansing; Verify the reliability of the scale; Factor Analysis. A factor analysis tool is used to check and classify items (questions) into different factor groups; Descriptive statistics; Correlation analysis; Linear regression analysis; CHAPTER 4 CURRENT SITUATION OF LIFE INSURANCE MARKET IN VIETNAM AND THE EFFECTS OF FACTORS TO DECISION ON BUYING LIFE INSURANCE 4.1. Situation of life insurance market in Vietnam 4.1. Situation of life insurance market in Vietnam According to the Insurance Supervisory Authority, as of December 31, 2017, the insurance market has 64 enterprises operating in the fields of non-life, life insurance, reinsurance and insurance brokers. Total assets of the market reached VND 316,300 billion, up 25% compared to 2016, the total market revenue reached 132,369 billion VND, of which insurance premium revenue reached VND 107,821 billion (up 23.4% over the year). In 2016, the investment revenue reached VND 24,548 billion, the payment of insurance benefits was VND 31,904 billion. In particular, at the end of 2017, the life insurance market continued to maintain impressive growth momentum, with total premium revenue estimated at VND 66,235 billion, up 31% compared to 2016, in which, new sales revenue was estimated at VND 22,558 billion, up 28.7%; The number of valid contracts (under the main contract) is estimated at 7,447,242 contracts, up 16.4% over the same period. In the period from 2013 to the end of 2018, the life insurance market has many big changes with the acquisition and change of brand name. 4.2. Analysis of factors affecting the decision to buy life insurance products 4.2.1. Results of statistical analysis describing the survey samples Regarding to genders of survey subjects, the results showed that 166 male and 176 female participants answered the questionnaires. The number of women is higher than that of men, but is not significant (51.5% and 48.5%). Regarding to the survey age, the results of the analysis of the number of surveyed people aged from 31 to 40 years old accounted for the largest proportion. The second proportion is the object from 41 to 50 years old, the third is from 20 to 30 and the last is from 50 years old. The age structure of the surveyed subjects is quite similar to the potential buyers of insurance that insurance enterprises need to exploit. Regarding to education level, we see that 29 people with lower education level accounted for 8.5%, 11 people accounted for high school level as 3.2%, university degree accounted for 180 people equivalent to 52.6%. This figure also reflects quite accurately the educational level of Hanoi area with the majority of intellectual and business labor. Regarding to income, from the interview data, it can be seen that the number of respondents surveyed with income below VND 9 million accounts for a relatively low rate of 7.3% corresponding to 25 votes. The number of subjects with income from VND 9 million to VND 15 million accounts for 45.6%, this takes the highest proportion of income level. With 25.1% and 86 choices of income levels over VND 20 million, this rate indicates that the people's income is not too low, but the rate of buying insurance is still limited. Therefore, factor analysis is expected to produce significant results. Regarding the buying status, the survey results about the purchase of life insurance of the participants have 90.1% of the respondents never bought the insurance and only 9.9% of the respondents selected the status of ever participated. This data is relatively consistent with the current situation in Hanoi City. 4.2.2. Analyze the reliability of the scales by Cronbach's alpha coefficient The results of the reliability analysis of the scales show that the alpha coefficients are all greater than 0.6, ensuring the reliability for subsequent analyzes. 4.2.3. EFA discovery factor analysis The results of the combined EFA factor analysis after using the rotation have produced a feasible result with the observed variables converging on the same factor and the weight of all factors is greater than 0.5 with the corresponding KMO coefficient is 0.819> 0.5, which shows the suitability of the observed variables in the model. It is therefore possible to use this result for further analysis steps. 4.2.4. Regression analysis of the influence of factors in the research model 4.2.4.1. Correlation check Pearson correlation test results show that the correlation coefficients ensure the reliability as the basis for the next analysis step. 4.2.4.2. Influence of factors on attitude towards life insurance purchase. The results of linear regression analysis are as follows: TDBH = -0.39 * TDRR + 0.363 * CNLI + 0.11 * NTRR For factors affecting the attitude of buying life insurance (model 1) Hypotheses H1, H2, H3 are all tested with expected results by regression model with r squared as 39%. In which, TDRR is the factor that has the most negative and strong influence on the attitude of buying life insurance with a correlation coefficient of (-0.39) units. The independent variable CNLI also has a strong influence with every 1 increase of CNLI unit will increase the TDBH by 0.363 units. Meanwhile, NTRR has less influence with standardized correlation coefficient of 0.11. 4.2.4.3. The influence of factors on the intention to buy life insurance The results of linear regression analysis are as follows: YD = 0.446 * HBTC + 0.158 * TCSP + 0.191 * CMCQ + 0.1122 * TDBH + 0.168 * NTRR (R squared 45.2%) The data processing results show that the model with R2 = 0.452 shows that the regression results explain 45.2% of the significance of the model and the beta values of the factors ensure statistical significance levels with sig all very small (<0.05). Therefore, the hypotheses H4, H5, H6, H7 and H8 of the model all meet the expected values. Accordingly, 2 cognitive behavioral control variables including financial knowledge and product accessibility all influence the intention to buy life insurance. The powerful factor behind financial literacy is the subjective norm that influences the intention to buy life insurance. 4.2.4.4. The influence of factors on the decision to buy life insurance The results of linear regression analysis are as follows: QD = 0.218 * HBTC + 0.223 * TCSP + 0.192 * YD (R squared 25.5%) With model 3, when considering the factors affecting the decision to buy life insurance, the hypotheses H9, H10, H11 are tested with the possibility that TCSP is the most powerful influence on the formation of buying behavior. The beta coefficient is 0.223. The level of HBTC is less with the impact of 0.218 units on the QD while the intention is only 0.192. This shows that unlike normal products, the purchase of life insurance products is very fast. It is not necessarily that participants must have the intention at the beginning but sometimes only by meeting a persuasive consultant or if they meet an appropriate distribution channel, they can decide to buy life insurance. However, it can be seen in the research model that because of factors affecting purchasing intentions, it is likely to affect the decision to buy life insurance. Therefore, the thesis continues to test more hypotheses about the effects of the factors TDRR, CNLI, NTRR, TDBH, CMCQ to decision of purchasing life insurance in addition to direct impact variables such as HBTC, TCSP, YD. To eliminate the autocorrelation and multicollinear effects between these variables, all of the independent variables were run Mean Center before being included in the regression model and obtained the variables TDRR_AVER, CNLI_AVER, NTRR_AVER, TDBH_AVER; CMCQ_AVER; YD_AVER; HBTC_AVER; TCSP_AVER. At the same time, to compare the influence of these factors on the purchase of non-life insurance by two groups of customers who have purchased and have not purchased, the split file technique is used to compare the regression results of these two groups. The regression results obtained show the variables CNLI_AVER, NTRR_AVER, TDBH_AVER; CMCQ_AVER are not guaranteed sig values (<0.05) then they are excluded from the model. Accordingly, the regression results are as follows: Compare the impact of factors in the buying decision model on two groups of customers For those who have not yet purchased life insurance (285 people), the independent variables in the model have a better level of explanation than the overall analysis (R 2 = 58.9%). In particular, besides the influence of factors such as HBTC; TCSP; YD, TDRR is also a factor with statistically significant impact on sig level (<0.05). Therefore, it can be seen that the participant has not purchased the non-life insurance can be due to the influence of all 4 factors: TDRR, knowledge, ability to access the product and the customer has no intention. Additionally, apart from their intention, the factor HBTC makes the most impact on people who have not purchased life insurance. This is also in line with current market practices. Therefore, it is necessary to change the approach to products for these target groups to increase the understanding and intentions for customers. For the group of people who have bought life insurance (85 participants), as the R square of 51.6% with the correlation coefficients and the strongest influence of HBTC in addition to the influence of HBTC and TCSP are respective, it strongly promoted the buying decision. Meanwhile TDRR is really a barrier to the decision to buy life insurance. This result is also consistent with the studies of Le Long Hau, 2017 ; Mai Thi Huong and Bui Thi Thu Ha, 2019. However, the above results have not indicated how the effect takes place during the purchase process from intention to customer behavior. Therefore, the study will continue to implement regression model with 2 regulatory variables including financial knowledge and product accessibility to see the impact of these 2 variables on the motivating process from intent to behavior. 4.2.4.5. The process influences intent from behavior through moderator variables The results of linear regression analysis are as follows: Model 4: QD = 2.5410 + 0.2324 * YD_AVER + 0.1678 * HBTC_AVER + 0.178* TCSP_AVER + 0.11811 * YD_AVER * HBTC_AVER With the model considering the process of motivating from intent to behavior (QD), the addition of regulatory variable makes the model more meaningful and the regression results also show that, once there is the intention to buy insurance along with financial know-how, customers can find the appropriate distribution channels and access insurance. (hypothesis H12 is rejected and hypothesis H13 is accepted). Comparing 3 models 2,3,4, you can see: The independent variables in the model better explain intention than behavior. Factors TCSP influence stronger to the QD than to YD due to “cognitive control behavior” factor only becomes reality when considering the decision for this in terms of intentions, this factor does not affect much because consumers have not been difficult or hindered to enter the market but only the intention exists. Thus, except for rejecting the H12 hypothesis, the other research hypotheses of the model have been fully tested to meet the thesis's expectations. CHAPTER 5 SOLUTIONS AND RECOMMENDATIONS TO INCREASE THE DECISION TO BUY LIFE INSURANCE PRODUCTS 5.1. Review on the analysis of the influence of factors on the decision to buy life insurance products 5.1.1. Test results of research hypotheses for factors affecting the attitude of purchasing life insurance (model 1) Hypotheses H1, H2, H3 are all tested with expected results by regression model with r squared as 39%. In which TDRR is the strongest and most negative influencing factor on the attitude of buying life insurance with a correlation coefficient of (-0.39) units. This is also consistent with the research results of Le Khuong Ninh and Huynh Huu Tho (2013) and Le Long Hau (2017). Research results show that those with high-risk or risk-prone attitude tend to choose other investment channels that bring higher profit margins instead of buying life insurance. Luciano et al. (2015) also confirmed that the financial risk attitude negatively affects th