Risk Awareness for Vietnamese’s Life Insurance on Financial Protection: The Case Study of Daklak Province, Vietnam

Abstract

This paper aims to identify risk awareness through factors that influence the intention to buy people’s life insurance in Daklak province of Vietnam and provide implications for life insurance companies. The data resources were conducted from the survey of 250 people in Daklak Province and applied the ordinal logit model for the analysis. Remarkably, as we conducted the study during the COVID-19 pandemic period, a dummy variable of COVID-19 was included in the analysis. The results of this research have some similarities and differences with other studies. As with the references, saving motivation was the most crucial factor affecting the dependent variable. Saving motivation, financial literacy, brand name, and risk awareness have a positive impact. While age and gender were differences that have a negative effect on the intention to buy life insurance, which means that young people and women have more intention to purchase life insurance than younger men. The four factors consisting of financial literacy, brand name, risk awareness, and gender were considered the second most important factors. COVID-19 and attitude were the third critical effect on the intention to purchase life insurance. Income was the less important factor.

1. Introduction

Risks always exist in human life. It often happens unexpectedly, without knowing the damage it will bring. One of the ways to deal with risks is holding insurance. Insurance can provide financial protection against risks to property, civil liability, or people. There was a question if a person had a goose that laid golden eggs, would he insure the goose or insure the eggs? (Bautis Financial 2013). It is a fact that many people in Vietnam own all kinds of insurance for material assets such as houses, cars, goods, and construction works, but they have not yet insured themself who create material wealth. As we raise a goose that lays golden eggs, we only hedge the risks for the eggs, while the goose that produces those eggs is completely unprotected. Especially, in Vietnam, at the end of 2020, the number of people who had life insurance policies was only about 10% of the population (Helen 2021).

COVID-19 is also an associated risk factor. Although Vietnam had succeeded in keeping zero cases in the first, second, and third waves of COVID-19, at the end of April 2021, the highly transmissible Delta strain began to spread, and the country faced a fourth wave (Tough 2021). In the fourth wave, the number of COVID-19 cases in Vietnam was around 100 times higher than the total of the previous three waves. The number of deaths due to COVID-19 increased (Health Minister 2021). The risk of the COVID-19 pandemic required life insurance companies to pay more for the customers’ higher risk of illness and mortality rate (Kirti and Shin 2020; Babuna et al. 2020). However, people may be aware of the risks leading to the financial loss of individuals and families during the pandemic, so they may look for financial protection measures against hazards, such as participating in life insurance (Babuna et al. 2020). Usually, products and services are bought when people need them immediately. However, insurance products are different due to the characteristics of insurance, collecting premiums in advance and indemnifying or paying when there is risk or maturity. Thus, when a person wants to get insurance but has already suffered a financial loss related to the insured object, he cannot participate in insurance anymore. For example, when a person is over 70 years old, they cannot participate in life insurance to enjoy old age. When participating in life insurance, the younger the age, the lower the premium, and vice versa. That is, with an insurance product, when the buyer does not need to use it, he can buy it, and if the buyer needs to use it, he cannot buy it. Insurance is not only needed for the insured but also meaningful for the whole economy. When buying insurance, both the buyer and the insurance company must apply safety measures to the insured object, which helps to ensure the safety of each individual and the whole society. Insurance also holds a large amount of idle capital to reinvest in the economy. Up to now (2022), there has been neither any specific information about the life insurance market in Daklak province, Vietnam, nor research about life insurance in this area. When conducting a trial survey before the official study, randomly 30 people in Buon Ma Thuot city, Daklak province, only 5/30 people have purchased life insurance for their families. Therefore, more than 80% of people surveyed still did not have a life insurance policy yet. Primarily, there is still no research on the intention to buy life insurance during the COVID-19 pandemic in Vietnam as well as over the world. With the desire to help increase the participation rate of life insurance in the future to help families in Daklak province have solid financial protection in life during normal times as well as during the COVID-19 (Covid) pandemic time, and help the economy attract more idle capital from life insurance.

The study “Risk Awareness for Vietnamese’s Life Insurance on Financial Protection: The Case Study of Daklak Province, Vietnam,” was proposed to determine the factors affecting the intention to buy life insurance.

In this research, we add one more factor, Covid, besides the conventional factors to investigate whichare the key factors affecting the intention to buy life insurance in the research area. The study can help life insurance companies in the research area to have appropriate solutions to ensure finances for the family in all life situations, increase the rate of life insurance ownership in the entire population and attract idle capital for the economy.

The rest of the paper is organized as follows: Section 2 provides a literature review. Section 3 presents the research methodology, including group discussion, data collection, research model, and data analysis. Section 4 presents the research results. Section 6 is the conclusion, along with our remarks.

2. Literature Reviews

According to the insurance law of Vietnam, “Life insurance is a type of insurance in case the insured lives or dies.” “The subject matter of a life insurance contract is the life span, human life” (Law No. 08/2022/QH15 2022). The most extensively utilized personal financial security planning product in the world is life insurance (Ayenew et al. 2020; Chen et al. 2006; Lin et al. 2017; Tien 2021). It is a type of insurance against risks related to human life that came from an idea by an English captain in 1583. He asked the insurance company for his life insurance with his explanation that if the boat was insured then the owner of the boat should also be insured (Fouse 1905). Due to the specificity of the insurance, collecting premiums in advance and indemnifying or paying at risk or maturity, there exists a period of capital idleness from the accumulated premiums. That idle money will be invested back into the economy. An essential principle in insurance is that the many compliments the few. Therefore, the more people participate in the same insurance business, the larger the risk-offering fund, the more accessible, and the more idle money is available to invest back into the economy. Several previous studies have found a beneficial association between economic growth and insurance (Arena 2008; Ćurak et al. 2009; Ege and Saraç 2011; Vadlamannati 2008).

According to the Theory of Planned Behavior, purchase intention influences customer behavior (Ajzen 1991). In the covid pandemic, especially in the fourth wave, people in Vietnam face a lot of risks such as loss of breadwinners and health risks during and after covid. Therefore, people may intend to buy insurance for themselves and their family members against financial losses caused by covid risks. Managers use purchase intention as critical in forecasting future sales and identifying initiatives to influence client behavior (Jamieson and Bass 1989). Purchase intention is sometimes used to gauge demand for a new product. Purchase intention is often used by marketing executives to predict future demand for their products and to assess the impact of their marketing efforts on future sales (Morwitz 2014).

Many studies suggest that purchase intention is a type of rational decision-making when a buyer chooses to purchase from a specific brand name (Brand_name) (Jackson 2017; Saad et al. 2012; Tariq et al. 2013). According to the Theory of Reasoned Action (TRA) proposed by Ajzen and Fishbein (1975) cited in Rutter and Bunce (1989), and the Theory of Planned Behavior (TPB), an individual’s purchase intention is influenced by two factors, namely attitude and subject norm (Sub_norm) (Ajzen 2011; Ajzen and Fishbein 1980; Rutter and Bunce 1989; Sheppard et al. 1988). Life insurance is a type of insurance that protects against financial risks related to people’s lifespans. Therefore, risk awareness (Risk_awa) is an essential factor affecting people’s intention to buy life insurance (Qin and Zhang 2012; Jahan and Sabbir 2019). Saving motivation (Sav_moti) and financial literacy (Financial_lite) are two more factors that influence the desire to get life insurance (Zakaria et al. 2016; Jahan and Sabbir 2019). In addition, the COVID-19 pandemic is also a factor affecting the insurance industry. Although there have been no studies on the intention to participate in life insurance and the COVID-19 (Covid) pandemic. Moreover, demographics are also one of the important factors that affect the intention to buy life insurance (Beck and Webb 2003)

Based on the theoretical overview, the author would like to divide the factors into two groups: subjective and objective factors. The hypotheses about the factors affecting the intention to buy life insurance are proposed in this study as follows:

H1. 

Subjective factors consist of Age, Gender, Education, Marital status, Income, Attitude, Sub_norm, Risk_awa, Sav_moti, Financial_lite, influence the intention to purchase life insurance.

H2. 

Objective factors consist of Brand_name and Covid effect on the intention to purchase life insurance.

The explanation of H1 and H2 are illustrated in

Table 1. Explanation of the hypothesis.

Categories	Explanations
H1: Subjective factors influence the intention to purchase life insurance
Age	Age has a positive effect on the intention to purchase life insurance (Ćurak et al. 2013; Yusuf et al. 2009)
Gender	- Men have more intention to purchase life insurance than women (Gandolfi and Miners 1996 ) - Men and women equally demand life insurance (Ćurak et al. 2013)
Education	Education has a positive effect on the intention to purchase life insurance (Jahan and Sabbir 2019).
Marital status	Single individuals have the most intention to purchase life insurance, followed by married individuals and the last one was individuals who divorced (Mahdzan and Peter Victorian 2013 )
Income, Attitude, Sub_norm Risk_awa, Sav_moti, Financial_lite	Income, Attitude toward life insurance, risk awareness, saving motivation, and financial literacy have a positive effect on the intention to purchase life insurance (Jackson 2017; Saad et al. 2012; Mahdzan and Peter Victorian 2013; Tariq et al. 2013; Qin and Zhang 2012; Jahan and Sabbir 2019; Zakaria et al. 2016)
H2: Objective factors influence the intention to purchase life insurance
Brand_name	Brand name has a positive effect on the intention to purchase life insurance (Jackson 2017; Saad et al. 2012; Tariq et al. 2013; Qin and Zhang 2012; Jahan and Sabbir 2019; Zakaria et al. 2016 )
Covid	The COVID-19 pandemic may have a positive effect on the intention to purchase life insurance

.

Due to the characteristics of insurance, collecting premiums in advance and indemnifying or paying when there is risk or maturity. Therefore, there exists an idle period of capital from the collected premium. That amount of idle money will be invested back into the economy. An essential principle in insurance is that the majority complement the few; hence the more people participating in the same insurance business, the greater the monetary fund for more accessible risk compensation, and the amount of idle money is also more to invest back into the economy. Some prior research had found a beneficial association between economic growth and insurance (Arena 2008; Vadlamannati 2008).

In general, previous studies have identified factors affecting the intention to purchase life insurance for people in many parts of the world. However, there has not been a study conducted in Daklak province of Vietnam, and no research has mentioned the risk factors of COVID-19 to the intention to buy life insurance. In this study, the author develops previous studies with adjustments to suit the research area and adds the COVID-19 factor, which is one of the new risk events appearing in 2019 that badly affected the lives of people.

3. Methodology

3.1. Group Focus Discussion

In designing the survey questionnaire for data collection, we formed a group of specialists, current customers, and potential ones through collective efforts of forming, norming, and storming.

3.2. Data Collection

This study was conducted in Buon Ma Thuot city, the largest city in the Central Highlands, located in the center of the province and the city in the center of the Central Highlands of Vietnam (Statistic office of Daklak 2019). According to Vietnamese civil law 2015, a minor is a person under the age of eighteen. The people who sign the insurance policy must be older than 18 (Vietnamese Civil Law 2015). Therefore, in this study, we used stratified random sampling to select a survey with 250 citizens over 18 years old in Buon Ma Thuot city Daklak province. A pre-survey with 30 questionnaires was sent to the respondees to ensure that all the questions were understandable. The actual survey was conducted from May to June 2021. The research area is illustrated in Figure 1.

3.3. Research Model and Data Analysis

With the hypotheses given in Table 1 the author proposes the following research model (Figure 2).

Purchase intention is the probability that a person will choose to buy a product or service in the future. This probability ranges from 0–1 or 0 to 100%. To measure purchase intention, researchers often conduct surveys with a sample and ask respondents to answer questions about purchase intent, such as “Do you intend to purchase item x shortly?”. On a scale with feedback options such as “will buy” (100% probability), “probably will buy,” “may or may not buy,” “probably won’t buy,” “will buy,” and “do not buy” (0% probability) (Morwitz 2014). In this study, the dependent variable (intention) was proposed with five options (0 to 4) as above.

An ordered logit model investigated the correlation between the dependent variable (intention) and the other independent variables. In H1 and H2, the independent variables, including Brand name, Risk, Covid, Attitude, Sub_norm, Sav_moti, and Financial were developed using a 5-point Likert scale with 5 indicating high agreement and 1 indicating significant disagreement (detail in the Appendix A). Demographics including Age, Gender, Literacy, Marital status, and Income are explained in Table 1.

To eliminate non-conforming variables, reliability analysis and exploratory factor analysis were performed with the variables that consist of the question in the 5-point Likert scale before running the ordinal logit model. Furthermore, the multicollinearity diagnosis was established to assess the link between the independent variables, resulting in an erroneous model.

Cronbach’s alpha was used to examine the association in the study’s reliability analysis, revealing the close relationship between the object groups. Correct Item-total Correlation indexes in Cronbach’s alpha analysis reflect the contribution of variables. When Cronbach’s alpha coefficient is 0.7 and the Correct Item-total Correlation is more than 0.3, reliability is regarded as appropriate (Nunnally 1978).

The incorrect variables were deleted after the reliability analysis, and Exploratory Factor Analysis (EFA) was performed to define the elements determining the significance of participation in life insurance.

Bartlett test (Sig. ≤ 0.05) and Total Variance Explained > 50%

Load Factor > 0.5 and Kaiser-Meyer-Olkin (KMO) 0.5 KMO 1

Variables with a KMO of less than 0.5 will be eliminated from the model (Nunnally 1978).

The phenomenon of multicollinearity occurs when the independent variables are strongly connected. A regression model with multicollinearity will make quantitative research findings inconsistent and inaccurate. VIF (Variance Inflation Factors) is employed to determine multicollinearity, and Tolerance Values are used to evaluate multicollinearity. Tolerance Values of 0.20 are a serious concern. Multicollinearity can occur when tolerance values are less than 0.10. Because Tolerance Values are the polar opposite of VIF, Tolerance Values 0.20 applies to rule 5 and 0.10 to rule 10 (Menard 2001). Hair et al. (1995) demonstrated that if the VIF is less than ten, the model is not multicollinear (Hair et al. 1995). VIF > 10 indicates undesirable collinearity for the study model’s conclusions (Kennedy 2008).

An ordered logit model was applied after analyzing Reliability, Exploratory Factor Analysis, and Multicollinearity.

The model to determine the influencing factors is as follows:

Intention = β₀ + β₁Age + β₂Gender + β₃Literacy + β₄Maritalstatus +
β₅Income + β₆Brand_name + β₇Sub_norm + β₈Financial_lite +
β₉Covid + β₁₀Sav_moti + β₁₁Attitude + β₁₂Risk_awa + ε

(1)

In this study, 250 individuals were surveyed to find out about their intention to buy life insurance. Suppose the proportion of people who intend to purchase life insurance who would answer “do not intend to purchase life insurance,” “may not purchase life insurance,” “may or may not purchase life insurance,” and “may purchase life insurance” are p1, p2, p3, p4, p5, respectively. The logarithms of the odds (rather than the logarithms of the probabilities) of answering in particular ways are then calculated as follows.

Do not intend to purchase life insurance: $log\frac{p1}{p2+p3+p4+p5}$, 0

Do not intend to purchase life insurance, or may not purchase life insurance: $log\frac{p1+p2}{p3+p4+p5} ,$ 1

Do not intend to purchase life insurance, may not purchase life insurance, or may or may not purchase life insurance: $log\frac{p1+p2+p3}{p4+p5}$, 2

Do not intend to purchase life insurance, may not buy life insurance, may or may not purchase life insurance, or may purchase life insurance: $log\frac{p1+p2+p3+p4}{p5}$, 3.

According to the proportionate odds assumption, the number added to each of these logarithms to generate the next is the same in all cases. These logarithms, in other words, create an arithmetic series. Ordinary least squares cannot be used to estimate the coefficients in the linear combination consistently. Most of the time, the greatest likelihood is used to estimate them. Iteratively reweighted least squares are used to calculate maximum-likelihood estimates.

Assume that the underlying process to be described is

Y* = X^Tβ + ε

(2)

• ε: logistically distributed error;
• Y* where y* is the precise but unknown dependent variable;
• X is the vector of the independent variables defined in Equation (1);
• β is the regression coefficients vector that we want to estimate.

While we are unable to observe y*, we can only observe the answer categories. Y = 0 if Yj * $\le \mu$₁,

Y = 1 if $\mu$₁ $<Y* \le \mu$_2,

Y = 2 if $\mu$₂ $<Y* \le \mu$_3,

Y = ………………………,

Y = N if $\mu$_N $<Y*$

The parameters $\mu$_i are the observable categories’ externally imposed endpoints. The ordered logit approach will then fit the parameter vector β using the observations on y, which are a type of censored data on y*.

SPSS v.20 software program was used to define calculations and perform other analyses.

4. Results and Discussion

4.1. Demographics of Respondents

In terms of demographics of the respondents, the author surveyed with information related to the respondents’ age, gender, income per family member in the previous year, education, marital status, and the number of people holding life insurance policies. Details of the survey sample with demographics are shown in

Table 2. Respondents’ profile.

Explanation	Category	Scale That Put in the Model	Frequency	Percentage
	Total (Households)		250	100
Age (In the year)	18–20	1	1	0.4%
	21–30	2	14	5.6%
	31–40	3	57	22.8%
	41–50	4	134	53.6%
	51–60	5	44	17.6%
	>60	6	0	0%
Gender	Female	0	125	50
	Male	1	125	50
Income (Income per family member in the previous year (USD))	<2000	1	2	0.8%
	2000–4000	2	66	26.4%
	4001–6000	3	91	36.4%
	6001–8000	4	58	23.2%
	8001–10,000	5	27	10.8
	>10,000	6	6	2.4%
Literacy	Primary school	1	0	0
	Secondary school	2	1	0.4
	High school	3	72	28.8
	Undergraduate	4	142	56.8
	Postgraduate	5	35	14
Marital status	Married	1	171	68.4
	Single	2	77	30.8
	Divorced	3	0	0
	Widow	4	0	0
	Separate	5	2	0.8
Have life insurance policies	Yes	-	55	22.0
	No	-	195	78.0

Source: Calculated from the survey data.

.

In the sample of 250 people in Buon Ma Thuot city, there were equal males and females. The number of people who graduated from college or university was 56.8%. Income per family member of around 4001–6000 USD accounted for the largest group in the survey (36.4%). The following was the income of 2000–4000 USD and 6001–8000 USD (26.4% and 23.2%, respectively). Most of the people had graduated from high school and university (28.8% and 56.8%, respectively). A total 68.4% of people in the survey were married, while 30.8% of them were single. None of them was divorced and a widow. Only 0.8% were separated. Almost all the people in the survey knew about life insurance. However, only 22% of them had life insurance policies. The detailed information is in Table 2.

4.2. Reliability Statistics

Before running EFA, Cronbach’s alpha was estimated to define the reliability of all variables that consist of the question in the 5-point Likert scale in H1 and H2. Cronbach’s Alpha ≥0.7 and the corrected item-total correlation > 0.3 was acceptable (Nunnally 1978).

As can be seen in

Table 3. Reliability.

	Scale Mean If Item Deleted	Scale Variance If Item Deleted	Corrected Item-Total Correlation	Cronbach’s Alpha If Item Deleted
Brand1	17.78	22.815	0.698	0.873
Brand2	17.77	23.054	0.689	0.874
Brand3	17.68	22.660	0.708	0.871
Brand4	17.52	22.837	0.704	0.872
Brand5	17.64	22.384	0.733	0.867
Brand6	17.74	23.006	0.719	0.870
Cronbach’s Alpha of Brand_name is 0.891
Risk1	7.32	4.797	0.695	0.878
Risk2	6.98	4.738	0.768	0.808
Risk3	6.97	4.971	0.811	0.775
Cronbach’s Alpha of Risk is 0.872
Covid1	12.48	7.327	0.710	0.814
Covid2	12.43	7.082	0.651	0.827
Covid3	12.38	7.242	0.604	0.840
Covid4	12.33	7.241	0.599	0.841
Covid5	12.38	6.685	0.782	0.792
Cronbach’s Alpha of Covid is 0.853
Attitude1	6.44	2.095	0.820	0.819
Attitude2	6.64	2.289	0.776	0.857
Attitude3	6.59	2.435	0.774	0.860
Cronbach’s Alpha of Attitude is 0.892
Sub_norm1	24.75	19.169	0.001	0.675
Sub_norm2	24.69	15.531	0.594	0.522
Sub_norm3	24.71	15.917	0.566	0.533
Sub_norm4	24.76	16.055	0.498	0.545
Sub_norm5	24.82	16.001	0.544	0.537
Sub_norm6	24.42	15.988	0.509	0.542
Sub_norm7	25.22	16.415	0.158	0.655
Sub_norm8	25.17	17.032	0.114	0.667
Cronbach’s Alpha of Sub_norm is 0.620
Sav_moti1	7.82	6.467	0.513	0.773
Sav_moti2	7.78	4.630	0.660	0.706
Sav_moti3	7.76	5.378	0.722	0.670
Sav_moti4	7.74	6.195	0.518	0.770
Cronbach’s Alpha of Save_moti is 0.787
Financial1	11.43	18.367	0.747	0.876
Financial2	11.35	18.356	0.735	0.878
Financial3	11.44	18.770	0.694	0.887
Financial4	11.44	17.902	0.739	0.878
Financial5	11.42	17.747	0.825	0.859
Cronbach’s Alpha of Financial is 0.898

Source: Calculated from the survey data.

, most of the value of Cronbach’s Alpha is greater than 0.7, except that of Sub_norm (0.620). In which, the Corrected item-total Correlation of Sub_norm1, Sub_norm7, and Sub_norm8 were lower than 0.3. Therefore, those three items must be removed from the model. Revised Cronbach’s alpha for Sub_norm with the same process as shown in

Table 4. Cronbach’s Alpha of Sub_norm after removing Sub_norm1, Sub_norm7 and Sub_norm8.

	Scale Mean If Item Deleted	Scale Variance If Item Deleted	Corrected Item-Total Correlation	Cronbach’s Alpha If Item Deleted
Sub_norm2	14.74	7.896	0.760	0.815
Sub_norm3	14.76	8.296	0.706	0.830
Sub_norm4	14.81	8.349	0.635	0.848
Sub_norm5	14.87	8.417	0.667	0.839
Sub_norm6	14.47	8.274	0.653	0.843
Cronbach’s Alpha of Sub_norm after removing Sub_norm1, Sub_norm7, and Sub_norm8 is 0.864

Source: Calculated from the survey data.

.

4.3. Exploratory Factor Analysis (AFA)

After the two were removed, all the independent variables were appropriated. Then EFA was conducted without Sub_norm7 and Sub_norm8. Regarding this step, KMO was found to equal 0.864, which lined between 0.5 and 1 which is suitable for the model. The Bartlett test was 0.000, and the variance explained was 69.485%. All factor loading was greater than 0.5. The detail of factor loading in the rotated component matrix is illustrated in

Table 5. Rotated Component Matrix.

	Component
	1	2	3	4	5	6	7
Brand5	0.790
Brand1	0.786
Brand2	0.768
Brand6	0.762
Brand3	0.751
Brand4	0.705
Sub_norm3		0.824
Sub_norm2		0.824
Sub_norm5		0.772
Sub_norm6		0.736
Sub_norm4		0.728
Sub_norm1		0.727
Financial5			0.828
Financial2			0.771
Financial1			0.741
Financial3			0.731
Financial4			0.717
Covid5				0.844
Covid1				0.813
Covid2				0.765
Covid3				0.722
Covid4				0.704
Sav_moti3					0.852
Sav_moti2					0.768
Sav_moti4					0.692
Sav_moti1					0.680
Attitude1						0.877
Attitude2						0.826
Attitude3						0.790
Risk2							0.874
Risk3							0.867
Risk1							0.813

Source: Calculated from the survey data.

.

4.4. Multicollinearity Diagnostics

If multicollinearity existed, the results would not be correct and meaningful. Thus, before running an ordinal logistic model, multicollinearity must be diagnosed.

As mentioned in the methodology section, multicollinearity exists when VIF values are greater than 5 and tolerance values are less than 0.2. In this study, all the VIF and tolerance values met the requirement. Thus, no multicollinearity was in the model. The detail of VIF and Tolerance are shown in

Table 6. Multicollinearity diagnostics.

Coefficients a
	Collinearity Statistics
	Tolerance	VIF
(Constant)
Age	0.978	10.023
Gender	0.934	10.071
Marital_status	0.974	10.026
Literacy	0.984	10.016
Incom	0.926	10.079
Brand_name	0.652	10.535
Sub_norm	0.805	10.242
Financial_lite	0.549	10.822
Sav_moti	0.778	10.286
Covid	0.751	10.332
Attitude	0.696	10.437
Risk_awa	0.788	10.269

Source: Regression output. ^a. Dependent Variable: Intention.

.

4.5. Ordinal Logistic Model

The ordinal logistic model was used to assess the intention to purchase life insurance of a sample of 250 people in Buon Ma Thuot City, Daklak Province, after the reliability, EFA, and multicollinearity requirements were demonstrated. As previously noted, the intention to purchase was an ordinal dependent variable, with 5 values (0 to 4). Brand name, Subject norm, Financial literacy, Covid effectiveness, Saving motivation, Attitude, and Risk Awareness were the model’s independent variables, as indicated in Table 4, and corresponded to the seven factors generated from the following items:

• Brand_name, = mean (Brand1, Brand2, Brand3, Brand4, Brand5, Brand6);
• Sub_norm = mean(Sub_norm2, Sub_norm3, Sub_norm4, Sub_norm5, Sub_norm6);
• Financial = mean(Financial1, Financial2, Financial3, Financial4);
• Covid = mean(Covid1, Covid2, Covid3, Covid4, Covid5);
• Sav_moti = mean(Sav_moti1, Sav_moti2, Sav_moti3, Sav_moti4);
• Attitude = mean(Attitude1, Attitude2 Attitude3);
• Risk = mean(Risk1, Risk2, Risk3).

Equation (1) was used to predict the dependent variable.

There was 39.2% out of the total 250 respondents indicated that they might intend to purchase life insurance. Only 10% of them intended. Up to 40.4% showed that they may or may not intend to buy life insurance (

Table 7. Summary of Case Processing.

		N	Marginal Percentage
Intention	0	6	2.4%
	1	20	8.0%
	2	101	40.4%
	3	98	39.2%
	4	25	10.0%
Valid		250	100.0%
Missing		0
Total		250

Source: Regression output.

).

As shown in Table 7, the number of people who are hesitant to buy and the number of people considering whether to purchase or not account for a large proportion of the survey sample, 40.4% and 39.2%, respectively. To pursue these people to change the idea of buying life insurance, it is necessary to consider the influencing factors in

Table 8. Results of ordinal logistic model for determining factor effect on the intention to purchase life insurance.

Model Fitting Information
Model		−2 Log-Likelihood		Chi-Square		Df	Sig.
Intercept Only		627.548
Final		426.681		200.867		12	0.000
Goodness-of-Fit
		Chi-Square				Df	Sig.
Pearson		696.976				984	1.000
Deviance		426.681				984	1.000
Pseudo R-Square
Cox and Snell						0.552
Nagelkerke						0.601
McFadden						0.320
Parameter Estimates
		Estimate	Std. Error	Wald	Odd ratios	95% confidence Interval
						Lower Bound	Upper Bound
Threshold	[Intention = 0]	8.450 *	1.733	23.775	4675.255	5.053	11.847
	[Intention = 1]	10.528 *	1.737	38.755	37,360.049	7.125	13.932
	[Intention = 2]	14.211 *	1.860	58.375	1,485,179.660	10.566	17.857
	[Intention = 3]	17.974 *	2.013	79.758	63,961,874.82	14.029	21.918
Location	Age	−0.371 **	0.169	4.819	0.690	−0.702	−0.040
	Gender	−0.669 **	0.277	5.841	0.512	−0.1.210	−0.127
	Merital_status	−0.154 ****	0.202	583	857	−0.549	0.241
	Income	0.275 **	0.131	4.424	1.316	0.011	0.531
	Literacy	0.416 **	0.207	4.049	1.516	0.019	0.821
	Brand_name	0.618 *	0.178	12.068	1.854	0.269	0.966
	Sub_norm	0.535 **	0.212	6.357	1.707	0.119	0.950
	Financial_lite	0.619 *	0.173	12.734	1.857	0.279	0.959
	Sav_motive	0.837 *	0.203	16.957	2.309	0.438	1.235
	Covid	0.469 **	0.235	3.989	1.599	0.009	0.929
	Attitude	0.533 **	0.219	6.938	1.704	0.104	0.962
	Risk_awa	0.646 *	0.145	19.918	1.908	0.362	0.929

Source: Regression output. Ordinal logit model: * p < 0.01, ** p < 0.05, **** p > 0.1.

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Regarding Model Fitting Information, the two model deviances were given by a −2 Log Likelihood value. The −2 Log-Likelihood of Intercept only (null model) was 627.548, which was greater than the value of the Final model (426.681). Thus, the final model with a full predictor was better than the null model. The Chi-Square was 200.867, the Sig was 0.000, and the df = 12 value was equivalent to the number of independent variables in the model. Therefore, the independent variables of the model were appropriated. In terms of Goodness-of-fit, the sig values of Pearson and Deviance were well greater than 0.05, indicating a good fit to the data of the ordinal logit model. R² cannot be calculated in the logit model, but Pseudo R-Square can be done due to the characteristic of R² (Sarstedt 2019). The Pseudo R-Square values revealed the study model’s fit with all independent variables. In this research, the fit of the research model was 0.552 based on Cox & Snell method, 0.501 based on the Nagelkerke method, and 0.320 based on the McFadden method. McFadden suggested that the Pseudo R-Square value was between 0.2–0.4, suitable for the probability research model (McFadden 1973).

Regarding Parameter Estimates in Table 8, the model had four thresholds value because the dependent variable was divided by five values due to the ordinal scale; thus, they were suitable for the model. All the sig. value of the thresholds was lower than 0.05. Thus, they were significant. The slopes (Estimate values) reflected the change in the log-odds of falling from higher to lower dependent variable categories. Sig. value of Marital status was over 0.05. Thus, this factor was insignificant. The ($\beta )$ values of Age and Gender were negative, which means young people and women have a higher intention to purchase life insurance than older ones and men. All the $\beta$values of Income, Literacy, Brand_name, Sub_norm, Financial_lite, Sav_motive, Covid, Attitude, and Risk_awa were positive, so the higher values of $\beta$ on the independent variable would be associated with a greater intention to purchase life insurance. The odds ratios indicate the multiplicative change in odds per unit increase on the independent variables. The slope of Sav_moti is the highest number (0.837), indicating the most significant crucial factor impacting intention to buy life insurance. Moreover, the odds ratio of Sav_moti was the greatest (2.309), so for every one-unit increase on Sav_moti, the intention to purchase life insurance changes by 2.309 for each category of the dependent variable. The three factors, including Brand_name, Financial_lite, and Risk, had a similar impact on the dependent variable, and those factors were considered to be the second most important factor that affected the intention. These results show that Gender was also the second important factor, but the effect was negative. COVID-19 and Attitude were the third critical effect of the intention. Income was a less critical impact on the intention.

5. Discussion

The results show that Sav_moti was the key factor that influence the intention to purchase life insurance. Therefore, life insurance companies need to consider each family’s savings motivation. Design questions about people’s savings motives and recommend products to suit their needs. In this study, questions about saving associated with family knowledge about life insurance are as follows:

What do you think life insurance is? (you can choose more than one answer)

□ Protection of family’s income (i.e., will have a financial source to replace lost revenue in case the insured is affected by the risk).

□ Investment for the future.

□ Profitable investment, such as bank deposit or stock investment.

□ It is a preparation for old age that does not depend on children’s finances.

□ It is the payment of hospital fees for medicines, hospital beds... when sick.

□ It is payment for the schooling of children.

□Other comments (specify)…………………………………………………….

The results for saving with life insurance are shown in

Table 9. Saving with life insurance.

		Frequency	Percent
Valid	Protect family income	42	16.8
	Investment for the future	43	17.2
	Profitable investments such as bank deposits or stock investment	59	23.6
	It is a preparation for old age that does not depend on children’s finances	22	8.8
	It is the payment of hospital fees for medicines, and hospital beds when being hospitalized	34	13.6
	It is payment for the school of children	38	15.2
	Other comments	12	4.8
	Total	250	100.0

Source: Calculated from the survey data.

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The research results on risk awareness through factors that influence the intention to purchase life insurance in Daklak province of Vietnam were almost similar to the previous studies. The factors, namely attitude toward life insurance, risk awareness, saving motivation, financial literacy, and brand name have a positive effect on the intention to purchase life insurance. Saving motivation is the key factor that influences the intention to purchase life insurance. Therefore, life insurance companies need to consider each family’s saving motivation.

However, the results of this study have some differences compared with previous studies due to regional characteristics. Specifically, since the sig value of marital status is greater than 0.1, the author still cannot conclude the influence of this factor on the intention to purchase life insurance. Mahdzan and Peter Victorian (2013) show that marital status has a significant effect on the intention as single individuals have the most intention to purchase life insurance, followed by married individuals and the last one was individuals who divorced. Factors such as age and gender also have a difference compared with the research of Ćurak et al. (2013), Yusuf et al. (2009), Gandolfi and Miners (1996), and Ćurak et al. (2013); young people and women intend to buy life insurance more than older people and men. This also helps insurance companies in Daklak province better approach their target customers.

6. Conclusions

Life insurance is a necessity for protecting financial risks in the life of people and providing a large of idle capital for the economy. However, only around 10% of people in Vietnam hold life insurance policies. The study aims to identify the risk awareness through factors that influence the intention to buy people’s life insurance in Daklak province of Vietnam to encourage people in the research to hold life insurance policies for their life and help the life insurance companies have the right glance to make suitable policies for promoting their life insurance product. The resource data was based on a survey of 250 individuals in Buon Ma Thuot City, the largest city in the Central Highlands, located in the center of the province and the city in the center of the Central Highlands of Vietnam. The data were analyzed by the ordinal logit model to give the research results. The results show that all people in the survey know about life insurance and the most significant number (40.4%) hesitate to buy or not buy a life insurance policy. The majority of them (39.2%) may purchase. Saving motivation is the key factor that affects the intention to buy life insurance. The brand name was also a significant influence. The effect of COVID-19 was added as one of the factors that affect the intention to buy life insurance policies. Still, it is the third critical factor effect on the independent variable. There are differences between this study and previous studies that help policymakers, life insurance companies, and consultants have the best access to customers in the research area. At present, it is still not possible to conclude the influence of marital status on the intention to purchase life insurance in Daklak province. Moreover, the limitation of this study is that it was concentrated only in one province in Vietnam (Daklak province). Therefore, in the future, the effect of factor marital status on the intention to purchase life insurance in the research area will be conducted to clarify this, and future research will be expanded to other areas in the country. The effect of COVID-19 was mentioned and has a significant effect on the intention to purchase life insurance. This could be a new factor that contributes to the literature.