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Int. J. Financial Stud. 2022, 10, 84. https://doi.org/10.3390/ijfs10040084 www.mdpi.com/journal/ijfs
Article
Risk Awareness for Vietnamese’s Life Insurance on Financial
Protection: The Case Study of Daklak Province, Vietnam
Tran Thi Lan
Faculty of Economics, Tay Nguyen University, Buon Ma Thuot , Daklak 63000, Vietnam;
tranthilan@ttn.edu.vn; Tel.: +84-(0)-935013329
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 insur-
ance companies. The data resources were conducted from the survey of 250 people in Daklak Prov-
ince 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 anal-
ysis. 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. Sav-
ing 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 gen-
der were considered the second most important factors. COVID-19 and attitude were the third crit-
ical effect on the intention to purchase life insurance. Income was the less important factor.
Keywords: intention; life insurance; COVID-19; Daklak; risk awareness; Vietnam
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. Insur-
ance 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 construc-
tion 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 pan-
demic, so they may look for financial protection measures against hazards, such as par-
ticipating in life insurance (Babuna et al. 2020). Usually, products and services are bought
Citation: Lan, Tran Thi. 2022. Risk
Awareness for Vietnamese’s Life
Insurance on Financial Protection:
The Case Study of Daklak Province,
Vietnam. International Journal of
Financial Studies 10: 84.
https://doi.org/10.3390/ijfs10040084
Academic Editor: Sabri Boubaker
Received: 18 July 2022
Accepted: 20 September 2022
Published: 22 September 2022
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
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tional affiliations.
Copyright: © 2022 by the author. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).
Int. J. Financial Stud. 2022, 10, 84 2 of 17
when people need them immediately. However, insurance products are different due to
the characteristics of insurance, collecting premiums in advance and indemnifying or pay-
ing 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 in-
surance 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 econ-
omy. 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 insur-
ance 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 eco-
nomic growth and insurance (Arena 2008; Ćurak et al. 2009; Ege and Saraç 2011;
Vadlamannati 2008).
Int. J. Financial Stud. 2022, 10, 84 3 of 17
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 Vi-
etnam 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. Pur-
chase 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) pro-
posed 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 insur-
ance and the COVID-19 (Covid) pandemic. Moreover, demographics are also one of the im-
portant 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 in-
tention 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.
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, Finan-
cial_lite
Income, Attitude toward life insurance, risk awareness, saving motivation,
and
financial literacy have a positive effect on the intention to purchase life insur-
ance (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
Int. J. Financial Stud. 2022, 10, 84 4 of 17
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 pur-
chase life insurance
Due to the characteristics of insurance, collecting premiums in advance and indemni-
fying or paying when there is risk or maturity. Therefore, there exists an idle period of cap-
ital 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 eco-
nomic 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 previ-
ous 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 spe-
cialists, 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 High-
lands, 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 ran-
dom 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.
Figure 1. The study area (Source: Dak Lak Provincial People’s Committee Portal 2022).
Int. J. Financial Stud. 2022, 10, 84 5 of 17
3.3. Research Model and Data Analysis
With the hypotheses given in Table 1 the author proposes the following research model
(Figure 2).
Figure 2. Research Model.
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% proba-
bility) (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, Gen-
der, 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 Lik-
ert scale before running the ordinal logit model. Furthermore, the multicollinearity diag-
nosis 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 anal-
ysis, revealing the close relationship between the object groups. Correct Item-total Corre-
lation 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 re-
search findings inconsistent and inaccurate. VIF (Variance Inflation Factors) is employed
to determine multicollinearity, and Tolerance Values are used to evaluate
Int. J. Financial Stud. 2022, 10, 84 6 of 17
multicollinearity. Tolerance Values of 0.20 are a serious concern. Multicollinearity can oc-
cur when tolerance values are less than 0.10. Because Tolerance Values are the polar op-
posite 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 multicol-
linear (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:
= + + + + +
+
_ +
_ +
_ +
+ _ + + _ + ℇ
(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:
, 0
Do not intend to purchase life insurance, or may not purchase life insurance:
, 1
Do not intend to purchase life insurance, may not purchase life insurance, or may or
may not purchase life insurance:
, 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:
, 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 coeffi-
cients 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 maxi-
mum-likelihood estimates.
Assume that the underlying process to be described is
Y* = XTβ + 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 * ≤ 1,
Y = 1 if 1 < ∗ ≤ 2,
Y = 2 if 2 < ∗ ≤ 3,
Y = ……………………….,
Y = N if N < ∗
The parameters 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 anal-
yses.
Int. J. Financial Stud. 2022, 10, 84 7 of 17
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.
Table 2. Respondents’ profile.
Explanation Category
Scale that Put in the
Model Frequency Percentage
Total (House-
holds) 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 fe-
males. The number of people who graduated from college or university was 56.8%. In-
come 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. How-
ever, only 22% of them had life insurance policies. The detailed information is in Table 2.
Int. J. Financial Stud. 2022, 10, 84 8 of 17
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, 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.
Table 3. Reliability.
Scale Mean If Item
Deleted
Scale Variance If
Item Deleted
Corrected Item-To-
tal 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.
Int. J. Financial Stud. 2022, 10, 84 9 of 17
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.
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
Int. J. Financial Stud. 2022, 10, 84 10 of 17
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.
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 in-
tention 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 cor-
responded 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).
The following was the equation that was used to predict the dependent variable:
= + + + + +
+
_ +
_ +
_ +
+ _ + + _ + ℇ
(3)
Int. J. Financial Stud. 2022, 10, 84 11 of 17
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).
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.
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, indi-
cating 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).
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
Int. J. Financial Stud. 2022, 10, 84 12 of 17
[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.
Regarding Parameter Estimates in Table 8, the model had four thresholds value be-
cause 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 () values of Age and Gender
were negative, which means young people and women have a higher intention to pur-
chase life insurance than older ones and men. All the values of Income, Literacy,
Brand_name, Sub_norm, Financial_lite, Sav_motive, Covid, Attitude, and Risk_awa were
positive, so the higher values of 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 inten-
tion 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 pur-
chase 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 reve-
nue 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.
Int. J. Financial Stud. 2022, 10, 84 13 of 17
□Other comments (specify)…………………………………………………….
The results for saving with life insurance are shown in Table 9.
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 de-
pend 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.
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 moti-
vation, financial literacy, and brand name have a positive effect on the intention to pur-
chase life insurance. Saving motivation is the key factor that influences the intention to
purchase life insurance. Therefore, life insurance companies need to consider each fam-
ily’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 inten-
tion 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 individ-
uals 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 prov-
ince 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
Int. J. Financial Stud. 2022, 10, 84 14 of 17
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 be-
tween 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 concen-
trated 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.
Funding: This research was funded by the grant from Tay Nguyen University (T2022- 81CBTĐ).
Institutional Review Board Statement: The study was conducted in accordance with the Declara-
tion of Helsinki, and approved by the Institutional Review Board of Tay Nguyen University (T2022-
81CBTĐ, 1 January 2022).
Conflicts of Interest: The author declares that there is no conflict of interest.
Appendix A. Explanation of the Variables That Consist of the Question in the 5-Point
Likert Scale in H1 and H2
The brand name has a positive effect on the intention to purchase life insurance (Brand_name)
Brand1 I find out the reputation of the company when I intend to buy a life insur-
ance policy
Brand2 I believe that a life insurance company with a strong brand will ensure
better
benefits for customers than other companies.
Brand3
I am interested in a life insurance company with a good after-sales policy
Brand4
I intend to join life insurance at a company with good financial potential
Brand5 I will buy life insurance at a company that always focuses on the interests
of the community
Brand6
I look for life insurance companies with foreign brands
Intention to buy life insurance is influenced by risk awareness (Risk)
Risk1
Buying life insurance makes me feel comfortable and comfortable because
I have found an effective financial risk management tool for individuals
and families
Risk2 Long-term premium rates of life insurance allow the company to best pro-
tect interests during the long-term life insurance contract
Risk3
Life insurance helps me fulfill my responsibilities to my family because
when the unfortunate happens that I can no longer generate income for
my family, I have life insurance to compensate financially for my depend-
ents.
The COVID-19 pandemic affects the intention to purchase life insurance (Covid)
Covid1 The COVID-19 pandemic made me realize the necessity and importance
of life insurance for each individual and family
Covid2 I will purchase life insurance for myself and my relatives if there is a pay-
ment clause for the risk of COVID-19
Covid3 I will convince my friends and my relatives to buy life insurance to offset
the financial risks caused by covid
Covid4
I think people will buy more life insurance when the mortality rate, the
cost of illness, and the amount for emergency use in case of illness during
the COVID-19 pandemic
Covid5 Securing the future with life insurance is very important, especially dur-
ing the COVID-19 pandemic
The intention is positively influenced by one’s Attitude toward life insurance (Attitude)
Int. J. Financial Stud. 2022, 10, 84 15 of 17
Attitude1
I have a positive attitude towards life insurance
Attitude2 Purchasing life insurance is not only beneficial for me but also the finan-
cial security of my loved ones
Attitude3 Life insurance companies in Vietnam have high safety and security and
always ensure the interests of customers under the contract.
Subject norm influences the intention (Sub_norm)
Sub_norm1
All the people who are important to me think that I should buy BHNT
Sub_norm2
My close friends want me to buy BHNT
Sub_norm3
My close colleagues want me to buy BHNT
Sub_norm4 I feel relaxed and can enjoy life more when my loved ones are protected
by
life insurance.
Sub_norm5 I will be able to save money with life insurance so that old age is not a fi-
nancial burden for my children and those around me.
Sub_norm6
I am an income generator, so I need life insurance to protect my family’s
income if there is a force majeure event that I can no longer generate in-
come.
Sub_norm7
Purchase life insurance helps me maintain the habit of saving for long-
term financial plans in the future such as sending my children to study
abroad and preparing for life after retirement.
Sub_norm8
Life insurance helps me live a socially responsible life because if I’m lucky
I don’t have any risks, and I will save for the less fortunate. Moreover,
when the life insurance policy matures, I will still receive the amount for
the lucky
Intention to purchase life insurance is affected by the saving motivation of each person
(Sav_moti)
Sav_moti1
I save money to cover my retirement expenses
Sav_moti2
I save money to use for emergencies
Sav_moti3 I save money to ensure the future of my dependents if I, unfortunately,
run the risk of not generating my current income
Sav_moti4
I want to save money to inherit for those who are important to me
Financial literacy of each person affects the intention (Financial)
Financial1
I know several financial products that can cover my financial needs
Financial2
I understand the terms contained in the life insurance contract
Financial3 Having life insurance is an important factor in taking care of myself and
my family financially
Financial4 I feel less stressed when my family members and I are financially pro-
tected by life insurance
Financial5
Life insurance is an important element of my financial plan
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