ADVANCES IN BUSINESS-RELATED
SCIENTIFIC RESEARCH JOURNAL (ABSRJ)

Volume 14, Number 1 (2023)
ISSN 1855-931X

Entrepreneurial activity in Slovenia: the role of gender and
household income
Ana Malnar, Karin Širec
Assess the role of career exploration in expanding social cognitive
career theory
Quoc Bao Lam, Minh Pham
Adoption of FinTech services in Uttar Pradesh: using extended
TAM
Shanu Srivastava, Aashna Sinha, V Shunmugasundaram
Analysis of MBA skills from the employers’ perspective: a case
study
Valentina Jošt Lešer, Dario Berginc
A Bayesian analysis of determinants of net interest margins of
commercial banks in Vietnam
Tien Thuy Ho, Oanh Kim Thi Tran, Diep Van Nguyen

ABSRJ, Volume 14, Number 1

ENTREPRENEURIAL ACTIVITY IN SLOVENIA: THE
ROLE OF GENDER AND HOUSEHOLD INCOME
Karin Širec
University of Maribor,
Slovenia
karin.sirec@um.si

Ana Malnar*
University of Rijeka,
Croatia
ana.malnar@efri.hr

Abstract
Entrepreneurial activity is considered an important aspect of the organization
of industries most conducive to innovation and unrestricted competition and,
at the same time, it is viewed as one of the most important factors in the
economic progress of a country. Entrepreneurial activity depends, among
other, on several individual factors. This paper investigates the correlation
between entrepreneurial activity and individual predictors such as gender
and household income based on the data from Global Entrepreneurship
Monitor (GEM). The paper aims to help fill the gap in the literature by
providing a quantitative analysis of the relationships and effects between
entrepreneurial activity, gender, and household income in Slovenia. The
empirical results show that the correlations between entrepreneurial activity
and gender could not be confirmed, while the correlations between
household income and entrepreneurial activity can be demonstrated for the
bottom and the top third of the household income category.

Key Words
Entrepreneurial
activity;
Entrepreneurship Monitor.

*Corresponding Author

gender;

household

income;

Global

Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

INTRODUCTION
Entrepreneurship can be defined as a phenomenon that involves identifying,
evaluating and exploiting opportunities, which requires people to have
different beliefs about the value of resources (Shane & Venkataraman,
2000). Both researchers and policy-makers have widely recognized the
prominent role of entrepreneurship in an economy. Overall,
entrepreneurship is seen as a key mechanism for boosting employment and
productivity growth, increasing competitiveness and innovation in
economies, and promoting social inclusion and equal opportunity (Svetek &
Drnovsek, 2022).
It is widely believed that entrepreneurial activity is an important aspect of
the organization of industries most conducive to innovation and unrestricted
competition (Stel et al., 2005), as well as one of the most important factors
in a country's economic progress. Entrepreneurship can invigorate
development by driving progress, bringing about change, and improving
competition. In terms of a developing economy, entrepreneurs are an
important resource as they also contribute to social development, act as
problem solvers by bringing ground-breaking plans to the market, drive
significant developments by bringing new products and services on the
market, and create an attractive business climate by enabling the creation of
new organizations (Voda et al., 2020). As recently as 2012, Slovenia had
one of the lowest participation rates of women in early-stage entrepreneurial
activities (last in Europe, second to last in the world). After 2018, women's
early-stage entrepreneurial activity started to increase, reaching a new high
in 2021. Therefore, we wanted to investigate if there are still gender
differences and if we can identify the income level as an influencing factor.
The aim of this study is to analyse the specific role of gender and
household income as explanatory variables for early-stage entrepreneurial
activity in Slovenia in 2021. Data from GEM (Global Entrepreneurship
Monitor) is used, which includes the rate of Total Entrepreneurial Activity
(TEA), which may be defined as the “percentage of 18-64 population who
are either a nascent entrepreneur or owner-manager of a new business”
(Bosma et al., 2021). This variable is measured uniformly across a range of
countries covered by the research and represents a useful index for
measuring the extent of “entrepreneurship”. The introductory section is
followed by an overview of the concept of entrepreneurial activity and the
individual factors of gender and household income, and hypotheses
development. The following section provides a description of the research
methodology and data. The paper ends with research findings and
discussion followed by the conclusion.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
There are numerous definitions of entrepreneurship in the literature, but
there is no doubt that entrepreneurship has been an essential component of

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economic development for quite some time. There are several observational
studies (Johannessen et al., 2001) that focus on characterizing the idea of
entrepreneurship and examining the positive relationship between
innovative activities and economic indicators. An entrepreneur is usually
characterized as a person who tries to seize an opportunity, provides the
important means to start a business, and faces challenges (Bloch, 2020). As
an innovator, an entrepreneur launches new products and developments,
explores new business areas for existing products, and develops new
advertising techniques (Steyaert & Hjorth, 2003). Entrepreneurship
contributes to capital raising by pooling reserve funds and ventures, creating
conditions for society to thrive, contributing to adjusted territorial
improvement of the country, and helping to reduce the convergence of
monetary power owned by a single person (Kafaji, 2019). The
entrepreneurial consumption offer in the form of new goods and services
leads to new jobs, which affect the economy by contributing to an increase
in national income through higher tax revenues and can be used for interest
in various areas (Tleuberdinova et al., 2019).
Entrepreneurial activity depends on a number of individual factors as well
as regional and national aspects. Economics, political science,
epistemology, sociology and cultural and institutional theory all recognize the
importance of individual characteristics, sociocultural perceptions, cultural
values, and regulatory and normative dimensions on the level of
entrepreneurial activity in different countries (Santos et al., 2017).
Moreover, entrepreneurial activity is a global economic and social
phenomenon in which entrepreneurs take on various commitments and
accept the risks associated with starting a new business, with the
expectation that this attitude will have an impact and lead to higher levels of
productivity and income (Leitão & Capucho, 2021). Entrepreneurs'
motivation, inspiration, and enthusiasm for independence and freedom in
their new ventures are crucial. However, the literature shows that more
empirical studies are needed at the macro level to define the nature of
institutional climate that determines entrepreneurial activity (Smallbone &
Welter, 2020). Considering that entrepreneurship is one of the variables that
contribute to structural change in nations, it can be considered as an
important switch for economic turnaround and development, especially by
strengthening the competitive elements and innovation frontier of small and
medium enterprises (Stam et al., 2011). Chowdhury et al. (2019) state that
entrepreneurship is fundamental to the vitality of economies, emphasising
that enterprises are at the core of the scope and nature of entrepreneurial
activity.
Entrepreneurial activity in the form of various operating companies and
self-employed individuals is consistently influenced by various determinants
on the supply and demand side of the market (Dvouletý, 2021).
Over the years, various authors have analysed entrepreneurial activity
with the use of TEA1 as part of the model in their work. Furthermore, new
1

Total early-stage Entrepreneurial Activity (TEA) is the % of 18-64 year old population that is
either a nascent entrepreneur, or owner of a business.

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business creation is assumed to be the result of individuals' decision to
improve themselves, which is consistent with Schmitz’s (1989) theoretical
framework. According to data from the 2020 and 2021 reports of GEM
(Bosma et al., 2021), TEA includes early-stage entrepreneurs driven by
opportunity, aged 18-64, who are driven to entrepreneurial ventures out of a
need to become independent and increase their income, and early-stage
entrepreneurs who engage in business creation because there are no
accessible jobs (necessity-oriented entrepreneurs). TEA rates relate to
opportunity and necessity distinguish between entrepreneurs who are driven
to seek business opportunities from those whose entrepreneurial activity
results from an inability to pursue other economic options (Voda et al., 2020).
Gender as an enabler of entrepreneurial activity
In addition to entrepreneurial activity, as measured by the TEA index, this
study also considers predictors such as gender and household income.
When one group in society does not start a business on an equal footing with
other groups, it limits job creation, innovation, income accumulation, the
availability of new products and services, and many different benefits that
new businesses bring to the economy and society.
There is a growing body of research on the role of gender in
entrepreneurial activity, and the findings suggest that gender can have both
facilitating and inhibiting effects on entrepreneurial activity. Research has
shown that certain gender-related characteristics, such as risk-taking and
access to networks, can promote entrepreneurial activity. For example, a
study by Cooper et al. (1994) finds that men are more likely than women to
start a business because they are more willing to take risks. Other research
suggests that men have better access to networks and resources that can
provide them with the support and resources they need to start and grow a
business (Neumeyer et al., 2019). However, research has also identified a
number of barriers that may limit women's entrepreneurial activity. These
include societal expectations and stereotypes about women's roles and
capabilities, as well as structural and institutional barriers such as access to
funding and a lack of supportive policies and programs (Brindley, 2005).
Overall, research findings suggest that gender can both promote and inhibit
entrepreneurship and that efforts to promote and support female
entrepreneurship may be necessary to level the playing field and ensure that
women have equal opportunities to start and build businesses.
While a number of research studies document a growing number of
women entrepreneurs and women-owned businesses, the results show that
men's entrepreneurial activity is still higher than women's (Tsyganova &
Shirokova, 2010). Bosma et al. (2021) find that in most cases men are the
ones who are more likely to start a business venture, although some
examples prove otherwise, and there are others where the difference is
small. When discussing the role of gender in the development of
entrepreneurial activity, it is important to note that authors such as Voda et
al. (2020) find that women's innovativeness and pioneering potential add to
entrepreneurial development and the creation of new jobs. As indicated by

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information presented by the European Commission (2019), women make
up 52% of the absolute European population, but represent only 34.4% of
the self-employed and 30% of start-up entrepreneurs. In addition, the results
show that businesses run by women have the fastest development. Due to
the importance of female businesses in the monetary and social
development and improvement of a country, some authors take an allencompassing viewpoint and utilize an appropriate structure suitable for
understanding women’s businesses and the elements that add to the
entrepreneurial movement (Brush et al., 2009).
Moreover, the findings of a research conducted by Santos et al. (2017)
show a notable impact of gender on entrepreneurial activity in its initial
stages. Similarly, men are more bound to take part in the initial stages of the
entrepreneurial movement than women. Moreover, Özdemir and Karadeniz
(2011) focus on business visionaries in Turkey and show that men with
higher pay and training levels can identify business opportunities and have
a higher probability of becoming entrepreneurs. The authors note that
entrepreneurs in Turkey are predominately men and that men are twice as
likely to become entrepreneurs than women. Likewise, Haus et al. (2013)
analyse gender differences in entrepreneurial intentions and activities of
entrepreneurs in Europe and the United States. and find that the average
entrepreneurial intention is higher among men than women. Although
important, gender differences in entrepreneurial intentions and activities
were too small to adequately explain the differences in starting ventures.
There is some empirical research data suggesting that there may be
differences between men and women in the motives for entrepreneurship.
However, it should be noted that these differences may vary depending on
the specific context and individual circumstances. In their study by Greene
et al. (2003), find that men are more likely to start a business to achieve
financial gain, while women are more likely to start a business to pursue their
passion or to fill a need in the marketplace. Another study by Wong-MingJi
et al. (1999) argues that men are more likely to start businesses to gain
status and power, while women are more likely to start businesses to achieve
work-life balance and to have control over their own careers. It should be
noted that these results should be interpreted with caution, as there may be
other factors that contribute to differences in motives for entrepreneurship
between men and women, such as cultural and societal expectations and
biases. In addition, it is likely that there is a high degree of overlap between
men's and women's motives for starting a business.
Given the importance of gender to entrepreneurship, the study proposes
to test the following hypothesis:
H1: Men are more likely to engage in early – stage entrepreneurial activity
than women.
Household income as an enabler of entrepreneurial activity
Household income can play a role in enabling entrepreneurial activity in
several ways. For example, higher household income may provide

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individuals with the financial resources and stability needed to start a
business, as well as the ability to take risks and withstand potential financial
losses. There is empirical research data supporting the relationship between
household income and entrepreneurial activity. A study by Gaglio & Katz
(2001) finds that individuals with higher income and education are more likely
to be entrepreneurial. Another study argues that individuals from higherincome households are more likely to start a business, especially in
industries with higher start-up costs (Nandamuri & Gowthami, 2013). In
addition, individuals with higher household incomes are more likely to report
having the financial resources needed to start a business, such as access to
capital and savings. On the other hand, authors such as Hurst and Lusardi
(2004) find that the relationship between income and entry into
entrepreneurship is flat across most of the wealth distribution, while only at
the top end of the wealth distribution-after the ninety-fifth percentile-can a
positive relationship be found.
Other research also suggests that household income may be a predictor
of entrepreneurial success. For example, a study by Bae et al. (2014) finds
that individuals from households with higher incomes are more likely to
achieve greater success in their ventures. Overall, the results suggest that
household income may be an important factor enabling entrepreneurial
activity and success. However, it should be noted that other factors such as
education, skills, and access to resources and networks may also play a role
in enabling entrepreneurial activity.
The relationship between household income levels and levels of
entrepreneurial activity is necessarily complex, both within and across
economies. Financial wealth in the form of household income is another
important precursor to entrepreneurial activity. Not only are high-income
households more able to provide the necessary financial resources for
entrepreneurial activity, but high-income households are also more likely to
have more entrepreneurial growth opportunities because of their social
status (Dunn & Holtz-Eakin, 2000). Exogenous influences (such as
demographics, society, characteristics, financial support, and culture) affect
attitudes and indirectly affect intentions and behaviours to become
entrepreneurs (Shapero, 1982). Among the exogenous factors, the
household may be considered as one of the most important because it acts
as a backstop for the entrepreneur. Several studies (Evans & Jovanovic,
1989) argue that a lack of financial resources in the form of household
income limits entrepreneurial activity.
High-income economies, like high-income individuals, tend to have more
opportunities and better access to the resources needed to take advantage
of those opportunities. On the other hand, starting one's own business in a
high-income economy can involve high opportunity costs in terms of
foregone wages, while social security systems can break the direct link
between work and income. In low-income economies, as with low-income
individuals, there are likely to be fewer alternative sources of income, so
starting one's own business may be an economic necessity (Bosma et al.,
2021). Individuals whose family's incomes are higher might be keen on more
worthwhile business opportunities than low-income individuals, as monetary

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abundance may provide the better quality of living. Higher level of financial
resources gives more significant monetary assets, permitting business
visionaries to attempt bigger endeavours prior to looking for outer sources of
funding. However, when resources are unavailable or low, development is
challenging to accomplish, whether or not inspiration is available (CarreónGutiérrez & Saiz-álvarez, 2019). On the other hand, Kim et al. (2006) find no
significant effect of household income and wealth on entrepreneurship entry.
Following the above reasoning, the second hypothesis is stated as
follows:
H2: Household income is positively and significantly associated with early –
stage entrepreneurial activity.
DATA AND METHODOLOGY
The Global Entrepreneurship Monitor (GEM) database provides insights into
the characteristics of
entrepreneurs, ranging from standard
sociodemographic characteristics to more specific entrepreneurial
characteristics, perceptions, and attitudes. In this study, the focus is on data
related to entrepreneurial activity as well as gender and household income
as factors influencing entrepreneurial activity. The data used in this study
were collected as part of the National Adult Population Survey.
In this study, the research is based on the GEM dataset for Slovenia from
2021, which includes 2000 observations. The dependent variable used in
this study is entrepreneurial activity (it indicates how entrepreneurial
societies actually are). To quantify the level of entrepreneurial activity, the
proxy variable for total early-stage entrepreneurial activity (TEA) was
utilized. The variable incorporates the classification of the population aged
18-64 that is either actively trying to start a new business or is involved with
a business that is less than three and a half years old. Estimated as a
dichotomous variable that takes the value of "1" if respondents affirm their
involvement in early-stage entrepreneurial activities and "0" otherwise. The
aforementioned approach of measuring entrepreneurial activities with a
single proxy item is widely accepted and used by researchers (Voda et al.,
2020).
Regarding household income, this GEM variable categorizes household
income into thirds according to the national distribution (lowest 33%, middle
33%, highest 33%). The annual income of the whole household, including
the respondent, must be guaranteed. Among the start-ups, those who
belong to the upper third of household income are represented with high
expectations (Carreón-Gutiérrez & Saiz-álvarez, 2019). The variable gender
can appear as “1” (for the male gender) and “2” (for the female gender).
The hypotheses are tested using the binary logistic regression for
predicting the probability of the effects of the previously stated influencing
factors on entrepreneurial activity. The SPSS 20 software is used to perform
the analysis. The dichotomous dependent variable takes the value "1" with
a probability of success q (where 1 represents participation in TEA) or the

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value "0" with a probability of 1-q (no participation in entrepreneurial
activities). This form of analysis is applied because the dependent variable
is dichotomous.
Figure 1: Graphical form of hypotheses
Gender

Household Income

Entrepreneurial
Activity

Figure 1 represents the graphical form, while regression model can be
presented in the stochastic form in the formula (1) as follows:
log (

𝜋(𝑦)
) = 𝛽0 + 𝛽1 𝑥1 + 𝛽2 𝑥2
1 − 𝜋(𝑦)

Where:
y - dependent variable: value of the TEA index
x1 - independent variable: gender
x2 - independent variable: household income
β0, β1, β2 – value of the regression coefficients

(1)

Based on the priory stated the formula takes the form (2):
log (

𝜋(𝑇𝐸𝐴)
) = 𝛽0 + 𝛽1 𝑔𝑒𝑛𝑑𝑒𝑟 + 𝛽2 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑖𝑛𝑐𝑜𝑚𝑒
1 − 𝜋(𝑇𝐸𝐴)

(2)

RESEARCH RESULTS AND DISCUSSION
In this section, the results of the hypotheses testing are presented and
discussed. From the case processing summary, the sample size is N=1771,
which means that 229 cases are missing from the 2000 cases originally
recorded. Furthermore, the coding of the dependent variable shows that the
dependent variable is indeed dichotomous, ranging from 0 to 1 depending
on whether the respondents selected "no" or "yes" as their answer to the
question about entrepreneurial activity.
Further on, categorical variables coding represents a description of the
coding of additional variables included in the logistic regression equation for
those categorical variables that have more than two possible values. The
variable related to the household income appears in the lowest 33% tile in
646 cases, in the middle 33% tile in 580 cases and in the top 33% tile in 545
cases. Individuals living in households that belong to the top third are the

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category with which we will compare the remaining individuals below.
Variable (1) describes households in the bottom third and variable (2)
describes households in the middle third, while the gender variable has only
two possible values. Omnibus tests of model coefficients contain the values
for the chi-squared statistic and significance level. Since stepwise logistic
regression or blocking were not used the results for step, model, and block
are identical. The value in the Sig. column is the probability of obtaining the
chi-squared statistic if the null hypothesis is true, comparing the p-value to a
critical value of 0.05. Since the significance level of 0.012 is less than 0.05,
the overall model is statistically significant. Omnibus tests of the model
coefficients can be considered significant since p < 0.05, confirming the
causal relationship of the proposed model and the hypothesis that the βcoefficients are different from zero.
In addition, the model summary shows the values of the "pseudo" Rsquares, since the logistic regression does not have the R-square normally
found in OLS regression. The Cox & Snell R-squared is based on the logical
probability for the model compared to the logical probability for a baseline
model. Furthermore, Cox & Snell R-squared and Nagelkerke R-squared
indicate that the variables under consideration explain a significant amount
of the variance in entrepreneurial activity. To determine how much of the
variance of the dependent variable is explained by the observed binary
logistic model, the values of Cox and Snell's R-squared and Nagelkerke Rsquared were analysed. The explained variance of the criterion in the model
ranges from 0.6% to 1.9%, depending on the observed measure, but it is
generally considered that Nagelkerke R-squared is the preferred measure
for the analysis of explained variance in binary models, since the second
measure cannot reach the value 1 and the explained variance is expressed
by values between 0 and 1. The model summary shows the variance of
information explained by the standard binary logistic model, i.e., the amount
of unexplained information that remains after fitting the model. The result of
1.9% explained variance in the model is not entirely satisfactory. The value
of the variance indicator (-2LL=682.95) suggests the statistical acceptability
and adequacy of the model at this step.
The classification shows the analysis of the quality of the model as a
whole - percentage of correct classifications. Assuming the results of the
logistic regression and the obtained equation, the "calculated" value of the
variable "Total involved in early-stage entrepreneurial activity" would be
correct 95.1% of the time (i.e., 0 if it is also 0 in the data; or 1 if it is also 1 in
data). The data regarding the variables in the equation can be seen in Table
1, including information on the significance level and odds ratio.
Table 1: Variables in the Equation
Variables in the Equation
B
Fender
-,308
Step 1* GEMHHINC
GEMHHINC(1) -,724

S.E. Wald
,224 1,890
7,645
,295 6,030

df
1
2
1

Sig. Exp(B)
,169 ,735
,022
,014 ,485

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GEMHHINC(2) ,015
,251 ,004
1
Constant
-2,309 ,356 41,936 1

,952 1,015
,000 ,099

* Variable(s) entered on step 1: gender, GEMHHINC.

Table 1 demonstrates that the variable “gender” is not statistically
significant, since the Sig. > 0.05, therefore the hypothesis H1 cannot be
proved. Regarding the variable “household income”, the hypothesis H2 can
be proved only in the sections of the lowest 33% and in the top 33%, where
the significance level < 0.05, while the significance level of Sig. = 0.952
shows that the middle 33% in the household income category do not support
the hypothesis H2. The odds ratio for the bottom third of household income
(Exp(B)= 0.485) shows that the surveyed population is 0.4 times more likely
to engage in entrepreneurial activity than the population in the top third of
household income.
Based on the above principle, it can be said that hypothesis H1 cannot
be proved, while hypothesis H2 can be proved for the lowest and the highest
third of the household income category. Looking at the previous research in
Chapter 2, especially Subchapter 2.1, it can be seen that the results for
Hypothesis H1 obtained through this research do not support the findings of
Bosma et al. (2021), Santos et al. (2017), and Haus et al. (2013), which state
that men are more likely to be entrepreneurial. Considering the results of
testing hypothesis H1, the obtained results are more related to the research
of Voda et al. (2020) and Brush et al. (2009), which stated that women are
more likely to be entrepreneurial due to their psychological characteristics.
Having the priory stated results in mind, there are a number of ways to
support and promote female entrepreneurship through policies that
governments and other organizations can adopt. Examples include loans,
grants, and investment capital specifically targeted to women-owned
businesses, or the provision of business development and training
programs, including mentoring programs, incubators, and accelerators that
provide women entrepreneurs with the skills and knowledge they need to
succeed. In addition, it is important to combat unconscious bias by having
governments and organizations undertake initiatives to raise awareness and
promote equality in the workplace. This could include training programs for
business owners and employees on diversity and inclusion. Furthermore,
work-life balance can be promoted through policies such as paid family leave
and flexible work arrangements to support women who want to balance work
and family. Governments and organizations can create an ecosystem that
supports and encourages female entrepreneurship by organizing networking
events, promoting women-led businesses, and partnering with organizations
that support women in business. It is important to note that these policies
should be tailored to the specific needs and challenges of women
entrepreneurs in a given context.
The results obtained for hypothesis H2 can be interpreted in different
ways or even considered contradictory. Since hypothesis H2 was confirmed
for the lowest and the top third of household income, the following
explanation can be given: The relationship between entrepreneurial activity
and the respondent population from the lowest 33% of income can be

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explained by the need of these people to engage in entrepreneurial activity
and in this way contribute to financial well-being. This does not preclude their
potential entrepreneurial intention or enthusiasm, but rather suggests that
necessity plays an important role in their business creation. Policy
implications can be one of the tools to encourage low-income individuals to
engage in opportunity driven entrepreneurship. There are a number of policy
actions that governments and other organizations can take to support and
encourage entrepreneurship among low-income individuals, including
initiatives such as loans, grants, and investment capital. Tools also include
business development and training programs that act as mentors,
incubators, and accelerators to provide low-income entrepreneurs with the
skills and knowledge they need to succeed. Access to education and training
can also be an important step, as low-income individuals may not have the
same access to education and training as higher-income individuals.
Governments and organizations can provide affordable or free education
and training programs to help low-income individuals acquire the skills
needed to start and run a business.
In addition, participants from the top 33% of household income can be
characterized as those who are entrepreneurial by choice, based on
internally created or externally identified opportunities and potential. Since
financial wealth is not the only reason for this group of entrepreneurs to start
a new business, various motives and drivers for their engagement in starting
a new business can be discussed. The results of this study should also be
considered in the context of its limitations in terms of time scope and number
of variables covered. The fact that only the year 2021 was included in the
study might be too short a time period to obtain truly representative results,
especially when 2021 can still be considered a (post-)pandemic year and
certainly differs from the objective reality before the pandemic.
CONCLUSION
Entrepreneurial activity is a financial and social peculiarity around the world,
in which entrepreneurs assume various obligations and take the risks
associated with the creation of a new venture, trusting that this venture will
have an impact and contribute to higher levels of efficiency and income
(Leitão & Capucho, 2021).
Based on the literature review, the paper provides insight into the
relationships between entrepreneurial activity and its predictors in terms of
gender and household income. The paper aims to help fill the
aforementioned gap in the literature by providing a quantitative analysis of
the relationships and effects between entrepreneurial activity, gender and
household income in Slovenia. As mentioned above, gender differences in
early – stage entrepreneurial activity are decreasing and that may be the
reason why the empirical results of the paper show that the correlations
between entrepreneurial activity and gender in Slovenian sample could not
be confirmed. On the other hand, the correlation between household income
and entrepreneurial activity can be demonstrated for the lowest and the top

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third of the household income category. By examining the factors that
influence entrepreneurial activity, this approach contributes to the existing
literature by reviewing the results regarding the influence of gender and
household income. The results regarding the gender dimension are not
consistent with some previous research, but may highlight the work of other
authors who have a different view of the role of masculinity and femininity in
business creation. In addition, the results related to household income open
up new research questions on motivation and opportunity perceptions
among entrepreneurs with the lowest and highest household incomes.
Future research questions should clarify whether the difference in household
income, and thus entrepreneurial activity, depends on the motivation for the
entrepreneurial venture; do opportunity costs, opportunity perceptions and
motivation play different roles in different income groups. However, further
research should be conducted to gain deeper insight into the relationships.
In addition, the study's limitation to measuring direct relationships between
the above concepts and the sample limited to a single country provides a
starting point for future research directions that could cover a longer time
span and include samples from additional countries, either in the
surrounding area or at the European Union level.
The differences in the intensity of entrepreneurial activity by gender
structure underscore the need to include other dimensions when assessing
the extent to which the entrepreneurial environment enhances participation
in entrepreneurial activities by all. As far as gender differences are
concerned, the entrepreneurial environment should also ensure conditions
that allow for a more equal organization of family life (child care,
kindergarten, meals at school and at work, more intensive use of maternity
leave by fathers, care for the elderly, cultural attitudes towards the role of
women in the family and the like).
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14

ASSESS THE ROLE OF CAREER EXPLORATION IN
EXPANDING SOCIAL COGNITIVE CAREER THEORY

Quoc Bao Lam
Ho Chi Minh City Open University,
Vietnam
1954082006bao@ou.edu.vn

Minh Pham*
Ho Chi Minh City Open University,
Vietnam
minh.p@ou.edu.vn

Abstract
The issue of career choice plays an important role in students' future.
Researchers have conducted many studies to explain the career choice
process. However, choosing the right career is still very difficult. This article
examines the role of career exploration and determines how it works
throughout social cognitive career theory. Data were collected using a
questionnaire designed on Google Forms in June 2022. In addition, 340
Vietnamese students participated in this study. The research hypotheses
were tested through SmartPLS 3. The results have once again confirmed the
role of social cognitive career theory in explaining the career selection
process. Another interesting finding was that environment exploration had a
more substantial effect on the relationship between self-efficacy and career
choice than self-exploration. Finally, the article further clarified the role of
career exploration and helped universities devise tailored study programs to
make the career choice process more efficient.

Key Words
Career choice; self-efficacy; outcome expectation; career exploration.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

INTRODUCTION
Throughout life, an individual has to make many decisions that sometimes
dramatically affect their entire life. Career choice plays a core role in forming
related factors, such as career orientation or career development (Gati et al.,
2019). Many studies have confirmed the important role of career choice
(Alinea, 2022) in determining job satisfaction or enjoyment (Lamanauskas
and Augienė, 2018), and Maree (2018) argues that choosing the right career
helps students adapt more quickly to the working environment.
Choosing a satisfying career is always tricky. Regarding personal causes,
individuals are less conscious of proactive career planning and often expect
help (Preston and Biddle, 1994), or information disturbances cause
hesitation (Udayar et al., 2018). Objective causes often include the
disappearance of occupations (Hite and McDonald, 2020). In another
aspect, an imbalance occurs due to the lack of highly qualified human
resources, while unskilled labour is gradually replaced by machinery
(Heinrich and Witko, 2021). As a result, many individuals experience job
dissatisfaction (Kulcsár et al., 2020) because they cannot adapt to the
environment or obtain low efficiency; consequently, regret and other
negative emotions emerge (Li et al., 2015).
This article focuses on students because they are still at the early stage
of discovering their abilities, values, and interests (Gati and Saka, 2001). In
addition, despite having experience in part-time or freelance jobs, students
were still unsatisfied with their choice. Therefore, to answer how to choose
a career effectively, the process of forming professional behaviour needs to
be considered and elaborated on in a general way. Therefore, this study
focuses on examining the role of individuals and how they interact with the
environment through career exploration to overcome the lack of information
(Gati and Kulcsár, 2021) about the environment and the individual self. As
stated by Betz and Voyten (1997), Shea et al. (2007), and Chen et al. (2021),
this research examines the role of career exploration and determines how it
works throughout the career choice process.
LITERATURE REVIEW
Career choice
A career is defined as “the progress and development of the person in
working life” (Kirpik and Yilmaz, 2020) or “the sequence of work experiences
that evolves over the individual’s life course” (Van der Heijden and De Vos,
2015). Career choice is the process by which individuals “choose an
occupation and the educational training involved, then a job and then
whether to stay at a job or switch to another, what formal and informal
advanced training to take” (Kulcsár et al., 2020). In addition, Manjooran et
al. (2021) defined career choice as “the selection of a type of profession”.

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Social cognitive career theory (SCCT)
Since it was developed by Lent et al. in 1994 based on Bandura's social
cognitive theory (1986), SCCT has argued that career choice intentions and
behaviours are governed by self-efficacy and outcome expectations. Due to
its comprehensiveness, SCCT and related models are now widely used in
academia, especially in career choice (Owusu et al., 2018; Tetteh et al.,
2021; Hatane et al., 2020).
The definition of Outcome expectation was stated by Zhou et al. (2014):
“one’s judgement about the potential outcomes of a given behaviour”. Tien
et al. (2009) suggest that outcome expectations positively predict career
choice behaviour. Kuthea Nguti et al. (2021) tested the role of outcome
expectations on efficiency and individual satisfaction with the chosen
profession. On the other hand, the statement of Lindley (2005) asserts that
outcome expectations explain and motivate behavioural choice. Finally,
Komarraju (2014) also supports the positive influence of outcome
expectations on career choice:
H1: Outcome expectations positively influence career choice.
Career exploration is how individuals improve their knowledge about
careers and related information (Heymann et al., 2022). Chan (2018) defines
career exploration as collecting information about selectable occupations to
make more effective decisions. Chen et al. (2021) believe that career
exploration is the beginning of sustainable career development. Selfexploration helps individuals increase their understanding of their abilities,
personalities or interests, thereby eliminating careers they consider
unsuitable. On the other hand, environment exploration helps gather more
objective information from which individuals can assess whether the
environment is suitable. Specifically, self-exploration allows choosing a
career that matches the individual’s abilities. Exploring the environment
enhances adaptability to a new job. In addition, evidence suggests a strong
link between effective career exploration and career choice (Presbitero and
Teng-Calleja, 2022; Railey and Spector, 2022). Based on the above
argument, the following hypothesis is proposed:
H2: Self-exploration positively influences career choice.
H3: Environmental exploration positively influences career choice.
Self-efficacy is a subjective assessment of an individual’s ability to
perform a behaviour (Zulkosky, 2009). On the other hand, Landino and
Owen (1988) claim individuals’ confidence in using their abilities to perform
or control behaviour (Çalli and Kartal, 2021). However, considering the
relationship between self-efficacy and career exploration, Chan (2018)
argues that self-efficacy will increase individual engagement in the career
exploration process. On the other hand, high self-efficacy incentivises
individuals to participate in career exploration as a form of preparation for
making career decisions (Tsai et al., 2017).

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In particular, the stronger the belief in self-efficacy, the more individuals
want to learn about themselves to verify their beliefs. In addition, individuals
will also promote environment exploration to ensure more effective career
choice behaviour. Self-efficacy helps guide the process of better career
exploration. At the same time, self-efficacy makes individuals more proactive
when reducing mental or cognitive difficulties during career exploration
(Storme and Celik, 2018; Glessner et al., 2017). This positive relationship is
also demonstrated in many studies, such as Gushue et al. (2006) and Penn
and Lent (2019). From the above arguments, this study proposes the
following hypothesis:
H4: Self-efficacy positively influences self-exploration.
H5: Self-efficacy positively influences environmental exploration.
According to SCCT, self-efficacy strongly influences and explains
outcome expectations (Lent et al., 1994). In other words, self-efficacy
indicates outcome expectations (Baglama and Uzunboylu, 2017; Brown and
Cinamon, 2016). Specifically, Alexander et al. (2011) explain that a firmer
belief in an ability to perform a behaviour will lead to "more hopeful of
benefiting in a meaningful way from the positive outcomes". According to
Jiang and Zhang (2012), self-efficacy for the performance of the behaviour
enhances the individual's positivity in assessing the expected outcome. This
view of this positive relationship, as well as its role in practical career choice,
has also been demonstrated by Nguti et al. (2021), DeFreitas (2012), Sawitri
(2015), and Dickinson et al. (2017). From the above arguments, the following
hypothesis is established:
H6: Self-efficacy positively influences outcome expectations.
RESULTS
Career exploration adopts the scale of eleven observed variables from the
study of Stumpf et al. (1983). Two variables of SCCT are measured based
on eight observed variables from the scale of Betz et al. (1996) for selfefficacy and the ten observed variables for outcome expectation (Metheny
and Mcwhirter, 2013). Finally, this research uses a scale including six
observed variables to measure career choice in the study of Mu (1998). The
data were collected by questionnaire via Google Forms with 340 valid
samples. The proportion of women accounted for 70.9%, men accounted for
29.1%, and most were in the "economics - administration" category (69.7%).
Data were analysed using SPSS version 20 and SmartPLS 3.0 software to
verify the research results.
Table 1: Descriptive statistics
Classification

Categories

Frequency

Percentage

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Gender

Male

99

29.1

Female

241

70.9

Economics Administration

237

69.7

Social
Sciences
Humanities

74

21.8

29

8.5

Major

Natural
ScienceEngineering

-

Measurement model assessment
Data were analysed by measurement model assessment. According to Hair
et al. (2017), scale reliability requires composite reliability (CR) and
Cronbach’s α (CA) greater than 0.7. Table 2 shows that the minimum CA
value is 0.854, and the minimum CR value is 0.895. All scales have outer
loading coefficients all over 0.7, and an AVE index greater than 0.5 indicates
that the observed variables of the scale can explain more than 50% of the
variance of the concept it represents (Henseler et al., 2015). It is concluded
that the scale achieves the required reliability and convergence and does
not experience multicollinearity (VIF<5) (Hair et al., 2014).
Table 2: Convergent validity and multicollinearity test

CA

CR

Outer Loading

VIF

Min

Max

Min

Max

AVE

Career Choice

0.898

0.922

0.662

0.782

0.814

1.903

2.491

Environment exploration

0.888

0.915

0.643

0.736

0.870

1.711

2.885

Outcome expectations

0.913

0.928

0.562

0.703

0.814

1.802

2.491

Self-exploration

0.854

0.895

0.631

0.758

0.831

1.614

2.046

Self-efficacy

0.899

0.919

0.587

0.727

0.797

1.761

2.156

Table 3 presents the discriminant validity, which represents the extent to
which the factors are distinct and uncorrelated. This value was evaluated by
comparing the square root of the AVE of each construct and its
intercorrelation with other constructs (Fornell and Larcker, 1981). Thus, it
can be seen that this coefficient (bold numbers) is greater than other
intercorrelations satisfying the Fornell–Larcker criteria (Henseler et al.,
2015).

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Table 3: Discriminant validity
Career
choice

Environment
exploration

Outcome
expectations

Selfexploration

Career choice

0.814

Environment
exploration

0.656

0.802

Outcome
expectations

0.746

0.631

0.750

Self-exploration

0.603

0.655

0.594

0.795

Self-efficacy

0.688

0.645

0.715

0.625

Selfefficacy

0.766

Structural model assessment
Hair et al. (2017) prioritise the evaluation of the structural model to test
through the coefficient of determination (R2). According to the standards of
Hair et al. (2011), the analysis results (Table 4) show that the concepts in
the model have the smallest R2 value of 0.39, and all values reach the
average predictive level.
Table 4: Results of determination R2, predictive capacity Q2
R Square

Q Square

Career Choice

0.624

0.406

Environment exploration

0.416

0.263

Outcome expectations

0.511

0.282

Self-exploration

0.390

0.241

The study carried out a bootstrapping test (N=5000) to examine the
relationships between the factors in the model. In Table 5, all hypotheses
have statistical significance (P value < 0.05). Next, the path coefficients (β)
are all positive, proving that Hypotheses H1, H2, H3, H4, H5, and H6 are
supported (Figure 1).
Table 5: Hypothesis assessment
Hypothesis

β

Standard
Deviation

t value

p value

Environment exploration→Career
Choice

0.242

0.063

3.871

0.000

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Outcome expectations→Career Choice

0.508

0.063

8.064

0.000

Self-exploration→Career Choice

0.143

0.063

2.286

0.022

Self-efficacy→Environment exploration

0.645

0.041

15.601

0.000

Self-efficacy→Outcome expectations

0.715

0.037

19.250

0.000

Self-efficacy→Environment exploration

0.625

0.048

12.928

0.000

DISCUSSION
This study examined the relationship among the SCCT backgrounds (selfefficacy, outcome expectations), career exploration, and career choices of
Vietnamese students. The results showed that all proposed hypotheses
were supported. First, self-efficacy has a positive effect on outcome
expectation (β=0.715), self-exploration (β=0.625), and environment
exploration (β=0.645). This result suggests that self-efficacy is the basis for
building outcome expectations. In detail, individuals own their capacities,
which leads to different prospects about the behaviour results. In addition,
this shows the role of personal capacity and the correct perception of
individuals about the skills they possess to make appropriate assessments.
In addition, similar to the result of Rogers and Creed (2011), career
exploration is positively affected by self-efficacy, which means that the
higher the self-efficacy, the more efficient the exploration. Based on selfefficacy, students can be more confident in discovering their skills and
comparing themselves to the explored information. Finally, after
understanding self-efficacy, they can choose a suitable career environment.
Figure 1: Model analysis

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Next, the impact of career exploration on career choice includes selfexploration (β=0.143) and environment exploration (β=0.242) (Table 4). This
result shows that students must explore themselves to understand their
interests, strengths or limitations. When combined with environment
exploration, individuals create a strong connection between themselves and
the professional environment, which is the basis for making effective career
choices, thereby adapting their careers and developing better. Finally, the
factor that has the most decisive influence on career choice is outcome
expectation (β=0.508). It is inferred that outcome expectations will change
career choice behaviour based on individuals' evaluations of what they will
receive in return if they choose that career. In other words, Lent et al. (1994)
declared that outcome expectations describe what individuals receive after
performing the expected behaviour.
CONCLUSIONS
This study contributes to the theoretical system in two ways. First, applying
SCCT explains the formation of career choice more comprehensively. The
combination of SCCT and career exploration helps to strengthen the
relationship between the individual and the environment more effectively.
The second contribution, the independent testing of the role of selfexploration and environmental exploration, is a prominent advantage
compared with previous studies, such as Chen et al. (2021) or Heymann et
al. (2022). Most previous research only referred to career exploration as a
simple information search. Meanwhile, exploration is essential for career
orientation and goals (Presbitero and Teng-Calleja, 2022). Therefore,
concretising the role of each form of career exploration helps to more
comprehensively reflect the nature of career behaviour.
The outcome expectation of a person with the essential mediator role
strengthens the relationship between self-efficacy and career choice. For
example, if an individual has a high career outcome expectation, self-efficacy
will cause them to develop a tendency to choose that career (Lent and
Brown, 2013). Therefore, universities need to be equipped with practical
knowledge about jobs to make it easier for students to choose a career that
can achieve their expectations.
The results also show that environment exploration has a more decisive
role than self-exploration in shaping career choice trends. Surprisingly, this
result is in contrast to the study of Gross-Spector and Cinamon (2018).
Vietnamese students belong to a collective culture, so self-discovery
receives little attention. Giving students more opportunities to explore the
professional environment through business semesters, seminars, or the
faculty's experience will help them become more confident in their career
choices.
This study has some limitations. First, the study did not consider the
interference between other occupational theories but only built a research
model based on the SCCT context. This limit may cause partiality in drawing

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relevant or misleading conclusions in different contexts. Second, the model
focuses on building factors positively related to career choice, but it is also
necessary to consider answering the question, "How can students overcome
barriers in career choice?"
Regarding research for the future, in addition to looking at the process or
ways of choosing a career, examining the change or comparing the
difference in career choice behaviour among many subjects, such as social
roles or age, should also be considered (student‒student-worker). Next,
differences between national or regional cultures can also be an idea when
researching career choices or related fields. Finally, longitudinal research
can be applied to observe the overall change in career choice throughout
career development. On the other hand, future studies also need to examine
the mechanism of barriers in the career choice process to obtain a more
comprehensive view of career choice.
ACKNOWLEDGEMENTS
This research is funded by the Ho Chi Minh City Open University under grant
number E2022.05.2.
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ADOPTION OF FINTECH SERVICES IN UTTAR
PRADESH: USING EXTENDED TAM
Shanu Srivastava*
Banaras Hindu University,
India
shanu.0108@bhu.ac.in

Aashna Sinha
Banaras Hindu University,
India
aashna2407@bhu.ac.in

V Shunmugasundaram
Banaras Hindu University,
India
sundaram@bhu.ac.in

Abstract
Rapid progression in technology has resulted in the tremendous growth &
development of FinTechs, which significantly transformed the framework of
the Indian Financial System. So, the present research aims at contributing
to the literature by employing extended TAM to examine Perceived
Usefulness (PU), Perceived Ease of Use (PEOU), Government Influence
(GI) & Security (S) as the factor influencing users’ Intention to Adopt (ITA)
FinTech Services in India. Our findings revealed that PEOU, PU & GI
significantly impact the usage of Financial Technological Services while
Security has an insignificant influence on users’ intentions. Moreover, it was
found that PU mediates the association of PEOU with ITA. Therefore, the
present research contributes to the literature concerned with factors affecting
users’ ITA FinTech Services and validating Technology Acceptance Model
(TAM) in anticipating users’ ITA FinTech Services by adding Security &
Government Influence as additional constructs.

Key Words
Behavioural intention; financial technology; FinTech services; perceived
usefulness, TAM.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

INTRODUCTION
The global recession, of 2008 was the catalyst of the growth & development
of FinTechs that become users’ favorites in no time (Capgemini Research
Institute, 2021) because they turned the limitations of conventional financial
institutions into their strength. It is one of the rapidly emerging fields in the
financial ecosystem, and also the confluence of financial services with
expanding technologies is crucial in building a vigorous digital nation that
would lead India toward digital transformation (Deloitte, 2017). FinTech
services are the digitalized & technology-enabled financial products &
services that are provided by FinTech companies & financial institutions
(Osman et al., 2021). Such services are altering the way users think while
accessing, investing, managing, and spending their savings, in order to
achieve their demand of availing financial services at an affordable cost
(Shah et al., 2019). The inefficiency of conventional financial services like
labor-dependent financial services, paper-based and manually done
procedures, etc. offers a great chance for FinTechs to acquire and retain
users at low cost, and that would lead to greater adoption of financial
services (Deloitte, 2017). The FinTech sector of Asia region is highly
dominated by two developing nations namely, India & China (KPMG, 2018).
According to Goyal et al. (2021), India has experienced a tremendous rise in
FinTech companies as there are more than 2,100 FinTech companies, out
of which around 67 percent of them have come into existence in the past five
years. Also, the worldwide FinTech industry is anticipated to expand at a
CAGR of 23.58 percent from 2021-2025, and with the predicted market
opportunity of $1.3 trillion by 2025 and more than 4200 active FinTech firms,
India is quickly emerging as the global center for the FinTech industry (EY,
2022). It is because of the initiatives taken by the government & regulatory
bodies for the digital nation, with the help of more internet and smartphone
usage which led to greater utilization of technology-enabled services
(Deloitte, 2017). According to Invest India (2023), “India has the highest
FinTech Adoption rate globally of 87 percent which is significantly higher
than the Global average rate of 64 percent”. And it has one of the fastest
budding FinTech ecosystems globally having a FinTech industry market size
of $50 Billion which is projected to be ~$150 billion by 2025 and also it
documented the highest absolute number of real-time transactions globally
crossing around 48 billion that is around 6.5 times of the collective volume
of world’s leading nations like U.S, Canada, U.K., France & Germany in 2021
(Invest India, 2023).
The usage rate of Fintech services has enormously increased after the
COVID-19 outbreak indicating greater financial inclusion (Gupta and
Agrawal, 2021). The main reason behind the greater adoption rate of
FinTech services is that FinTech companies are mostly aiming at techfriendly users resulting in reduced budgetary & resource exploitation in
convincing uninterested users also these companies deliver financial
products & services to the financially underserved & unserved section of the
society that is high in masses in developing nations (Slazus and Bick, 2022;
EY, 2016). Users with less financial literacy can also use FinTech services,

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so it has the potential to serve the unbanked population that has low financial
literacy (Setiawan et al., 2021). The growth & development of FinTech
Services has greatly influenced individuals’ day-to-day life as services like
online mobile payment, e-wallets, InsurTech, TradeTech, cryptocurrencies,
etc. have made financial transactions, effortless & scalable for them (Osman
et al., 2021).
FinTech offers a wide range of benefits to its users, still, it is an alien term
for a large section of the nation even if they know then also, they restrain to
use and it is because of various factors that affect users’ behavioral intention
while adopting technology-enabled financial Services (Singh et al., 2021).
According to Indian Ambassador Taranjit Sandhu, “Uttar Pradesh is
destined to become a powerhouse of India, with a population of 240 million
it is the most populous state”. Also, over the past four years, Noida and
Greater Noida (districts in Uttar Pradesh) have become preferred locations
for establishing Uttar Pradesh financial technology (FinTech) companies
(Express News Service, 2022). UP government has also signed a MoU with
a US-based fintech firm for investing 500 crores for setting up its operations
in Lucknow and Noida.
Therefore, the present research aims at contributing to the literature by
employing extended TAM to examine PU, PEOU, GI & S as the factor
influencing users’ intention to use FinTech Services in Uttar Pradesh.
However, there are past studies that have addressed the adoption of a
particular type of FinTech Services like Mobile payments, Online Banking,
etc. but there is very fewer literature as per our knowledge that is conducted
in India and addressed FinTech Services adoption as a whole. And
absolutely no research that is conducted in Uttar Pradesh and attempted to
assess the users’ intent towards FinTech services usage.
Since “Uttar Pradesh favored destination for Fintech companies” (News
Desk, 2022) and also there is an excessive rise in the number of FinTech
users in Uttar Pradesh so there is a need to explore more on this topic as it
will enable FinTech service providers & system developers to better
understand the users’ attitudes & perceptions towards using such services
so that they can propose solutions and design services to retain users’ and
also attract potential consumers. Therefore, this study attempts to bridge the
gap by tackling this issue.
Hence, the rest of the paper is presented in various sections, where
Section 2 outlines the prior works of literature that were reviewed based on
FinTech services, TAM constructs (PU & PEOU), Security & GI as additional
constructs, Intention to use FinTech services and accordingly conceptual
research model and hypothesis were developed & framed. Section 3
explains the methodology used in the study involving questionnaire
preparation, data collection & scale reliability are explained. In section 4, the
result of the study is presented and section 5 comprises findings &
discussions. Section 6 concludes the research work while sections 7 and 8
contain the implications, limitations & future research scope.

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BACKGROUND & HYPOTHESIS FRAMING
FinTech Services
FinTech is the portmanteau of Financial Technology, which refers to an
organization or a firm that delivers financial services & products with the
employment of technology and their main goal is to grab the attention of
consumers by offering convenient products, easily accessible & efficient
services as compared to traditional financial services (Susilo et al., 2019).
As per Financial Stability Board (FSB), “FinTech is technologically enabled
financial innovation that could result in new business models, applications,
processes, or products with an associated material effect on financial
markets and institutions and the provision of financial services”. Basically, it
refers to a financial processing unit that facilitates the nation’s financial
sector in providing efficient customer care and assistance by using
technologically-equipped methods (Gupta and Agrawal, 2021).
Theoretical Background
For assessing users’ adoption intention and behavior towards a technology,
prior studies have adopted several theories like TRA, UTAUT, TPB, etc. but
among all TAM is most widely adopted by researchers to understand
consumers’ adoption & usage intent towards a technology or system. Theory
of Reasoned Action (TRA) is the base model of TAM, which was originally
designed for examining IBM employees’ acceptance of word processor
technology (Patel and J. Patel, 2017).
Previous works of literature have correlated FinTech services adoption
with various theories but TAM is extensively used by past studies (Susilo et
al., 2019; Slazus and Bick, 2022; Chong et al., 2010, Cheng et al., 2006)
while conducting research that is concerned with the adoption of FinTech
Services and its related fields. Therefore, since TAM has been enormously
backed by empirical findings so the present study has used TAM as the base
model to examine the factors that affect the users’ intention to adopt FinTech
Services in India with special reference to selected districts of Uttar Pradesh.
Framing hypothesis for the proposed model
As TAM has enormous empirical and pragmatic support from prior studies
so in this study TAM is used as our foundation model. However, in the initial
model of the Technology Acceptance Model Davis (1989), only PU & PEOU
were used as the predictor variable and Behavioural Intention (BI) was the
outcome variable but in the present research, we have used TAM as the
base model and augmented it by adding two more independent variables
namely, Government Influence (GI) & Security (S). Also in the original model,
the attitude was included as it mediates the relationship of PU & PEOU with
behavioral intention but recent works of literature have excluded it since it
does not mediate the relation of PU & PEOU with behavioral intention (Patel
and J. Patel, 2017). So, in this study, attitude is not included in the model.

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Therefore, we have utilized the original constructs of TAM which are PU &
PEOU but also added GI & S, considering that they are crucial for users’
intention to adopt FinTech Services in India. So, we assume that the present
study will be a literature contribution to the augmented Technology
Acceptance Model by using GI & S along with the actual constructs of TAM
for examining whether PU, PEOU, GI & S influence users’ intention while
adopting FinTech Services. Table 1 presents the prior studies that
administered the same association. Figure 1 shows the conceptual model of
the study.
Figure 1: Conceptual Research Model

Government Influence (GI) & Intention to Adopt (ITA) Financial
Technological Services
The government of any nation plays an important role in developing its
FinTech Sector, as it frames laws & regulations that help the FinTech sector
to grow & develop (Setiawan et al., 2021), facilitate users with the proper
infrastructure needed, and also promote it among its citizens by making them
aware about the benefits of such services. The government efforts would
encourage individuals. So, government influence is an important factor that
affects users’ intention to use Technology-enabled Services as studied by
prior researchers (Chong et al., 2010; Hu et al., 2019; Osman et al., 2021)
while Setiawan et al. (2021) found that there is the insignificant influence of
GI on users’ intention. The present study hypothesizes that:
H1: GI positively influences users’ ITA FinTech Services.
Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and
Intention to Adopt (ITA) FinTech Services
As per Liébana-Cabanillas et al. (2014), the Perceived Ease of Use refers to
users’ perception that adopting a particular technology requires no or very
less effort. Users are expected to use FinTech services if they believe them
to be user-friendly, easy to use, & less complicated (Patel & and J. Patel,
2017). While Perceived Usefulness (PU) is the degree to which a user’s

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subjective probability that adopting a certain technology will result in his/her
improved job performance (Venkatesh and Bala, 2008). And Intention to use
refers to the user’s intent to adopt, in contrast to their actual use of FinTech
services (Cheng et al., 2006). PU & PEOU are considered the prime
constructs that are capable to influence users’ intention to use any
technology (Davis, 1989). Therefore, in the present study, we examined the
association of PEOU with users of ITA FinTech Services with PU as a
mediating variable, as PU is a crucial factor in technological usage (Setiawan
et al., 2021). Hu et al. (2019) found that PEOU has a significant impact on
PU while Patel and J. Patel (2017) concluded that PEOU has a significant
impact on Users’ intentions. On the contrary, Setiawan et al. (2021) & Chong
et al. (2010) found that PEOU had an insignificant influence on users’
intentions. Cheng et al. (2006) & Setiawan et al. (2021) revealed that the
relationship between PEOU and Users’ intentions is mediated by PU. So,
based on prior literature, the hypothesis was framed as:
H2: PEOU has a significant positive impact on users’ ITA FinTech Services.
H3: PEOU is positively related to PU.
H4: The relationship between PEOU and users’ ITA FinTech Services is
mediated by PU.
Perceived Usefulness (PU) and Intention to Adopt (ITA) FinTech
Services
PU is the users’ perception to adopt or not to adopt certain technology to the
extent that it will facilitate them to enhance their job performance (Davis,
1989). It is expected that users mostly prefer to use FinTech services if they
find them to be more useful as compared to conventional financial services
(Patel and J. Patel, 2017). Prior studies found that there is a positive
correlation between PU and users’ ITA FinTech services (Setiawan et al.,
2021; Chong et al., 2010; Cheng et al., 2006; Patel and J. Patel, 2017). So,
the hypothesis was developed as:
H5: PU has a significant positive impact on users’ ITA FinTech Services.
Security (S) and Intention to Adopt (ITA) FinTech Services
Security & privacy is considered vital factor to make sure that the users’
information is unavailable to suspicious or unauthorized consumers who can
misuse the data (Ismail et al., 2018). Security issues like personal & financial
data leakage, cyber threat, identity threat, etc. are the major issues that may
negatively affect users’ intention to use FinTech services (Osman et al.,
2021). The study by Cheng et al. (2006) & Patel and J. Patel (2017) revealed
that there is a strong impact of security on users’ intentions contradicting this
Chau & Ngai (2010) found that security is not a significant factor that

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influences young users’ intention to adopt fintech services. So, we
hypothesize that:
H6: S positively influences users’ ITA FinTech Services.
Table 1: Effects of the Proposed Relationships
ASSOCIATION
S→ITA
GI→ITA
PEOU→ITA
PU→ITA
PEUO→PU
PEOU→PU→ITA

PRIOR LITERATURE
(Cheng et al., 2006), (Ismail et al., 2018), (Alwi et al.,
2019), (Osman et al., 2021), (Patel and J. Patel, 2017)
(Setiawan et al., 2021), (Chong et al., 2010), (Hu et al.,
2019), (Osman et al., 2021)
(Liébana-Cabanillas et al., 2014), (Daragmeh et al.,
2021), (Setiawan et al., 2021), (Chong et al., 2010),
(Patel and J. Patel, 2017)
(Liébana-Cabanillas et al., 2014), (Daragmeh et al.,
2021), (Setiawan et al., 2021), (Chong et al., 2010),
(Cheng et al., 2006), (Patel and J. Patel, 2017)
(Daragmeh et al., 2021), (Hu et al., 2019)
(Daragmeh et al., 2021), (Setiawan et al., 2021), (Cheng
et al., 2006)

METHODOLOGY
Data Collection
In the present study, we attempt to ascertain empirically whether constructs
(namely, Government Influence, Perceived Usefulness, Perceived Ease of
Use, & Security) influence users’ intention to adopt FinTech Services.
Further, we test the mediation effect of Perceived Usefulness between
Perceived Ease of Use & Users’ Intention to use FinTech Services. So, we
constructed a research instrument consisting of five constructs, which are
GI, PU, PEOU, S & ITA. All constructs excluding demographic variables
were measured on a 5-point Likert scale (5 = strongly agree and 1 = strongly
disagree) as used by Singh et al. (2021); Upadhyay and Jahanyan (2016) &
Daragmeh et al. (2021) to assess users’ intention towards technology-abled
services. There were 22 items for 5 variables in the research questionnaire
that were taken from past literature and further amended as per the need of
our study. Before sharing the research instrument, a pilot study was
conducted with 40 FinTech Users, to check the reliability of the statements
of the questionnaire, and accordingly, modifications were made.
Further, the sample size of the study in case the population is unknown
is the minimum number of indicators multiplied by 5 (Hair et al., 2014), there
were 21 indicators in this study so the minimum sample size required is 105
respondents. So, we distributed questionnaires to 280 respondents and
received 220 responses in total. After filtering invalid questionnaires
consisting of missing & incomplete questionnaires, there were 209 valid
responses, which is above the minimum required sample size. Overall, we

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achieved a response rate of 75 percent, which is adequate & acceptable for
the analysis. The data was collected through hybrid (online & offline) mode
using a combination of convenience & snowball sampling and the target
population of the study was FinTech Service users residing in four cities of
India namely, Prayagraj, Noida, Lucknow & Varanasi. Microsoft Excel &
SmartPLS 4 were employed for data analysis. Table 2 depicts the
demographic profile of the respondents, in which 43.5 percent of the
respondents were male while 56.5 percent were female. 18.2 percent of the
respondents were below 25 years, 27.8 percent were between 25-40 years,
28.2 percent were between 41-55 years and 25.8 percent were above 55
years of age. Educational qualifications show that the majority of the
respondents were graduated (34.4 percent) and post-graduated 49.3
percent) while 10.5 percent had intermediate degrees, 1.6 percent held
Ph.D. degrees & above and 3.8 percent had any Diploma/ Professional
Degree.
Table 2: Demographic Profile
GENDER
Male
Female
Total
AGE
Below 25 years
25-40 years
41-55 years
Above 55 years
Total
EDUCATION
Intermediate
Graduation
Post-Graduation
Ph.D. & above
Any Diploma/ Professional Degree
Total

FREQUENCY
91
118
209

PERCENT (%)
43.5
56.5
100.0

38
58
59
54
209

18.2
27.8
28.2
25.8
100.0

22
72
103
4
8
209

10.5
34.4
49.3
1.9
3.8
100.0

Source: Own survey.

Common Method Bias (CMB)
CMB, which is also known as common method variance or just method bias.
Common method variance (CMV) is the systematic amount of fictitious
covariation shared between various variables due to a similar method
employed for the collection of data (Buckley, Cote, & Comstock, 1990).
Since, all variables in the present study are measured using a common
survey questionnaire so there might be chances of the presence of
covariance among them (Malhotra et al., 2016). So, to address common
method variance-related issues authors have used Harman’s single factor
test, which is a broadly employed method used for assessing CMB in a
single-method research design (Podsakoff et al., 2003). Basically, in this test

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Exploratory Factor Analysis (EFA) is done on all the items, and CMB is
present if a single factor comes from unrotated factor solutions or a first
factor ascertains that there are many variations in the variables (Podsakoff
and Organ, 1986). It is widely accepted that if the result of a single-factor test
is less than 50 percent of the variance, there are fewer chances of CMV
(Daragmeh et al., 2021). The result of Harman’s single factor of the study
was within the acceptable range. Therefore, there is an absence of CMV in
the study.

RESULTS
For testing the research hypothesis, SEM is applied in the study. It’s a
multivariate technique, that is the amalgamation of factor analysis and
multiple regression. It facilitates the researcher to assess a string of
interrelated dependence associations among the measured variables as well
as the latent constructs and also between various latent constructs, all at the
same time (Hair et al., 2015). Further, for estimating the parameters Partial
Least Square SEM was employed. In the present study, SmartPLS 4
software is used for validating the research hypothesis. Data was examined
in two steps, firstly measurement model was analyzed by assessing the
construct validity & reliability of each construct and then the structural model
was studied. The structural model also known as an inner model in PLSSEM depicts the constructs and shows an association among the constructs
while measurement models of the constructs also known as the outer model
in PLS-SEM show the association among the constructs and the indicator
variables (Hair et al., 2017).
Measurement model of the study
Firstly, we measured the reliability & validity of the research data. A reliable
measuring instrument provides consistent and stable results (Kothari, 2004).
As per Hair et al. (2019), indicator loadings of items more than 0.70 are
regarded as within the acceptable range. Table 3 shows that every item of
the data is within the acceptable limit. Moreover, Cronbach alpha and
Composite reliability were measured for determining the internal reliability of
the data. According to Daragmeh et al. (2021) & Fornell and Larcker (1981),
Convergent reliability is more than 0.70 while Cronbach’s alpha of more than
0.80 is the indicator of good internal consistency. And since the present
variables have convergent reliability & Cronbach alpha above 0.70 & 0.80,
so the data has good internal consistency.
Table 3: Construct reliability and validity
Constructs

Item
Loadings

Cronbach's
alpha

Composite
reliability
(rho_a)

Composite
reliability
(rho_c)

Average
variance
extracted (AVE)

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023
Government
Influence
(GI)
GI1
0.775
GI2
0.885
GI3
0.904
GI4
0.777
GI5
0.862
GI6
0.705
GI7
0.875
Intention to Adopt (ITA)
ITA1
0.863
ITA2
0.892
ITA3
0.915
ITA4
0.921
Perceived Ease of Use
(PEOU)
PEOU1
0.838
PEOU2
0.845
PEOU3
0.869
PEOU4
0.873
Perceived
Usefulness
(PU)
PU1
0.922
PU2
0.904
PU3
0.901
Security (S)
S1
0.944
S2
0.914
S3
0.905

0.923

0.929

0.939

0.687

0.920

0.923

0.943

0.806

0.878

0.879

0.916

0.733

0.895

0.897

0.934

0.826

0.911

0.925

0.944

0.849

Source: SmartPLS 4 (v.4.0.8.6).

Validity is the extent to which a measuring instrument exactly and
accurately depicts what it is needed to do (Hair et al., 2015). For assessing
the validity of the present study, convergent and discriminant validity is used.
The average variance extracted (AVE) is used to determine convergent
validity. AVE above 0.50 or higher indicates that the construct describes 50
percent of variance and it is the acceptable range of AVE, and in Table 3 it
can be seen that the AVE of all constructs is above 0.50. Further,
discriminant validity is calculated to ensure that a constructed measure is
unique and has no relation with other measures (Henseler et al., 2015).
Firstly, discriminant validity was calculated as per Fornell and Larcker
(1981), in which the AVE of each construct should be more than its
correlation with other constructs.
Table 4: Fornell-Larcker criterion
GI
ITA
PEOU
PU
S

GI
0.829
0.627
0.503
0.469
0.269

ITA

PEOU

PU

S

0.898
0.698
0.716
0.410

0.856
0.747
0.491

0.909
0.517

0.921

Source: SmartPLS 4 (v.4.0.8.6).

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

In Table 4, it can be seen that all constructs satisfy the condition, so DV
is acceptable. Further, DV was calculated by using the Heterotrait-monotrait
ratio (HTMT), which was suggested by Henseler et al. (2015) as they were
against the Fornell-Larcker criterion. The threshold value required for
structural models with similar constructs is 0.90 while for distinct constructs
is 0.85 (Henseler et al., 2015). In Table 5, it can be seen that the values of
all constructs are within the threshold range.
Table 5: Heterotrait-monotrait ratio (HTMT)
GI
ITA
PEOU
PU
S

GI

ITA

PEOU

PU

0.673
0.558
0.510
0.287

0.775
0.787
0.443

0.842
0.545

0.566

S

Source: SmartPLS 4 (v.4.0.8.6).

Structural equation modelling
SEM is a process used for assessing a sequence of interrelationships
between a set of constructs expressed by various measured variables and
merged into an integrated model (Malhotra and Dash, 2022). For evaluating
the structural model of the study, the collinearity of the formative indicators
was calculated using the Variance Inflation Factor, and VIF values of more
than 5 suggest that there are major collinearity issues between the indicators
of formatively calculated constructs (Hair et al., 2019).
Table 6: Collinearity statistics (VIF)
Constructs
VIF
Government Influence (GI)
GI1
2.129
GI2
3.973
GI3
4.751
GI4
2.109
GI5
3.185
GI6
1.928
GI7
3.323
Intention to Adopt (ITA)
ITA1
2.831
ITA2
3.161
ITA3
4.682
ITA4
4.839
Perceived Ease of Use (PEOU)
PEOU1
2.088
PEOU2
2.140
PEOU3
2.672
PEOU4
2.686

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

Perceived Usefulness (PU)
PU1
2.928
PU2
2.605
PU3
2.609
Security (S)
S1
4.737
S2
4.004
S3
2.395
Source: SmartPLS 4 (v.4.0.8.6).

Table 6 shows that all VIF values are below the threshold limit so there
are no collinearity issues. Further, for determining the explanatory power of
the structural model, the value of R2 i.e., coefficient of determination. is
calculated. R2 shows the variance that is explained in each of the
endogenous constructs and R2 values range between 0 to 1 indicating a high
explanatory power (Hair et al., 2019). Table 7, it is observed that exogenous
constructs can interpret 64.8 percent of the variance of the dependent
variable i.e., intention to adopt, which is within the range and has moderate
explanatory power.
Table 7: Values of R-square
ITA
PU

R-square
0.648
0.558

R-square adjusted
0.641
0.556

Source: SmartPLS 4 (v.4.0.8.6).

After examining the explanatory power of the model, the next step
bootstrapping process was carried out with 5,000 samples for testing the
framed hypothesis of the study. In Figure 2, the results of the structural
model are explained.
Figure 2: Structural results of the research model

Source: SmartPLS 4 (v.4.0.8.6).

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

Table 8 shows the results of the hypothesis testing. In hypothesis 1, the
effect of Government Influence (GI) on Intention to Adopt (ITA) is examined.
Government Influence (GI) has a significant positive association with ITA (β
= 0.323 & p < 0.05). Hence, hypothesis 1 is supported.
Hypothesis 2 studies the effect of PEOU on ITA while hypothesis 3
examines the effect of PU on PEOU. The result shows that PEOU has a
direct significant positive association with ITA and PU (β = 0.256, p < 0.05
& β = 0.747, p < 0.05). So, hypotheses 2 & 3 are also supported.
Hypothesis 4 determines the effect of PU on ITA and PU has a significant
positive association with ITA since β = 0.370 & p < 0.05. Therefore,
hypothesis 4 is supported.
Hypothesis 5 examines the impact of Security (S) on ITA and results
reveal that Security (S) has an insignificant positive association with ITA
since β = 0.007 & p > 0.05. So, hypothesis 5 is rejected.
Hypothesis 6 assesses the association between PEOU and ITA Fintech
Services as mediated by Perceived Usefulness (PU). It was found that
PEOU has a positive indirect association with ITA mediated by PU with β =
0.276 & p > 0.05. Hence, hypothesis 6 is also supported.
Table 8: Hypothesis results
Relationship

GI -> ITA
PEOU -> ITA
PEOU -> PU
PU -> ITA
S -> ITA
PEOU -> PU > ITA

Original
sample (O)
Direct Effect
0.323
0.256
0.747
0.370
0.007
Indirect Effect
0.276

Sample
mean (M)

Standard
deviation
(STDEV)

T
statistics
(|O/STDEV|)

P
values

0.321
0.256
0.749
0.371
0.003

0.058
0.072
0.049
0.066
0.053

5.556
3.534
15.191
5.560
0.126

0.000
0.000
0.000
0.000
0.900

0.278

0.056

4.969

0.000

Source: SmartPLS 4 (v.4.0.8.6).

FINDINGS AND DISCUSSIONS
Our study aims at examining the factors that affect users’ ITA FinTech
Services by validating extended TAM for understanding the users’ intent
toward FinTech Services. Each hypothesis concerned with the association
among the variables is framed and validated through reliability testing and
Structural Equation Modelling. The outcome of the research revealed that
there is a significant positive influence of PU, PEOU & GI on users’ ITA
FinTech Services while S has an insignificant influence on the adoption
intention of users. It was also found that PU mediates the relationship
between PEOU & users’ ITA FinTech Services which means that PEOU
indirectly influences users’ intentions via PU. So, Hypotheses: 1, 2, 3, 4 & 6
are accepted while H5 is rejected.
The survey outcome of the research reveals that GI has a significant
association with users’ ITA FinTech services as previously concluded by

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Chong et al. (2010); Hu et al. (2019) & Osman et al. (2021). In the last
decade, the Indian Government has been very active in promoting FinTech
services among its citizens and also encouraged them to use FinTech
services. Therefore, the government should implement proper laws
concerning FinTech services, provide required infrastructures and ensure
security & privacy to its citizens to protect them from cybercrimes & frauds.
Further, it was found that PEOU has a direct significant association with
users’ intentions and it is validated by the study of Patel and J. Patel (2017)
but Setiawan et al. (2021); Chong et al. (2010) & Cheng et al. (2006)
contradict our results as they found out that PEOU does not have a direct
influence on users intention. However, the results of the initial Technology
Acceptance Model also support our results. Since the FinTech service is a
quite new concept for Indian users especially the users above 41 years of
age who prefer to adopt those services that are easy to use, therefore the
FinTech companies should offer user-friendly services that are easy to learn
& use.
Similar to the results of Hu et al. (2019) & Daragmeh et al. (2021), our
study has also identified that PEOU significantly influences PU. While PU
mediates the relationship of PEOU with users’ ITA FinTech services, our
results are consistent with the results of past studies (Daragmeh et al., 2021;
Setiawan et al., 2021; Cheng et al., 2006). It means that users find those
services useful that are easy to use and hence, prefer to use them. Hence,
there is an indirect relation between PEOU & users’ intention that is mediated
via PU, it implies that users want to put less effort into learning & using
FinTech services.
Our results show that PU has a strong association with users’ intention to
use FinTech Services. Past studies (Setiawan et al., 2021; Chong et al.,
2010; Cheng et al., 2006) validate our results. Since PU is a significant factor
in determining users’ intention to FinTech Services, service providers should
let their users know about the various benefits offered by FinTech services
in comparison with conventional financial services. Also, fintech companies
should focus on promoting and enhancing their features and offer their users
with latest innovative services so that they find them useful in their day-today life.
Our study found that there is an insignificant influence of security on
users’ intention to use FinTech Services. Chau and Ngai (2010) validate our
finding as they concluded that security isn’t a significant factor that influences
young users’ ITA mobile banking. While Ismail et al. (2018); Alwi et al. (2019)
& Osman et al. (2021) contradict our result as they concluded that security
& privacy positively affect users’ intention to use technology-abled services.
This contradiction may be because users find FinTech services more secure
than conventional financial services. However, the FinTech service providers
need to enhance the security & privacy issues and create awareness about
it among their users to protect them from cyber fraud and ensure greater
adoption of FinTech services.

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

CONCLUSIONS
The objective of the present study was to investigate the factors affecting
users’ intention to adopt FinTech services in India with special reference to
four districts of Uttar Pradesh namely Noida, Prayagraj, Lucknow, &
Varanasi. We attempted to investigate the direct & indirect impact of PEOU,
PU, S & GI on users’ ITA FinTech services. Statistical results of our research
show that PEOU, PU, & GI have a strong positive impact on users’ intentions
while security has an insignificant influence on users’ intentions. Moreover,
the PU was found to be a mediator for the indirect influence of PEOU on
Users’ intentions. The augmentation of TAM by adding GI & S as an
additional variable shows the theoretical input to the literature of factors
affecting users’ ITA FinTech Services & validating the Technology
Acceptance Model (TAM) in anticipating users’ ITA FinTech Services.
IMPLICATIONS OF THE STUDY
The present study has theoretical implications as the model framed in this
research is an augmented form of TAM by affixing Government Influence &
Security as additional constructs. Augmented TAM will facilitate researchers
& academicians in better understanding & explaining factors affecting users’
intent to adopt FinTech Services like mobile payment apps, digital lending
services, etc. Also, the outcome of the research will facilitate FinTech service
providers & system developers to design & deliver services that are as per
the preference of the consumers, easy to use, secure, and can also consider
various factors that influence users’ intention towards FinTech services.
The present research validates that Government Influence is a significant
determinant that influences users’ intent towards FinTech services in Uttar
Pradesh, this suggests that Indian Government should frame effective laws
and regulations concerning FinTech services to protect the interest of
FinTech adopters. Also, Government should provide the proper
infrastructure like proper internet services, smartphones, etc., and organize
FinTech literacy and awareness programs so that more and more individuals
can adopt it.
Further, PEOU & PU are also significant predictors while examining
users’ intent toward FinTech services so FinTech system developers &
service providers should provide users’ websites & applications interfaces
that can be easily operated, adaptable & convenient for users. They should
also provide proper steps or videos on how financial transactions can be
carried out on FinTech apps & websites. Also, FinTech service providers
should upgrade & enhance their services by considering the feedback given
by users so that users’ needs & requirements can be catered to successfully.
This research found that security has an insignificant impact on users’
intent towards FinTech services and this is because of the reason that users
in Uttar Pradesh prefer to use FinTech services like mobile payment apps,
digital lending apps, etc. over conventional financial services as they find
them more secure and safe. But since prior studies show that security is the

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

key determinant of users’ intent to adopt technology-abled services so
FinTech service providers must ensure the security and privacy of their users
against cyber frauds.

LIMITATIONS AND FUTURE DIRECTIONS
Our research has certain limitations the study was conducted only from the
viewpoint of Indian users from several districts of Uttar Pradesh but the
model used in our study can be applied by future studies for assessing users’
intention to adopt technology-enabled services by other developing nations.
We have used only Government Influence (GI) & Security (S) as an
additional construct to Technology Acceptance Model (TAM) for evaluating
users’ intention to adopt FinTech services. Future studies can employ
Perceived Risk, Subjective Norm, Quality of service, etc. as additional for
assessing users’ behavioral intention toward FinTech services. Also, they
can use Age, Gender, etc. as moderating variables for studying their
influence on users’ behavioral intentions.

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ANALYSIS OF MBA SKILLS FROM THE EMPLOYERS’
PERSPECTIVE: A CASE STUDY
Valentina Jošt Lešer*
GEA College – Faculty of
Entrepreneurship,
Slovenia
valentina.jost-leser@gea-college.si

Dario Berginc.
GEA College – Faculty of
Entrepreneurship,
Slovenia
dario.berginc@gea-college.si

Abstract
The paper outlines the importance of distinguishing between different types
of MBA programmes (professional and academic MBAs) and thus related
outcomes. Furthermore, the paper presents a case study from Slovenia
where we obtained the data crucial to adapt the MBA curriculum to
employers' requirements. The research was conducted to offer a new MBA
program in Slovenia by GEA College - Faculty of Entrepreneurship. The
most significant discrepancy has been recognised in interpersonal,
leadership, and entrepreneurial skills. Emotional intelligence and empathy
are proposed directly by employers as essential for the manager’s job. Our
results confirm some existing research in this area. The importance of the
study is seen as a good practice. Moreover, findings are beneficial for
leaders in education, especially for MBA programme managers: to enhance
their programme relevance, adopt more innovative ways of designing and
delivering courses, and as a good starting point for further extensive
research of the MBA skill gaps in Slovenia. The latter is very important, as
our study was limited to GEA College’s business partners.

Key Words
MBA; MBA skills; MBA curriculum; soft skills; case study.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

INTRODUCTION
The Master of Business Administration programme (hereinafter MBA)
created in the USA was established on market needs. Its advantage
(compared to other programmes) has been in its practical value and
employability of MBA graduates. In the 1960s, MBA studies entered the
European market, where most schools followed the U.S. example of Harvard
Business School (Dhoul, 2019). Knowing that European higher education
stands on a very different predisposition, the question arises, how do the
higher educational institutions implement MBAs and what types?
Furthermore, changing the organisational environment puts educational
institutions that educate executives and leaders in an unenviable position
since an MBA, as Tan and Ko state (2019, 64), “holds the promise of
systematically preparing its graduates for their managerial roles”. Nowadays,
some authors (Navarro 2008; Blass and Weight 2005; Wensley, 2005;
Mintzberg, 2004) criticise MBA programmes for their relevance, value, and
overall quality. Some professionals and researchers perceived the gaps
between the skills the MBA curriculum attempts to impart and those
managers need while on the job. (Kumar and Jain, 2010; Kamal, 2020;
Passarelli et al., 2018). Emphasis on hard skills over soft skills is considered
to be a disadvantage of current MBA degree programs (Cruz and Wood,
2015; Mintzberg, 2004). Accordingly, a constant process of harmonising
programs with the labour market requirements is necessary. Mintzberg’s
(2004) viewpoint is that since management is deeply embedded in everyday
living practices, educators should focus on developing managers who are
deeply embedded in local life.
GEA College – Faculty of Entrepreneurship from Slovenia (hereinafter
GEA College), in cooperation with the University of New York in Prague, has
outlined the implementation of the MBA program in Slovenia in the year
2021. In addition to the preliminary literature review, the purpose was to
research what kind of education the MBA program represents in the EU and
to research the market to align the MBA curriculum to employers’ needs. The
paper presents the skills gap perceived by GEA College’s business partners,
programme co-creators, and potential employers of future MBD graduates
of the programme. The research shows a case study of how GEA College
reviewed the market needs before establishing the MBA program and
adjusted the curriculum accordingly.
Recognising the Need to Research What MBA Is in the EU
Among the first to help develop the original form of the programme known
as the Master of Business Administration were American schools operating
in the early 20th century: Wharton School of the University of Pennsylvania,
founded in 1881, followed by The Haas School of Business at the University
of California in Berkely, and Tuck School of Business at Dartmouth College.
The first MBA programme was established at the Harvard Graduate School
of Business Administration in 1908 to respond to the needs of the industry
(Kaplan 2014; MBAcentral 2022). The programme started as an experiment

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Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

with 59 students and has become one of the most popular graduate degree
programs in the U.S. (Herringto, 2010). During the 1950s to the 1960s, a
significant internationalization of MBA occurred, beginning with Asia,
Europe, and Africa. The programme entered the European higher education
market through different business associations (Hull, 2000). Therefore
European universities were initially reluctant to welcome the MBA (Alam et.
al 2021, p. 1243).
Nowadays, the MBA is among the most internationally popular
programmes and is globally present (Blass and Weight, 2005, p. 231). How
many different MBA programmes there are in Europe is not known, since a
joint European or world database on MBA programmes does not yet exist.
The number can be estimated from different web pages for
searching/ranking MBA programmes, such as MBA STUDIES,
FINDMYMBA, STUDY PORTALS, MBA CENTRAL, MBA TODAY, QS
Global MBA, etc. However, the numbers are not reliable, since institutions
have to pay for their MBA program to be included in some of the registers
and different methodologies used for rankings etc.
There are various variants of the programme on the market, a variety of
specializations, a variety of study lengths, and modes of study: MBA
programmes in International Business, Human Resources, Finance,
Management, IT, Marketing etc. A three-year, a two-year, a one-year
programme, and a mini MBA. Moreover, we have an opportunity to study
MBA part-time, full-time, face-to-face, blended, or online.
Along with the growing popularity and rise of the MBA programme,
accreditation agencies were established. The development of accreditation
bodies in the UK and Europe was much more recent than in the USA and
has been fuelled primarily by concerns about protecting the MBA brand
(Lock, 1999). Among many, three accreditation bodies are today recognised
as the most significant in the world: from the USA the American Assembly of
Collegiate Schools of Business (AACSB), form the UK the Association of
MBAs (AMBA), and form the EU the European Quality Improvement System
(EQUIS) (Nassereddine, 2018). Programmes accredited by all three bodies
are also known as “triple crown” accredited MBA programmes (FIND MBA
2022; MBA CENTRAL 2022; MBA TODAY 2022). There are many
accreditation agencies from different parts of the world. However, no
institution on a global or European level is in charge of what can be named
the MBA.
Concerning the European Higher Education Area and Bologna Process
(hereinafter EHEA)1 and a three-cycle system: bachelor’s (3 years), master’s
(2 years) and doctoral programmes (3 years), the question arises: what is
the difference between the master’s program in business and the MBA?
“Technically, any Master’s in the field of business administration can be
labelled as an MBA since any education provider is free to label their
Master’s degrees in business as MBAs” (IESE, 2022). Is the MBA just
1

29 European Ministers of Education, meeting in Bologna, signed the Bologna Declaration in 1999. The
basic purpose of the declaration is to establish European higher education and the basis for a common
path and cooperation for an open higher education area by 2010 (Zgaga 2004, 24).

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another name for a master’s program and master’s degree? Some authors
(Alam et al., 2022, 883) agree that there is no common understanding of
whether it is a ‘terminal degree’ or customised training and should not be
ideally considered as a Master’s program’. Moreover, market changes have
affected debates about the MBA’s purpose and efficiency. Vaara and Fay
(2011, 28) argue that there are critical issues in the ongoing debate about
MBA programs: the outcomes of MBA programs, the inculcation of values
and practices in the process, and self-regulation in the form of accreditation
and ranking. The first issue (no more important than the others) is the
primary concern of this paper, the knowledge and learning provided by MBA
education. Many authors perceived the gap between the skills the MBA
curriculum attempts to impart and the skills the managers need while on the
job (Kumthe and Jain, 2010; Kamal, 2020; Passarelli et.al., 2018; Costigan
and Brink, 2015).
Approaching this issue, the answer to this question has to be resolved:
what is the difference between the Master’s program in business and the
MBA? Clearly, if any business program can be called an MBA, then there
can be enormous differences. Originally, the MBA started out as an elite
qualification for potential senior managers and leaders, minimum age and
quantity of experience were the selection criteria. Nowadays, the market is
flooded with a range of MBA programmes, offering different modes of
delivery and specialisations, with some being accredited by national
accreditation bodies, some by international/foreign accreditation bodies, and
others by none (Blass and Weight, 2005, 229). Researching MBAs without
proper classification and expected outputs adds confusion to the literature
review. Blass and Weight (2005) state, »the MBA is positioned here as a
qualification that is plagued by market confusion as to what it actually
represents and what its value is«.
Concerning the European higher education system with 3 cycles,
master’s (2 years) are nationally recognised programs accredited by the
national higher education accreditation agency2. Accreditation means that
the operation of an institution or particular programme is authorised by a
legal body: a government ministry or an accreditation or quality assurance
agency (NUFFIC 2020, 27). Master’s programmes (2nd cycle) are meant to
continue academic studies after the Bachelor’s (1st cycle) level. They last for
two years, are credited with 120 ECTS3, and can be delivered as full-time or
part-time. We can consider it an academic MBA since the students can
continue their education after their bachelor’s without any work experience
and acquire an academic master’s degree upon successful completion.
Teachers are also academics with habitations4 since most national
accreditation agencies in the EU have this prerequisite for programme
accreditation. The prevailing teaching style is traditional (not necessarily);
lectures are normally the leading teaching method, and the teaching
2

The European Consortium for Accreditation in higher education (ECA) is an association of recognised
accreditation and quality assurance agencies in Europe.
3
European Credit Transfer and Accumulation System.
4
According to NAKVIS all teachers at the master level have to be habilitated, the minimum level is that
of Assistant Professor.

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language is a mother tongue since most national accreditation agencies
have this prerequisite for programme accreditation (the right to study in the
mother language). Students with a master's degree have access to the third
level, PhD. In that manner, MBA is just another name for a master’s program
in business and a master’s degree.
Another type of an MBA that can be found in the European and Slovenian
higher education market5 is a programme aimed at experienced
professionals who want to acquire a strong foundation in business. Usually,
programmes are not nationally recognised (depending on the national
accrediting agencies' prerequisites concerning habitation, language, and
other prerequisites) and require considerable full-time work experience from
students. The teaching style is interactive and practically oriented. Most MBA
programmes combine lectures, case studies, experiential teaching methods,
project-based work, role games etc. The idea is to create an atmosphere
with a group of students to learn from each other and mutually enrich the
classroom experience (IESE, 2022). Teachers are professionals and experts
from business (not necessarily habilitated academics) since “a training
ground for managerial work requires establishing a priori a comprehensive
understanding of the nature of contemporary managerial work and the
associated work role requirements” (Rubin and Dierdorff, 2009). The
teaching language is usually English since teachers are foreign
professionals. It can be delivered in different varieties as a full-time or parttime, three-year, two-year or one-year MBA program, also called a mini
MBA.
In this regard, we identified two types of MBAs, an academic MBA (a
Master’s program in business) and a professional MBA. However, a different
typology could be made regarding other considered characteristics. There is
no standard agreement on whether an MBA should be regarded as a
‘Master’s program’ or a form of ‘customised training’ (Alam et al., 2022, 883).
However, having identified two different MBA types, different answers to the
questions will be provided: are the business schools’ faculty trainers or
teachers, are the business schools of learning or training centres, and should
curricula be concentrated on theories or providing practical skills (Kamal and
Kumar 2010, 71).
In Slovenian higher education the Ministry of Education, Science and
Sport keeps a public record of all higher education institutions and study
programmes accredited by the National Agency for Quality Assurance in
Higher Education (hereinafter NAKVIS) which provides nationally
recognized degrees (Gov.si, 2022). The MBA programme can have a Triple
Crown, being accredited by AACSB, AMBA, and EQUIS. However, if
NAKVIS does not accredit it, it is not nationally recognized. Potential MBA
students have to examine the accreditations of desired MBA programs on
their own. At the same time they must pay special attention to their lack of
skills, desired study outcomes, and employer's expectations.
5
According to webpage research (year 2020), there are three institutions in Slovenia that are advertising
MBA programmes: IEDC - Bled School of Management, University of Ljubljana - School of Economics
and Business, University of Maribor - Faculty of Economics & Business.

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Concerning the identified MBAs, the academic MBA (Master’s
programme in business) and the professional MBA, different outcomes are
expected. Insofar as we really sum up the fundamental difference, the
academic MBA equips the student with more theoretical knowledge and the
professional MBA with practical skills. An MBA programme presents a
conspicuous and systematic effort to equip individuals with the skills needed
to manage people and organizations (Varela and Burke, 2013, 436).
However, what are the professional MBA competency and skills
requirements? Considering that a professional MBA prepares “students for
future management roles in organizations, by providing them with a better
skill in the understanding of business management and people” (Kowarski,
2019), we have to ask the employers. We have to bear in mind that beside
the market demand other indicators can have an impact on MBA output as
curricula content coordinated by the program manager (or academic
director) or program structure and faculty expertise (Tan and Ko, 2019, 65).
For example, Natarajan and Kumar (2014 in Buenviaje et al., 2016, 18)
identified the most important problems for low employability among students
aspiring to take up a career in Human Resources Management: lack of
competent faculty and outdated curriculum, improper selection of students,
lack of communication skills, and lack of industry-institute interaction.
Usually, the core MBA curriculum is built on finance and accounting,
operations management, marketing, and strategy (Stoten, 2018). The
studies such as The Skills Gap survey (Financial Times, 2018) and MBA
Jobs & Salary Trends Report (QS TopMBA, 2018; QS TopMBA, 2021) show
that employers are increasingly looking for soft skills. MBA employers rate
soft skills as more important than hard skills to emphasize the ability to
interact, communicate, and lead, which remains paramount to successfully
moving organizations forward (Chisholm, 2021). Traditionally, the skill gap
has been most pronounced in soft skill areas: communication, interpersonal,
and leadership skills (QS TopMBA.com Jobs & Salary Trends Report 2018).
Skills with the lowest satisfaction scores expressed by employers in the
consulting sector are interpersonal, leadership and strategic thinking skills
(Chisholm, 2021). Passarelli, Boyatzis, and Wei (2018, 57) state that
emotional and social competencies, which represent the behavioural level of
emotional intelligence are becoming very important for managers.
In order to equip managers with the needed skills for their career and
success, understanding how MBA programs effectively foster competency
development is critical for the future of management education (Passarelli,
Boyatzis, and Wei, 2018; QS TopMBA 2018).
RESEARCH FINDINGS – MBA CURRICULUM ADJUSTMENT DATA
In 2021, GEA College tended to offer an MBA program (professional MBA)
in the Slovenian higher education market. For this purpose, a case study
with the following research question was designed: which skills do
employers recognize as the most important for managers and to what extent

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have their managers developed these skills? To answer the research
question we gathered qualitative and quantitative data. Through a literature
review we gathered the newest knowledge and studies about MBA
programmes and skill gaps.
For collecting the quantitative data, an online questionnaire was used.
The paper present researched skills gaps perceived by GEA College partner
companies. The population in this case represents 285 companies6, whom
we see as co-creators of programs and potential employers of MBA
graduates. The online questionnaire was conducted in January 2020 and
was sent to all partner companies through the online survey tool 1KA. In the
first phase, we asked company directors to assess the importance of the
skills for the manager’s job and in the second, the development of selected
skills of their managers.
238 respondents participated in the survey7. 44.1% of respondents were
men and 55. 9% were women. Participants were divided into four age
groups: up to 20 years of age (1.3%), 21 to 40 years (26.1%), 41 to 60 years
(61.8%), and above 61 years (10.9%). The largest group was a group of
participants between 41 and 60 years. 18.9% of the participants come from
micro companies, 19.3% from small companies, 30.3 % from medium
companies, and 31.5% from large companies.
The next step during our research was to carry out a data analysis
followed by the interpretation of the findings. The findings are significant for
preventing skill gaps and aligning MBA curricula to the employers’ needs.
From the employers’ point of view, the most important skills for managers
are (see Table 1) interpersonal skills (4.73), leadership skills (4.69), and
entrepreneurial skills (4.46). Male and female employers assess the
competencies of managers with approximately the same importance. Only
when assessing interpersonal skills, a statistical difference8 is shown.
Namely, male employers assess it (4.27) as more important than female
employers (3.83). As the literature review showed our findings also support
the fact that soft skills are essential for employers in the sample. Academic
achievements (2.66), research skills (3.64), and IT/computer skills (3.92) are
presented as less important for managers.
Table 1: Expressed level of Importance and development of skills

1
2
3
4

Skills

Level of Importance for managers

Interpersonal Skills
Leadership Skills
Entrepreneurship
Multilingualism

4.73
4.69
4.46
4.12

Developed
skills
employed managers
3.39
3.48
3.27
3.67

in

6

In 2020, there were 52011 companies in Slovenia. Observed companies: Micro enterprise (2-9) Small
enterprise (10-49) Medium enterprise (50-249) Large enterprise (250+), SURS classification, 2020
(SURS 2020).
7
We have conducted the Kolmororov-Smirnov test, statistic shows (p>.0.5) that the sample of the test is
not significantly different from a normal distribution. On this preposition in further statistical analysis, we
used parametric statistical tests.
8
SPSS, t-test (sig 0.032).

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5
6
7
8
9
10
11
12

Finance Skills
E-Business
Relevant Experience
International Awareness
Marketing skills
It/Computer Skills
Research skills
Academic Achievements

4.11
4.08
4.08
4.01
3.99
3.92
3.64
2.66

3.34
3.26
3.58
3.19
3.21
3.29
3.16
2.60

The most significant discrepancy or skill gap (see Graph 1) has been
perceived in interpersonal skills (1.34), leadership skills (1.21), and
entrepreneurial skills (1.19). Results inform which skills MBA programme
directors have to put more effort into. Skills that are most in line with what
employers deem important and those with the smallest discrepancy are
academic achievements (0.06), multilingualism (0.45), research skills (0.48),
and relevant experience (0.50). There was no statistically significant
difference between company groups and the skills gap and perceived gaps
between men and women9.
Graph 1: The skill gap between the importance and development of skills

Academic Achievements
Multi-lingual

0,06
0,45

Research skills

0,48

Relevant Experience

0,50

It/Computer Skills

0,63

Finance Skills

0,77

Marketing

0,78

International Awareness

0,82

E-Business

0,83

Entrepreneurship

1,19

Leadership Skills

1,21

Interpersonal Skills

1,34

Through open questions in the survey, we investigated which skills that
were not listed employers also perceive as significant for a manager’s job.
We got 16 answers. Respondents most often pointed to empathy and
emotional intelligence as essential competencies.

9

SPSS, ANOVA, (p>.0.5).

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Graph 2: Skills that were not listed, but perceived as significant
agility, proactivity,
Professional experiences
Teamwork
Moral values
Optimization, innovativeness
Vision, energy
Multiculturalism
Target performance, setting priorities
Project management
To be a good example, learning by doing
Personal and professional credibility
Coaching
Charisma
Knowledge transfer
Empathy
Emotional intelligence
0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

The findings of our research are also supported by the literature review
and research of the Bloomberg Businessweek survey. Research results
show that soft skill development is a priority now, both for MBA programmes
and students (University of Northern Colorado, 2021). Further, research also
shows that soft skills will continue to be relevant in the future. According to
recruiters, the importance of interpersonal skills will only increase in the next
five years. The greatest emphasis on interpersonal skills in the future is
placed in Asia, followed by Europe and the USA (GMAC, 2020).
Furthermore, our research confirms that there is a lack of development of
managerial skills among MBA programmes (Varela et al., 2011). The authors
make a distinction between specific managerial skills (e.g. setting goals) and
complex managerial skills (e.g. leading others, negotiating in intercultural
contexts), whereas the latter is much more difficult to implement in MBA
programmes. Based on our research, the most important skills for managers
are interpersonal skills, leadership skills, and entrepreneurial skills - all of
which can be classified as complex managerial skills. The authors give an
example of one of AMBA’s (2011) accreditation criteria (“Employers can
expect graduates over time to […] have well-developed interpersonal skills”),
where learning goals are not specific enough. They advocate for greater
emphasis on learning goals tied to intermediate stages of skill development,
that are operationalized with respect to measurable knowledge structures.
We agree that soft skills segmentation should be structured and quantifiable,
and this can also be a challenge for future research.
Similar results can also be observed from the research evaluating MBA
programmes in the Czech Republic from the perspective of employers. A

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quantitative survey was conducted among 127 HR managers to identify the
value of the MBA as perceived by the employer. The sample included micro,
small, medium-sized, and large organizations. Results have shown that
micro-organizations have the greatest expectations for the acquisition and
development of hard skills by their employees.
Also, micro and small organizations are not interested in the personal
development of their employees. On the other side, the development of soft
skills, especially leadership, is an important factor for large organizations.
Results have shown that large companies strongly require the development
of managerial skills, especially leadership skills to be developed as an
outcome of MBA programmes (Balcarova et al., 2016). The great importance
of leadership skills among soft skills was also highlighted in our study. On
the other hand, we did not find any significant differences between
employers' preferences regarding hard and soft skills in relation to the size
of the company.
Our study further confirms the results of the integrative literature review
on employability and skill gap at the post-graduate level of management
education (MBA and Post-graduate Diploma in Management - PGDM) in
India (Bhatnagar, 2021). The study on 19 relevant selected papers,
published from 2012 to 2019 in India, found that soft skills and non-technical
aptitudes should be emphasised in MBA education. Organisations also seek
other attributes that are associated with employability, such as
communication, emotional intelligence, critical thinking, problem-solving
skills and interpersonal skills. In our study interpersonal skills have shown
the most significant discrepancy or skill gap and emotional intelligence and
empathy are proposed directly by employers as essential for the manager’s
job.
In addition, Bedwell et al. (2014) highlighted interpersonal skills as crucial
in today's business environment. Research in the field of integrating
interpersonal skills into the MBA classroom has shown that executives
consistently rank interpersonal skills as crucial for a successful workforce.
Results suggest that students must acquire effective interpersonal skills
before entering the workforce, as well to use the science of training as a
stepping-stone to facilitate instructors' integration of interpersonal skills into
existing MBA courses. The authors also presented some example teaching
tools to help integrate interpersonal skills teaching within existing courses.
Furthermore, the authors warn that there must be an understanding of
exactly what constitutes interpersonal skills effectiveness. Authors based
their research on a scientifically rooted taxonomy (Klein et al., 2006).
CONCLUSION
An MBA program should be specifically designed to add value to candidates
in the context of practical/applied business knowledge and leadership skills.
As the business environment of organizations in the global world is changing
rapidly, and so are the operations of organizations themselves, key skills that
MBA graduates need, have to be identified. The paper presents a case study

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of research data needed to adapt the MBA curriculum to employers'
requirements in order to offer the MBA programme at GEA College, in
cooperation with UNYP. The relevance of researching the skill gap is
supported by an escalating criticism that MBA programmes are losing their
significance and that competencies indicated by managers to be most critical
are least represented in core MBA curricula (Tan and Ko, 2019, 64). The
biggest discrepancy has been recognized in leadership, interpersonal skills,
and entrepreneurial skills. Emotional intelligence and empathy are proposed
directly by employers as essential for a manager’s job. Findings support the
literature review since many authors acknowledge the very importance of
soft skills.
Accordingly, the research outlines the importance of distinguishing
between different types of MBAs (professional MBAs and academic MBAs)
in Slovenia and thus related outcomes. Much confusion in the European
higher education market concerning the MBA is observed. It seems that
more and more MBA programmes are being created without a common
direction and definition of what an MBA programme is or what it should be.
As some authors observe (Alam et al., 2021, 1250) a large number of
programmes and the confusion about what an MBA programme is can lead
to circumstances where MBA programmes become just money-making
machines.
Findings allow us to adopt more innovative ways of designing and
delivering the MBA courses enabling us to acquire soft skills and to add
some relevant subjects based on the results derived from GEA College’s
partner organizations.
The results of the study are confirmed by some existing research, e.g.
(Varela et al., 2021, Balcarova et al., 2016; Bhatnagar, 2021; Bedwell et al.,
2014) and open up new dimensions in the exploration of soft managerial
skills. The importance of the study is seen as good practice, the findings of
this study are useful for programme managers, so that they are able to
enhance their programme relevance and as a good starting point for further
extensive research of the MBA skill gaps in the Slovenian higher education
market. The latter is very important, as research was limited to GEA
College’s business partners only.
RECOMMENDATION FOR FUTURE RESEARCH
Research should be expanded, suggesting checking the survey results
through a quantitative survey of a broader sample of employers in Slovenia,
thus increasing the representativeness of the findings. A quantitative survey
on a wider sample would be very useful and would show the possible
significant differences between employers' preferences regarding hard and
soft skills in relation to the size of the company.
Conducting qualitative methods of getting data (in-depth interviews, focus
groups) among employers should also be considered. Nonetheless,
qualitative data would give us a deeper insight into the topic and a better
understanding. It would be necessary to segment each soft skill in order to

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systematically plan the progress of MBA students' competencies. Similar
scientific taxonomy as used by Klein et al. (2006) for interpersonal skills,
could be used. Such research would produce a systematic model of soft
skills and their sub-skills, which could further lead to a competency model of
soft skills for MBA students.
We further assume that the results differ between countries or regions.
Some research has shown the difference between soft skills perception in
different parts of the world, e.g. Asia, the USA and the EU (CMAC, 2002).
From this point of view, it is necessary and reasonable to investigate the
factors influencing these differences.
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A BAYESIAN ANALYSIS OF DETERMINANTS OF NET
INTEREST MARGINS OF COMMERCIAL BANKS IN
VIETNAM
Tien Thuy Ho
University of Finance
and Marketing,
Vietnam
tienht@ufm.edu.vn

Oanh Kim Thi Tran
University of Finance
and Marketing,
Vietnam
kimoanh@ufm.edu.vn

Diep Van Nguyen*
Ho Chi Minh City
Open University,
Vietnam
diep.nv@ou.edu.vn

Abstract
The current article investigates the impact of bank-specific and
macroeconomic factors on the net interest margin (NIM) of commercial
banks in Vietnam. In order to explore the association, we performed
Bayesian linear regression on a dataset of 24 banks from 2008 to 2020. Our
research result reveals that bank size (LNSIZE), profitability (ROA),
operational cost to operating income ratio (BOPO), loan-to-deposit ratio
(LDR), and non-performing loan ratio (NPL) of a bank positively affect the
NIM of banks. On the contrary, bank liquidity (GWM) and loan market power
(MPR) negatively affect the NIM of banks. Moreover, we suggest that
macroeconomic factors, including GDP growth (GDP) and M2 money supply
growth (M2), have a negative impact on NIM. Furthermore, the impact of the
inflation rate (INFL) on NIM is relatively low. Our article highlights new
information that improves the understanding of the NIM of banks in emerging
economies like Vietnam.

Key Words
Commercial banks; net interest margin; Bayesian linear regression;
Vietnam.

*Corresponding Author

Advances in Business-Related Scientific Research Journal, Volume 14, No. 1, 2023

INTRODUCTION
A commercial bank is a financial intermediary operating for profit. In
particular, a commercial bank provides financial services to customers and
collects fees and charges interest from the provided services to create profits
(Asmar, 2018; Khoa et al., 2022). Moreover, a commercial bank acts as an
intermediary for transferring capital from overfunded to underfunded places
through deposit mobilization and lending to customers (Tarus, Chekol, &
Mutwol, 2012). Commercial banks are the main pillars of maintaining a stable
economic and financial system, particularly in developing countries, where
bank loans are vital for development (Rudhani & Balaj, 2019; Tran, Nguyen
& Duong, 2022). Therefore, the performance of banks is a key influencer of
the economic and financial stability of any country. Rapid technological
advancement, the introduction of new financial products, and the trend of
globalization have increased the size and complexity of banking institutions
(Moch, 2018). Furthermore, regulators have advocated the creation of larger
and more complex institutions by encouraging other banks to acquire failed
banks to limit the negative consequences during crisis (Barth & Wihlborg,
2016). Although banking institutions have become more complex,
performance is still the main driver of their operations. NIM is an indicator
reflecting the bank's performance, which is measured by the difference
between interest income and expenses payable to bank investors. However,
in order to achieve the profit target, commercial banks can increase the
lending rate and reduce the deposit rate. However, this will make it difficult
for individuals and enterprises that need capital to have access to
unemployed capital and reduce the customer's debt repayment ability.
Additionally, in the context of complicated and prolonged pandemic
development, business activities of individuals and enterprises become
more difficult, resulting in a reduction in their debt repayment ability on
schedule if they have to repay too much interest. Therefore, commercial
banks have to reduce lending interest rates to support business activities as
well as stimulate economic development. The income of the majority of
banks comes from credit extension activities, specifically lending activities.
A reduction in lending rates will reduce a bank's income as well as deposit
interest rates in order to ensure that the bank's operation is still profitable. If
the deposit interest rates are too low, it will be difficult for commercial banks
to mobilize unemployed capital from the public.
Figure 1: NIM trend of Vietnam banks

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6,0000 %
5,3662 %
5,0000 %

4,4928 %
4,0426 %
3,4794 %
4,0000 % 3,6305 % 3,4619 %
3,6925 % 3,4984 %
3,2919 %
3,4118 % 3,2274 % 3,4215 %
3,1730 %
3,0000 %
2,0000 %
1,0000 %
0,0000 %
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Figure 1 illustrates that the NIM decreased slightly between 2008 and
2009 and increased slightly between 2009 and 2012. Then, NIM fluctuated
insignificantly in value from 3.1730% to 3.6925% between 2013 and 2019
and increased significantly in 2019–2020. In particular, in 2008–2009, the
impact of the Global Financial Crisis of 2008–2009 negatively influenced
Vietnam's economy. The reduction in lending interest rates to a single digit
in early 2009 and interest rate support policies made credit activities of banks
become tightened, resulting in a reduction of interest margin from 3.6305%
in 2008 and 3.2919% in 2009. In 2010–2012, the economy gradually
recovered from the crisis through monetary and fiscal policy in a strict
direction to control inflation and stabilize the government's macroeconomy,
and signs of further improvement were observed in the activities of the
banking industry. In 2010–2011, the high inflation rate led to an increase in
the lending and deposit interest rates of commercial banks, which made the
NIM of banks increase significantly to 11.9075% in 2011. In 2013–2019, the
State Bank of Vietnam (SBV) issued decisions to reduce lending interest
rates, market interest rates, and control inflation, such as Decision No.
2646/QD-NHNN 2012, Decision 643-NHNN 2013, Decision 1424/QD-NHNN
2017, and Decision No. 1870/QD-NHNN 2019. Meanwhile, those policies
affected the NIM of commercial banks, which fluctuated around 3.41% on
average. 2019–2020 was the period when the whole world, in general, and
Vietnam, in particular, faced a health crisis. Particularly, the outbreak and
prolongation of the COVID-19 pandemic made life and economic activities
become worse. People's production and business activities became difficult.
SBV issued Circular No. 01/2020/TT-NHNN stipulating that credit institutions
and foreign bank branches rescheduled debt repayment terms, applied
exemption or reduction in interests and fees, and kept unchanged debt
groups to support customers affected by the COVID-19 pandemic. Although
lending interest rates were adjusted down to support customers, such

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adjustment resulted in a significant reduction in deposit interest rates, only
3–5% per year on average, which created a large difference, helping the
operation of commercial banks in this period gain high profits. In particular,
the average NIM of commercial banks increased markedly from 3.4794% in
2019 to 5.3662% in 2020.
The present article contributes to the existing literature in numerous ways.
First, unlike previous studies, we investigated the factors affecting the NIM
of commercial banks through the use of the Bayesian regression method. In
contrast to the frequency school regression, Bayesian regression models
utilize poor distributions allowing us to incorporate external knowledge into
our research model (van de Schoot et al., 2021). Second, we utilized 24
Vietnamese banks from 2008 to 2020 to study the Vietnamese banking
system. Therefore, the sample of the article is small (only 312 observations).
In this case, the Bayesian regression method performs better than the
frequency school regression models (Bui & Thach, 2023). Third, because we
consider both bank-specific factors and macro factors to analyze their effect
on NIM, we have an overview of the set of factors affecting NIM and the
effect of each factor on NIM. Our findings will assist bank managers in
providing appropriate strategies to increase the NIM for banks in the context
of Vietnam. Besides, the results of the present paper are reference material
for researchers in this field.
The rest of the current article is structured as follows. Section 2 briefly
presents the literature review. Section 3 describes the dataset, the
experimental data, model, and method and provides some summary
statistics. Section 4 presents and discusses the research results. Finally,
Section 5 summarizes the main conclusions.
LITERATURE REVIEW
NIM is a measure of the performance as well as the profitability of banks
(Kumar, 2014). NIM is calculated by subtracting operating expenses from
gross income (Ben Khediri & Ben-Khedhiri, 2011). From the perspective of
industrial organizations, the difference between the price of bank
intermediation and the cost of its output represents the bank's interest
profitability covering the cost of intermediation. Bank-specific factors,
information premiums for evaluating and monitoring investments, market
structure, and risk management premium should all be reflected in the
margin. NIM is a metric summarizing a bank's net interest rate of return on
its core borrowing and lending activities (Khoa et al., 2022). NIM indicates
the ability of the board of directors as well as the bank's staff to maintain the
growth of revenues in comparison with the increase in costs (Sarwar,
Muhammad, & Zaman, 2020). The change in NIM assists bank managers in
making reasonable investment decisions by assessing the received income
rate and the costs incurred. From an economic aspect, a higher NIM
indicates that the banks are operating more efficiently, controlling payable
costs more strictly, and managing profitable properties better. Hence, banks
earned high profits, helping them to increase competitiveness in the market

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as well as limit risks when the macro environment fluctuates. Additionally,
from a social aspect, when the NIM is high and banks make a lot of profits,
bank borrowers suffer from high interest rates, resulting in numerous
difficulties in production and business activities and limiting the development
of the economy.
The starting point of the factors affecting the bank's NIM may be the
dealership model proposed by Ho and Saunders (1981). In the dealership
model, Ho and Saunders (1981) considered banks as intermediaries
between borrowers and lenders in the financial market. In order to analyze
the determinants of bank profitability, the model proposed by Ho and
Saunders has been extended by other researchers to consider the
determinants of the bank's NIM in various countries and regions worldwide.
Most studies divide the determinants of the bank's NIM into two main groups:
the first is the group of bank-specific factors, and the second is
macroeconomic factors.
Bank-Specific Factors
Bank Size
Bank size is measured in different ways, such as total assets, number of
employees, number of customers, number of branches, customer deposits,
and capital base. When evaluating the effect of bank size on the NIM of the
bank, there are 2 groups of different views. According to resource-based
theory, organizations with strategic resources have a competitive advantage
over other organizations (Barney, 1991). Strategic resources are
characterized by specific attributes, including essential resources that can
enhance the performance of an organization and repel threats to its
development, in addition to resources that the competitors cannot imitate,
known as limited or irreplaceable resources. In light of this, Raharjo, Hakim,
Manurung, and Maulana (2014) found a positive effect of bank size on NIM.
The second group believes that the larger the size of assets, the lower the
NIM because the higher the total assets, the less flexible, rigid, and
bureaucratic it becomes, reducing the NIM of the bank. This research group
received the support of Lestari, Chintia, and Akbar (2021), Khan and Jalil
(2020), Hussain (2014), and Gul, Irshad, and Zaman (2011). They detected
a negative effect of bank size on NIM. However, Pham, Tran, and Vo (2018),
Megawaty and Ugut (2022), and Hanzlík and Teplý (2022) found that bank
size did not affect banks' NIM.
Profitability
Bank profitability is a key indicator of bank performance. It represents the
return rate a bank has been able to generate from using the resources at its
command to produce and provide services. Raharjo et al. (2014) observed
the factors affecting the NIM of banks and detected that the profit margin
was remarkably affected by the bank’s profitability. Moreover, Endri and
Marlina (2020) revealed that banks with high profitability have high NIM.
Operational Cost to Operating Income Ratio

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The operating expenses to gross income ratio capture the bank
management’s efficiency showing the number of expenses for every dollar
of income the bank generates. In previous studies, Hanzlík and Teplý (2022),
Sarwar et al. (2020), Pham et al. (2018), Khanh and Tra (2015), and Ben
Khediri and Ben-Khedhiri (2011) indicated that operational cost to operating
income ratio negatively affected NIM. Even so, Mustafa-Zatriqi and Ahmeti
(2022), Lestari et al. (2021), Lee and Isa (2017), and Raharjo et al. (2014)
found that operational cost to operating income ratio positively affected NIM.
However, Islam and Nishiyama (2016), Khan and Jalil (2020), and Endri and
Marlina (2020) demonstrated that the operating cost to operating income
ratio had no effect on the bank's NIM.
Bank Liquidity
Liquidity is a measure of the available cash and other assets of a bank to
quickly pay bills and meet short-term business and financial obligations.
Raharjo et al. (2014) and Hussain (2014) revealed that liquidity negatively
impacted the bank's NIM. On the contrary, Islam and Nishiyama (2016)
demonstrated that banks with higher liquidity had higher NIM. Meanwhile,
Mustafa-Zatriqi and Ahmeti (2022) and Megawaty and Ugut (2022)
concluded that liquidity had no effect on the bank's NIM.
Loan-to-Deposit Ratio
The loan-to-deposit ratio (LDR) is the ratio between the bank's total loans
and deposits. The ratio is generally expressed in percentage. If the ratio is
less than one, this means the bank relies on its deposits to lend its customers
without borrowing outside. On the contrary, if the ratio is more significant
than one, this means the bank borrowed the money it lent back at a higher
interest rate rather than relying entirely on its deposits. Banks may only make
optimal returns if the ratio is high. Banks may need more liquidity to cover
unforeseen funding requirements or economic crises if this ratio is too high.
Mustafa-Zatriqi and Ahmeti (2022), Endri and Marlina (2020), Asmar (2018),
and Raharjo et al. (2014) indicated that the loan-to-deposit ratio positively
affected NIM. Studies by Hanzlík and Teplý (2022), Lestari et al. (2021),
Islam and Nishiyama (2016), and Pham et al. (2018) demonstrated that the
loan-to-deposit ratio and NIM had no relationship.
Non-Performing Loan
A non-performing loan (NPL) is a loan that is not repaid because the
borrower has not made scheduled payments within a certain period. While
the exact elements of default can vary based on the terms of the particular
loan, "nonpayment" is generally defined as nonpayment of principal or
interest (Tran et al., 2022). Islam and Nishiyama (2016) illustrated that the
non-performing loan ratio had no effect on the NIM. However, MustafaZatriqi and Ahmeti (2022), Raharjo et al. (2014), and Tarus et al. (2012)
detected a positive impact of non-performing loan ratio on NIM. On the
contrary, Endri and Marlina (2020), Lestari et al. (2021), Khan and Jalil
(2020), and Sarwar et al. (2020) demonstrated that the non-performing loan
ratio negatively influenced NIM.

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Market Power Ratio
Raharjo et al. (2014) found no evidence regarding the effect of loan market
power on the NIM of banks. Meanwhile, the research results of MustafaZatriqi and Ahmeti (2022), Khan and Jalil (2020), Islam and Nishiyama
(2016), Khanh and Tra (2015), and Tarus et al. (2012) indicated that loan
market power had a negative effect on the NIM of banks. In contrast, Sarwar
et al. (2020), Lee and Isa (2017), and Hussain (2014) concluded that loan
market power and banks' NIM had a positive relationship.
Macroeconomic Factors
Inflation
Inflation is used as a measure of macroeconomic stability, and it is computed
by the annual consumer price index. Several pieces of literature have
empirically examined the relationship between NIM and inflation. Some
researchers demonstrated that an increase in inflation motivates bank
performance and NIM. For instance, Hanzlík and Teplý (2022), Pham et al.
(2018), Khanh and Tra (2015), Hussain (2014), Raharjo et al. (2014), Tarus
et al. (2012), and Gul et al. (2011) found that the inflation rate positively
affected the NIM. Notwithstanding, some literature reported otherwise. The
negative impact of the inflation rate on NIM was noticed by Lestari et al.
(2021) and Khan and Jalil (2020). This result implies that banks' performance
and lending decrease when the inflation rate rises in an economy. However,
the research results of Megawaty and Ugut (2022), Endri and Marlina (2020),
and Islam and Nishiyama (2016) indicated that the inflation rate had no effect
on NIM.
GDP Growth
The metric of gross domestic product (GDP) growth measures the
economy's overall health. The results of the impact of this factor on NIM in
previous studies are different. In previous studies, Hanzlík and Teplý (2022),
Islam and Nishiyama (2016), Tarus et al. (2012), and Gul et al. (2011) found
that economic growth had a negative effect on NIM. However, Megawaty
and Ugut (2022), Khanh and Tra (2015), and Hussain (2014) found no
evidence regarding the effect of GDP growth on NIM.
Money Supply
Monetary policy is the framework used by the Central Bank to perform its
regulatory function to facilitate economic growth and stability (Nikhil &
Deene, 2023). Monetary policy is determined by the relationship between
the cost of borrowing money in an economy and the total amount of money
available. Additionally, the Central Bank adjusts the interest rate and
required reserve along with purchasing the country bond to control the
money supply. The link between monetary policy and NIM has gained
prominence recently, particularly after the 2008-2009 Global Financial Crisis.
Khan and Jalil (2020) researched the role of factors and their impact on the
NIM of banks in Pakistan and found that money supply growth and banks'

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NIM had a positive relationship. Khan and Jalil (2020) explained that an
increase in money supply resulted in more cash in the banks. Therefore, they
increased lending, hence increasing their profits.

DATA, MODELS, AND EMPIRICAL STRATEGY
Dataset
We gathered secondary data on the variables at the bank level from audited
consolidated financial statements of 24 joint-stock commercial banks from
2008 to 2020. In 2008–2020, we eliminated banks involved in mergers and
acquisitions (M&A) events because the data of these banks were volatile
and heterogeneous, thus distorting the estimation result. In addition, banks
with incomplete data in 2008-2020 were removed from the sample. We
tested outliers before estimating the model. Due to outliers, errors
decreased, and the generalizability of the results was enhanced. Therefore,
the final data included 312 observations of 24 joint-stock commercial banks
in 13 years. Meanwhile, annual data on macroeconomic variables were
obtained from the World Development Indicators of the World Bank.
Models
In order to examine the effect of factors on the bank's NIM, we first consider
the bank-specific factors affecting the bank’s NIM as follows:
NIM = 0 + i BSFi +  .
(1)
Next, we add macroeconomic factors to equation (1) in order to examine
the effect of these macroeconomic factors on the NIM of banks. Therefore,
equation (2) has the following form:
NIM =  0 + i BSFi +  j MFj +  .
(2)
where

 0 ,  i , and  j are the coefficients of determinant variables; ε is the

error term; NIM is the net interest margin (dependent variable); BSF is a
vector of bank-specific factors; and MF is a vector of macroeconomic factors.
Bank-specific factors include bank size, profitability, operational cost to
operating income ratio, bank liquidity, loan-to-deposit ratio, non-performing
loan ratio, and loans market power of banks. Macroeconomic factors
comprise inflation rate, economic growth, and growth of money supply M2.
Details of the measurement of each variable based on the definition are
given in Table 1.
Table 1: Research variables measurements.
Variables

Symbol

Measurements

Dependent variable

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Net interest margin

Interest earned minus interest payments
divided by total assets

NIM

Independent variables
Bank-specific variables
Bank size

LNSIZE

Profitability

ROA

The logarithm of bank’s total assets
Total net income over total asset
The ratio of operating expenses to gross
income

Operational cost to operating
income ratio

BOPO

Bank liquidity

GWM

Liquid assets over total assets

Loan-to-deposit ratio

LDR

Total loan to total deposit ratio

Non-performing loan

NPL

Non-performing loan to total loan ratio

Market power ratio

MPR

The ratio of bank loans at time t to total loans
within the banking sector

Inflation

INFL

Annual rate of inflation (%)

GDP growth

GDP

Annual growth rate of GDP (%)

Macroeconomic variables

Money supply

Annual growth rate of M2 (%)

M2

Empirical Strategy
Unlike previous studies using the frequency approach, we utilized the
Bayesian approach, particularly Bayesian linear regression, in order to
explore the factors affecting the NIM of banks in Vietnam. Bayes' theorem
delineates an event's conditional probability based on data and previous
information about the event or conditions associated with the event (van de
Schoot et al., 2021).
Following the Bayesian view (Bayes, 1763), we built a Bayesian linear
regression using a probability distribution of the following form:
y N ( T X ,  2 I ) . (3)
Here, y is formed from a normal distribution described by mean and variance
values. The mean of Bayesian linear regression is the displacement of the
prediction matrix multiplied by the weight matrix. The variance is the identity
matrix multiplied by the square of the standard deviation (σ).
Not only is the output (y) formed from the probability distribution, but also
the model parameters are also derived from the distribution. The posterior
probability of the conditional model parameters based on the inputs and
outputs has the following form:

P(  y, X ) =

P( y  , X )( P(  X )
P( y X )

.

(4)

Here, P (  y, X ) is the posterior probability distribution; P ( y  , X ) is the
likelihood of a dataset; P (  X ) is a prior probability distribution; and

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P ( y X ) is a standard constant and can be eliminated. Therefore, equation
(4) is often simplified as follows:

P(  y, X ) = P( y  , X )( P(  X ) .

(5)

Bayesian regression will proceed through the following three steps when
testing the hypothetical relationship between bank's NIM and explanatory
variables. Firstly, we provide a prior assumption to have a normal distribution
with a mean of zero for the total coefficients for the coefficients. Such a prior
specification signifies that coefficients resulting from Bayesian regression
are more likely to have values close to zero than non-zero values. Most
importantly, we do not favor the Bayesian analysis results of research
hypotheses in a negative or positive direction. Secondly, regarding the
likelihood functions of the coefficients, we suppose normal distributions with
the parameters obtained from equations (1) and (2). Finally, we employ
Markov Chain Monte Carlo (MCMC) combined with Gibbs Sampling
techniques to approach the corresponding posterior distributions of the
coefficients by 12,500 withdrawal times of estimation and simulation drawn
from a posterior distribution. As usual, we will have the first 2,500 withdrawal
times removed. MCMC technique is frequently utilized for adjusting complex
models in various fields (Gelman & Rubin, 1992; Roy, 2020).
RESULTS AND DISCUSSION
Some Facts
Table 2 presents descriptive statistics of all variables in the model. The main
descriptive criteria comprise mean, standard deviation, and maximum and
minimum value. The first is a group of bank-specific variables: banks' NIM in
2008–2020 had an average value of 3.7069%, the highest value of
49.1020%, and the lowest value of -1.2709%. The average total assets size
of commercial banks is 18,3769, with the highest value of 21.1398 and the
lowest of 14.6987. The average return on assets (ROA) ratio is 0.9640, with
the lowest value of -5.9929 and the highest of 5.9518. The average of
operational cost to operating income ratio is 0.7881, with the largest value of
86.3019 and the smallest of 0. Generally, the liquidity of commercial banks
is quite stable, having an average value of 0.2099, a maximum of 1.2566,
and a minimum of 0.0452. The loan-to-deposit ratio had an average value of
88.5189%, a maximum of 209.1146%, and a minimum of 23.5094%. Banks
have an average non-performing loan ratio of 1,8092%, a maximum of
11,4017%, and a minimum of 0%. The loan market power of banks has an
average value of 16.2057%, a maximum of 100%, and a minimum of
0.2116%. The second is a group of macroeconomic factors: Vietnam's
inflation rate average is 7.222%, with the largest value of 23.115% (in 2008)
and the lowest of 0.631% (in 2015). In addition, Vietnam had an average
economic growth of 5.929%, a maximum growth rate of 7.076% in 2018, and
a minimum growth rate of 2.906% in 2020. Finally, broad money growth had

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an average annual rate of 18.5592%, a minimum rate of 11.94% in 2011,
and a maximum rate of 29.71% in 2010.
Table 2: Descriptive statistics
Variables

Mean

Std. Dev.

Min

Max

3.0509

-1.2709

49.1020

21.1398

Dependent variable
NIM

3.7069

Independent variables
Bank-specific variables
LNSIZE

18.3769

1.2733

14.6987

ROA

0.9640

0.8960

-5.9929

5.9518

BOPO

0.7881

4.8591

0.0000

86.3019

GWM

0.2099

0.1252

0.0452

1.2566

LDR

88.5189

21.0779

23.5094

209.1146

NPL

1.8092

1.4471

0.0000

11.4017

MPR

16.2057

22.2103

0.2116

100.0000

0.6310

23.1150

Macroeconomic variables
INFL

7.2220

6.3870

GDP

5.9290

1.0570

2.9060

7.0760

M2

18.5592

5.5019

11.9425

29.7146

Table 3 presents the Pearson correlation matrix of the variables. It shows
that NIM is negatively correlated with LNSIZE, BOPO, GWM, GDP, and M2,
while other factors are positively correlated. Meanwhile, all absolute values
of the correlation coefficients among the independent variables in Table 3
are less than 0.4, so multicollinearity does not appear in our study.
Table 3: Correlation matrix of the variables.
NIM

LNSIZE

ROA

BOPO

GWM

LDR

NIM

1.00

LNSIZE

-0.02

1.00

ROA

0.32

-0.04

1.00

BOPO

-0.10

-0.07

0.46

1.00

GWM

-0.11

-0.23

0.09

0.07

1.00

LDR

0.10

-0.10

0.21

-0.09

-0.14

1.00

-0.02

0.14

-0.04

-0.05

-0.09

NPL

MPR

INFL

NPL

0.00

MPR

0.01

0.38

0.05

-0.04

-0.08

0.14

0.11

1.00

INFL

0.02

-0.39

0.17

0.10

0.33

0.11

-0.07

-0.02

1.00

GDP

-0.16

0.02

0.08

0.02

0.02

0.00

-0.04

0.00

-0.08

GDP

M2

1.00

1.00

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M2

-0.03

-0.35

0.19

-0.07

0.16

0.00

0.06

-0.02

0.19

-0.14

1.00

Estimation Results
Tables 4 and 5 display the results of our Bayesian linear regression. Table
4 presents the effect of bank-specific variables, while Table 5 presents the
influence of bank-specific variables and macroeconomic variables on NIM.
In order to describe an impact, we indicate a 95%-credible interval for
coefficients, that is, an interval containing the parameter of interest with a
certain probability.
Table 4: Regression of bank-specific variables on the NIM
Dependent variable: NIM
Independent
variables
LNSIZE
ROA
BOPO
GWM
LDR
NPL
MPR
constant

Posterior mean
0.0933
[-0.0230; 0.2106]
1.2117
[0.7994; 1.6170]
0.0484
[-0.0250; 0.1225]
-1.1504
[-2.6873; 0.4436]
0.0067
[-0.0081; 0.0217]
0.1286
[-0.0943; 0.3526]
-0.0054
[-0.0204; 0.0097]
0.2766
[-1.5832; 2.0925]

Posterior
probability

Efficiency

Rc

0.9401*

1.0000

1.0001

1.0000*

1.0000

1.0000

0.9003*

1.0000

1.0000

0.9221**

1.0000

1.0001

0.8166*

0.9836

1.0000

0.8712*

1.0000

1.0000

0.7611**

1.0000

1.0000

0.6185*

0.9637

1.0000

8.3817
─
0.9305
1.0001
[8.3458; 7.1397]
Notes: 95% credible interval in brackets. *Probability of mean > 0. **Probability of mean < 0.
var

Firstly, we consider the effect of bank-specific factors on a bank's NIM.
Tables 4 and 5 demonstrate that a higher bank size (LNSIZE) strongly
affects the bank's NIM, with a probability that the bank size variable has a
positive influence of 94.01% (Table 4) or 99.50% (Table 5). The posterior
mean coefficient is β = 0.0933 or β = 0.1858. Our result demonstrates that
the larger the bank's asset size is, the higher the NIM will be. Large banks
have reputable brands and high confidence among customers and investors.
They may invest in more contemporary technologies and have a competitive

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advantage owing to scale, favoring scale efficiency. Thus, large commercial
banks earn NIM more. Our result is compatible with that of Raharjo et al.
(2014).
There is a probability that the profitability variable (ROA) positively affects
the NIM of 100% (Table 4 and Table 5). The posterior mean coefficients are
β = 1.2117 (Table 4) and β = 1.2326 (Table 5). Similar to the study by Endri
and Marlina (2020) and Raharjo et al. (2014), we detected strong evidence
regarding the positive influence of profitability on NIM. According to the
results of this analysis, a high NIM is more attainable for a bank with a higher
ROA. Bank profitability indicates the ability of banks to generate income
exceeding costs in relation to the bank capital base. A high ROA of banks
reflects that such banks have had reasonable policies and plans to make
optimal use of assets in generating revenue, increasing profits, and at the
same time helping NIM increase.
Our results demonstrate that the operational cost to operating income
ratio (BOPO) positively affects the NIM. In particular, the mean coefficient of
BOPO variable is β = 0.0484 (Table 4) or β = 0.0505 (Table 5). There is a
probability that the BOPO variable has a positive influence of 90.03% (Table
4) or 90.64% (Table 5). The research result illustrates that the operational
cost to operating income ratio has a strong and positive impact on the NIM.
From 2008 to 2020, Vietnamese commercial banks modernized the banking
system and administration following international standards. Banks also
altered their operations for safety and health. Bank management and
administration have become more professional, approaching global banking
governance requirements. Our result agrees with the conclusions of
Mustafa-Zatriqi and Ahmeti (2022), Lestari et al. (2021), Lee and Isa (2017),
and Raharjo et al. (2014).
The mean coefficient of the GWM variable is β = -1.1504 (Table 4) or β =
-0.9291 (Table 5), and there is a probability that the GWM variable has a
negative effect of 92.21% (Table 4) or 87.39% (Table 5). The research result
indicates that liquidity negatively impacts the NIM. For banks with high
liquidity, public trust will be maintained, and liquidity risks are also limited.
However, maintaining a high rate of liquidity also imposes a significant
opportunity cost on banks and reduces their NIM. Our result is consistent
with the conclusion of Raharjo et al. (2014) and Hussain (2014).
LDR variable has a mean coefficient of β = 0.0067 (Table 4) or β = 0.0098
(Table 5), and there is a probability that the LDR variable has a positive
influence of 81.66% (Table 4) or 89.58% (Table 5). The research result
demonstrates that the loan-to-deposit ratio has a strong and positive impact
on the NIM. This result refers to the efficiency with which a bank can turn
customer deposits into interest income, hence expanding its NIM. A high
loan-to-deposit ratio results in greater conversion of customer deposits into
loans, as observed by its positive effect on the NIM. As a result of this
efficiency, the LDR may raise its NIM. Our result is in accordance with the
conclusions of previous studies by Mustafa-Zatriqi and Ahmeti (2022), Endri
and Marlina (2020), Asmar (2018), and Raharjo et al. (2014).
Similar to the studies of Mustafa-Zatriqi and Ahmeti (2022), Raharjo et al.
(2014), and Tarus et al. (2012), we detected that the NPL ratio had a positive

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and strong effect on NIM. In particular, NPL variable has a mean coefficient
of β = 0.1286 (Table 4) or β = 0.1482 (Table 5), and there is a probability
that the NPL variable has a positive influence of 87.12% (Table 4) or 90.01%
(Table 5).
For the bank's loans market power variable, the mean coefficient of the
MPR variable is β = -0.0054 (Table 4) or β = -0.0078 (Table 5). There is a
probability that the MPR variable has a negative influence on the NIM of
banks of 76.11% (Table 4) and 84.66% (Table 5). The research result
demonstrates that the higher a bank’s lending market power is, the lower the
bank’s NIM is. This result is similar to those of Mustafa-Zatriqi and Ahmeti
(2022), Khan and Jalil (2020), Islam and Nishiyama (2016), Khanh and Tra
(2015), and Tarus et al. (2012). As part of the roadmap to restructure the
national financial system of the SBV, mergers and acquisitions (M&A)
activities between large banks and inefficient banks have been strongly
encouraged in recent times. When the number of players decreases, the
market share is still in the hands of the four largest banks in Vietnam,
namely, Vietcombank, Vietinbank, Agribank, and BIDV. Consequently, the
government can quickly seize the industry and impose restrictions on the
NIM.
Secondly, we consider the effect of macroeconomic variables on NIM.
Consistent with previous empirical studies (Gul et al., 2011; Raharjo et al.,
2014; Tarus et al., 2012; Hussain, 2014; Khanh & Tra, 2015; Pham et al.,
2018; Hanzlík & Teplý, 2022), we found that the inflation ratio had a positive
influence on NIM. Table 5 presents that the mean coefficient of the INFL
variable is γ = 0.0021. Thus, the level of inflation, measured by CPI, had a
positive effect on the NIM of the bank. This result implies that inflation was
anticipated. Hence, the bank management had a chance to adjust interest
rates accordingly, further increasing the spread between interest revenue
and interest expense, leading to a positive effect on NIM. However, there is
a probability that the INFL variable has a positive effect of 50.02%. The
probability of this influence implies that the impact of the inflation rate on the
NIM is relatively low.
For the economic growth variable, the mean coefficient of the GDP
variable is γ = -0.3026. There is a probability that the GDP variable had a
negative impact on the NIM of 98.09% (Table 5). The research result
demonstrates that economic growth has a strong and negative impact on
NIM. Our result is consistent with those of Islam and Nishiyama (2016),
Tarus et al. (2012), and Gul et al. (2011).
Similar to the GDP variable, we also found a negative and strong effect
of money supply growth on NIM. In particular, variable M2 has a mean
coefficient of γ = -0.0339, and there is a probability that variable M2 has a
negative effect of 87.49% (Table 5). Our result contradicts that of Khan and
Jalil (2020).
Table 5: Regression of bank-specific and macroeconomic variables on the
NIM

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Dependent variable: NIM
Independent
variables
LNSIZE
ROA
BOPO
GWM
LDR
NPL
MPR
INFL
GDP
M2
constant

Posterior mean
0.1858
[0.0422; 0.3290]
1.2326
[0.8016; 1.6534]
0.0505
[-0.0258; 0.1258]
-0.9291
[-2.5406; 0.6644]
0.0098
[-0.0054; 0.0251]
0.1482
[-0.0772; 0.3733]
-0.0078
[-0.0228; 0.0071]
0.0021
[-0.0517; 0.0556]
-0.3026
[-0.5872; -0.0160]
-0.0339
[-0.0913; 0.0243]
0.6458
[-1.2338; 2.5384]

Posterior
probability

Efficiency

Rc

0.9950*

0.9802

1.0000

1.0000*

0.9877

1.0000

0.9064*

1.0000

1.0000

0.8739**

0.9863

1.0000

0.8958*

1.0000

1.0000

0.9001*

1.0000

1.0000

0.8466**

1.0000

1.0000

0.5302*

0.9565

1.0000

0.9809**

1.0000

1.0002

0.8749**

1.0000

1.0000

0.7506*

1.0000

1.0001

8.2900

─
0.9163
1.0003
[7.0689; 9.7420]
Notes: 95% credible interval in brackets. *Probability of mean > 0. **Probability of mean < 0.
var

MCMC Diagnostics
As presented in Section 3.3 (Empirical Strategy), the posterior distribution is
created based on the MCMC technique. Thus, the quality of the sample
generated by the MCMC algorithm must accurately estimate the target
distribution (Roy, 2020). Therefore, MCMC diagnostics are necessary to test
the convergence of Markov chains and MCMC sampling cessation. In the
current article, we utilized the Gelman-Rubin statistic, also known as the Rc
coefficient, to test the convergence of the Markov chain and efficiency index
to consider MCMC sampling cessation.
Tables 4 and 5 display that the coefficient Rc of all parameters is less
than 1.1. According to Roy (2020), the Rc coefficient of less than 1.1 is
evidence that the MCMC algorithm has generated representative samples.
That is, Markov chains have converged. Meanwhile, the efficiency index of

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all Markov chains greater than 0.01 indicates that MCMC-based estimates
are more accurate and stable in terms of the characteristics of the posterior
distribution. Thus, MCMC diagnostics through the Rc coefficient and the
efficiency index demonstrate that the sample quality generated by the
MCMC algorithm has provided an accurate estimate of the posterior
distribution.
CONCLUSION
The present article analyzes the factors affecting the NIM of 24 joint-stock
commercial banks in Vietnam from 2008 to 2020. Although several previous
studies examined factors, including bank-specific and macroeconomic
factors affecting banks' NIM, these studies were carried out based on the
frequentist approach. The current study utilizes the Bayesian approach,
specifically Bayesian linear regression, to discover the relationship. Our
result indicates that LNSIZE, ROA, BOPO, LDR, and NPL positively affect
the NIM of banks while GWM, MPR, GDP, and M2 negatively affect NIM.
The effect of the INFL on NIM is ambiguous.
The result of our article has provided important information for bank
managers by showing the effect of bank-specific and macroeconomic factors
on the NIM so that they give solutions to enhance the operational efficiency
of banks. Furthermore, our findings provide investors with investment
opportunities based on the factors affecting a bank's NIM. However, the
study has limitations as follows. Future research may consider comparing
NIMs of domestic and foreign capital banks in Vietnam. Furthermore, our
study only examined one country for a limited period, implying that future
research needs to investigate this association in other emerging markets
with different banking structures. For instance, future studies can be
extended by comparing and analyzing the determinants of NIM of banks
among other countries in Southeast Asia. The comparison result may
provide more insight for governments to plan their monetary policies.
ACKNOWLEDGEMENTS
The authors are supported by the University of Finance and Marketing and
Ho Chi Minh City Open University, Vietnam.

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