1 
Education of women entrepreneurs 
Mateja Vadnjal*, Jaka Vadnjal** 
 
Abstract: In this paper, we discuss the education of women 
entrepreneurs in different life stages. Education is one of the most 
widely used indicators of human capital. We assume that there are 
significant differences among women entrepreneurs at different 
stages of their lives, in the Early Career and Idealistic Achievement 
phase, Mid-Career or phase of Pragmatic Endurance, and thirdly, 
Advanced Career or Reinvention and Recontribution phase. 
Entrepreneurial women are well aware of and value the knowledge 
of their businesses. A sizable portion of female entrepreneurs is 
pursuing higher levels of education and training throughout their 
careers. A rather low index of business training before starting 
entrepreneurship and also a low index of ongoing training in business 
areas, especially in the area of business strategies and management, 
may mean that this is one of the weak points of female 
entrepreneurship in our country. Based on the findings of this 
research we also suggest that educating and promoting 
entrepreneurship among highly educated female professionals is a 
challenge to policymakers. 
Keywords: woman entrepreneurs, female entrepreneurship, life 
stages, human capital, woman education 
JEL classification codes: M13, J16 
 
Izobraževanje podjetnic 
 
Povzetek: V prispevku obravnavamo izobraževanje podjetnic v 
njihovih različnih življenjskih fazah. Izobrazba je eden najbolj 
razširjenih kazalnikov človeškega kapitala. Predvidevamo, da 
obstajajo pomembne razlike med podjetnicami v njihovih različnih 
življenjskih fazah, oziroma v Zgodnji karieri ali fazi idealističnih 
dosežkov, Srednji karieri ali fazi pragmatične vzdržljivosti in tretjič, 
Napredni karieri ali fazi ponovnega odkrivanja (izumljanja) in 
ponovnega prispevanja. Podjetnice se dobro zavedajo pomena 
svojega poslovnega znanja in ga zelo cenijo v svojih podjetjih. 
Precejšen del podjetnic si v svoji karieri prizadeva za višjo stopnjo 
izobrazbe in usposabljanja. Precej nizek indeks poslovnega 
usposabljanja pred vstopom v podjetništvo in tudi nizek indeks 
sprotnega usposabljanja na poslovnih področjih, predvsem na 
področju poslovnih strategij in vodenja, lahko pomeni, da je to ena 
izmed šibkih točk ženskega podjetništva pri nas. Na podlagi 
ugotovitev raziskave tudi menimo, da izobraževanje in spodbujanje 
podjetništva med visoko izobraženimi strokovnjakinjami 
predstavljata izziv za oblikovalce politik. 
Ključne besede: podjetnice, žensko podjetništvo, življenjske faze, 
človeški kapital, izobrazba žensk 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
*
  
 
* PhD, Alma Mater Europaea - ECM, 
Mateja.vadnjal@gmail.com 
 
** PhD, GEA College - Faculty of 
Entrepreneurship  
& Swiss School of Business 
Management,  
Jaka.vadnjal@gea-college.si 
 
©Copyrights are protected by = 
Avtorske pravice so zaščitene s: 
Creative Commons Attribution-
Noncommercial 4.0 International 
License (CC BY-NC 4.0) = Priznanje 
avtorstva-nekomercialno 4.0 
mednarodna licenca (CC BY-NC 4.0) 
Mednarodno inovativno poslovanje 
=  
Journal of Innovative Business and 
Management  
DOI 10.32015/JIBM.2022.14.2.9 
 
ISSN 1855-6175 
 
 
 

 
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1 Introduction 
 
In the introduction chapter, we discuss two important elements of the research construct 
which are (i) the theory of women entrepreneurs' life stages and (ii) the dimensions of 
measuring human capital 
1.1 Women entrepreneurs’ life stages 
 
Given the research (e.g. Bennett & Dann, 2000; Davis & Shaver, 2012), we assume that there 
are significant differences between women entrepreneurs at different stages of their lives. 
This is also the finding of O’Neil & Bilimoria (2005). In their research on women's career 
development. Career orientation, pattern and beliefs manifest themselves differently in 
three stages, which we will also use in our study: 
Early Career and Idealistic Achievement phase between the ages of 24 and 35; Mid-Career 
phase between the ages of 36 and 45, women are driven by pragmatic resilience; and 
Advanced Career phase between the ages of 46 and 60, women are driven by re-contributing 
to the community, family, or organization.  
Education and knowledge are the basic elements of human capital, which increases an 
individual's cognitive capacity and enables him or her to function more effectively and 
productively (Becker, 1975; Mincer, 1985; Schultz, 1959). When defining human capital, it 
is not only the level of education that is important but also its product, i.e. knowledge and 
skills (Unger et al., 2011). The degree of development and the importance of a particular 
type of human capital in an organisation determines the extent to which a firm will outsource 
(Mayer et al., 2012), as well as the likelihood that employees will choose to become 
entrepreneurs after a certain period of time (Amaral et al., 2011). The ability to identify 
entrepreneurial opportunities is related to education, work experience and entrepreneurial 
experience (Stuetzer et al., 2013; Davidsson & Honig, 2003; Ucbasaran et al., 2010). 
Entrepreneurs with higher levels of human capital identify many more entrepreneurial 
opportunities (Ucbasaran et al., 2010) and exploit them more effectively (Ravasi & Turati, 
2005). The human capital of an entrepreneur is an important prerequisite for the further 
acquisition of other resources for the organisation, such as physical and financial capital 
(Brush et al., 2009). The impact of traditionally measured human capital in the form of 
education and prior entrepreneurial experience is greater at the beginning of the 
entrepreneurial journey, but its impact on firm development and performance is smaller or 
more difficult to determine at later stages (Unger et al., 2011; Davidsson & Honig, 2003). 
The highest level of education is shown to be important for success in developing innovative 
ideas in high-tech sectors (Obschonka et al., 2011). Samuelsson & Davidsson (2009), in a 
comparison of innovative and imitative firms, find that the highest level of education, i.e. a 
science degree, is extremely important in the pursuit of innovative entrepreneurial ideas 
and in obtaining all kinds of resources for the growth of technology firms. Unger et al. (2011) 
suggest that entrepreneurs should invest in targeted knowledge acquisition, which is more 
important than prior experience for the success of the venture. Research on human capital 
outflows to new ventures (Campbell et al., 2012) shows a higher propensity to 
entrepreneurship among employees with higher levels of human capital. Employees with a 
high level of knowledge and experience decide to pursue an independent path after some 
time in an existing organisation, despite high incomes, because they are driven by motives 
such as a desire for autonomy and independence, or because they see an opportunity that 
they cannot exploit in their existing organisation. However, Koellinger et al (2013) point out 
that age, work experience and education influence attitudes towards entrepreneurship 
through their impact on individuals' perceptions. Millan et al. (2014), studying the impact of 
human capital in society on entrepreneurial performance, show a marked positive impact of 
the population's tertiary education enrolment on entrepreneurial success, employment and 
persistence. In societies with higher educational attainment, self-employed individuals are 

 
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more likely to become and remain employers, and educated personnel are also better able 
to provide appropriate services for the higher demands of highly educated customers. Some 
researchers (Davidsson & Honig, 2003; Delmar & Shane, 2006; Ployhart & Militerno, 2011) 
consider that educational attainment influences an individual's decision to become an 
entrepreneur, while Unger et al. (2011) find that human capital is not necessarily a 
determinant of entrepreneurial entry, but is generally an advantage for the individual who 
chooses to venture into entrepreneurship. Women entrepreneurs with higher levels of human 
capital are better able to identify new entrepreneurial opportunities, generate new ideas 
and demonstrate creativity in the areas of new product introductions, services, production 
processes and locations, all of which have an impact on the growth potential of their firms 
(Roomi, 2013). For women in transition countries, the importance of specific human capital 
for growth is evident (Manolova et al., 2007). 
Measuring general educational attainment and prior experience is likely to be a good 
predictor of an entrepreneur's success over a lifetime, but entrepreneurial success is often 
measured in a specific context, so it is better to use targeted human capital for such 
assessments (Unger et al., 2011). The importance of knowledge and proactive learning is of 
great importance for the success of firms in developing and transition countries (Unger et 
al., 2011).  
 
1.2 Dimensions for measuring human capital 
 
Human capital theory (Becker, 1976; Mincer, 1975) assumes that individuals with more 
knowledge, skills and other competences outperform those with less knowledge, skills and 
competences (Ployhart & Moliterno, 2011). The probability of early entrepreneurial activity 
increases with each year of education (Davidsson & Honig, 2003). Entrepreneurial success is 
influenced by investments in general human capital (Unger et al., 2011), as well as 
investments in specific human capital, both in the form of academic entrepreneurship 
education and entrepreneurial skills training (Martin et al., 2013). The importance of the 
form of human capital changes with the evolution of entrepreneurial activity (Martin et al., 
2013; Stuetzer et al., 2013).  
Investing in human capital development helps the performance of women's firms more than 
men's firms (Bird et al., 2001). The process of discovering entrepreneurial opportunities for 
women is linked to learning and innovation, which means that women entrepreneurs are 
more likely to consider and develop their entrepreneurial idea towards specialisation. This 
approach may be due to different motives and also to different sets of entrepreneurial 
competences. In any case, the research contributes to the understanding of our differences, 
which all lead to successful and innovative outcomes, but shows that approaches to 
entrepreneurial training should be different for different groups (DeTienne & Chandler, 
2007).  
We conclude that different groups of women entrepreneurs also need different approaches 
to investing in human capital. As Samuelsson & Davidsson (2009) note, imitative firms are 
affected by different types of human capital than innovative firms. 
 
2 Hypotheses 
 
Based on the previous findings, we hypothesize that educational attainment is general 
human capital (DeTienne & Chandler, 2007) and specific human capital is specific knowledge 
acquired in school and specific training (DeTienne & Chandler, 2007; Kreiser et al., 2013; 
Bird et al., 2001). 

 
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Hypothesis H1: There are differences in levels of schooling attainment between groups of 
women entrepreneurs at different stages of life. GENERAL HUMAN CAPITAL 
Hypothesis H2: There are differences in the business background acquired in school or 
specific training between groups of women entrepreneurs at different stages of life. SPECIFIC 
HUMAN CAPITAL 
Hypothesis H1 refers to the educational attainment with which an individual invests in human 
capital that enables her to choose a job (Baker, 1995). In human capital theory, education 
is also used as a measure of entry into entrepreneurship (Bates, 1995), as it increases general 
knowledge and helps to integrate and acquire other types of knowledge and experience, to 
adapt to new situations and acquire tacit knowledge (Davidsson & Honig, 2003).  
As a measure of schooling attainment, we use the level and direction of current education 
and the level and direction of education attained when the first firm was founded to test 
hypothesis H1. We summarize the questions from previously conducted studies (Koellinger 
et al., 2013; Manolova et al., 2007;). When asking for possible answers for the level of 
education completed, we take into account the possibility that the women entrepreneurs 
completed their education in the years before the introduction of the Bologna system, so 
the possible answers are as follows: primary school, vocational school, secondary school, 
college, college or university degree, specialization, master's degree, doctorate (Davidsson 
& Honig, 2003; Manolova et al., 2007; De Carolis et al., 2009). We measure specific human 
capital in the form of education major in line with findings that educational major is an 
important factor in women's entry into entrepreneurship (Bird et al., 2001). The measure is 
set up in line with Manolova et al. (2007) and Davidsson & Honig (2003) as a binary variable, 
namely technical/natural science education or business education.  
There are several types of training for budding entrepreneurs to prepare them for their entry 
into entrepreneurship and their first steps. Some programmes offer a broad range of skills, 
while others are more narrowly focused. To test hypothesis H2, which refers to differences 
in business knowledge acquired at school or in specific training, we ask women entrepreneurs 
whether they have ever attended any seminars or workshops related to starting a business. 
The answer is binary. 
We are also interested in human capital in the form of knowledge that women entrepreneurs 
acquire while running their businesses. We, therefore, ask women entrepreneurs about the 
frequency of their training in specific areas. The areas of training are divided into seven 
groups: leadership, communication and relationships; planning and organization; specific 
skills (finance and accounting, law, technology, HR); computer tools; languages; 
occupational safety and ecology; teamwork. The frequency of training is measured on a scale 
of 1 to 5, where 1 means 'never' and 5 means 'very often'. 
3 Methods, population and sample 
 
Slovenia does not have a data collection system in place that would allow for the immediate 
identification of women-owned enterprises. For the survey, we collected the companies that 
we assessed as women-owned enterprises from the set of the basic database of the Business 
Register of Slovenia. The data was provided to us for research purposes by Bisnode. The 
questionnaire was fully completed by 340 women entrepreneurs, giving us a response rate 
of 3.4%. 
In our study, we test the null hypothesis that the social and human capital of women 
entrepreneurs of all age groups is equal. The null hypothesis, therefore, assumes that the 
arithmetic means of the selected variables are the same for all groups. The groups of women 
entrepreneurs are compared and stratified according to their age. Our dependent variable 

 
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is therefore categorical or nominal, while the independent variables are numerical. A 
statistical technique that is suitable for dealing with such cases is multiple discriminant 
analysis (Hair et al., 2010, 236). Before we perform multivariate analysis, we use univariate 
or bivariate techniques to analyse the variables.  
In the first stage, we used the chi-square test and analysis of variance to test for differences 
in the identified dimensions of human and social capital. The first method is used to measure 
the association of categorical variables. From the contingency table, it is possible to see 
whether the proportions of respondents differ by age group. To test our hypotheses, we used 
analysis of variance to test for differences in index values by age group. Each index was 
derived from the average of a different number of variables that make up a given construct. 
4 Results and discussion 
 
Each construct consists of a different number of variables. To finally test the hypotheses, 
we transformed them into composite variables, resulting in nine composite variables, which 
were further analysed. To test the hypotheses, an analysis of variance was used to test for 
differences in the values of these nine composite variables according to age groups.  
In the next stage, a discriminant analysis was carried out to check whether the selected 
criteria represented by the composite variables explained the differences between the 
predefined groups of women entrepreneurs.  
4.1 Educational view of human capital 
 
To test for differences in human capital, we developed two constructs: (i) educational 
attainment and (ii) business knowledge acquired through education. Each of these constructs 
is tested with a different number of variables. The criteria for these constructs are taken 
from previous studies and the scales of their answers are not uniform. While the criteria 
have been widely used in previous studies for analyses and comparisons of performance, our 
research aims to compare human and social capital among women entrepreneurs, and here 
we analyse the differences between each group of women entrepreneurs. In the first step, 
we compare each variable separately using a chi-square test or analysis of variance, and only 
in the next step do we unify and combine the variables into nine constructs, i.e. five 
constructs of human capital and four constructs of social capital.  
First, we present the cross-tabulations of the variables by life stage. For the individual 
variables, we had to aggregate some categories to satisfy the condition for using chi-square 
statistics as a measure of association, according to which the theoretical frequency should 
have at least five units in each cell of the contingency table. 
4.2 School education achieved 
 
Educational attainment is one of the most common and widely used indicators of general 
human capital (Becker 1964; Boden and Nucci 2000). Higher general human capital is thought 
to better prepare entrepreneurs to adapt to external changes in their entrepreneurial path 
(Welter and Smallbone 2010). We construct school attainment from four variables relating 
to educational attainment: (i) educational attainment at the time of business start-up, (ii) 
educational attainment today, (iii) technical education today, and (iv) business education 
today.  
First, we compare the educational attainment of women entrepreneurs at the time of their 
business start-up. Just under 40 % of female entrepreneurs had completed either secondary 
or less than secondary education (132) or higher education (134) at the start-up of their 
business. Almost half of the youngest female entrepreneurs (40) had already completed 

 
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higher education at the start-up of their business, and this proportion decreases with age. 
Among female entrepreneurs aged 45 and over, 32% (48) are such. However, this group has 
the highest proportion of those with higher education at the start of the business, with 18% 
(27) of the oldest female entrepreneurs, 13% (14) of those in the middle group and only 8.5% 
(10) of the youngest female entrepreneurs having such qualifications. 27% (10) of the oldest 
female entrepreneurs have higher education at the start of the business.  
Based on the chi-square statistic, we cannot confirm that there are statistically significant 
differences in education at the start of the business between female entrepreneurs at 
different stages of life (p = 0.137; df = 6; χ² = 9.721).  
The higher share of university graduates among younger women entrepreneurs and the 
lowest share among older women entrepreneurs at the time of business start-up could be 
explained by the reasoning that older women entrepreneurs are more likely to be those who 
were driven into entrepreneurship out of necessity due to job losses during the economic 
transition. On the other hand, there are more younger and more educated women 
entrepreneurs, perhaps because the recruitment of young educated people has become more 
complex in the last years or decade.  
Women entrepreneurs have increased their level of education since starting their businesses. 
The share of all women entrepreneurs who have attained tertiary education or more has 
increased to 43.8% (149), while the share of women entrepreneurs with secondary education 
or less (27.6%, 94) is lower compared to the time when they started their business, and the 
share of women entrepreneurs with postgraduate education is higher, increasing from 7.6% 
(26) to 12.1% (41). More than half of female entrepreneurs aged 45 and under (120) now 
have a university degree or higher. The statistically significant proportion of the oldest 
entrepreneurs with a university degree is probably due to changes in the education system. 
The oldest female entrepreneurs have the statistically significantly lowest share of those 
with higher education (35.3%, 53) and the statistically significantly highest share of those 
with completed secondary education (33.3%, 50). Based on the chi-square statistic, we can 
confirm that there are differences in the level of educational attainment between female 
entrepreneurs at different stages of life (p = 0.021, df = 6; χ² = 14.92).  
It is also interesting to note that the proportions of all but the lowest level of education of 
the surveyed female entrepreneurs (secondary and below) have significantly improved over 
the observed periods, indicating that a significant proportion of female entrepreneurs are 
pursuing higher levels of education and training throughout their careers and therefore 
understand and value the importance of knowledge for their business success.  
Just over a third of women entrepreneurs (124) have a degree in a technical or natural 
science discipline. The proportion is slightly higher among the youngest (39%, 32) and oldest 
(37.8%, 56) female entrepreneurs, while in the middle group the proportion is 33.3% (36), 
but there are no statistically significant differences between them. Based on the chi-square 
statistic, we cannot confirm that there are statistically significant differences between the 
proportions of female entrepreneurs at each life stage who have obtained a science and 
technology degree (p = 0.67, df = 2; χ² = 0.800). 
Half of the women entrepreneurs (165) answered that they had attained any level of business 
education. Among them, the share of older women entrepreneurs is predominant. As many 
as 60% (90) of female entrepreneurs aged 45 and over and 43% (46) of female entrepreneurs 
aged 35-45 have attained a business degree. Among the youngest, the proportion is reversed, 
with only 38.3% (31) of female entrepreneurs aged 35 and under having attained some type 
of business qualification. In contrast to Slovenian women entrepreneurs, for example, British 
women entrepreneurs are less educated in business, with 83% having no business degree.  

 
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Based on the chi-square statistic, we can confirm that there are differences between female 
entrepreneurs of different ages who have obtained a business degree (p = 0.002; df = 2; χ² 
= 12.678). 
The common variable or education attainment index was created by first transforming the 
scale for each of the variables to an interval between 1 and 5. The new variable was 
calculated as the average of these four variables. The educational attainment index is 3.01 
for the youngest age group, 2.91 for the 36-45 age group and 3.04 for the over-45 age group, 
which is higher than the average of 2.99. Based on the chi-square statistic, we cannot 
confirm that there is a statistically significant difference in educational attainment between 
women entrepreneurs at different stages of life (p = 0.469, F = 0.758) 
4.3 Business knowledge acquired through training 
 
For our research, the next construct that constitutes human capital is the business 
knowledge acquired by women entrepreneurs at school or in specific training courses. We 
constructed it from three sets of variables.  
The first set of variables relates to the frequency of participation in training and education 
in eight areas. The possible answers were given on a Likert scale from 1 to 5 (never, rarely, 
sometimes, often, very often). To compare the answers between the different age groups, 
we used analysis of variance and Tukey's post hoc test if the assumption of the equality of 
variances held, which was tested using Levene's test. Otherwise, we performed the Tanhane 
post hoc test, which does not require the assumption of the equality of variances. The next 
set of variables relates to the attendance of a seminar on setting up and running a business, 
where two answers were possible (yes, no). The last set of variables relates to pre-founding 
training in nine different areas, where two answers were possible (yes, no). The association 
of these variables with the age of the women entrepreneurs was tested with a chi-square 
test. 
Women entrepreneurs rarely attend different training courses and seminars. In the last year, 
they have most often attended training in specific skills, but the average score of 2.53, 
which ranges between rarely (score 2) and sometimes (score 3), is still low. On average, all 
respondents say that they rarely attend the other training courses listed. Analysis of variance 
shows that there are no specific differences between women entrepreneurs at different ages 
in the frequency of attending seminars and training in the areas of management (mean score 
1.91), communication and relationships (mean score 1.96), planning and organisation (mean 
score 1.93), languages (mean score 2.2) and teamwork (mean score 1.91). The post hoc 
analysis showed that there is a difference between the oldest and the youngest female 
entrepreneurs in the frequency of attending training courses on specific skills such as finance 
and accounting, legal aspects, technology, and human resources (p = 0.001; F = 6.716), and 
seminars on occupational safety and environmental protection (ecology) (p = 0.027; F = 
3.639) and computer science (p = 0.041; F = 3.216). Older women entrepreneurs are more 
likely than younger women entrepreneurs to receive training in all areas, which is probably 
due to the difference in the activities carried out by women entrepreneurs from the different 
groups, as these seminars are more of a compulsory nature to meet the legal criteria for 
carrying out a certain activity. 
Less than one-third of the respondents (100) had attended a seminar on setting up and 
running a business before setting up their own business. The proportion decreases with the 
age of the women entrepreneurs. 46.3% (37) of the youngest respondents and only 22.3% 
(33) of the women entrepreneurs over 45 years of age had received such training. Based on 
the chi-square statistic, we can confirm that there is a statistically significant difference (p 
= 0.001; χ² = 14.272; df = 2) between the female entrepreneurs of different ages who have 
received initial entrepreneurship training. This difference may be attributed to the fact that 

 
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younger women entrepreneurs attend such seminars and training, as they have recently 
become very accessible, even free of charge, by various institutions with project funding. A 
large amount of this type of content is also available online. 
When asked about the different forms of training before setting up a business, the answers 
of women entrepreneurs of different years do not differ statistically significantly. Before 
starting a business, 32.8% of all female entrepreneurs (108) had received training on legal 
aspects of the business (p = 2.143; df = 2; χ² = 0.343), 30.7% (101) had received training on 
marketing (p = 5.383; df = 2; χ² = 0.068), 26.5% (86) in sales (p = 3.208; df = 2; χ² = 0.201), 
49.7% (164) in accounting and finance (p = 3.411; df = 2; χ² = 0.128), 33% (107) in 
management (p = 1.312; df = 2; χ² = 0.519), 16.6% (53) in international business (p = 0.250; 
df = 2; χ² = 0.883), 29% (94) in business strategy (p = 2.836; df = 2; χ² = 0.242), only 7.1% 
(23) in the field of e-commerce (p = 1.415; df = 2; χ² = 0.493) and 12.1% (39) in the field of 
management information systems (p = 4.474; df = 2; χ² = 0.107).  
Although the proportions of female entrepreneurs at different stages of life do not differ 
statistically significantly according to their training before starting their entrepreneurial 
career, some differences can be observed between younger and older women. For example, 
younger women entrepreneurs were more likely to have received training related to new 
technologies, marketing and sales, while older women entrepreneurs were more likely to 
have received training in legal aspects of business and accounting. However, we do not know 
whether the difference is due to different offerings or different interests of women 
entrepreneurs from different age groups. 
 
4.4 Overview of the composite human capital variables 
 
The two constructs of human capital are composed of different numbers of variables. Each 
of these variables has so far been analysed using chi-square statistics or analysis of variance 
to see how they differ between women entrepreneurs at different stages of their lives.  
For each group of women entrepreneurs, we have created a composite variable or a total 
index of each construct of human and social capital. This common variable was constructed 
by first transforming the response scale for each of the variables to an interval between 1 
and 5. The composite variable or index of each factor was calculated as the average of these 
individual variables. In the following, we show once again all the calculated composite 
variables based on which the individual hypotheses were accepted in the first stage. They 
are always denoted by the hypothesis being tested with a particular composite variable.  
Table 1 summarises the composite variables or indices of all the constructs that were used 
to compare the level of the human capital of women entrepreneurs in the three age groups. 
For all women entrepreneurs, the highest index is that of educational attainment, with a 
score of 2.99 on a five-point scale. This score is also consistent with findings on the relatively 
high educational attainment of women entrepreneurs in transition countries (Manolova et 
al.; Manev, 2007). The composite variable for prior industry experience follows with a score 
of 2.33, the index of business background reaches 2.11, the index of prior entrepreneurial 
experience reaches 1.89, and the index of prior managerial experience reaches the lowest 
value with a value of 1.65.  
Our study aims to compare the differences in human capital levels between women 
entrepreneurs of different age groups. The value of the composite variable of prior 
entrepreneurial experience is 2.02 for the youngest age group, 1.93 for the 36-45 age group 
and 1.80 for the over-45 age group. According to the composite variables, younger female 
entrepreneurs have gained more entrepreneurial experience before setting up their 
businesses than older female entrepreneurs. The higher index of prior entrepreneurial 
experience for the youngest entrepreneurs may indicate that these entrepreneurs are more 
deliberate in embarking on their entrepreneurial journey, but it would also be worth 

 
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comparing differences in motives for entrepreneurship. For women entrepreneurs of the 
younger generation, entrepreneurship may in many cases be a solution to their employment 
status rather than an entrepreneurial ambition.  
Based on the analysis of variance and the post hoc test carried out for the composite 
variables, it can be argued that there are statistically significant differences in the levels of 
prior entrepreneurial experience between women entrepreneurs of different ages (p = 
0.037; F = 3.328), namely between the group aged up to 35 and the group aged over 45. On 
this basis, we can confirm hypothesis H1. 
The value of the composite variable for educational attainment for all women entrepreneurs 
is 2.99. The lowest index of 2.91 is achieved by female entrepreneurs aged between 35 and 
45. The index of educational attainment for female entrepreneurs in the youngest group is 
3.01, while the index for the oldest group is only slightly higher at 3.04. Based on the analysis 
of variance and the post hoc test of the composite variables, we cannot confirm hypothesis 
H2, as the differences in the indices are not statistically significant (F = 0.758; p = 0.469). 
Table 1: Composite variables (indices) of the individual human capital constructs 
  
N Mean St. D Leven's F p F p 
Level of 
education 
(H1) 
up to 35 years 82 3,01 0,89 1,03 0,358 0,758 0,469 
36 to 45 years 108 2,91 0,88 
over 45 years 150 3,04 0,81 
Total 340 2,99 0,85 
Business 
skills 
(H1c) 
up to 35 years 82 2,15 0,87 3,059 0,048 1,001 0,369 
36 to 45 years 108 2,02 0,74 
over 45 years 150 2,15 0,81 
Total 340 2,11 0,80 
 
The index of business knowledge for all female entrepreneurs is 2.11, which is also the lowest 
for the middle-aged group with a value of 2.02, while the oldest and the youngest groups 
have an index of 2.15. Based on the analysis of variance and the post hoc test of the 
composite variables, we cannot claim that there are statistically significant differences 
between them (F = 1.001; p = 0.369). Based on the analysis so far, we can accept hypotheses 
H1a and H1e. 
5 Discussion 
 
We created two constructs—educational attainment and business knowledge gained through 
education—to test for variations in human capital. The number of variables used to test each 
of these notions varies. The scales of their responses are not uniform, and the criteria for 
these notions are drawn from earlier studies. While performance measurements have been 
extensively employed in prior research for analyses and comparisons, the goal of our study 
is to compare human and social capital among women entrepreneurs. In this section, we 
examine the variations among the various categories of women entrepreneurs. We compare 
each variable individually in the first section using a chi-square test or analysis of variance, 
and only then do we unify and combine the variables into nine constructs, five of which are 
related to human capital and four of which are related to social capital. 

 
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Mednarodno inovativno poslovanje = Journal of Innovative Business and Management 2022 / Vol. 14 / No. 2  
The fact that older women entrepreneurs are more likely to have been forced into 
entrepreneurship out of necessity due to job losses during the economic transition may 
account for the higher share of university graduates among younger women entrepreneurs 
and the lower share among older women entrepreneurs at the time of business start-up. On 
the other side, there are younger and more educated women entrepreneurs, maybe as a 
result of the recent complexity in the hiring of young educated individuals. 
Since they first launched their businesses till the present, female entrepreneurs have 
expanded their level of education. The percentage of all female entrepreneurs who hold a 
bachelor's degree or more has increased to 43.8%. Today, just 27.6% of women entrepreneurs 
have a secondary degree or less, a lower percentage than it was when the business was first 
established. With an increase from 7.6% to 12.1%, the proportion of female entrepreneurs 
with postgraduate degrees is higher. Today, more than half of female entrepreneurs who 
are 45 years old or younger have a higher degree. 
Intriguingly, all but the lowest level of education (secondary and below) among the surveyed 
female entrepreneurs have seen a significant increase throughout their entrepreneurial 
careers, suggesting that a sizable portion of female entrepreneurs are pursuing higher levels 
of education and training throughout their careers. Entrepreneurial women are well aware 
of and value the value of knowledge to their businesses. 
Younger women entrepreneurs are more likely to be able to utilize a variety of different 
sources for business growth and development given their high educational attainment, which 
could result in the formation of profitable, innovative businesses. Our study compares the 
variations in levels of human capital among female business owners in various age groups. 
The composite variable's value for those under the age of 35 is 2.02, 1.93 for those between 
the ages of 36 and 45, and 1.80 for those beyond 45. The composite variables show that 
younger female entrepreneurs had more business experience before starting their own 
company than older female entrepreneurs. Younger entrepreneurs may be more thoughtful 
in starting their businesses, as evidenced by their greater index of past entrepreneurial 
experience, but it would also be worthwhile to compare different entrepreneurial 
motivations. For women entrepreneurs of the younger generation, entrepreneurship may in 
many cases be a solution to their employment status rather than an entrepreneurial 
ambition. 
 
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