Does gender really matter when becoming an entrepreneur?

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Does gender really matter when

becoming an entrepreneur?

A study that examines possible associations between

gender, performance, push-pull factors and both the

nonprofit and for-profit sectors in the UK

Master’s Thesis 15 credits Department of Business Studies Uppsala University

Spring Semester of 2020

Date of Submission: 2020-06-03

Görkem Keskin

Andrea Visiedo

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Acknowledgement

First of all, we would like to thank all our teachers in the Master's program of Entrepreneurship who have provided us with new knowledge and education within this specific field. Also, we would like to thank our classmates who participated in this program, supported us and contributed to many fun and unforgettable memories. More than anything, we wish them continued success in future projects. Moreover, we appreciate the help and guidance provided by professor James Sallis whom quantitative skills were of high importance.

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Abstract

The present study aims to identify the association between gender and three different aspects: performance, pull-push factors, and nonprofit-for-profit sectors. In order to respond to the research questions, a quantitative approach was applied. Secondary data from the Global Entrepreneurship Monitor 2016 was collected as it includes self-reported information of established business owners from the UK. The results provided by the cross tabulation analysis executed by the SPSS, show a deeper and quantitative understanding regarding the associations between gender and the three aspects. Findings demonstrated that gender only had a significant association with the for-profit and nonprofit sectors. The other aspects clearly showed that they did not have any association with gender. Also, this study discusses the unequal number of female and male entrepreneurs shown in each of the aspects in order to provide acknowledge about the current situation in the UK. It is recommended that future research collects a higher number of variables or responses, preferably primary data that includes more information about the aspirations and preferences of the individuals and combines quantitative with qualitative methods. In conclusion, the gender of entrepreneurs should not be considered to be a break-dealer factor.

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1.Introduction

In contemporary society, women are more actively involved in business life than in the past. The desire of women to boost their self-confidence and achieve economic independence is one of the most pronounced points for them to be innovative and become entrepreneurs. The participation of more women in the workforce has increased the number of female entrepreneurs. Women entrepreneurs have started to be more effective in creating jobs both for themselves and for society and strengthen their position in the community. Therefore, female entrepreneurship is in line with the empowerment of women. However, it is important to acknowledge that both female and male entrepreneurs contribute tremendously to the growth of the economy. Entrepreneurs may take different forms and strategies when making a business out of innovative ideas. In other words, some entrepreneurs may feel more encouraged to promote sustainable entrepreneurship, others may want to provide solutions to specific problems in their community. Therefore, it is possible to find entrepreneurs in several fields of the workforce, they can be found in the nonprofit and for-profit sector. Nevertheless, it could be said that entrepreneurs may encounter different obstacles and motivations when becoming entrepreneurs.

According to Brush's (1992) research, it is harder for a woman to become an entrepreneur than a man. This argument is still supported by Alison Rose's Entrepreneurship Review (2019) with the opinion that women are less familiar with other entrepreneurs compared to men. Rose (2019) mentions that knowing other entrepreneurs who can offer guidance with their knowledge and experience increases the chances of someone starting their own business (p.90).When it comes to non-financial issues like charity businesses, women are more sensitive than men, and they are innovators who tend to take a more active role in this field. In a study by Orhan and Scott (2001), while men tend to focus on the economic reason of business ownership, women prefer to contribute socially as an entrepreneur. Women have more commitment to local needs and participation in social entrepreneurship activities than men (Levie and Hart, 2011), but they still continue to face obstacles that affect their entrepreneurial journey, whether it is within the nonprofit or for-profit sector.

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unemployed to create their own businesses, it is demonstrated that European countries show an inconsistent number of self-employed women. The Organisation for Economic Co-operation and Development (OECD) report shows through its statistics that not only factors such as performance, motivation, and sectors vary across Member States’ entrepreneurship rates, but also gender. Gender differences are not reflected merely in general terms between countries in this report, but also within each of the three factors mentioned above, which can highly affect an entrepreneur’s success and entrepreneurial journey.

According to the statistics from the OECD, the United Kingdom (UK) has, for example, a higher rate of women self-employed than Sweden, which might be unexpected as Sweden is the first ranked country in the European Union (EU) in terms of gender equality (European Institute for Gender Equality, EIGE, 2019). Even though the gender gap in self-employment is lower in Sweden (6,6% percentage points (p.p.)) than in the UK (7,5% p.p.), it is interesting to observe how more women in the UK, as stated in the report, show a higher tendency for opting for an entrepreneurial journey than the ones from Sweden (OECD, 2019). Another contradictory fact explained is the proportion of women entrepreneurs that are actively involved in the creation of a new firm they will own or co-own and the proportion of women that currently own or co-own a firm. The UK shows that only 2,9% of the adult proportion are women that are involved in business creation while Sweden has a percentage of 3,5%. On the other hand, Sweden shows a lower rate of women in terms of business ownership, 3,2%, while the UK shows that 3,7% of the adult proportion are women that currently own an established business (Ibid, p.58,60).

There are different causes that might influence these figures and the gender gap within entrepreneurship. Some factors such as: lack of knowledge, child-rearing, lack of business contacts, and limited networking may affect female entrepreneurs’ potential of their performance and success (Minniti and Arenius, 2003, p.12; Rose, 2019, p.67; OECD, 2019). Given the fact that the UK shows a higher rate of a proportion of women that own businesses in comparison to other European countries, such as Sweden, and that is in the transition of becoming political and economically independent from the EU since a while ago, it is interesting to take a more in-depth look at the entrepreneurship status of this country (OECD, 2019).

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other key players (Rose, 2019, p.52,54). On the other hand, female entrepreneurs’ business ideas usually become discarded by venture capital investors unless they can demonstrate performance (Ibid). This might explain, to some extent, why male entrepreneurs have a higher tendency to be involved in the total early-stage entrepreneurial activity (TEA) than women (House of Commons Library 2020, p.3,14).

Contrarily, according to the report provided by the report of Federation of Small Business (FSB), female entrepreneurs have lower levels of growth in fast-growing firms, but when firm and individual characteristics are taken into account, most of the women-owned firms in the UK outperform the ones male-owned (FSB, 2018, p.9). FSB also explains different arguments depending on the sector. It is highlighted in the report that there is a lower number of women-owned businesses when it comes to a sector that overall generates a higher average gross value added (GVA) per person, i.e., technology. While a sector below average GVA per person, such as care services, there has been an increase in the number of women-owned businesses (FSB, 2018, p.16).

Does this mean that it can be argued that gender has any significant effect on firm performance? Is it possible to assume that sectors are linked and dependent to the gender of the entrepreneur? What motives can gain female and/or male entrepreneurs’ interest making them shift into an entrepreneurial career? Therefore, it is necessary to examine if gender actually matters in the UK when it comes to aspects such as performance, sectors and push and pull motivations, as they all affect the entrepreneurial journey of individuals.

1.1 Aim

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This paper will focus on the UK due to an observed lack of information regarding gender differences among entrepreneurs of this region. According to the Rose (2019) report, only 5.6% of UK women run their own businesses, compared to Canada (15%), US (11%), Australia, and the Netherlands (over 9%) (p.31). The fact that the rate of female entrepreneurs in the UK is less than expected from a developed country, such as Canada, has made this region more attractive for the study (Ibid). Focusing on established entrepreneurs of a specific region, in this case the UK, will facilitate research within entrepreneurship to understand the factors that make entrepreneurs feel more suitable bringing their business ideas into the nonprofit and for-profit sector. Acknowledging the possible factors behind why an entrepreneur decides to enter into a specific sector could be a way of understanding possible impediments that they may encounter along the way. Therefore, the results of this study might be able to explain furthermore why some entrepreneurs might tend to opt for one sector rather than the other and if there is any need from a specific sector to improve in order to rectify its potential existent inequality in terms of male-female ratio.

By applying the push-pull theory and a quantitative approach, this study aims to provide more quantitative evidence to prior research findings regarding gender and factors that might possibly affect the decision of entrepreneurs when deciding to start their own entrepreneurial journey. In such decision, it is assumed by the authors of this paper that entrepreneurs may take into account the sector they intend to execute their business idea, and specific motives for entering into the entrepreneurial environment. Therefore, looking into the gender of established UK entrepreneurs within each aspect: performance, the nonprofit and for-profit sector and the push and pull factors, will provide a better understanding in statistical terms about whether gender plays any role or not. It is in this study’s interest to create a space for discussion for how future entrepreneurs and other actors of the entrepreneurial might perceive gender when it comes to the creation of new businesses. Consequently, it could be possible that this space can even be interesting for researchers that examine different entrepreneurs-related factors at a national level and expand it even further.

1.2 Research question

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questions. In this case, it is considered that it is needed to provide and support research by applying a quantitative approach when responding research questions. Examining data from entrepreneurs based in the UK that includes information regarding established entrepreneurs’ positions towards the three aspects described earlier, will hopefully provide a clearer insight about how gender really matters itself in the entrepreneurial environment of the UK. The questions this study intends to respond to are the following:

● To what extent can it be argued that the UK’s entrepreneurs’ gender is a significant factor that can actually influence the growth performance of a firm?

● To what extent do any particular push and /or pull factors differ greatly depending on the gender that is being examined?

● To what extent can it be argued that the for-profit sector is more male-dominated than the nonprofit sector?

2. Literature Review

2.1 Push-Pull Theory

The push-pull theory has over the years been applied by, among others, researchers within entrepreneurship, especially in papers that have investigated self-employment. The terminology of self-employment itself has consequently been developed and changed into different directions by researchers (Dawson and Henley, 2012, p.698-699). Some authors may refer to it as push vs pull

entrepreneurship, others as necessity entrepreneurship, refugee entrepreneurship or even lifestyle/ family entrepreneurship (ibid). Literature shows that authors who have examined push vs pull entrepreneurship, whereas entrepreneurship refers to self-employment (Dawson and Henley,

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genders showed a high rate, 86.4 %, when “one pull/motivation factor” was given as a motive for starting their entrepreneurial career (Dawson and Henley, 2012, p.706,713).

Based on previous research, there has not been any pronounced focus on the gender-differences of the push and pull factors regarding what motivates individuals into becoming entrepreneurs (Kirkwood, 2009, p.347). Nevertheless, literature seems to show a common definition for determining push factors, negative and external drivers that are usually related to work, such as unemployment, work-family balance, job dissatisfaction (McClelland, et al., 2005, p.85; Dawson and Henley, 2012, p.700-701). On the other hand, pull factors are positively perceived as they are considered to be driving factors that draw individuals to launch their own ventures, i.e. autonomy, self-achievement, monetary and general control (Kirkwood, 2009, p.346-349; McClelland, et al., 2005, p.86; Dawson and Henley, 2012, p.700-701).

According to the literature review, one of the factors that push women to entrepreneurship, in comparison to men, is to achieve a balance between work and family, and this balance is not possible with fixed scheduled work programs in corporate firms. (Patil and Deshpande, 2019, p.31; Ummah and Gunapalan, 2013). Other authors support this by arguing that women see self-employment as an effective alternative to achieve this balance (Orhan and Scott, 2001). On the other hand, women have difficulties in proving themselves in the male-dominated hierarchy system (push factor), while the factor that pulls women to be entrepreneurs is the autonomous and self-achievement opportunity of entrepreneurship (Patil and Deshpande, 2019, p.31).

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Figure 1 - Motivations for Female Entrepreneurs

As the figure shows above, environment conditions have an impact on the factors that push women to become entrepreneurs (Patil and Deshpande, 2019, p.32). Additionally, factors that we identified from other authors, such as loss of life partner, economic/financial burden, loss of job, male-dominated system are also environmental factors that by necessity push women to become entrepreneurs (Orhan and Scott, 2001, p.233; Van der Zwan, Thurik, Verheul, and Hessel, 2016; Patil and Deshpande, 2019, p.32). Moreover, the pull factors such as opportunity, a desire to contribute to the community, a pursuit of self-achievement, an independent work atmosphere, and flexible working hours are the facultative motivations that attract specially women, more than men, to become entrepreneurs (Hisrich, et al.,1996; Ummah and Gunapalan, 2013, p.3; Van der Zwan, et al., 2016, p.274; Patil and Deshpande, 2019, p.32-34).

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2.2 Social vs Business entrepreneurs and their performance

According to the previous studies, it is mentioned that business growth is an indicator for business success (Cooper, Gimeno-Gascon, and Woo, 1994; Dijkhuizen, Gorgievski, Veldhoven, and Schalk, 2014). Also, it has been demonstrated that job demands are related to the success and performance of the business owners (Dijkhuizen, et al., 2014). Although the indicators are associated with performance, previous research on female entrepreneurs' performance is limited (Jaffee and Johnson, 2015). However, it is argued that there are historical differences in business performance among genders (Jaffee and Johnson 2015). According to Jaffee and Johnson (2015), there are differences between female and male entrepreneurs in terms of business ownership and business size, and those differences may suggest that women's entrepreneurship performance is not sufficient compared to men. On the other hand, as a result of the research conducted by Amezcua and McKelvie (2011), it is said that businesses (mostly incubation firms) owned by females perform better than men. Besides, it has been suggested that sales growth and employment growth levels of women entrepreneurs are higher than men (Amezcua and McKelvie, 2011). Moreover, prior research that has focused on entrepreneurs based in the UK, have found gender-based differences in the time spent into the firm. This has led research to argue that women are much more likely to become social entrepreneurs (individuals that have innovative solutions to society’s social problems) rather than business entrepreneurs (individuals who creates a new business) (Levie and Hart, 2011, p.200).

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women show a will into improving and contributing their communities (Levie and Hart, 2011, p. 214-215).

Other previous research from this certain region, also, indirectly stresses how this desire of female entrepreneurs contributing to the community and how the flexibility such desire entails might affect female entrepreneurs’ ventures in terms of performance and growth (Outsios and Farooqi, 2017, p.185). It is argued that women’s motives in entering into the entrepreneurial atmosphere rely on the balance between family and work giving them the possibility of distributing their time in a more flexible way. This could be perceived as a way of strengthening what was mentioned above regarding Levie and Hart’s findings, that female entrepreneurs are more likely to become social entrepreneurs than men and that female social entrepreneurs tend to work on a part-time basis.

Despite the fact that female entrepreneurs tend to work part-time, it is recognized by the UK government the economic contribution women-owned ventures provide to the national economy (Roomi, Harrison and Beaumont-Kerridge, 2009, p.271). This economic contribution is strengthened by the report executed by the Department of Business Innovation and Skills (DBIS) of the UK government, whereas it is stated that the number of females in self-employment has risen by more than a third since 2008 (DBIS, 2016, p.4). Specifically, the DBIS found that SME women-led businesses contribute with 85£billion in GVA to the UK economy (DBIS, 2015, p.3). Still, women based in the UK show a distinctive lower number in terms of willingness to start their own venture in comparison to men. (Rose, 2019, p.35)

2.3 Nonprofit sector

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(Rates Male 41.01% - Female 44.89%), (Themudo, 2009 p.672). The Global Entrepreneurship

Monitor (GEM) in the United Kingdom rose 17.3% (a 13.29% relative increase in non-profit sector size) in between 1995-2000 (Themudo, 2009 p.675).

According to the GEM, women in every part of the world (including the UK) are dominant in the health / social services sector (GEM Women’s Entrepreneurship Report, 2019, p.50). Also, women are more likely than men to start their enterprises in some low-valued or lower productivity sectors such as health-social work and education (Rose, 2019). In line with this, it is stated that health and social work and education are among the most common employment sectors for women in the UK (Devine and Foley, 2020). In a previous study, Keen and Audickas state that according to research done by the National Council for Voluntary Organisations (NCVO), the most common service of the UK volunteer-nonprofit organizations is social services ∼18% and Human Health service is ∼5% (Keen and Audickas, 2017). Also, according to the UK Civil Society Almanac 2019 demographics (UK Civil Society, 2019) women in the United Kingdom are more likely to prefer voluntary jobs than men. Based on these data, we estimate that women have more roles in non-profit initiatives on the social-health services and education sector. As portrayed in the report of RBC Wealth Management (RBC, 2018), women business owners in the UK place more emphasis on making a positive charitable impact on their communities.

As of their establishment, charities (as another example of the nonprofit sector) were considered feminine enterprises (Broadbridge and Parsons, 2007). The founders and managers of charity and charity stores are mostly female compared to male domination in the managerial positions (Broadbridge and Parsons, 2007). In a national survey conducted in the UK, 94 percent of charity directors were found to be women, and it was stated that voluntary aid organizations were created by women (Broadbridge and Parsons, 2007, p.5,6). Moreover, it is known that women are more likely to find low-paid or unpaid voluntary jobs in the charity sector than men. For instance, The Charities Association reflected the average full-time employee salary in 2007 at £ 9,596 - £ 16,000 (Broadbridge and Parsons, 2007, p.7)

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“glass ceiling” (unrecognized barriers affecting women) and the male-dominant work atmosphere (Ummah and Gunapalan, 2013). Especially in patriarchal societies, women are prone to staying under masculine power and experiencing difficulties such as male domination and the glass ceiling are gathered under the push factors for women who cannot achieve higher executive positions (Ummah and Gunapalan, 2013). Women are particularly disturbed by the business culture and hierarchy, in which the male domination presents itself in forms of hierarchy, networking and in the use of power (Cocburn, 1991; Kanter, 1977). Most of the women moved away from positions under masculine domination, motivated themselves to create jobs/careers (Orhan and Scott, 2001, p.233). Besides, as an example of push factors, the lack of recognition by employees (more male dominance in the workplace) pushes women to set up their businesses and satisfy their desire to prove themselves in a managerial position. Supporting this argument, Hisrich et al. (1996) stated that some pull factors for women, such as the desire for independence and self-achievement triggers entrepreneurial motivation. According to Kim Williams, CEO of Interfaith Housing Coalition, women want to take part in nonprofit sectors because they believe they can make some difference for society (Forbes, 2015). The idea of benefiting the society behind charity organizations, health / social works and education sectors will foster the desire to prove themselves for women who do this job. At the same time, nonprofit sectors such as charity and voluntary fields create opportunities for women entrepreneurs to do useful work with passion.

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and Scott (2001) mentioned that women tend to become entrepreneurs because it provides professional flexibility in terms of hours worked and independence at work (p.232).

Motivations of female entrepreneurs for entering into business questioned, in particular, their need for achievement and their risk-taking propensity (Cooper, Hampton, and McGowan, 2010, p.6). According to Orhan and Scott (2001), women are more motivated by the social contribution their business can make. Instead of salary, job satisfaction and taking part in useful and helpful fields for society are more valued and these motivations are the pull factors that attract women to entrepreneurship (Cooper, et al., 2010, p.5). Baruch (2004b) sees career success as internal satisfaction, life balance, and freedom (p.26,168). Accordingly, the measurement of success of employees varied according to traditional measurements. It was measured based on objective measures such as success, income, professional status, and promotion (Broadbridge and Parsons, 2005, p.84). In addition, success was determined around men performing specific career steps or measured according to masculine norms (Broadbridge and Parsons, 2005, p.84). For this reason, we believe that the methods used to measure the entrepreneurial success of women until recently were not fair. A male-dominated measuring system should not express women's success rates. On the other hand, women associate entrepreneurial success, especially in voluntary work, with more subjective data such as feelings of success and satisfaction and the effects of their work on society (Broadbridge and Parsons, 2005, p.84).

2.4 For-profit sector

Besides the perception of female entrepreneurs is dominant in the nonprofit sector (Themudo, 2009), women-owned businesses represent less than 25% of enterprises in the UK’s high-value sectors, such as financial services, IT, or manufacturing (Rose, 2019). The most common for-profit industries determined for males are wholesale and retail trade, manufacturing, and construction (Devine and Foley, 2020, p.6). Moreover, some high-value/for-profit sectors, such as construction, mining, transportation, and storage, are less attractive for female entrepreneurs in the UK than males (Devine and Foley, 2020).

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job opportunities in the technology field. However, these data represent self-employed women and all women in the labor force. As mentioned above, female entrepreneurs in the UK are only 25% likely to start a business in the for-profit sector, which is considered to be an efficient and high-value sector (Rose, 2019, p. 37). The reason for this is argued as the higher probability of men working in high-income sectors such as technology, engineering, financial services and construction. In this case, the most important point to look at is why women are less active in the for-profit field? It was determined with the research that the reason behind this is the entrepreneurs' background skills (Rose, 2019).

According to Rose (2019), entrepreneurs tend to start businesses in sectors they know well (p.38). For instance, the establishment of a technology-based enterprise includes different information related to both the market and technology information of the business (Cooper, et al., 2010). The reason women are underrepresented in the high-value sectors is linked to Science, Technology, Engineering and Mathematics (STEM) skills (Rose, 2019, p.36). Alison Rose’s Review (2019) stated that there is a gender gap in the education system when it comes to STEM fields. In more productive industries, such as financial services, IT, or manufacturing, STEM background has an impact on the women's awareness of the entrepreneurship journey and helps them to develop the necessary self-reliance (Rose, 2019, p. 63). For example, as a result of their predominantly technical background and their education in this field, it has been observed that women who joined the workforce in STEM fields before becoming self-employed in technology, acquired the skills required to set up their own businesses (Cooper, et al., 2010). However, since female entrepreneurs are not encouraged to develop their STEM skills, their lower efficiency in high-value sectors affects both their financial expectations and their ability to achieve entrepreneurial success (Rose, 2019 p.40). From the UK's economic growth perspective, it shows that women and girls should be supported in the STEM skills because these skills will pave the way for more female entrepreneurs in the higher productivity sectors such as technology, production, engineering (Bughin, et al., 2018).

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the technology sector being more male-dominated is also supported by authors that do not focus on this specific region. According to Cooper et al. (2010), even though many opportunities exist in technology-related fields, they are mostly followed by men, the number of female technology entrepreneurs are less than their male counterparts. Kepler et al. (2007) found in their analysis, focused on the US, that male entrepreneurs were more likely to start technologically intensive

businesses than women (p.53).

Compared to the US, the rate of women in technology has increased due to the dot-com bubble in the UK. However, when we look at the big picture, it is evident that there is not enough female participation (Rose, 2019). Even if women entrepreneurs in the UK have been able to enter into the e-business and start new ventures within this industry, research stresses the fact that only one in 10 jobs taken by women are considered to be within the manufacturing and technology sector (Forson and Özbilgin, 2003, p.15). This paper, studied by Forson and Özbilgin (2003) focused on dot-com businesses owned by women entrepreneurs in the UK and found that this kind of facts were a result of a mix of pull and push factors for why women in this specific region, UK, started their businesses within this sector. Besides the pull factor of independence, they showed as well a gender-related push factor, the glass ceiling effect, which goes in accordance to other research findings’ as i.e. in McClelland et al. (2005) and Kirkwood (2009) (Forson and Özbilgin, 2003, p. 20-21). Findings also highlight that women entrepreneurs are more likely to face firm performance-related challenges than men, which could be considered to be a push factor too due to its negative effect. These challenges could be: raising finance and operational problems (Forson and Özbilgin, 2003, p.21-22).

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2.5 Motives for expanding previous research

This research concentrates on female entrepreneurs and initially focuses on the underlying reasons and factors which lead them to become entrepreneurs. These reasons are classified in previous research as push and pull factors (Dawson and Henley, 2012) which are required for quantitative research. By adding those factors to our variables, this study will focus on examining gender impact on push-pull factors and which business sectors are preferred by female entrepreneurs (in this case, female entrepreneurs in the UK). The literature review indicates that there is a relationship between the reasons that lead women to become entrepreneurs and the business sectors they choose (Hisrich, et al.,1996; Orhan and Scott, 2001; Ummah and Gunapalan, 2013; Rose, 2019). Although the secondary data may not be optimal for defining this relationship, it will contribute to quantifying the sectors preferred by male and female entrepreneurs. Therefore, this research determines nonprofit and for-profit sectors as a variable. It will focus on the industries and education levels of women and support them quantitatively.

Regarding female entrepreneurs in the UK, it is necessary to consider the recurring factors (i.e., gender) in literature research. For example, according to the previous literature, entrepreneurship research has focused on the similarities and differences of male / female entrepreneurs and dominant or common traits of them (Jianakoplos and, Bernasek, 1998; Shmailan, 2016). Accepting “gender” as a variable to support the hypotheses with quantitative research methods will be beneficial for this study. In this case, gender is considered as a variable of this research. Besides, the overall performance of female entrepreneurs is considered as a variable. This research aims to reveal the association between determined performance values and gender. It is the interest of this study to quantitatively investigate whether entrepreneurs’ willingness to pursue growth differ by gender. In this way, it can be shown whether there is a connection between the performance variables and male and female entrepreneurs, and it can be expressed for the UK specifically.

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when it comes to gender differences. For this reason, we will have four different hypotheses that will contribute to the examination of this study’s purpose and research questions:

1. The first hypothesis of this study is: The gender of established entrepreneurs from the UK is related to performance.

2. The second hypothesis is as follows: The gender of entrepreneurs is related to the industry

in such way that established male entrepreneurs from the UK are related to the for-profit sector.

3. The third hypothesis is as follows: The gender of entrepreneurs is related to the industry in such way that established female entrepreneurs from the UK are related to the nonprofit sector.

4. The fourth hypothesis is as follows: Push factors are related to gender, in such way that

the motive for beginning an entrepreneurial depends on the gender of the entrepreneur.

5. The fifth hypothesis is as follows: Pull factors are related to gender, in such way that the

motive for beginning an entrepreneurial depends on the gender of the entrepreneur.

Eventually, depending on the results, this study will contribute and expand previous research in terms of giving an indication for what might be needed from the UK government in order to encourage individuals becoming self-employed (entrepreneurs) and reduce the existing gender gap in the two distinctive sectors, nonprofit and for-profit.

3. Methodology

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differences in the UK’s entrepreneurial environment. Using a research method based on numerical data and statistical results will ensure that the findings are as clear as possible and at the same time, to some extent, free from misinterpretation. Opting for a qualitative approach would not have provided more statistically significant results as we consider it is needed after reviewing previous literature. However, it can be argued that combining quantitative and qualitative methods would achieve more optimal results than the current one as we aim to identify at an individual level any gender differences in our different selected aspects, i.e. performance, sectors and push and pull factors. Mixing methods would have entailed e.g. having semi-structured interviews with a selected number of entrepreneurs from both sectors and asking them a combination of types of questions that could reflect and be converted into useful quantitative data. In this way, we would have been able to base our study’s findings on primary data and in that way also ensure that the paper is providing to entrepreneurship research the most recent information regarding entrepreneurs' actual attitudes and status. Unfortunately, due to the limited time period and unique economic situation the world and not less entrepreneurs are experiencing at the moment, it was concluded to move forward with the quantitative method.

This quantitative approach has great importance, as this paper can still offer a perspective on the gender differences within the selected aspects, but in a statistically significant manner which can be limited and hard to access in previous research. Applying a secondary analysis was determined to be more beneficial in terms of saving time, money and still having access to high quality and reliable data. These four points were considered to be of extreme importance to this paper as we aspire to contribute to further research while looking into a more profound manner how and if gender differences are related to performance and businesses’ distinctive sectors. Additionally, we decided to examine the possible presence of a relationship between gender and the different aspects by using a cross tabulation in the following analysis software program: IBM SPSS Statistics.

3.1 Data collection

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executed between 2014-2018 including EU Member States and OECD countries with the exception of some of the countries1 that did not participate every year according to some parts of their report (OECD, 2019). Additionally, the data collected and presented by the OECD report also includes another reliable data source, the Eurostat Labour Force Survey (LFS). This household survey collects data through individual interviews from all EU Member States (OECD, 2019, p. 22-23). The compiled data provided by the OECD report was considered to be reliable and of very high quality given the aim of this study and therefore we decided to reach out to the GEM UK team that was located at Aston Business School and Queen’s University of Belfast and in charge of managing the GEM data. Unfortunately, when contacting them, they explained that 2014-2018 was not available for external researchers due to the 3-year embargo. This basically meant that the only individual level data that we could have access to was GEM 2014-2016. The OECD report could only provide information on a national level and did not include any individual level data which was required for the aim of this study. We decided to go ahead with the available data, GEM 2016 Annual Population Survey (APS) Global Individual-Level Data. The GEM data is composed either by the APS and the National Expert Survey (NES) making sure to keep a high quality individual data of each country. The individual interviews take place via telephone or face-to-face and each GEM country must have a minimum of 2000 participants (Global Entrepreneurship Research Association, GERA 2020). The selected data, GEM 2016 APS Global Individual-Level Data contains data from the APS and information about the entrepreneurial attitudes, aspirations and aspirations of the participants (GERA, 2020).

Firstly, when introducing this data into the SPSS software program in order to observe what it contained, we found that it had a total number of 194 824 individual cases. In order to identify the variables, we wanted to analyze and that provided specific data for the UK we had to filter out all other countries but the UK. Having a manageable size of the UK data would be easier to handle and our main focus is the UK for the purpose of this study. After obtaining 10.011 UK individual cases we decided to once again minimize the number of cases, but this time by adjusting it to the

1_{Countries that did not participate at all or that did not participate specific years: Czech Republic, Malta,}

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number of participants that considered themselves to be established business owners. The total UK data size ended up being 365 individual cases. We believe that selecting data that only included established owners (EB) that were sole owners, meaning that did not have any other partner/ co-founder in their business, would provide a better understanding about each individual’s entrepreneurial journey. Moreover, the reason why we did not reduce further the size of the data was because we considered that a higher number of individual cases, sample size, would decrease the risk of obtaining a high margin of error in the study and the validity of it. Even though most of the data provided by this individual level data could be included in this study, we found a limitation regarding the nonprofit sector and thought that we might be able to find more specific data in one of the UK's databases of statistics, the Office National Statistics (ONS). The data provided by the ONS is collected in the same way as the Eurostat Labour Force Survey and the GEM APS data, via individual interviews, that can be taken face-to-face or telephone but unfortunately it was presented at a national level and without the possibility of gaining access to the individual level one. Despite the obstacle encountered, the limitation regarding the nonprofit sector was resolved by using a variable that represented the different industries in which the established business owners were involved. Therefore, at the end, we considered that using the GEM 2016 APS Global Individual-Level Data and information from the OECD report was highly adequate for the purpose and approach of this study.

3.1.1 Variables

Most of the data collected for the different aspects we aim to examine; performance, nonprofit and for-profit sector and push and pull-factors, includes information about the gender of established entrepreneurs from the UK. The year period we take into account for this study is 2016. Although having data from a more recent year, such as 2018 or 2019 would be ideal, the database that was accessible, as mentioned earlier, provided SPSS data from this time period. This might affect to some extent the validity of the research and therefore, we recommend future research to get means that are required in order to collect data from more recent years.

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Another fact that is important to highlight is the fact that all the variables of this study are categorical and that their SPSS abbreviation are found next to each of the variables. The SPSS abbreviation was included as means of facilitating further research the search of the variables that have been selected from this specific data.

Hereunder, the independent variables will be described together with the motives behind their selection between the subsections 3.1.1.1 to 3.1.1.3 while the gender variable will be explained under subsection 3.1.1.4.

3.1.1.1 Performance

The data for performance has been divided into two different subcategories, expected performance and job tenure. The independent variables that have been selected for this aspect are shown below:

Expected performance:

● Expected Number Of Jobs 5 years from now (EB_yyJ5Y) - Expected Number Of Jobs 1 to 5

- Expected Number Of Jobs 6 to 19 - Expected Number Of Jobs more than 20 Job tenure:

● Manages a business that is older than 42 months (ESTBBUSM)

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The OECD briefly noted the fact that this variable, despite being part of their performance section, does not entirely mean that entrepreneurs will actually create the number of jobs (OECD, 2019). This could somehow, to some extent, harm the validity of the study as it is not possible to know whether the established owners would create that specific number of job opportunities in the future. The second variable shows us how many of these established entrepreneurs have actually been managing their firms for more than 42 months. For this one, the participants could only choose between “Yes” or “No” as a response alternative. We discarded some other factors that were included under the performance section of the OECD report, such as: median net income for those working full-time, percent of entrepreneurs that sold to customers in another country because it is not in this study interest to look at those facts given the purpose and research questions we aim to answer and because the GEM dataset did not provide that kind of data either (OECD, 2019, p.73,78).

3.1.1.2 Sectors: Nonprofit and for-profit

For the for-profit and nonprofit sectors, we have introduced the following independent variables:

● Sector For Profit (EB_ISIC4_1D)

- For-profit includes: “Agriculture, forestry, fishing”, “Mining, construction”, “Manufacturing”, “Utilisation, transport, storage”, “Wholesale trade”, “Retail trade, hotels & restaurants”, “Information and communication”, “Financial intermediation, real estate activities”, “Professional services”, “Administrative services” and “ Personal/consumer service activities”

● Sector Nonprofit (EB_ISIC4_1D)

- Nonprofit includes: “Government, health, education, social services”

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trade”, “Retail trade, hotels & restaurants”, “Information and communication”, “Financial intermediation, real estate activities”, “Professional services”, “Administrative services”, “Government, health, education, social services” and “ Personal/consumer service activities”.

In order to keep as many respondents as possible we decided to assume, although the limitation we encountered when collecting data, that this variable was useful as we decided to divide this variable into two. The first variable included the participants that responded to all fields that we considered to be part of the for-profit sector: “Agriculture, forestry, fishing”, “Mining, construction”, “Manufacturing”, “Utilisation, transport, storage”, “Wholesale trade”, “Retail trade, hotels & restaurants”, “Information and communication”, “Financial intermediation, real estate activities”, “Professional services”, “Administrative services” and “ Personal/consumer service activities” (more details about how this was processed can be found under the subsection 3.2). On the other hand, the second variable included all the participants that chose “Government, health, education, social services” as their industry, which we considered to belong to the nonprofit sector (more details about how this was processed can be found under the subsection 3.2). In order to make this assumption we did some research in different UK's databases. Even though we did not use any data into our variables from the ONS, we did find a document that could support our assumption. The information provided by the ONS’s statistics from 2018 called “All in employment by status, occupation, and sex” (ONS, Dataset Employment by Occupation 2018) showed that the industry called “Human health & social work activities” includes social workers, clergy, welfare professionals are among the ones considered to be social workers (Ibid). This document can be interpreted with the support of the information provided by ONS SOC2010 (ONS, 2016). Therefore, we consider that this variable (EB_ISIC4_1D) that asks participants about their firm type, can be used in order to see how many established entrepreneurs are actively involved within the for-profit and the nonprofit sector.

3.1.1.3 Push and pull-factor variables

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● Entrepreneurial skills (SUSKILyy)

● Motive For Established Business Owner-Managers (EB_yyWHY) - Push Motive Necessity

We considered, as previous research and reports did too, that the ability of having entrepreneurial skills is a push factor. Earlier investigations have argued that women entrepreneurs do not have the skills required in order to start a business on their own even though some women might show a higher education level (OECD, 2019). Thereupon, we decided to include this variable to our analysis. The response alternatives for this variable were: “Yes”, “No”, “Don’t Know” and “Refused”. This led us to split the variable into 4 but, for the purpose of the study, we considered it to be interesting, to only include the entrepreneurs that chose the alternatives “Yes” and “No” (more details about how this was processed can be found under the subsection 3.2). Our second variable was chosen because it is argued by earlier studies that unemployed and employed individuals overall might find different motives to find an entrepreneurial career as a unique alternative for their professional careers and living. In this variable, the entrepreneurs could choose between the following alternatives: “Purely opportunity motive”, “Partly opportunity motive”, “Necessity motive” and “Cannot code/don’t know”. So, in other words, this variable shows how many established entrepreneurs considered starting a new business as a necessity and how many perceived it as a purely opportunity motive and as a partly opportunity motive. Because of the aim of the study we decided to only focus on the entrepreneurs that responded “Necessity motive” (more details about how this was processed can be found under the subsection 3.2).

On the other hand, for pull factors we decided to have the following independent variables:

● Opportunity to offer new and innovative products and services (FUTSUPyy)

● Motive For Established Business Owner-Managers (EB_MOTIV) - Pull Motive Income

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3.1.1.4 Gender

We chose gender as an additional variable in this study because we aspire to observe if there are any associations between gender and the rest of the other variables. The gender variable included in this study is composed of both female and male entrepreneurs. The entrepreneurs had as alternatives the following: “Refused”, “Don’t Know”, “Male” and “Female”. Given that we did not have access to proper information about these alternatives, we double checked the content of gender in our SPSS and found out that out of these 365 established owners, only the genders named male and female were chosen (more details about how this executed can be found under the subsection 3.2). This means that there might be a risk for not entirely knowing if individuals really consider themselves to be female or males as they might have to choose, to some extent involuntarily due to the lack of more options, between a range of options that did not really represent themselves.

3.2 Classification of variables and SPSS

Because of our ambition of identifying any associations between gender and the different aspects performance, sectors and push and pull factors, we chose to execute a cross tabulation analysis in the SPSS as it is considered to be the best one for studies that have such purpose, in this case, the examination of categorical variables (Bryman and Duncan, 2003, p.153). It is important for us to know whether, if there is a presence of a relationship between the variables that go in accordance with our hypotheses, the association exists or not. The reason why we consider this to be important when looking at the findings provided by the SPSS, is because we want to explore if gender really has a significant association with the selected aspects and therefore help potential entrepreneurs that their gender should not be seen as an impediment (Ibid.).

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It is important to mention that some of our variables were continuous but in order to run the cross tabulation with the responses we wanted to include, we decided that having all those variables as dummy variables would be best. This recoding step was taken several times for each of our aspects. For instance, when it comes to performance, we had to create three different dummy variables for the variable named “Expected Number Of Jobs 5 years from now” as we focused on the entrepreneurs that chose either “1-5 jobs, “6-19 jobs or “more than 20 jobs'' as a response. In order to know how many female and male (separately) entrepreneurs were expecting i.e. 1-5 jobs in the upcoming 5 years, we introduced 1 as the new value that represents “1-5 jobs''. We introduced 0 as the value that represents all other values, which includes all of them that responded to one of the other alternatives (even those ones that we did not want to include in the analysis).

The same procedure was applied for some of the push and pull factors variables. For push factors, we followed the same procedure for the variable named “Motive For Established Business Owner-Managers (EB_yyWHY) ” so we could create two dummy variables, one as a push factor while the other as a pull factor (“Push Necessity Motive” and “Pull Motive Purely Opportunity”). Likewise, it was done for the pull factor variable named “Motive For Established Business Owner-Managers (EB_MOTIV)”, whereas one dummy variable was also created “Pull Motive Income”. However, for the aspect of sectors (nonprofit and for-profit) we did a similar procedure. The difference in this step was that we used the range option in order to aggregate all of the industries2 we considered it to be the for-profit sector and gave this range the value of 1. At the same time, we could include a new value for the industry3 we consider to be representative of the nonprofit sector, the value of 0. In other words, for the variable “Sector For-profit” the value of 1 represents the for-profit sector and the value of 0 represents the nonprofit sector. For the variable “Sector Non-profit”, the recoding step was the same with the slight difference that we gave the industry that we considered to represent the nonprofit the value of 1 and while the other industries, aggregated also here, that represented the for-profit sector were given the value of 0. The rest of the independent variables,

2_{For-profit sector includes aggregate industries: “Agriculture, forestry, fishing”, “Mining, construction”,}

“Manufacturing”, “Utilisation, transport, storage”, “Wholesale trade”, “Retail trade, hotels & restaurants”, “Information and communication”, “Financial intermediation, real estate activities”, “Professional services”, “Administrative services” and “ Personal/consumer service activities”.

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ie., 1) Manages a business that is older than 42 months (ESTBBUSM), 2) Entrepreneurial skills (SUSKILyy) and 3) Opportunity to offer new and innovative products and services (FUTSUPyy), were adjusted so their new value, 1, represented “Yes” and 0 represented “No”. For the gender variable, we followed what Bryman (2011) argued and let it still be nominal and as it was. As mentioned above, before taking such a decision, we double checked the gender variable alone using the frequencies option. In this case, we could observe that out of the 365 established entrepreneurs, the data was divided into female and males. This means that all of the 365 respondents considered themselves to be either a female or male entrepreneur and did not choose any of the other additional alternatives. The secondary data provided had already adjusted the values of female as = 2 and male as =1. Also, we did run the frequencies on the rest of variables in order to ensure the total number of participants that responded to each of our selected variables.

Below you will find the table 1 that summarizes and shows all the variables introduced and what information is provided by each gender:

GENDER

● Female (2) vs Male (1)

SECTORS

For-profit:

● Dummy variable for “Sector For-profit: -Whereas value of 1: for-profit

-Whereas value of 0: nonprofit Nonprofit sector:

● Dummy variable for “Sector Nonprofit: -Whereas value of 1: nonprofit

-Whereas value of 0: for-profit

PERFORMANCE

Expected Performance:

● Dummy variables for “Expected Number Of Jobs 5 Years From Now:

- Expected Number Of Jobs 1 to 5 - Expected Number Of Jobs 6 to 19 - Expected Number Of Jobs more than 20

PUSH AND PULL FACTORS

Push factor Motives:

● Entrepreneurial skills

● Dummy variable for “Motive For Established Business Owner-Managers:

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Job tenure:

● Manages a business that is older than 42 months

Pull factor Motives:

● Opportunity to offer new and innovative products and services

● Dummy variables for “Motive For Established Business Owner-Managers:

- Pull Motive Income ● Dummy variable for “Motive For

Established Business Owner-Managers: - Pull Motive Purely Opportunity Table 1. Summary of All Variables

When our secondary data was adjusted in accordance with the aim of the study, we consequently executed the cross tabulation by selecting the option of crosstabs in the descriptive statistics. For each aspect, we introduced our independent dummy variables in the so-called “column” and our gender variable in the so-called “row”. Then we selected “row” as a percentage option and “observed” as counts under cells in order to make the illustration of the tables easier to understand. These options are considered to be helpful for comparing and summarizing groups which in this study are genders (Karadimitriou and Marshall, 2020, p.1). Selecting row as a percentage option facilitates getting an insight about the percentage of X within gender (Ibid). For example, when examining gender and the entrepreneurial skills- variables adjusted in this manner, it will be shown the percentage of females that responded “Yes” to having these skills, as well as the percentage of females that said no (Ibid). As there are two categories within gender, female and male established entrepreneurs, the same will be illustrated in the table for males. In the process of testing our hypotheses, it is crucial as researchers to understand the statistical significance of the association between categorical variables. This is the reason for why the Chi-squared test (X2_{) was selected}

before running the cross tabulation analysis. The Chi-square test provides information whether the association, if there is an association between the variables, is statistically significant or not (Ibid.).

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4. Findings

All the crosstabs will be included in the Appendix. For now, we will keep the screenshot of each of them until we have executed the T- test so we can describe each correctly.

4.1 Frequencies: 4.1.1. Gender

Running frequencies on gender allowed to confirm that all of the established entrepreneurs defined themselves as either female or male.

Table 2. Gender Frequency

The table 2 describes that 69% of the respondents (established entrepreneurs) were male while only 31% of them were female. This information also demonstrates a gender difference when it comes to the number of established entrepreneurs.

4.1.2 Other Variables

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The frequency value is the same for all variables except for the nonprofit and for-profit sector variables. There are 6 missing data points for the sector variables because these 6 participants did not choose any of the specified industry categories for sector variables.

According to the frequency table created for the sector variables (see in the appendix), 81,9% of the participants are in the for-profit sector. Only 1,6% is determined as missing value, which represents the 6 participants. The remaining percent of participants represent the nonprofit sector. Likewise, in the frequency table for performance (see in the appendix), 34.2% of the 365 participants are included in the variable named the Expected number of jobs 1-5. The 9,6% segment has an expectation number of jobs of 6-19 while 5.2% of 365 participants have more than 20 job expectations. The ratio for Manage firm more than 42 months (job tenure), another variable of performance, is 97.8%. In other words, 357 of 365 participants are managing their firms for more than 42 months.

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4.2 Findings in Performance

4.2.1 Expected number of jobs 1-5 and Gender

According to the crosstab named “1. Gender and Expected Number of Jobs 1-5” (see appendix 5), only a total of 125 entrepreneurs expect a number of jobs between 1 to 5 in the upcoming 5 years. However, it is interesting to see that the number of male entrepreneurs that have responded to this alternative, doubles the number of female entrepreneurs, 84 males vs. 41 females. Comparing the percentages between male (33,3%) and female (36,3%) it is demonstrated that there are no differences. Moreover, the Chi-square test indicates that given the fact that it is a 2x2 table the value that should be observed is the continuity correction asymptotic significance (2-sided). This value will be from now on, i.e. in the upcoming variables of all aspects called as the continuity correction significance. In this case, the continuity correction significance shows a higher value (.667) than 0.05, which means that there is no association or statistically significant differences between genders and the expectation of the number of jobs 1-5.

4.2.2 Expected number of jobs 6-19 and Gender

For the second dummy variable of performance, the crosstab named “2. Gender and Expected Number of Jobs 6-19” (see appendix 5), shows that only a total of 35 entrepreneurs expect a number of jobs between 6 to 19 in the upcoming 5 years. Moreover, it is observed that the number of male entrepreneurs triples the number of females, 27 vs.8. Comparing the percentage between male (10,7%) and female (7,1%) it is demonstrated that there are no such remarkable differences. Consequently, looking at the Chi-square test and its continuity correction significance which shows a higher value of 0.05, in this case of .369, it indicates that there is no association or statistically significant difference between genders and the expectation of the number of jobs 6-19.

4.2.3 Expected number of jobs more than 20 and Gender

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responded to this alternative almost triples female entrepreneurs, 15 males vs. 4 females. Comparing the percentage female (3,5%) and male (6,0%) it indicates that there are no major differences. The Chi-square test and its continuity correction significance of .481, once again shows a higher value of 0.05 leading to the fact that there is no association between genders and this variable.

4.2.4 Manages a firm more than 42 months and Gender

The fourth dummy variable with the name “4. Gender and Manage firm more than 42 months” (see appendix 5), shows the number of total established entrepreneurs that have been managing the firm for more than 42 months, which is 357 total entrepreneurs. Comparing the percentages between male (97,2%) and female entrepreneurs (99,1%) that have affirmed to have been managing the firm for that time period, it is demonstrated that there are barely any differences. It is interesting to observe how there is a larger number of male entrepreneurs (7) than female (1) that have not managed a firm for over 3 years. Nevertheless, the Chi-square test and its continuity correction significance (.450) indicates, due to its higher value of 0.05, that there is no association or significant differences between genders and the ability of managing a firm for over 3 years.

4.3 Findings in Sectors

The following crosstab table describes information regarding the variables: Sectors for- and nonprofit and Gender:

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As earlier mentioned, the crosstab Table 4 for the for-profit sector (the first shown above on the left side) shows results regarding the nonprofit sector by the value of 0 and the for-profit sector by the value of 1, while the crosstab for the nonprofit sector, also Table 4 (the second one shown above on the right side) demonstrates results for the for-profit sector by the value of 0 and the nonprofit by the value of 1. The for-profit Table 4 describes the unequal number of entrepreneurs in terms of gender. In this case, there are 225 male entrepreneurs that have established their venture in the for-profit sector while only 74 females have entered into that sector. On the other hand, looking at the nonprofit results, it is demonstrated that there is a higher number of female established entrepreneurs (36) than male (24). Comparing the percentages between female (32.7%) and male (9.6%) entrepreneurs within the nonprofit sector, it is demonstrated that there is an important difference between genders. Moreover, the percentage results for the for-profit sector also show a high difference between female (67,3%) and male (90,4%) entrepreneurs. Moreover, it should be observed the fact that there are some entrepreneurs that have not responded to either of the sectors, a total number of 6 entrepreneurs out of 365. The reason behind this is that these 6 entrepreneurs have selected the alternative response of “Not classified/Missing” which has not been included in the dummy variable that was created for the cross tabulation. Furthermore, both tables indicate that the Chi-square test shows a continuity correction significance of .000 which is lower than 0.05. For this reason, these two crosstabs indicate that there is an association and statistically significant difference between genders and sectors.

4.4 Findings in Push factors

4.4.1 Push Motive Necessity and Gender

Considering the crosstab named “1. Gender and Push Motive Necessity” (see appendix 6),

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compared for the necessity variable, it is obvious that there is no difference for women and men that responded positively with low rates (16.8% and 21.8%) to the variable. The ratio for both genders is quite similar. The lack of difference indicates that male and female entrepreneurs do not differ for this variable. In short, according to the crosstab, there is no relationship between gender and necessity, and there is no difference between the male and female entrepreneurs for the push factor - “necessity”.

4.4.2 Entrepreneurial Skills and Gender

The table “2. Gender and Entrepreneurial Skills” (see appendix 6) indicates that a total number of 325 entrepreneurs feel to have the required entrepreneurial skills to start a business. Among them it is observed that the number of males is considerably higher than female entrepreneurs, 230 vs 95. Nevertheless, comparing the percentages between female (84,1%) and male (91,3%) that feel to have these entrepreneurial skills, it is demonstrated that there are not such remarkable differences between genders. This is supported by the continuity correction significance of the Chi-square test which is of .064. Such value, which is above 0.05, indicates that there is no association or significant differences between genders and the ability of having entrepreneurial skills in order to start a business. However, it is interesting to see how the percentage of females that do not consider possessing the required skills and knowledge, is higher than male entrepreneurs.

4.5 Findings in Pull factors

4.5.1 Pull Motive Income and Gender

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However, it is obvious that the ratios are quite close to each other, and it indicates that there is no difference between male and female entrepreneurs when it comes to income.

4.5.2 Pull Motive Purely Opportunity and Gender

The table “2. Gender and Pull Motive Purely Opportunity” (see appendix 7) indicates the results for the Purely Opportunity motive variable and gender. This table demonstrates that 67 women entrepreneurs respond positively to this variable while there is a total of 129 men. The Chi-square test and its continuity correction significance quantitatively indicate that there is none significant association between gender and the variable. The reason for this is that the continuity correction significance (.186) is over 0.05. While 51.2% of men agree with this variable, almost the same rate of women think the same (59.3%). The fact that no significant difference between the rates indicates that there is also no difference between the male and female entrepreneurs for this determined push factor.

4.5.3 Opportunity to Offer New Products & Services and Gender

The last dummy variable of pull factors, shown in the crosstab named “3. Gender and Opportunity to Offer New Products and Services” (see in appendix 7), indicates that the majority of entrepreneurs (337 out of 365) do not expect to be in the position of offering any new products or services. On the other hand, it is observed from some entrepreneurs to be willing and expecting to offer their targeted customers new solutions. Comparing the percentages between female and male among the ones that do expect to offer new products and services, it is shown that there are basically no differences among them, 7.5% for male against 8.0% females. The Chi-square test supports this through its continuity correction significance, 1.000, which is above 0.05. This means that there is no significant association between genders and the opportunity to offer new products and services.

Does gender really matter when becoming an entrepreneur?