Culture and Entrepreneurship

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Culture and Entrepreneurship

A Comparison between Advanced and Emerging Economies

By

A.W.G. Abdullahi

099015026/S2658089

M.A. in Advanced International Business Management

&

M.Sc. in International Business and Management

Supervisors:

Faculty of Economics and Business - University of Groningen

Dr M.J. Klasing

&

Newcastle University Business School

Dr Tracy Scurry

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Abstract

The objective of this research is to explore how culture affects the entrepreneurship rate in advanced and emerging economies. This research also explores the cultural factors that affect necessity and improvement driven opportunity entrepreneurs. TEA rate defined as nascent entrepreneur or owner-manager of a new business is used as the measure for entrepreneurship. Hofstede culture variables are used as the measure for culture to investigate cultural factors that predict entrepreneurial rate. GDP per capita PPP adjusted is used to control for economic effect, total year of schooling is used to control for education and governance effectiveness is used to control for institution quality. Data from 64 countries is used to test the hypotheses. The finding from this research confirms that culture affects the entrepreneurial rate of all countries and emerging economies, there was no evidence found for advanced economies. Culture also affects necessity and improvement driven entrepreneurs.

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Acknowledgement

I will like to thank Dr Shirley Coleman, Technical Director & Principal Research Associate at the School of Mathematics and Statistics, Newcastle University for the help and support on data analysis. I will like to thank Professor Erik N. Mønness for providing me with his compilation of Hofsetde culture index data and advice on quantitative analysis. I will also like to thank Dr M.J. Klasing at the Faculty of Economics and Business at the University of Groningen for assisting with the development of the research question for this thesis and also for advice on quantitative analysis methods.

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INTRODUCTION

Entrepreneurship is the mind-set and process to create and develop economic activities by blending risk-taking, creativity and/or innovation with sound management, within a new or an existing organisation (Commission of the European Communities, 2003). Entrepreneurship has long been considered a significant factor for social, economic growth and development. It provides millions of job opportunities, offers a variety of consumer goods and services, and generally increases national propensity and competitiveness (Zahra, 1999). It is well known that the level of entrepreneurship, for instance expressed as the percentage of owner/manager of incorporated and unincorporated businesses relative to the labour force, differs strongly across countries (Stel, Carree and Thurik, 2005). This variation is related to difference in level economic development, but also to diverging demographics, cultural and institutional characteristics (Blanchflower, 200; Wennekers, 2006). Using data from the Global Entrepreneurship Monitor (GEM), Wennekers et al. (2005) have observed a U-shaped relationship between nascent entrepreneurship and the level of income per capita. The implication of the U-shape is that, as economies develop, the role of new business start-ups declines, but picks up again in highly developed economies (Pinillos and Reyes, 2009).

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The main objective of this research is to investigate how cultural factors affect entrepreneurship rate in advanced and emerging economies. Firstly, a view of how cultural factors affect entrepreneurs in advanced and emerging economies and how this in turn, affects entrepreneurship rate. Secondly, this research investigates the cultural factors matter to necessity and opportunity driven entrepreneurs.

This research adds to literatures on culture and entrepreneurship because it explores deeper into the relationship between culture and entrepreneurship at economy level. When comparing advanced and emerging markets Freytag and Thurik, (2010) found that, a number of individually relevant determinants of entrepreneurship had been widely explored but differences across countries remain relatedly unexplored. In addition, this research will provide a better understanding of culture and people across different countries and geographical regions around the world. Finally, this research will help Multinational Corporations and governments understand the differences in entrepreneurial behaviour of people in different countries and how entrepreneurship is affected and matters to them differently. It will also help them understand what matters more to entrepreneurs and how to assist them best especially to Multinationals corporations that rely on entrepreneurship and innovation in their firms.

Section 2 of this paper provides a background on entrepreneurship and culture. It looks at entrepreneurship and culture in detail: especially focusing on how culture affects entrepreneurship rate and why there is a significant difference across countries. This section also looks at Hofstede’s culture variables, the independent variable, and control variables that will be used for analysis. Section 3 and 4, cover the research strategy to be conducted, how data will be gathered, analysis and findings. Section 5 and 6 consist of the research limitations and conclusion.

1. LITERATURE REVIEW

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1.1 Relationship between culture and entrepreneurship

1.1.1 Culture

Many experts and scholars have come up with what they believe to be the concept and definition of culture, although there is no one true definition of culture. Browaeys and Price (2008) said the fundamental aspect of culture is that, culture is something all humans learn in one way or another. It is not something people inherit but rather a code of attitudes, norms and values, a way of thinking that is learnt within a social environment. The national culture of a person and the geographical region they live in help shape their culture. Other researchers like Inglehart (1990) and Kotze & Lombard (2003) believed that culture shifts from generation to generation especially in societies undergoing radical industrial change. Their research work on cultural change provided an explanation to why South African values changed from materialist to post-materialist and also the shift of American and Western Europeans shift to post materialism. This has shown that different countries have different cultural views and values; it has also shown us that cultural values shift over time as economic develop. As a result, it can be presumed that entrepreneurs from a developed country or an advanced economy will have different cultural values compared to entrepreneurs from an emerging economy or developing country. Since extensive research at the psychological level shows a link between values, beliefs and behavior, it is plausible that differences in national culture, in which these values and beliefs are embedded, may influence a wide range of behaviors including the decision to become self-employed rather than to work for others (Mueller and Thomas, 2000), (Noorderhaven et al., 2004).

2.1.2 Entrepreneurship

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entrepreneurship. Despite the interest, there remains a persistent difference of the level of entrepreneurship across countries (Audretsch et al., 2007). A lot of conclusions have been drawn on what the real reason are to why entrepreneurship rate differs across countries, some of which includes: culture, institutional development, financial system and education. For this research, TEA rate is used to measure the entrepreneurship rate of a country. TEA (Total Entrepreneurship Rate) - Percentage of 18-64 year old of the population of a country who are either a nascent entrepreneur or owner-manager of a new business.

Several studies have explored the relationship between culture and entrepreneurship Uhlaner and Thurik, (2007); Thurik and Dejardin, (2012); Mueller and Thomas (2001); Tiessen, (1997); Davidsson (1995), each using different theories to find the relationship between culture and entrepreneurship. Most especially, Thurik and Dejardin (2012) in their research on culture and entrepreneurship and Uhlaner and Thurik (2006) used ‘push vs. pull’ factor, aggregate psychological trait, social legitimation theory, and dissatisfaction approach to explain the relationship between culture and entrepreneurship.

2.1.3 Push Vs. Pull Factor

The primary theory developed around entrepreneurial motivations has been to classify motivations into categories of push and pull factors (Hakim, 1989; McClelland et al., 2005; Schjoedt and Shaver, 2007; Segal et al., 2005). Push factor takes into account the conflict between one’s current situation and one’s desired situation. Push

factor is often associated with some level of dissatisfaction (Uhlaner and Thurik,

2012). An individual’s dissatisfaction with his current situation can be a motivational or push to become entrepreneurial, this can be related to reason why some countries are more entrepreneurial that others. Research by Van Uxem and Bais (1996) found that 50% of almost 2000 new Dutch entrepreneurs mention dissatisfaction with their previous job as one of the motives to start a business for themselves.

Pull factors are those factors that draw or pull people to start businesses – for

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2.1.4 Theories on why culture affect entrepreneurship rate

The aggregate psychological trait perspective explains the difference in the rate of entrepreneurial activity as follows: if there are more people with entrepreneurial values in a country, there will be an increased number of people displaying entrepreneurial behaviour (Davidsson 1995). Extensive research conducted at the individual level shows that there is a link between individual values and beliefs, on one hand, and individual behaviour on the other (Thurik and Dejardin, 2012). Hence, it is plausible that cultural differences between countries or regions have a determining effect and influence on a variety of individual behaviours, including the decision to become self-employed rather than an employee (Mueller and Thomas, 2000).

Social legitimation or moral approach of entrepreneurship within a culture focuses on the impact of social norms and institutions on a society. Etzioni, (1987) claims that greater rates of entrepreneurs are found in societies where the entrepreneur is endowed with higher social status, attention to entrepreneurship is paid within the educational system, and more tax incentives exist to encourage business start-ups. This results in a higher demand for and supply of entrepreneurship. Institution and culture have a great influence in the social legitimation approach because the institution of a country can create a system where entrepreneurship is promoted through incentives and social status to entrepreneurs i.e. treating them like heroes. This can be used to provide some explanation to why entrepreneurship rate differs around the world, countries with higher education (colleges or universities) quality should have higher entrepreneurship rate based on this theory. Thus, for the social legitimation or moral approval approach, higher entrepreneurial activity within some countries can be explained by the general incidence of culture and institutions favourable to entrepreneurship (Thurik and Dejardin, 2012).

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(Thurik and Dejardin, 2012). Dissatisfaction as a motive at the micro-level has often been confirmed in survey studies with respect to both job mobility and business start-ups (Wennekers et al., 2001). Brockhaus (1982) finds the self-employed to be relatively strongly dissatisfied with several dimensions of job satisfaction, including the work itself, with supervision and opportunities for promotion (but more satisfied with actual pay). More generally, the state of being out of place or between things (Shapero and Sokol, 1982, p. 81) often precedes the formation of a company. So at the level of the individual, a strong dissatisfaction with life in general is probably associated with a stronger propensity to become self-employed (Noorderhaven et al., 2004).

These theories have provided some explanation to how culture affect entrepreneurship rate across countries. The article by Thurik and Dejardin (2012) on entrepreneurship and culture gave example of various theories on the relationship between cultural values and entrepreneurship, providing both a conceptual and an empirical test. The authors showed that, post-materialism, dissatisfaction, and aggregate psychological traits approach may help in contrasting and explaining the relationship between cultural values and entrepreneurship. This paper in contrast will build on these theories but will use Hofstede’s culture dimensions to explain the role cultural factors play on entrepreneurship rate. This will add to the literature by providing a clear difference between cultural factors and also identifying the specific cultural factors that matter most to entrepreneurs in advanced and emerging countries. 1.2 Geert Hofstede’s Culture Variables

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differences, the first being across employees working in subsidiaries of a multinational corporation (IBM) in sixty-four countries.

His research set-up, as well as the statistical methods used, were applied by other researchers to other groups, including students in twenty-three countries, commercial airline pilots in twenty-three countries, and civil service managers in fourteen countries. These studies together identified the four Hofstede dimensions of culture (Browaeys and Price, 2008). For each central issue faced by societies, he defines a cultural value “dimension” that reflects different possibilities for how members of a society can cope with a problem. Each value dimension represents a range of possible stances between two opposing limits (Freytag and Thurik, 2010). Hofstede four classic dimensions include: individualism/collectivism, power distance, masculinity/femininity, and uncertainty avoidance.

Individualism/ collectivism – “I or we” Individualism stands for a preference for a loosely knit social framework in society wherein individuals are supposed to take care of themselves and their immediate families only. Its opposite, Collectivism, stands for a preference for a tightly knit social framework in which individuals can expect their relatives, clan, or other in-group to look after them in exchange for unquestioning loyalty (Hofstede, 1984).

Power distance – is the extent to which individuals in a society accept that power in institution and organisation is unequally distributed. People in high power distance society accept a hierarchical order in which everybody has a place, which needs no further justification. People in low power distance societies strive for power equality and demand justification for power inequalities (Hofstede, 1984).

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Avoidance societies maintain a more relaxed atmosphere in which practice counts more than principles and deviance is more easily tolerated (Hofstede, 1984).

1.3 Link between Hofstede’s Culture variables and Entrepreneurship

Many articles and books discussing the relationship between culture and economy refer to the four cultural indices of Hofstede (1980), power distance (PDI), uncertainty avoidance (UAI), masculinity (MAS) and individualism (IDV). However, the existing hypotheses with respect to the influence of the indices on entrepreneurship, or the hypotheses that can be inferred from indirectly related phenomena, are often contradictory (Noorderhaven et al., 2004).

McGrath, MacMillan, and Scheinberg (1992) compared entrepreneurs and non-entrepreneurs using Hofstede’s cultural indices in eight countries, they argued that entrepreneurs tend to exhibit high power-distance (PDI+), low uncertainty-avoidance (UAV–), high individualism (IND+), and high masculinity (MAS+) when compared to non-entrepreneurs. In particular, they consider high PDI+ as a personal characteristic of entrepreneurs “regardless of whether the culture is high or low on power-distance” (Mcgrath et al., p. 119). Work done by Busenitz and Lau (1996) and Mueller et al. (2002) also shares this view. However, this view has been strongly disagreed by Hofsted et al. (2004), Hayton et al. (2002) and Busenitz et al. (2000).

Shane (1992) investigated the relationship between culture and inventions, and finds that countries with small power distance (PDI-) and high individualism (IND+) are more inventive than others. Shane (1993) examines the influence of culture on rates of innovation and finds that weak uncertainty avoidance (UAI-) has the strongest influence. PDI- and IND+ are also related to innovation (Noorderhaven et al., 2004). The result of the two researches suggests that entrepreneurial countries exhibit PDI-, IND+, and UAI-.

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Acs, Audretsch and Evans, (1994) examined culture and self-employment at the national level, they found that high UAI+ and low IND- are related to high entrepreneurship (Noorderhaven et al., 2004). This is a complete contradiction of the findings of Shane (1992, 1993). Consistent with the ‘aggregate psychological traits’ perspective, it could be surmised that PDI-, UAI-, MAS+ and IDV+ stimulate entrepreneurship (Shane, 1992; 1993). This is based on the assumption that countries with this cultural profile have relatively more individuals with entrepreneurial values (Brown and Ulijn, 2004).

2. HYPOTHESIS

It should come as no surprise to scholars examining the context of entrepreneurial activities, that cultural values are candidates for explaining persistent differences between levels of entrepreneurship between countries (Thurik and Dejardin, 2012). The theories discussed above provide an analytical framework for the explanation of the differences in entrepreneurship across countries.

Uncertainty avoidance (Hofstede, 2001) is a prominent explanatory factor that has been tested. Uncertainty avoidance is a cultural trait closely linked to attitudes of risk and uncertainty. Wennekers, Thurik, Van stel and Noorderhaven (2007) tested the contribution of uncertainty avoidance to the variance in business ownership across nations over time. They used a panel dataset of 21 OECD countries for 1976, 1990, and 2004 (Thurik and Dejardin, 2012). A positive direct influence of uncertainty avoidance on business ownership rate was found in the year 1976 through 2004. However, for data on year 2004, uncertainty avoidance no longer has any direct influence on business ownership (Thurik and Dejardin, 2012). Therefore:

H1 - Uncertainty avoidance does not affect entrepreneurship rate.

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GDP per capita, individualism has a negative effect while in advanced economies individualism has a positive effect. Advanced and emerging economy is a term used by the International Monetary Fund to describe developed and developing countries and economies. Advanced and emerging economies are determined based on GDP per capita or human development index.

H2 – The Entrepreneurship rate of a country is positively related to individualism in

advanced economies and negatively related to individualism in emerging economies.

The study by Zhao (2005) on synergy between entrepreneurship and innovation found that: entrepreneurship and innovation are positively related to each other and interact to help an organisation to flourish; entrepreneurship and innovation are complementary, and a combination of the two is vital to organisational success and sustainability in today's dynamic and changing environment (Zhao, 2005). The article ‘cultural influences on national rates of innovation’ by Shane, (1993) tests the proposition that cultural values influence national rates of innovation. He did this by comparing national score on Hofstede survey of cultural values with per capita rates if innovation in 1975 and 1980 across 33 countries. He found power distance, uncertainty avoidance, and individualism to be significant but masculinity was non-significant. Therefore:

H3 – Power Distance affects the entrepreneurship rate of a country.

H4 – Masculinity/Feminity does not affect the entrepreneurship rate of a country.

3. Research Method

3.1 Variables

- Independent variables – Cultural dimensions (individualism/collectivism, power distance, masculinity/femininity, and uncertainty avoidance).

- Dependent variable – TEA or entrepreneurship rate

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Economic development

Researchers trying to explain the different level of entrepreneurial activity have carried out comparative analysis of the economic conditions in different countries (Blau 1987, Blanchflower 2000). Such studies have provided empirical evidence on the existence of a U-form relationship between the level of economic development and the level of national entrepreneurial activity (Carree et al 2002; Thurik and Wennekers 2004; Sternberg and Wennekers 2005; Wennekers et al. 2005). There is evidence of a U-shaped relationship between the level of business ownership and per capita income (Blau 1987). This shows that, there is a relationship between the level of economic development and the entrepreneurship rate of a country. Hence, as economic growth increases, the conditions promoting entrepreneurship also improves (Wilken, 1979). By contrast, societies that are stagnating economically offer limited market incentives, and the level of capital accumulation is too small to enable potential entrepreneurs to take advantage of limited opportunities that do exit (Lee and Peterson, 2000).

Education

Le (1999) argues that there are two different channels (managerial ability and outside options) through which the level of education might influence the propensity to become self-employed. Calvo and Wellisz (1980), inspired by Lucas's (1978) general equilibrium model, explain the impact of educational attainment on the probability of selection into an entrepreneurial position through managerial ability. Education would enhance managerial ability, which in turn increases the probability of entrepreneurship. The other channel generates an opposite, negative effect on entrepreneurship selection. Higher levels of education may generate better outside options (i.e. more lucrative wage employment under better working conditions) and thus decrease the likelihood of entrepreneurship as the preferred choice (Van Der Sluis, Van Praag and Vijverberg, 2008). Other research also support the notion that education may be indirectly linked to a lower rate of entrepreneurship due to its inverse relationship to unemployment (Audretsch et al. 2002), which may be viewed as a push factor towards business ownership (Uhalaner and Thurik, 2007).

Research work by Robinson and Sexton, (1994) titled, ‘The effect of education

and experience on self-employment success’ supports the argument that high level of

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have more years of formal education than those who do not work for themselves’ was

confirmed with the years of education for the self-employed being 14.57 years for all workers. Hypothesis two ‘The number of years of formal education will increase the

probability of becoming self-employed’ was supported with the probability of

becoming self-employed increasing by 0.8% for each year of education providing a significant relationship. Hypothesis three ‘The relationship between years of formal

education and success of the self-employed, as well as the general population will be positive and significant’ was supported using the ‘Beta’ coefficients in a ‘Probit’

regression model, indicating that self-employment and wage and salaried earnings increase significantly for each year of education (Robinson and Sexton, 1994). For this research, ‘total year of schooling’ will be used as a measure for education.

Institution quality

In general sense, for the growth of entrepreneurship, a political system needs to be built on freedom of choice, individual rights, democratic rules, and a ‘checks-and-balances government’ (Friedman 1982). Specifically, the level of entrepreneurship that develops in a society is directly related to a society’s regulations and policies governing the allocation of rewards (Baumol, 1990). For entrepreneurship to flourish, it is essential that legal and regulatory systems recognize the corporate form of enterprise, allow limited liability, and protect contracts and intellectual property rights (Willis, 1985). Entrepreneurs are discouraged from starting ventures if they are forced to comply with too many rules and procedures requirements, are expected to report to an array of institutions, and spend extensive time and money in fulfilling the documentation requirements (Morris, 1998). For this research, institutional quality is measured by governance effectiveness of a country and Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies (Info.worldbank.org, 2014)

3.2 Data

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purposes which may or may not be research. Increasingly, such data are held in databases and databanks (Thomas, 2004).

The secondary data that will be used for this research will be collected primarily from these sources:

Total Entrepreneurship Rate (TEA) will be collected from General Entrepreneurship Monitor (GEM) database. GEM is one of the world’s foremost cross-national collaborative social science research projects. With a growing archive of high-quality data and a network of entrepreneurship experts, GEM has had a significant impact on the field of entrepreneurship scholarship (Gemconsortium.org, 2014). GEM collects two primary data, Adult Population Survey (APS) and National Expert Survey (NES). The APS is designed to collect detailed information on the entrepreneurial activity, attitudes and the aspirations of respondents. While the NES The NES is a survey instrument administered to a minimum of 36 ‘experts’ in each GEM country, allowing the measurement of the nine key Entrepreneurial Framework Conditions (EFCs): finance, government policies, government programs, entrepreneurial education and training, R&D transfer, commercial and professional infrastructure, internal market openness, physical infrastructure and services, and cultural and social norms (Gemconsortium.org, 2014).

TEA (Total Entrepreneurship Rate) - Percentage of 18-64 year old of the

population of a country who are either a nascent entrepreneur or owner-manager of a new business (Gemconsortium.org, 2014). Nascent entrepreneurs are the Percentage of 18-64 year olds of the population who are currently nascent entrepreneurs, i.e., actively involved in setting up a business they will own or co-own; this business has not paid salaries, wages, or any other payments to the owners for more than three months. While owner-manager of a new business is the percentage of 18-64 population who are currently an owner-manager of a new business, i.e., owning and managing a running business that has paid salaries, wages, or any other payments to the owners for more than three months, but not more than 42 months (Gemconsortium.org, 2014).

Necessity-driven Entrepreneurs – Percentage of people involved in TEA who

are involved in entrepreneurship because they had no other option for work (Gemconsortium.org, 2014).

Opportunity-driven Entrepreneurs – Percentage of people involved in TEA

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work; and (II) who indicate the main driver for being involved in this opportunity is being independent or increasing their income, rather than just maintaining their income (Gemconsortium.org, 2014).

GDP per capita (PPP adjusted) – Data for GDP is collected from the World

Bank database. GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars (Data.worldbank.org, 2014).

Governance Effectiveness – used as the measure for institutional quality, data

is collected from World Bank Worldwide governance indicator database. Governance effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies (Info.worldbank.org, 2014). It is measured on a scale of (-2.5 to 2.5) -2.5 being very poor governance effectiveness and 2.5 being a very effective governance system.

Avg. year of total schooling – this measures the level of education for this

research. It measures the average year of total schooling of people from 15 years and over. Data on this variable is collected from the ‘Baro-Lee’ (Baro R. and J.W. Lee) educational attainment database.

‘Advanced’ and ‘Emerging’ Economies – the IMF database was used to categorically

group countries into advanced and emerging economies. This classification is provided by the IMF world Economic Outlook WEO. The main classification used by the IMF to classify countries are; per capita income level, export diversification, and degree of integration into the global financial system (Imf.org, 2014).

Hofstede’s culture variables – data on the four dimensions of culture as described

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the four dimensions can be clearly related to and distinguished when comparing cultures across the world, a full data is readily available for most countries and a lot of research on culture and entrepreneurship used the first four culture variables. For instance, research by Noorderhaven et al., (2004) on ‘Culture’s role in entrepreneurship: self-employment out of dissatisfaction’ used the four classic Hofstede’s culture variables for their research.

3.3 Sample

The data that will be used for analysis will be collected from a period of 10-15 years; this is because some countries have data missing for a certain period of time over the years. For example; the TEA rate from the GEM database starts from 2001 until 2013, some countries like Austria only have data for 2005, 2007, and 2013. Therefore data is collected from 2001-2013 and then averaged for every country. The same method is applied to data collected for institutional quality, and education level. The data on GDP per capita PPP adjusted is collected from the year 2005 to 2013; most countries have data for the complete 9 years while others have data for some years missing. The only exception is the Hofstede’s variables because some countries have data available for them from one period in time only, that is the 1980’s. There is a total of 64 countries, 32 advanced and 32 emerging countries, all of which have data available for both control and dependent, and independent variables.

3.4 Data analysis

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4. Results

4.1 Descriptive statistics

Advanced Vs. Emerging economies

In order to get an understanding of the variables used for this research and how they differ between economies, the average mean value is used. Table 1 below show every variable with the mean value of advanced and emerging economies. The most important variables are the control variables, their importance and how they affect the entrepreneurship rate of a country has been explained earlier. It can be seen on the table below that GDP per capita in advanced economies is $39,422 while in emerging economies it is $13,363. This come as no surprise because this figures clearly explain the term advanced and emerging economies, IMF uses GDP as a measures in determining if a country is an advanced or emerging economy. Education quality measured by the total year of schooling of people 15 years of age and over is approximately 9 years in advanced economies and 5 years in emerging economies. Institutional quality (scale of -2.5 to 2.5) is 1.5 in advanced economies and -0.02 in emerging economies; this shows that the control variables have a significant difference between the two economies. Based on the literature review, it is expected that GDP per capita, education, and institution quality should have an effect on the entrepreneurship rate of a country. Table 1 shows a regression analysis of TEA rate against control variables, the result P = .000, P < 0.01 shows that there is a strong and negative significant effect of the three control variables on entrepreneurship rate. The negative effect of the control variables on entrepreneurship shows that an increase in any of the three variables will lead to a decrease to entrepreneurship rate. The lower the level of the three control variables, the higher the entrepreneurship rate. If the

push factor is taken into consideration, this could explain why emerging economies

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capita income, as economies develop, the conditions promoting entrepreneurship also improves. The Push factor is supported because advanced economies have 17% necessity entrepreneurs while emerging economies have 57%, institutional quality is 1.5 for advanced economies and -0.02 for emerging economies. This is based on a scale of -2.5 to 2.5; this shows that emerging economies have a very poor governance system.

Looking at the culture variable, we can define what category or score each economy belongs to on the Hofstede’s scale. Advanced economies scored 60% on individualism/collectivism while emerging economies scored 30%, this implies that advanced economies are an individualistic society while emerging economies are a collectivist society. On the power distance scale, advanced economies have less power distance with a score of 45%, i.e. they don’t believe in hierarchy and inequality unlike emerging economies that have a higher score of 71% (they believe in inequality and hierarchy). A score of 47% and 49% on masculinity/feminity scale for both advanced and emerging economies implies that both economies have a mixture of masculine and feminine elements. This means that both societies are driven by a ‘masculine’ competition, achievements, and success, and by a ‘feminine’ caring for others and equality in life. Uncertainty avoidance is also closely ranked with a score of 61% for advanced economies and 68% for emerging economies. Uncertainty avoidance is the ambiguity or the fact that the future is unknown. A high score in both economies means that ‘There is a focus on planning, and these plans can be altered at short notice and improvisations made. Emotions are not shown much in these societies; people are fairly relaxed and not averse to taking risks. Consequently, there is a larger degree of acceptance for new ideas, innovative products and a willingness to try something new or different, whether it pertains to technology, business practices, or food’ (Geert-hofstede.com, 2014). The variation in Hofstede’s cultural variables shows that culture varies around the world, it also shows that entrepreneurs in different countries will be affected by different cultural factors and this in turn affect the entrepreneurship rate of a country.

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Model 1 Model 2 Model 3

Variables β SE β SE β SE GDP -‐.547*** .000 Institution Quality -‐.538*** .682 Education -‐.458*** .293 R2 .288 .278 .197 N 64 64 64 *p < .10; **p < .05; ***p < .01

Table 2. Descriptive Statistics (Mean)

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Table 2 model shows that both advanced and emerging economies have high uncertainty

avoidance. This hypothesis is supported when TEA was regressed against uncertainty avoidance on all countries together. P = .506 and p > .10, the hypothesis is supported. When GDP, institution, and education are controlled for, the hypothesis is significant and negative (P = .029, P < .05). GDP and institutional quality are marginally significant but negative with P = .101 and P = .104 respectively. Also, when other predictor variables and culture variables were controlled for, uncertainty avoidance is negatively significant with P = .017, P < .05. The hypothesis is not supported when controlled variables were introduced (See Table 3

model 7, 8, and 9).

Advanced Economies – (Table 4 model 4 and 5) uncertainty avoidance is not significant in advanced economies with P = .118, even after introducing all control variables and other culture variables, it was not significant with P = .223. Education is the only control variable positive and significant in both regressions with P = .045 in the first regression and P = .086 in the second regression. This hypothesis is supported in advanced economies.

Emerging Economies – (Table 5 model 4 and 5) uncertainty avoidance is also not significant in emerging economies, P = .803 in the first multiple regression analysis with control variables, it was also not significant after adding the other three culture variables with P = .646. GDP is the only control variable that was significant in the first regression analysis with P = .060. This hypothesis is supported in emerging economies.

4.3 Hypothesis 2

The Entrepreneurship rate of a country is positively related to individualism in advanced economies and negatively related to individualism in emerging economies.

Advanced Economies – (Table 4 model 1 and 5) descriptive statistics on Table 2 shows that advanced economies are individualistic with a high score of 60%. Regression analysis of TEA with control variables carried out on advanced economies found that Individualism is non-significant with P = .724. Education is positive and significant with P = .061. Individualism is non-significant when more control variables were introduced with P = .945, education is significant with P = .086. In advanced economies, this hypothesis is not supported.

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(Table 5 model 1 and 5) Multiple regression of TEA on individualism/collectivism and control variables found that P = .004 which is strongly significant but negative, the control variable GDP is strongly significant and negative with P = .023. When the other three culture variables are introduced, there is also a strong and negative significance, P = .001. This hypothesis is supported.

Power Distance affects the entrepreneurship rate of a country.

First, a simple regression shows that power distance have a positive and significant effect on TEA with P = .058, when control variables were introduced, it was not significant with P = .161. When other culture variables were introduced, there is a strong significance with P = .024. GDP and institution were marginally significant with P = .112 and P = .108 respectively. On all countries, this hypothesis is supported in Table 3 model 3, 4, and 9.

Advanced Economies – (Table 4 model 2 and 5) there was no significance found in both multiple regressions with P = .524 and P = .654, the first regression with control variables, education is significant with P = .037, the second regression is also with control variables and the other three culture variables were also introduced, education is also significant with P = .086. This hypothesis is rejected with advanced economies.

Emerging Economies – (Table 5 model 2 and 5) no significance was found in the first regression, with control variables P = .460. GDP is significant with P = .069. In the second regression with control variables and other culture variables, there is a strong significance, P = .049. This hypothesis is rejected in emerging economies.

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Regression analysis found that there was no significance of masculinity/feminity on TEA, P = .632. When control variables were introduced, there is no significance, P = .845, GDP is significant P = .087. The final regression with the other three culture variables and control variables found no significance P = .573. GDP P = .112 and institution quality P = .108 are marginally significant. This hypothesis is supported. See Table 3 model 5, 6, and 9.

Advanced Economies – (Table 4 model 3 and 5) first regression with control variables was not significant but positive, P = .353, Education was significant P = .016. The second regression with control variables and other culture variables is also not significant, P = .509, education is significant with P = .086. This hypothesis is supported in advanced economies. Emerging Economies – (Table 5 model 3 and 5) there is also no significance found in emerging economies, masculinity/feminity has no effect on TEA rate P = .814 in the first regression with control variables, GDP is significant P = .039. The second regression with control variables and other culture variables also found no significance, P = .223. None of the control variables is significant. Hypothesis supported in emerging economies.

4.6 Necessity-driven entrepreneurs

Necessity-driven entrepreneurs as explained in the previous section, is the percentage of people in TEA who are involved in entrepreneurship because they had no other options for work. One of the main objectives of this research is to identify the cultural factors that matter to necessity-driven entrepreneurs (Gemconsortium.org, 2014). Using data from GEM dataset on the percentage of necessity-driven entrepreneurs in the TEA rate, a multiple regression was carried on necessity-driven entrepreneurship rate. First on 64 countries together, then on 32 advanced and 32 emerging economies. Using the four culture variables as predictor variables and GDP, institution quality, and education as control variables.

All Countries – (Table 6) in all the Models, masculinity/feminity is the only culture variable that is found to be significant. Masculinity/feminity is positively significant in model 3 and 5, P = .023 and P = .040. GDP and institution are negative and strongly significant in the entire 5 models, P < .01.

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positive and strongly significant with and without control variables, P < .01. Masculinity/feminity is positively significant with P < .05. Uncertainty avoidance is the only variable that was found to be non-significant (Model 7, 8, and 9). The control variable institution has a strong and negative significance in most occasions with P < .05. In model 5, GDP is also negatively significant with P = .032. Education is significant and positive in model 1, P = .069.

Emerging Economies – (Table 8) individualism/collectivism is the only culture variable significant, it is positively significant with P = .044 when control variables were introduced. It is also marginally significant in model 5, P = .109. All the three control variables are significant; GDP and institution quality negatively significant while Education is positively significant.

4.7 Improvement-driven opportunity entrepreneurs

Improvement-driven opportunity entrepreneurs is the percentage of people involved in TEA who (I) claim to be driven by opportunity as opposed to finding no other option for work; and (II) who indicate the main driver for being involved in this opportunity is being independent or increasing their income, rather than just maintaining their income (Gemconsortium.org, 2014). Using GEM database on the percentage of improvement-driven opportunity entrepreneurs in the TEA rate, a multiple regression was conducted. First on 64 countries together, then on 32 advanced and 32 emerging economies. GDP, institution quality, and education were used as control variables, while the 4 culture variables are used as predictor variables. All Countries – (Table 9) masculinity/feminity is the only culture variable that is found to be significant. It is negatively significant in model 3 and 5, P = .063 and P = .065. Institutional quality has a strong and positive significance, P < .05 in all 5 models.

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significant in all the 9 models. Education is positively significant P < .05 in model 6 and 9.

Emerging Economies – (Table 11) individualism/collectivism is the only culture variable significant. It is negatively significant when viewed with control variables in model 2 and 9, P = .043 and P = .076. GDP is positively significant while education is negatively significant, P < .05 in all occasions (model 1-9). Institution quality is only significant in model 2. It is negatively significant.

4.8 Test for robustness and normality

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5. DISCUSSION

This paper adds to the field of business and entrepreneurship by looking at how culture affects the rate of entrepreneurship in countries around the world. It looks at the differences between cultural values in advanced and emerging economies and the cultural factors that affect their entrepreneurship rate. This section looks at the results and findings from the quantitative tests conducted on each hypothesis, an explanation is provided on the findings in relation to academic theories.

The Push factor takes into account a persons current situation and their desired situation. An individual’s dissatisfaction with his current situation can be motivated or pushed to become entrepreneurial. In combination with the dissatisfaction

approach, this implies that countries with poor economic development, weaker

infrastructure, employee protection, low wages and unemployment, people are pushed into becoming entrepreneurs. This is termed as ‘necessity-driven entrepreneurship’, GEM defined necessity entrepreneurs as people who may be pushed into staring a business out of necessity because they have no other work options and need a source of income. On the other hand, opportunity driven entrepreneurs are people involved in TEA who (I) claim to be driven by opportunity as opposed to finding no other option for work; and (II) who indicate the main driver for being involved in this opportunity is being independent or increasing their income, rather than just maintaining their income (Gemconsortium.org, 2014). The result from the regression analysis carried out for each hypothesis will be discussed based on all the countries (advanced and emerging economies together) from the population together. This will be followed by an explanation on findings from analysis conducted on advanced and emerging economies separately. There will also be a discussion on cultural factors that affect necessity and improvement driven opportunity entrepreneurs in both advanced and emerging economies.

5.1 Results – Culture Variables

Uncertainty Avoidance

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quality are marginally significant on model 8 Table 3. The relationship between uncertainty avoidance and TEA rate in model 8 and 9 (Table 3) are significant and negative which implies that the higher the uncertainty avoidance, the lower the entrepreneurship rate. This is in line with the aggregate psychology trait approach which states that the higher the uncertainty avoidance, the less a society is inclined to be entrepreneurial (Thurik and Dejardin, 2011). In advanced economies, this hypothesis is supported even when GDP, institution quality, and education were controlled for. In Table 4, model 4 and 5 shows that education is positive and significant. A possible explanation is that the higher the level of education, the lower the level of self-employment. In emerging economies, uncertainty avoidance is negative and non-significant in Table 5, model 4 and 5, but GDP is negative and significant. This does not support the dissatisfaction theory that the higher uncertainty avoidance, the higher the entrepreneurship rate. The finding on uncertainty avoidance is inline with the findings by Wennekers et al. (2007) where uncertainty avoidance was found to have an influence on business ownership on OECD data in 1976 but the support vanished on more recent data. The findings by Shane (1992), McGrath, MacMillan, and Scheinberg (1992) shows that low uncertainty avoidance affects entrepreneurship rate, but Table 2 above shows that both advanced and emerging economies have a high uncertainty avoidance. This creates an interesting research gap that could be explored further on economies with low uncertainty avoidance.

Individualism/collectivism

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of development is high. Using a sample of 52 countries in GEM studies, they verified that although the individualist-collectivist spirit of a country is related to entrepreneurial activity, it cannot be stated that a greater level of individualism result in higher level of entrepreneurial activity, rather the relationship is moderated by the level of economic development.

Individualism has traditionally been positively linked with entrepreneurial activity, arguing that the search for personal objectives would be positively associated with the entrepreneurial spirit, and indeed, in some studies, empirical evidence has been found for this relationship. However, in other studies, the opposite relationship has been argued, with some empirical support, in other words, that there is a positive relationship between collectivism and entrepreneurial activity (Pinillos and Reyes, 2009). Also, Hui and Triandis (1986) argued that collectivism could be succinctly defined using the word “concern”, which refers to commitment and ties to others. In countries with a collectivist culture and low level of economic development, such ”concern” for others drives forward the search for a business opportunity that allows the group to improve while at the same time satisfying the need for affiliation, a characteristics trait business people in collectivist cultures, as verified by Braum et al (1993). Entrepreneurship rate was also found to high in emerging economies and low in advanced economies (see Table 2) and an explanation by (Pinillos and Reyes, 2009) was, in developed countries, entrepreneurial activity is associated with personal achievement, not forgetting the premise that the entrepreneurial spirit can also find a place among larger firms in developed countries without the need to create other new ones, and this is consistent with the fact that TEA is less in these countries. Further research is required to explicitly verify the effect of individualism and collectivism separately on entrepreneurship rate.

Power Distance

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Scheinberg (1992) argue that entrepreneurs tend to exhibit certain levels of those dimensions: high power-distance (PDI+), low uncertainty-avoidance (UAV–), high individualism (IND+), and high masculinity (MAS+). In particular, they consider high PDI+ as a personal characteristic of entrepreneurs “regardless of whether the culture is high or low on power-distance” (Mcgrath et al., p. 119). Initially, hypothesis 3 is supported (see Model 3) because there was a positive and significant relationship between power distance and entrepreneurship when a simple regression was ran without control variables on all countries. When control variables are introduced in Model 4, power distance was not significant but GDP was marginally significant. A possible explanation is, Wennekers et al (2005) stated that the level of development could interact with other determining factors of the level of entrepreneurial activity. They see the relationship between the rate of start-up firms and that of economic development as non-linear, but a U-shape, which indicates that the relationship changes at a certain level of economic development. In the sense that the level of economic development influences the effects of other determining factors (Pinillos and Reyes, 2009). Power distance does not have an effect on entrepreneurship rate in both advanced and emerging economies. Busenitz and Lau (1996) transfer the assumptions on entrepreneur’s characteristics to the national level. They suggest that cultures high on those values would favour the entrepreneurial activity of its members. Muller etal. (2002) Share this view, except for the PDI index. They argue that low power distance cultures would favour entrepreneurship. Power distance, defined as the extent to which individuals in a society accept that power in institutional settings and organisation is unequally distributed was found to have no effect on entrepreneurship rate in advanced and emerging economies.

Masculinity/Feminity

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In results from all four-hypothesis, there is a positive effect of education on entrepreneurship rate in advanced economies while a negative effect of GDP on entrepreneurship rate in emerging economies. An explanation to the positive effect of education on entrepreneurship rate (Uhlaner and Thurik, 2007) is that, higher level of education may attract would-be entrepreneurs to the non-material reward of entrepreneurship, such as greater autonomy (Van Gelderen and Jansen, 2006) or achievement (McClelland. 1975). This can be supported using the Pull factor where people are pulled into becoming entrepreneurial due to the rewards, achievements and social statue associated with entrepreneurship.

The negative relationship between GDP and entrepreneurship rate is in line with Wennekers et al (2007) who found higher self-employment in countries with lower GDP. Using the Push factor to explain this, it could be said that entrepreneurs in low GDP economies are pushed into becoming self-employed in order to attain a better income and standard of living. This could be a reason for the higher level of necessity entrepreneurs in emerging economies.

5.2 Necessity and opportunity driven entrepreneurs

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institutional quality and necessity-driven entrepreneurs (low GDP and poor institution quality) explain this. Opportunity-driven entrepreneurship is positively affected by high institutional quality. This explains why improvement-driven opportunity entrepreneurs are pulled into becoming self employed rather than pushed into it.

In advanced economies, necessity entrepreneurs are negatively affected by individualism/collectivism and positively affected by power distance and Mas.Fem even when control variables are introduced. Just as in all countries, institution quality and GDP have a negative effect on necessity-driven entrepreneurs, but here, education has a positive effect. Mas.Fem is the only culture variable that affects opportunity-driven entrepreneurs but power distance was found to have an effect when regressed without control variables. Institution quality and education positively affect opportunity-driven entrepreneurs.

In emerging economies, individualism/collectivism is the only culture variable that affects necessity entrepreneurs, the positive relation which implies that the higher the level of individualism/collectivism, the higher the rate of necessity entrepreneurs. One pattern that has been consistent with necessity entrepreneurs either in advanced or emerging economies is that GDP and institution quality have a negative effect on necessity entrepreneurs, this verifies the push factor theory. Education is also positively significant in both advanced and emerging economies on necessity-driven entrepreneurs. Individualism/collectivism is negatively related to opportunity-driven entrepreneurs, institution quality and GDP are positively related to opportunity-driven entrepreneurs in emerging economies while education is negatively related. One possible explanation to why education is negatively related is that, high education reduce self-employment as said by Uhlaner and Thurik (2007).

The findings on necessity and opportunity entrepreneurs have provided a starting point or foundation on which further research can be conducted. Further research can be conducted on the relationship and effect of culture, GDP, institution, and education on necessity-driven and improvement-driven opportunity entrepreneurs. 5.3 Limitations

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Hofstede between 1967 and 169 and again between 1971 and 1973 (Pinillos and Reyes, 2009). Sondergaard (1994), in his study of the reviews, cites and replies to the work of Hofstede, highlighting the fact that the differences foreseen by Hofstede’s dimensions have been widely confirmed. Kirkman et al (2006) concluded that studies published from the viewpoint of Hofstede (1980) study have upheld and increased their conclusions, moreover differences foreseen between countries are maintained, and thus “Hofstede’s values are clearly relevant for additional cross cultural research” (Kirkman et al. 2006, p. 308). In addition, Hofstede (2001, p. 34) claims “that national cultures are extremely stable over time”. He argued “… this stability can be explained from the reinforcement of culture patterns by institutions that themselves are products of the dominant cultural value systems”. In the long run, “cultures shift, but they shift in formation, so that the differences between them remain intact” (Hofstede 2001, p. 255).

Secondly, data on TEA, which is the measurement of entrepreneurship rate in a country, is missing for some years in some countries used in this research. As a result, the average or mean value was used between 2001-2013, but some countries only have data for two years period available. This is a limitation because the findings on the relationship between TEA and culture variables could differ if a full data set for every year in every country was available. Data was fully available for the independent variables GDP per capita, institution quality, and education. N = 64, advanced 32, and emerging 32. The total number of countries used for this research though large enough, could have been more because some countries in both economies were dropped because there were not Hofstede data available for them while other did not that any GEM TEA data available for them. Finally, only four hofstede’s culture variables were used, this is because the recently added two new variables LTO (long term orientation versus short term orientation) and (indulgence versus Restraint) has no data on them from most emerging economies. It will be interesting to know how they affect the entrepreneurship rate of a country and it would have added more to study on entrepreneurship and culture.

6. CONCLUSION

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entrepreneurship across advanced and emerging economies around the world. This research investigated the cultural factors that affect necessity and improvement driven opportunity entrepreneurs and their differences. Using a sample of 64 countries, and Hofstede’s 4 main culture variables as measure of culture, it can be concluded that culture affects the entrepreneurship rate of a country and different cultural factors affects entrepreneur in different countries. It can also be concluded that necessity and opportunity driven entrepreneurs are affected by different cultural factors in both advanced and emerging economies. The level of economic development, education, and institutional factors affect entrepreneurship rate as confirmed by the findings from this research. Academic papers on entrepreneurship and culture create an identity or characteristics to define entrepreneurs, some research support the argument while others didn’t, Thomas and Muller (2000) see the logic behind linking individualism and entrepreneurship as being related to the fact that most of the research conducted on entrepreneurship has been in the United States. Findings from this research have shown that individualism, power distance, and uncertainty avoidance are negatively related to entrepreneurship rate in all countries. In advanced economies, no cultural variable is significant. In emerging economies, individualism and power distance are negatively related to entrepreneurship rate.

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7. REFERENCE

Audretsch, D. (2002). Entrepreneurship. Boston: Kluwer Academic Publishers.

Audretsch, D., Grilo, I. and Thurik, A. (2007). Explaining entrepreneurship and the role of policy: a framework. The handbook of research on entrepreneurship policy, pp.1--17.

Audretsch, D. and Keilbach, M. (2004). Entrepreneurship capital and economic performance. Regional studies, 38(8), pp.949--959.

Barrolee.com, (2010). Full Dataset Download. [online] Available at: http://www.barrolee.com/data/dataexp.htm [Accessed 17 Nov. 2014].

Blanchflower, D. (2000). Self-employment in OECD countries. Labour Economics, 7(5), pp.471-505.

Blau, D. (1987). A Time-Series Analysis of Self-Employment in the United States. Journal of Political Economy, 95(3), p.445.

Braunerhjelm, P., Acs, Z., Audretsch, D. and Carlsson, B. (2009). The missing link: knowledge diffusion and entrepreneurship in endogenous growth. Small Bus Econ, 34(2), pp.105-125.

Browaeys, M. and Price, R. (2008). Understanding cross-cultural management. Harlow, England: Financial Times/Prentice Hall.

(46)

Busenitz, L., Gomez, C. and Spencer, J. (2000). Country institutional profiles: Unlocking entrepreneurial phenomena. Academy of Management journal, 43(5), pp.994--1003.

Busenitz LW, Lau CM (1996) A cross-cultural cognitive model of new venture creation. Entrepreneurship Theory and Practice 20(4): 25–39.

Calvo, G. and Wellisz, S. (1980). Technology, Entrepreneurs, and Firm Size. The Quarterly Journal of Economics, 95(4), p.663.

Carree, M. and Thurik, A. (2003). The impact of entrepreneurship on economic growth. Springer, pp.437--471.

Carree, M., van Stel, A., Thurik, R. and Wennekers, S. (2002). Small Business Economics, 19(3), pp.271-290.

coates, N. and sloan, d. (2008). Newcastle Business School Masters Dissertation. McGrew-Hill.

Creswell, J. (2003). Research design. Thousand Oaks, Calif.: Sage Publications. Data.worldbank.org, (2014). GDP per capita, PPP (constant 2011 international $) |

Data | Table. [online] Available at:

http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD [Accessed 20 Dec. 2014]. Davidsson, P. (1995). Culture, structure and regional levels of entrepreneurship. Entrepreneurship \& Regional Development, 7(1), pp.41--62.

Freytag, A. and Thurik, R. (2006). Entrepreneurship and its determinants in a cross-country setting. Journal of Evolutionary Economics, 17(2), pp.117-131.

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Geert-hofstede.com, (2014). Albania - Geert Hofstede. [online] Available at: http://geert-hofstede.com/albania.html [Accessed 23 Dec. 2014].

Gemconsortium.org, (2011). [online] Available at:

http://www.gemconsortium.org/docs/download/2409 [Accessed 17 Oct. 2014].

Gemconsortium.org, (2014). Academic Impact :: GEM Global Entrepreneurship Monitor ::. [online] Available at: http://www.gemconsortium.org/academic-impact [Accessed 20 Oct. 2014].

Hayton, J., George, G. and Zahra, S. (2002). ETJP National Culture and Entrepreneurship: A Review of Behavioral Research.

Hofstede, G. and Hofstede, G. (2005). Cultures and organizations. New York: McGraw-Hill.

Hofstede, G., Hofstede, G. and Minkov, M. (2010). Cultures and organizations. New York: McGraw-Hill.

Hofstede, G. (1984). Cultural dimensions in management and planning. Asia Pacific J Manage, [online] 1(2), pp.81-99. Available at: http://dx.doi.org/10.1007/bf01733682 [Accessed 19 Oct. 2014].

Imf.org, (2014). World Economic Outlook - Frequently Asked Questions. [online] Available at: https://www.imf.org/external/pubs/ft/weo/faq.htm#q4b [Accessed 21 Dec. 2014].

Info.worldbank.org, (2014). [online] Available at:

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