Entrepreneurial Profiles

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Entrepreneurial Profiles

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Entrepreneurial Profiles

A quantitative research to determine entrepreneurial segmentations within the E-Scan database

Master Thesis; Msc BA, Small Business and Entrepreneurship Gerrit Janssen s2232472

Supervisor: Prof. Dr. P. S. Zwart Second supervisor: Dr. A. Rauch Date: 05-11-2014

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Acknowledgements

This research is conducted as a master thesis for the Msc program Business

Administration at the University of Groningen with the specialization Small Business & Entrepreneurship.

The subject of this research is concerning the creation of entrepreneurial profiles from an existing database with (future) entrepreneurs.

I would like to thank my supervisor Prof. Dr. Peter Zwart for the time and effort he invested in supervising the research process. The extensive feedback sessions were of infinite utility to accomplish this research. I also would like to thank my second supervisor, Dr. Andreas Rauch, for the time and feedback he gave. Especially concerning his clear direction for this research.

I want to thank Dr. Martijn Driessen, from Entrepreneur Consultancy B.V., for the opportunity to perform such an interesting research and for the data he provided. I also want to thank my family and friends for the support shown during this research process. A lot of people gave me helpful advice, understood my absence or showed me a motivational trust.

Especially, I would like to thank my lovely wife, Wieke, for the support and patience she showed me during the research process. She showed love to me in such a loving way that I could concentrate and focus.

Finally, I would like to thank my new-born son, Izra. He is born during this research process and has been, since that day, my everyday “beam with joy”.

Gerrit Janssen

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Abstract

This research has created six profiles based on a literature review concluding with four factors; Age, Regional differences, Educational differences and Entrepreneurial experience. These profiles where statistically tested on ten characteristics and traits by mean comparison on data from the E-Scan. Hardly unexpected, the mean comparison gave interesting and significant differences. Especially interesting was that the overall scores on the ten characteristics and traits are lower for female entrepreneurs. This applies particularly for the risk taking propensity, the need for power, the creativity, the level of self-belief and the social orientation of an entrepreneur.

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1. Introduction

1.1 Introduction of research theme

When entering the working life, everyone needs to decide whether he will become employed or will become self-employed. More and more people see self-employment as a positive career step, despite the fact that more than 50% of the start-ups fail within five years (Driessen, 2005). This high level of failure is the reason why Driessen has developed the E-Scan in 1997. The E-Scan studies the personality of the entrepreneur. It does this for several reasons; the main reason is that if the entrepreneur knows his strengths and weaknesses he can better assess his business opportunities (Driessen, 2005). Furthermore, knowledge about the personality of the entrepreneur can reduce risks for potential stakeholders. The E-Scan is an objective tool for self-reflection for entrepreneurs and potential entrepreneurs to test their entrepreneurial capabilities to start a business of their own. The E-Scan provides insights into necessary traits (characteristics) and capabilities for entrepreneurship (Driessen, 2005).

Driessen (2005) has chosen to use these traits and capabilities after he had analysed which components are important for entrepreneurs. The most important components appeared to be motivation, knowledge and experience, characteristics and capabilities. The components motivation and knowledge are not (or superficial) assessed in the E-Scan. The component motivation can be detected in a personal interview and the component knowledge and experience can be adapted from a business proposal. The current research on the entrepreneur is mainly focusing on the average characteristics of a successful entrepreneur. And so far there has been little recent attention for differences between successful entrepreneurs. However, literature (e.g. Muller and Amit, 1995; Macmilland and Low, 1988) suggests that differences between entrepreneurs might be formed by demographic characteristics such as age, region or education.

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several goals, one of which is to create entrepreneurial profiles. The entrepreneurial profiles can give valuable insights into the differences between entrepreneurs and give an insight in how different entrepreneurs act and react. Moreover, this knowledge can then be used to improve the E-scan and help entrepreneurs to understand their behaviour in a wider context.

Entrepreneurial profiles can be created using the technique derived from marketing literature whereas multiple ‘Buyer personas’ (ideal buyer profiles) are created and named to make them visible and alive (Revella, 2001). These ‘Buyer Personas’ are created using market segmentation. This segmentation is based on specific matching characteristics such as socio-demographic information (Verhoef and Donkers, 2001). If the scores of the profiles are significantly different from the average scores, we might have found something generalizable for entrepreneurial profiles. The distinguishing of entrepreneurial profiles can give better insights in the differences of individual entrepreneurs.

For science, these profiles will lead to a better general understanding of the concept of the entrepreneur and a deeper understanding of why groups of entrepreneurs make certain decisions with a certain result.

If a certain standard profile normally excels in effectiveness and market awareness but lacks customer focus, entrepreneurs that fit that profile can be approached for focused training, coaching and support.

1.2 Research question

This research has the following research question:

How can the usage of entrepreneurial profiles contribute to the knowledge of entrepreneurship?

This research is completed based on the following sub-questions

• Which entrepreneurial profiles can be created based on entrepreneurship literature combined with marketing literature?

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1.3 Methodology

This research is an empirical research that will be conducted in a quantitative way. To give this quantitative research a solid base, the research starts with a literature review of the research on entrepreneurial characteristics and marketing literature.

The quantitative research is based on data from the E-Scan. The E-Scan is an objective tool for self-reflection for (future) entrepreneurs. The E-Scan is conducted with more than 100 online questions covering the 10 traits and capabilities of entrepreneurs. The E-Scan is based and funded in previous research (Driessen, 2005). The E-Scan can be found on www.ondernemerstest.nl.

The company Entrepreneur Consultancy B.V has provided this data. This data is originating from five different types of the E-Scan, shortly discussed in chapter three. A complete description of the research design can be found in chapter 3.

1.4 Structure

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2. Literature review

This chapter will present a literature review of the current literature available on the topic of entrepreneurship and consumer profiles. Firstly, it will discuss the theoretical background of the E-Scan. Secondly, it will discuss the main topics regarding entrepreneurial profiles, which is followed by the theory of consumer profiles from marketing literature. This chapter concludes with the development of E-Scan consumer profiles that is a start for the quantitative research.

2.1 The E-Scan

Ever since entrepreneurship is studied, personal characteristics of an entrepreneur are found to be key factors to be entrepreneurial. Schumpeter (1934) was one of the first well-known researchers about entrepreneurship. One of his important findings was the positive relationship between entrepreneurship and economic growth. In the same period Tuttle (1927) stresses the function of the entrepreneur as a motor of the economy. Since these days, researchers (e.g. Baumol, 1968, Audretsch & Thurik, 2004) agree about the importance of entrepreneurship to obtain economic growth. Ever since then entrepreneurship is known as socially important and interesting for scientific research. In 1999, Aldrich argued that the research of personality traits of the entrepreneur had come to a dead end because the correlations between the traits and entrepreneurial behaviour where to small to matter (Aldrich, 1999; Rauch & Frese, 2007). However, this statement is made invalid by the research of Rauch and Frese (2007) were they stress and prove the importance of certain traits for successful entrepreneurship. In example; the self-efficacy of an entrepreneur is proven to strongly correlate with business creation; also, need for achievement is strongly correlated with success (Rauch and Frese, 2007). Furthermore multiple other traits are important predictors of entrepreneurial behaviour: Risk-taking, innovativeness, proactive personality, self-efficacy, stress tolerance, need for autonomy and internal locus of control. Most of the traits discussed by Rauch and Frese (2007) are tested by the E-Scan and described and explained hereafter.

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This resulted in the E-Scan. Since this developing, the E-Scan has become an important method to objectively measure the entrepreneurs’ personal characteristics and capabilities. These personal measures are compared with an ideal profile, the so-called Norm-Profile, created by Driessen based on scientific data. Driessen (2005) has developed the E-Scan to give (potential) entrepreneurs insights in their personal characteristics and capabilities for entrepreneurship. There is a lot of literature available about personal characteristics of a firm owner to decide whether he is entrepreneurial or not. Carter and Jones-Evans (2006) come up with a list that is considered in almost every study on this topic. They state that a firm owner is entrepreneurial when he has a risk-taking propensity, a need for achievement, an internal locus of control and has a desire for autonomy. These four characteristics, completed by the need for power, social orientation and endurance, are measured with the E-Scan. Furthermore the capabilities necessary for the start-up period: creativity, flexibility and market awareness are measured (Driessen, 2005).

This research focuses on these ten variables.

Based on literature and advices from experts, Driessen has developed a Norm-score for successful entrepreneurship. Participants on the E-Scan can compare this score with their personal score to find out on which of the variables they need improvement. Risk-Taking

Risk taking is the entrepreneurs’ propensity to risk the financial well-being, career opportunities, family relations and physical well-being (Liles, 1974).

An entrepreneur takes risks because failure will have major financial consequences (Liles, 1974). An entrepreneur needs to take risks in order to outperform competitors and to be successful. In the late twentieth century, it became clear that risk-taking is not only based on rational calculations, but is influenced by the entrepreneurs’ attitude towards risk (March & Shapira, 1987).

Need for achievement

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there is a positive correlation between a need for achievement and a successful start up of a business (Hornaday & Bunker, 1970).

Internal locus of control

The internal locus of control suggests that an entrepreneur believes that he controls the events affecting him. Individuals with a higher internal focus of control believe that events result primarily from their own behaviour and actions (Rotter, 1975). Studies (e.g. Anderson, 1977) showed that people with a higher internal locus of control tend to be more achievement and result-oriented and get higher paid jobs. Entrepreneurs with a high internal locus of control are more willing to take risks and are actively seeking for new business opportunities. In the E-Scan, this factor is called self-belief and effectivity.

Desire for autonomy

Lumpkin & Dess (1996) refers to autonomy as the independent action of an individual or a team in bringing forth an idea or a vision and carrying it through to completion. Entrepreneurs with a high desire for autonomy do tend to control all events. They avoid the restrictions of established organizations and therefor become self-employed (Rauch & Frese, 2007). Rauch and Frese (2007) value a need for autonomy as an important predictor of entrepreneurial behaviour.

Need for Power

The need for power is defined by Atkinson (1958) as a persons’ talent that directs another persons’ behaviour towards the satisfaction of it’s own needs. People who have a high need for power are satisfied by seeing something moving in the direction they want, by their influence (Mccleland, 1967). They also have a desire to have impact on others and are likely to have a position in which they have control over others. If they have not a position in which they have the feeling that they are in control, they are likely to be frustrated (Conger and Kanungo, 1988).

Social Orientation

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entrepreneur. A successful entrepreneur knows the importance of getting in touch with the right people within his network to get their business to higher level. Wooldridge and Floyd (1999) argue that entrepreneurs need a good social network to become in a position in which they can push and flourish their ideas.

Endurance

Endurance is an entrepreneur’s likelihood that he will have the competence and commitment to continue the business from one period to another (Sørensen and Philips, 2011). The importance of endurance is inevitable in research. Independent entrepreneurs are found to have a higher level of endurance than dependent – franchising- entrepreneurs (Mescon and Montanaria, 1981). Eckhardt and Shane (2003) make clear how important endurance is to be a successful entrepreneur: “You cannot win a game if you do not stay to play”.

Creativity

Creativity is defined as the individual production of novel and useful ideas in any domain (Amabile et al, 1996).

An entrepreneur needs creativity to be able to deal with uncertain situations. An entrepreneur needs the type of creativity that gives him opportunities to be challenged by uncertain situation instead of getting rigid or freeze (Norton, 1975).

Flexibility

Flexibility is the capacity to adjust and adapt to changing circumstances (Hricovini and Hirsch, 1990). Simon et al. (2012) stresses the importance of an entrepreneurs’ need to adapt to changing conditions. When an entrepreneur identifies changes in the environment, it might be necessary to react with a new business strategy.

Market Awareness

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Norm Profile

To review the test results it is important to have a reference to compare the score with. The E-Scan makes use of a Norm-Score. The Norm-Score for the E-Scan is a norm developed together with 48 advisors from consultancy firms in different branches (Driessen, 2005). These advisors have the assessing of (starting) entrepreneurs as their occupation (i.e. in the role of a financial advisor or representing a bank). The advisors gave their independent opinions about what a good score on the different traits and capabilities would be and the averages of the scores from the advisors are currently representing the Norm-Score. Norm-Scores can be used for several goals like capacity tests for job applications or university enrolments. Within the E-scan the norm-score is used to compare a participants score with the norm score to see on which characteristics the participant has meeting the norm to be a successful entrepreneur and on which characteristics he will probably need training or complementary from an expert. The E-Scan had need for a standard to assess the traits and capabilities of an individual needed for entrepreneurial success. An entrepreneur meeting or exceeding the Norm-Score is expected to be a successful entrepreneur.

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Figure 2.1 shows an example of a person’s score (the orange web) and the Norm-Score (the blue line), in Dutch. The test-person (Gerrit Janssen) has on most factors a lower score than the Norm-Score, except for financial matters, dominance and social orientation.

2.2 Entrepreneurship literature and profiles

A lot of studies have been trying to typify the concept of the entrepreneur.

This paragraph reviews entrepreneurship literature regarding profile related factors. The current research focuses on socio-demographic factors of the entrepreneur from data of the e-scan. Therefor this literature review focuses on the socio-demographic factors within the literature of entrepreneurial typologies.

Van der Meulen (2013) has given a clear and useful overview of most well-known research on the topic of types of entrepreneurs.

Types of entrepreneur

Littunen (2000) assign the ability to take risks, being innovativeness, having knowledge and skills, and the ability to co-operate as typical characteristics of successful entrepreneurs. Following Van der Meulen (2013) this research will describe several typologies of entrepreneurs using existing theoretical frameworks. Smith

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Type of entrepreneur

Entrepreneurial variable Craftsman Opportunistic Breadth of education and training Education focused on current business

activity Education involved many different kinds of courses Breadth in type of jobs held Jobs were in the same type of business as

present company Jobs were not in the same type of business as present company Management reference group Previously associated mostly with fellow

workers Previously associated with managers and business owners Management sponsor or multiple role

models A fellow provided example for success The owner of a business provided example for success High social involvement Active in professional, trade or business

associations Active in community associations not related to profession or business Effective communication ability Does not communicate well in writing or

speeches Communicated well in writing and speeches Delegation of authority and

responsibility

Finds to do things right, one must do them oneself

Believes those at lower levels in the company should handle operations Universalistic criteria for employee

selection Attempts to hire people has known a long time Feels there are many available to work in company Multiple sources of capital used Two or fewer sources of capital used Three or more sources of capital used Multiple methods of establishing

customer relations Customers gained through prior relations or personal contact Customers gained neither through prior relations nor personal contact Table 2.1 Overview of Smith

Donckels and Fröhlich

Donckels and Fröhlich (1991) come up with another interesting typology of the entrepreneur. They have studied more than 1.100 businesses, and made a comparison between family and non-family businesses. They have found two important dimensions, resulting in four types of entrepreneur. An entrepreneur can be strong or weak in Administrative-Executive tasks and strong or weak in Dynamic-Creative tasks. These dimensions result in four types of entrepreneur; Allrounder, Pioneer, Organizer and Routineer. The allrounder is strong in administrative-executive tasks and strong in dynamic-creativ tasks. The pioneer is weak on administrative-executive but strong in dynamic-creative. The organizer is strong on administrative-executive but weak on dynamic-creative. The routiner, is weak on both dimensions. This makes him risk averse and cautious.

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Figure 2.2 Overview of Donkels and Fröhlichs’ typology

Miner

Miner (1997) has done some important research on this topic. Miner has found that every entrepreneur is different and an individual that creates his own career path. Miner states that an entrepreneur is not just one kind of person, but entrepreneurs can differ to succeed as an entrepreneur. Miner has found four types, all with their own characteristics. He found the personal achiever, the real manager, the expert idea generator and the emphatic salesperson. A personal achiever has a high need for achievement and likes challenges. The real manager is strongly committed to managerial tasks and tries everything to receive a promotion. The expert idea generator is the most creative and imaginative entrepreneur. Innovation and improving products is their second nature.

Miner does not use socio-demographic characteristics for his typology. Neither does he discuss any socio-demographic characteristic within the types.

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Muller and Gappisch

The research of Muller and Gappisch (2005) is an example of a more recent research about types of entrepreneurs. They are using the Myers-Briggs Type Indicator (Gartner and Martinko, 1996) and found four entrepreneurial personalities: The extraverted, the intuitive, the tough minded and the maladapted type.

Extraverted types value active relationships, the value relationships with customers, employees and subordinates. The intuitive entrepreneur is innovative and creative, actively seeking for new techniques and products. Tough-minded people communicate impersonal, are very analytic and objective. The maladapted entrepreneur does not like rules and authority. Freedom in what he is doing is an important value for him.

Figure 2.4 Overview of Muller and Gappisch

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Driessen and Zwart

Building on the E-Scan, Driessen and Zwart has determined four types of entrepreneurs based on Hermann (1996). These types are the expert, the pioneer, the manager and the salesman. Figure 2.5 illustrated the different types and shows the characteristics of these types. The types are distinguished by on the horizontal axes independence and sense versus dependence and emotion and on the vertical axes methodical versus instinctive.

figure 2.5 Overview of Driessen and Zwart’s types of entrepreneurs.

The research of Driessen and Zwart (2006) develops four different types of entrepreneurs based on some interesting characteristics of an entrepreneur, but not on socio-demographic characteristics like age, education or region.

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Korunka

The work of Korunka et al (2010) reviews existing longitudinal studies on the influence of personal characteristics, resources and environment on business survival. The personal characteristics of an entrepreneur they found are the internal locus of control, the need for achievement, the risk taking propensity, gender and age. They found a significant positive relation between female entrepreneurs and more severe work or family conflicts. Women also more often use unfavourable financial resources to finance their start up. There is also a significant interaction between age and financial resources. Older business founders often have more options to compensate unfavourable financial resources. In addition, younger business founders often have more difficulties to obtain external financing (Korunka et al, 2010). Another interesting conclusion in the research of Korunka et al (2003) is that they found that one-third of the people starting up a business was unemployed when they started the start up process. Other research of Korunka (2007) also finds some characteristics in most of the studied studies. They find the educational level, age of the entrepreneur and the gender of the entrepreneur as determining factors for business success.

Brockhause and Nord

The research of Brockhause and Nord (1979) gives an interesting insight in influencing factors for the entrepreneurs’ decision to become an entrepreneur. They look at personal characteristics as locus of control and risk taking propensity and at environmental conditions as age, sex, experience, years of education and years of residence in the current area (St. Louis). They found (weak) evidence that the entrepreneurs in the sample are lower educated and worked for fewer employers than the managers. There was no significant influence for the factors age and sex.

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Parker

Parker (2009) gives an interesting overview of characteristics influencing the choice to become an entrepreneur or not. He comes up with a list of influencing factors that are positively or negatively influencing the choice to become an entrepreneur. One of these factors is the factor human capital that contains age, regional differences, former experience and formal education.

Factors influencing entrepreneurship

Paragraph 2.2 has given an overview of entrepreneurship literature regarding typologies and socio-demographics. Table 2.2 gives an overview of the discussed socio-demographic factors. Surprisingly there is a consensus in literature about the most dominant socio-demographic factors. These are gender, age and education. Less dominant in the literature are the factors experience and region. All these five factors will be discussed in more detail in the remainder of this paragraph.

Overview researched demographic characteristics of an entrepreneur

Education Experience Age Gender Region

Smith X X

Korunka (2010 X X

Korunka (2007) X X X

Korunka (2003) X X

Brockhause and Nord X X X X X

Parker X X X X

Table 2.2 Overview of researched factors in entrepreneurship research

Education

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education (or more years of education) leads to better firm performance of the self-employed.

In the entrepreneurship literature, a lot of attention is being paid to the high-school dropouts, becoming extremely successful as an entrepreneur (e.g. Brockhaus, 1980). This is consistent with the findings of Casson (2003); the skills that make an entrepreneur successful are not the skills learned by higher education. Furthermore, education increases the likelihood to get a well-paid employment occupation that makes entrepreneurship less attractive for higher educated people (Le, 1999). Van Praag and van Stel (2013) show that entrepreneurs have a higher return on the investment in education compared with employees.

The factor ‘level of education’ is divided in two sections 1. Highly educated

2. Lower educated

Highly educated consists of a finished degree on a university of applied sciences (HBO) or a bachelor or masters degree on a academic university.

Lower educated embraces all lower degrees like senior vocational college (MBO) or high school.

Gender

The gender of an entrepreneur can have influence on multiple factors in the live of the entrepreneur. The previous literature review already discussed the work of Karunka (2010) and Korunka et al (2003). They found a significant positive relation between female entrepreneurs and more severe work or family conflicts. Women also more often use unfavourable financial resources to finance their start up. Furthermore, women have more chance that they realize the expectations they had in advance. Next to the previous discussed literature there is more literature available.

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are comparable, the entrepreneurial success of the black female entrepreneur is lower than the black male entrepreneur.

The current research distinguishes the following, rather obvious, groups for the factor ‘Gender’.

1. Male 2. Female Age

Regarding the factor age, Lévesque and Schade (2005), points out that older people have a lower risk-taking propensity and are less capable of working the long hours undertaken by entrepreneurs. Miller (1986) comes up with a “job-shopping” theory in which he predicts that people try riskier occupation (as entrepreneurship is) when they are younger, since riskier occupation relieves the worthiest information about the entrepreneurs’ job-matching opportunities. In other words; by trying riskier things you will sooner know what kind of occupation fits you the best. These are some arguments why elder people are less likely to choose for entrepreneurship.

On the other hand, discuss Weber and Schaper (2004) the concept of the grey entrepreneur. This entrepreneur is a person aged older than 55, having or starting a business. According to that research, older to-be entrepreneurs are getting more and more active as an entrepreneur when they reach the age of retirement. From older employees is known that these older people are less flexible but are valuable when it comes to knowledge and experience (Raad voor Werk en Inkomen, 2011). This might also be the case for older entrepreneurs. It is also known that younger people lack credibility and access to capital (Parker, 2009).

Furthermore, the human and physical capital requirements of entrepreneurship are often unavailable to younger workers (Parker, 2009). Parker also hints towards an specific reason why elder people get into self-employment; “There might also exist a particular type of human capital which is productive both in managing and in working for others, and which can be acquired most effectively by working initially as an employee.”

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entrepreneur, as they get older, up to their early fifties, after which the chances of becoming an entrepreneur diminish.

The current research distinguishes the following groups in the factor “age of the entrepreneur”.

1. Young- Younger than 25 2. Mature- Between 25 and 55 3. Old- Older than 55

Regions

In economic lagging regions, entrepreneurship is said and proven to be enhancing for economic growth (Stephens and Partridge, 2011). This is quite logic since it is generally known that entrepreneurship stimulates employment-growth and thus economic growth (Schumpeter, 1934; Acs and Audretsch, 2003; Carree and Thurik, 2003).

Reynolds et al (1994) studied multiple countries (France, Germany, UK, Italy and Ireland) on firm birth rate. They found that more fertile or fruitful regions have two to four times higher firm birth rate. They also state that high firm birth is a requirement for economic growth.

Reviewing the literature about the regional impact differences regarding entrepreneurial activities, the question arises if entrepreneurs in economic less successful regions need other characteristics and/or capabilities to be successful than their counterparts in successful regions.

Regarding “regional differences”, the current research distinguishes two groups. 1. Successful regions

2. Normal or unsuccessful regions

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Experience

Another interesting characteristic of an (to be) entrepreneur is the entrepreneurial experience the person has. Experience and learning are closely related. Learning about the daily practice of an enterprise or the learning about opportunities. Experience also embodies skills to exploit opportunities (Parker, 2009); Selling techniques, leading, planning, decision-making problem solving, organising and communication (Shane, 2003). Learning can also reduce uncertainty about the changes of success and the value of an opportunity (Jovanovic, 1982). Furthermore, previous self-employment experience has a positive impact on the probability entering self-employment (Evans and Leighton, 1989). This is in line with the results of different studies where they found that individuals with greater past entrepreneurial experience are more likely to start their own venture (Kauffmann and Dant, 1999; Williams, 1999). Baron and Ensley (2006) found out that experienced entrepreneurs are better in opportunity recognition and in connection the dots of a patter they recognize.

Finally, Zhang (2011) found that experienced entrepreneurs have more ease in gathering sources for venture capital. This was explained by the fact that experiences entrepreneurs have more skills and social connections than their counterparts.

Regarding the factor ‘experience’, the current research distinguishes two options; 1. Experienced entrepreneur

2. Non-experienced entrepreneur

An experienced entrepreneur is an entrepreneur with an established firm. A non-experienced entrepreneur is an individual with a lack of entrepreneurial experience. His current occupation can be student, employer or unemployed.

2.3 Marketing theory

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there is also attention for the specific factors that are combined to create the customer profile.

In the theory of traditional market segmentation or database marketing (DBM), the research of Verhoef and Donkers (2001), mainly based on Kamakura et al (1991), plays an important role. Based on the segmentation theory, they are able to predict the potential value of a consumer for the insurance industry. By using customer information stored in databases, companies can invest in customers that are (potentially) valuable for their company, and give less attention to customers that lack this (potential) value (Verhoef and Donkers, 2001). What the research of Verhoef and Donkers (2001) describes is that both, socio-demographic factors (e.g. age, educational level) combined with potential and current consumer value (e.g. average spending’s) gives prediction for future consumer value.

Another research of Verhoef et al. (2002) explains more about the techniques used for market segmentation. Segmentation in DBM is used to group customers into clusters that are internally homogenous and mutually heterogeneous. Segmentation is a technique in which customers are grouped based on multiple matching variables (Verhoef et al., 2002).

Another technique that research designates is cluster analysis. Cluster analysis is a statistical tool to automatically generate fitting clusters. It groups the objects within a dataset based on information within the dataset. Cluster analysis divides data into groups that are meaningful and/or useful. The goal is that the objects within a group need to be similar to one another and different from the other objects. The greater the similarity (homogeneity) within a group and the greater the difference between the groups (clusters) the better the clustering (Tan, et al., 2006).

That same research found that the most popular externally supplied information on customers is socio-demographics. Over 64% of the conducted firms purchase information on name, address, age or income statistics.

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profitability. If you have identified your most profitable segment, you can approach these customers for additional products and services (Coffey and Palm, 2003).

Also other authors use market segmentation as a basis to predict consumers’ lifetime value (Hoekstra and Huizingh, 1999; Spring et al, 2000). All these researches expound on the research of Blattberg (1987).

This research uses both techniques, segmentation- and cluster analysis, from marketing literature to encounter insights in the characteristics of the entrepreneur. 2.4 Entrepreneurial profiles

This paragraph consolidates the information derived from the entrepreneurial literature and the marketing literature. This leads to the E-Scan consumer profiles in the end of this paragraph.

From paragraph 2.3 –marketing literature- we have learned how the marketing section of the scientific world segment their customers. For this purpose they have develop customer profiles, often based on socio-demographics. From this literature review we use this specific technique to develop our own entrepreneurial profiles. The current research will segment entrepreneurs based on socio-demographics, using segmentation and cluster analysis.

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The abovementioned factors level of education, regional differences and gender are used to develop the entrepreneurial profiles. Looking at the three different factors, each divided into two sub-categories; a maximum of eight profiles can be created (see table 2.3). Factor/ Profile Educational level Gender Region Profile 1 Highly educated Man Successful region Profile 2 Highly educated Man Unsuccessful region Profile 3 Highly educated Woman Successful region Profile 4 Highly educated Woman Unsuccessful region Profile 5 Lower educated Man Successful region Profile 6 Lower educated Man Unsuccessful region Profile 7 Lower educated Woman Successful region Profile 8 Lower educated Woman Unsuccessful region

Table 2.3 overview of created profiles

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Highly educated Woman

Successful region

4. “Potentially powerful woman” Highly educated Woman Unsuccessful region 5. “Used-BMW driver” Lower educated Man Successful region

6. “The man who knows how it works” Lower educated

Man

Unsuccessful region

7. “Skewed worn Uggs-women” Lower educated

Woman

Successful region

8. “Potentially surprising woman” Lower educated

Woman

Unsuccessful region

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3. Study design

In this section the methodology of the research will be discusses in three paragraphs: Research method, data collection and data analysis. The goal of this research design is to answer the research question:

“How can the usage of entrepreneurial profiles contribute to the knowledge of entrepreneurship?”

3.1 Research method

The first part of this research was a literature study on entrepreneurship literature and marketing literature. That literature review gave insights in the factors and the methodology relevant to develop entrepreneurial profiles.

Reliability and Validity

According to Yin (2003) reliability and validity are important quality criteria for research.

Reliability is the degree to which an assessment tool produces stable and consistent results (Moskal and Leydens, 2000). A research is reliable when the results are the same if the research is repeated (Braster, 2000). To guarantee the reliability of the current research, the sequential phases of the current research are discussed rather elaborated. Within the dataset only real participants are existence. Participants that did not answer all the questions are erased from the dataset. Unfortunately, the data used in this research is not freely available, to assess the reliability of this research.

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relationships between concepts. To realize a high external validity, the current study uses a rather comprehensive dataset. This dataset will result in a higher

generalizability and thus a higher external validity than a smaller dataset would give. Literature review

This literature is carefully selected and needed to meet multiple criteria. Firstly, it needed to be scientific literature and secondly, it needed to be relevant for today’s entrepreneurs. The literature is mainly found using Google Scholar and downloaded using Business Source Premier. Google Scholar provided a simple way to find scientific data. From one place, it is easy to search in multiple sources of scientific data. The search-method for literature is less complex in Google Scholar because of Googles excellent search algorithm. The most articles were downloaded using Business Source Premier because of the collaboration with the University of Groningen. Business Source Premier is an important database for scientific data. Also, a lot of data is found by searching in references of interesting articles for other relevant articles. Finally, the developer of the E-Scan, Martijn Driessen, has applied a big part of the relevant literature; His Phd-dissertation has been an important director for this research.

Quantitative analysis

The upcoming part of this research will be a quantitative research. The quantitative data is derived from the E-Scan. The E-Scan tests ten characteristics and traits of an (potential) entrepreneur by more than 100 questions. These questions are predominantly statements to which the entrepreneur can give his opinion on a 7 point Likert-Scale. On this Liker-Scale the lowest score means disagree on the statement and the highest score is agree. After finishing the E-Scan, participants receive an individual report with their scores, compared with the Norm-Score. Data from over 16.000 participants is used for this quantitative analysis.

The exact method of data collection is shown in the next paragraph. 3.2 Data collection

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Data from all the five different forms of E-Scan are combined into one dataset. This consolidation of the difference datasets is justified by the need for a big dataset in order to attain statistically interesting profiles. The five different datasets are coming from five different types of the E-Scan. These are the English (international) version of the E-Scan, the Entrepreneur-Scan (voor ondernemers), the E-Scan for starters, the E-Scan for students and the E-Scan for self-employed without employees. All these are combined into one dataset. Also the English (international version) provided a sufficient quantity of usable data because apparently Dutch inhabitants did the English version of the test and filled in their postal code.

The different types of E-Scans have the same core, an assessment of 10 entrepreneurial characteristics. These ten characteristics are used for the statistical analysis. Since that data is basically the same for the different E-Scans, the combined dataset is completely used as an autonomous dataset.

This database is filled with results from participants of the E-Scan. The participants gave answers on the more than 100 questions regarding their traits and capabilities. Next to that, they provided socio-demographic information about themselves such as name, gender, postal code, level of education, current occupation and reason of participating to the E-Scan.

Based on three different characteristics of the entrepreneur; level of education, region and gender participants are segmented. This segmentation results in eight segments. These will be combined to create consumer profiles.

Education

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Region

The region data is withdrawn from a participant by asking his postal (Dutch ZIP) code. The notation for this postal code is four numbers combined with two alphabetical characters (NNNN CC, whereas N is a number and C is an alphabetical character). The successful versus the normal or unsuccessful regions in the Netherlands determine the regional division. Using the CBS data for defining the successful regions and the postal codes subtracted from metatapos.org. CBS defines “De Randstad” as the economic motor of the Netherlands. “De Randstad” is defined as the municipality Almere, the Provence’s North-Holland (excluding Alkmaar and surroundings and the north of North-Holland), South-Holland and Utrecht (excluding south-east Utrecht) (CBS.nl).

Figure 2.1 shows an overview of the postal codes within the Netherlands.

Figure 3.1 overview of postal codes in the Netherlands

Table 3.1 provides a list with the postal codes from the successful regions.

Successful regions

Region Postal codes

North-Holland 10NN CC – 15NN CC, 19NN CC- 21NN CC

South-Holland 22NN CC – 33NN CC

Utrecht 34NN CC – 37NN CC

Almere 13NN CC

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Gender

Respondents have specified whether they are a male or female using a selection window. This is found in the dataset as “female” or “male”.

3.3 Data analysis

This research is a quantitative research making use of existing provided data.

The data was, before it was provided, tested on consistency. Over 100 questions are testing the 10 variables discussed in chapter two. The reliability of these constructs is proven sufficient and tested using the Cronbach Alpha. The explanation of this consistency analysis is available in the research of Driessen (2006).

The database containing the data of the 16.000 respondents is filtered into eight profiles. Every respondent fitting a certain profile received an extra code (1-8) in the database to distinguish the different profiles from each other.

Then the means on the traits and capabilities were computed using the T-Test (using ANOVA) in SPSS. The output of these tested traits and capabilities was tested on significance and compared with the output of other profiles. This resulted in an overview of all mean differences for the characteristics compared from profile to profile. This test shows the differences in means for all the profiles and for the total dataset. These differences in means explain whether the profiles differ significant from each other or not.

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4. Results

This chapter presents and discusses the results of the quantitative analysis. Paragraph 4.1 present the descriptive statistics, paragraph 4.2 discusses the results of the analysis from the total database and paragraph 4.3 until 4.10 discusses the results regarding the analyses of the eight profiles. This chapter concludes with paragraph 4.11 that shortly discusses the cluster analysis. The conclusions and implications, overall and per profile, are discussed in chapter 5.

4.1 Descriptive statistics

The segmented dataset consist of 2.256 individual respondents. It is said that the original dataset consisted of 16.000 respondents. These 2.256 respondents are the ones that gave an (useful) answer to the questions used for the segmentation (gender, educational level and region).

The dataset was filled with five consolidated datasets. 8 came from the English (international) version of the E-Scan, 166 from the Entrepreneur-Scan (voor

ondernemers), 1.550 where provided by the E-Scan for starters, 84 by the E-Scan for students and 298 by the E-Scan for self-employed without employees. It is clear that starters provided the biggest part of the data (around 69%). The consolidation of the different datasets has no significant impact on the output of this study.

The new dataset contains 919 female entrepreneurs and 1.337 male.

The respondents where asked to give their birthdate, to calculate their age. Only 592 people gave information about their age. Years of birth vary between 1949 and 1998. Regarding the postal code, needed to determine whether the respondents live in a successful or unsuccessful region, 909 people originate from a successful region and 1.347 people come from an unsuccessful region.

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Overview of current occupation Current occupation Number of participants

Student 669

Starter 502

Employee 451

Living from benefits 341

Entrepreneur 293

Table 4.1 Overview of current occupation

As shown in table 4.1, the current occupation of the participants is also given in the dataset. 669 people were student, 502 people were a starter, 451 were working as an employee and 341 people were living from benefits. Only 293 people were currently working as an entrepreneur.

In table 4.2 an overview is given of the output after filtering the eight profiles from the database. 289 participants where meeting the requirements of profile 1, 385 people for profile 2, and so on. Table 4.2 clearly shows that the division of the profiles is rather good. The smallest profile contains 160 participants whereas the biggest contains 404, which is less than three times the smallest profile.

Number of participants of eight profiles Profile # Number of participants

1 289 2 385 3 201 4 242 5 259 6 404 7 160 8 316

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4.2 Average scores on E-Scan

The average scores on the E-Scan based on the current dataset are presented in this paragraph. Table 4.3 gives an overview of the average scores of the total dataset.

Average scores on E-Scan

Mean Min/Max score Std. Deviation Number of Participants

Need for Power 61,171 13,4/100 14,3004 2.256

Need for Achievement 79,82 38,4/100 11,0412 2.256

Need for Autonomy 66,964 16,7/100 13,0017 2.256

Social Orientation 75,17 2/100 14,1720 2.256 Self-Belief 68,434 16,6/100 12,5468 2.256 Endurance 75,63 12,5/100 13,1687 2.256 Risk Taking 50,64 0/100 16,953 2.256 Market Awareness 72,421 18,8/100 12,9654 2.256 Creativity 78,543 19,4/100 14,1197 2.256 Flexibility 73,206 11,9/100 10,6346 2.256

Table 4.3 average scores on E-Scan

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Means per segmentation factor

Gender Education Region

Male Female High Lower Successful Unsuccessful

Need for Power 62,593 59,103 61,453 60,895 62,119 60,532 Need for Achievement 80,823 78,350 79,852 79,780 80,780 79,165 Need for Autonomy 66,858 67,118 67,017 66,911 67,324 66,720 Social Orientation 75,782 74,281 75,345 75,00 75,691 74,820 Self-Belief 68,58 67,325 68,948 67,930 69,362 67,807 Endurance 76,139 74,888 75,855 75,408 76,672 74,926 Risk Taking 51,433 49,490 60,681 50,603 50,118 50,995 Market Awareness 72,821 71,838 72,143 72,693 73,148 71,930 Creativity 79,624 76,972 78,237 78,844 79,715 77,753 Flexibility 73,101 73,360 73,368 73,048 73,723 72,858

Table 4.4 Overview of means per segmentation factor

Appendix 1 gives an overview of the mean comparison differences. In that table, the mean comparisons with an asterix (*) are significant differences on a 0,05% level between all profiles and characteristics (using T-tests).

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4.3 Profile 1 (N=289)

This paragraph discusses the remarkable results regarding profile 1. Next, an overview is presented of the characteristics of the 289 respondents in profile 1.

1. “The ingredients for success” Highly educated Man Successful region Comparisons of means Factor --- Profile Need for

autonomy Need for achievement Need for power

Social

orientation Self belief Endurance Risk taking Market awareness Creativity Flexibility

Pr of ile 1 2 1,0448 2,3107* 1,5112 0,481 1,4746 1,9466 -0,523 1,2835 2,6580* 0,7939 3 -0,3765 2,8303* 2,8159* -2,56 1,4745 1,3498 0,765 0,6599 3,0662* -0,3713 4 0,1310 4,0580* 4,5241* 1,942 3,6201* 2,4768* 2,085 2,0129 5,3648* 0,8413 5 1,1510 0,6351 0,6162 -1,118 2,0197 -2,3368* 6,598* -2,3145* 3,5951* 0,5073 6 1,8350 2,2891* 1,9603 0,453 3,5164* 0,6994 4,625* -0,1907 4,5344* 1,6205* 7 2,0841 4,0691* 4,6729* 2,988* 4,9586* 0,8728 10,174* 0,2873 5,9001* 0,7467 8 1,4635 4,5300* 5,8629* 1,701 5,1266* 1,3666 5,759* 0,3553 6,9025* 0,8338 Table 4.5 mean comparison of profile 1

Appendix 1 has given an overwhelming general overview of the mean comparison between all the profiles. Table 4.5 gives an overview of the mean comparison only for profile 1. The numbers marked with an asterix(*) are significant (at a 0,05% level) mean differences. If we focus on the significant mean differences, we see that creativity (complete), need for achievement (six out of seven) and self belief (4/7) are higher than for the other profiles. There are only a couple significant negative mean comparisons, market awareness and endurance. This means that people in profile 1 scores particularly higher on the measured characteristics than the people in the other profiles. Especially profile 4 scores significant lower on 5 factors, need for achievement, the need for power, self-belief, endurance and creativity.

4.4 Profile 2 (N=385)

“Underdog” Highly educated Man

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Comparisons of means between profiles Factor --- Profile Need for

autonomy Need for achievement Need for power

Social

orientation Self belief Endurance Risk taking Market awareness Creativity Flexibility

Pr of ile 2 1 -1,0448 -2,3107* -1,5112 -0,481 -1,4746 -1,9466 0,523 -1,2835 -2,6580* -0,7939 3 -1,4213 0,5196 1,3047 -7,738 -0,0001 -0,5968 1,288 -0,6236 0,4081 -1,1652 4 -0,9138 1,7473 3,0129* 1,461 2,1455* 0,5302 2,608 0,7294 2,7068* 0,0473 5 0,1062 -1,6756 -0,8950 -1,599 0,5451 -4,2834* 7,121* -3,5979* 0,9370 -0,2867 6 0,7902 -0,0216 0,4491 -0,028 2,0418* -1,2472 5,148* -1,4742 1,8764 0,8266 7 1,0393 1,7584 3,1617* 2,507 3,4840* -1,0738 10,697* -0,9962 3,2420* -0,472 8 0,4187 2,2193* 4,3517* 1,220 3,6520* -0,5800 6,282* -0,9281 4,2445* 0,0398 Table 4.6 mean comparison of profile 2

Table 4.6 presents the mean differences of profile 2.

The significant differences are mainly on creativity, risk taking, self-belief and need for power. The factor creativity is lower for profile 2 compared with profile 1. Profile 2 has a higher score on creativity compared with profile 4, profile 7 and profile 8. Regarding the factor self-belief, profile 2 scores higher compared with profile 4, profile 6, 7 and 8. Profile 2 scores the highest on risk taking compared with all the other profiles. The differences are quite large for profile 5, 6, 7 and 8.

If we focuse on the significant differences compared to the other profiles we see that profile 2 scores lower on need for achievement and creativity compared to profile 1. The difference between profile 1 and profile 2 is the region the entrepreneur

originates from. It is interesting that a region difference determines a significant difference for creativity and need for achievement. Profile 4 versus profile 2 shows a higher score for profile 2 on need for power, self-belief, and creativity. This is

interesting because the only difference between profile 2 and profile 4 is the gender of the entrepreneur. Both profiles are highly educated and from a successful region. Compared to profile 5, profile 2 scores higher on risk taking but lower on market awareness and endurance. Profile 5 also contains men but differs in educational level (lower) and region (successful). Risk taking is, in this case lower for lower educated men combined with a successful region but market awareness and endurance is higher for these people.

4.5 Profile 3 (N=201)

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“Powerful woman” Highly educated Woman

Successful region

Table 4.7 shows the mean differences compared to the other profiles. Comparisons of means between profiles

Factor --- Profile Need for autonomy Need for achievement Need for power Social orientation Self belief Endurance Risk taking Market awareness Creativity Flexibility Pr of ile 3 1 0,3765 -2,8303* -2,8159 0,256 -1,4745 -1,3498 -0,765 -0,6599 -3,0662* 0,3713 2 1,4213 -0,5196 -1,3047 0,738 0,0001 0,5968 -1,288 0,6236 -0,4081 1,1652 4 0,5075 1,2277 1,7082 2,198 2,1457 1,1270 1,320 1,3530 2,2986 1,2125 5 1,5275 -2,1952* -2,1997 -0,862 0,5452 -3,6866* 5,834* -2,9744* 0,5289 0,8785 6 2,2114* -0,5412 -0,8556 0,709 2,0419 -0,6505 3,861* -0,8506 1,4683 1,9918* 7 2,4606 1,2388 1,8570 3,244* 3,4841* -0,4770 9,409* -0,3726 2,8339 1,1179 8 1,8400 1,6997 3,0470* 1,957 3,6521* 0,168 4,995* -0,3046 3,8364* 1,2050 Table 4.7 mean comparison of profile 3

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4.6 Profile 4 (N=242)

“Potentially powerful woman” Highly educated

Woman

Unsuccessful region

Comparisons of means between profiles Factor/ Profile Need for autonomy Need for achievemen t Need for power Social orientation

Self belief Endurance Risk taking Market awareness Creativity Flexibility Pr of ile 4 1 -0,1310 -4,0580* -4,5241* -1,942 -3,6201* -2,4768* -2,085 -2,0129 -5,3648* -0,8413 2 0,9138 -1,7473 3,0129* -1,461 -2,1455* -0,5302 -2,608 -0,7294 -2,7068* -0,0473 3 -0,5075 -1,2277 -1,7082 -2,198 -2,1457 -1,1270 -1,320 -1,3530 -2,2986 -1,2125 5 1,0200 -3,4229* -3,9078* -3,060* -1,6005 -4,8136* 4,513* -4,3273* -1,7697 -0,3340 6 1,7040 -1,7689* 2,5638* -1,489 -0,1038 -1,774 2,540 -2,2036* -0,8304 0,7793 7 1,9531 0,0111 0,1488 1,046 1,3384 -1,6040 8,089* -1,7256 0,5353 -0,946 8 1,3325 0,4720 1,3388 -0,241 1,5064 -1,1101 3,674* -1,675 1,5377 -0,0075 Table 4.8 mean comparison of profile 4

Table 4.8 presents the mean differences of profile 4 compared to the other profiles. It is striking that profile 4 has the lowest overall means for the factors endurance and market awareness. The significant differences are markes with an asterix (*). For most of the factors there are significiant differences. Seven out of ten characteristics have two or more significant differences. These are the factors need for achievement, need for power, self-belief, endurance, risk taking, market awareness and creativity. The need for achievement for people in profile 4 is lower than the need for achievement for most other profiles. Profile 1, 5 and 6 have a much higher need for achievement. All these profiles are filled with men whereas profile 4 contains women. The factor need for power is lower than profile 4 in profile 1. Interesting that highly educated woman from a unsuccessful region have a higher need for power than highly educated men from a successful region. The factor endurance in profile 4 is way lower than that factor in profile 1 and profile 5. Profile 1 and 5 are both filled with men. This suggests that men have a higher endurance capacity than women.

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female doing so. The factor gender is not likely to be a determining factor here, because profile 7 and 8, which are female profiles, do score reasonable higher than profile 4 does. For the factor creativity, profile 1 and 2 have significant higher scores. This suggest that gender is an determining factor here.

4.7 Profile 5 (N=259)

“Used-BMW driver” Lower educated Man

Successful region Comparisons of means between profiles

Factor/ Profile

Need for

autonomy Need for achievemen t

Need for

power Social orientatio n

Self

belief Endurance Risk taking Market awareness Creativity Flexibility

Pr of ile 5 1 -1,1510 -0,6351 -0,6162 1,118 -2,0197 2,3368* -6,598* 2,3145* -3,5951* -0,7073 2 -0,1062 1,6756 0,8950 1,599 -0,5451 4,2834* -7,121* 3,5979* -0,9370 0,2867 3 -1,5275 2,1952* 2,1997 0,862 -0,5452 3,6866* -5,834* 2,9744* -0,5289 -0,8785 4 -1,0200 3,4229* 3,9078* 3,060* 1,6005 4,8136* -4,513* 4,3273* 1,7697 0,3340 6 0,6840 1,6540 1,3441 1,571 1,4967 3,0362* -1,973 2,1237* 0,9393 1,1133 7 0,9331 3,4340* 4,0567* 4,106* 2,9389* 3,2096* 3,575* 2,6017* 2,3050 0,2394 8 0,3125 3,8949* 5,2467* 2,819* 3,1069* 3,7035* 0,839 2,6698* 3,3075* 0,3265 Table 4.9 mean comparison of profile 5

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4.8 Profile 6 (N=404)

Profile 6 has the following characteristics. “The man who knows how it works” Lower educated

Man

Unsuccessful region

Comparisons of means between profiles

Factor/ Profile

Need for

autonomy Need for achievement Need for power Social orientation Self belief Endurance Risk taking Market awareness Creativity Flexibility

Pr of ile 6 1 -1,8350 -2,2891* -1,9603 -0,453 -3,5164* -0,6994 -4,625* 0,1907 -4,5344* -1,6205* 2 -0,7902 0,0216 -0,4491 0,028 -2,0418* 1,2472 -5,148* 1,4742 -1,8764 -0,8266 3 -2,2114* 0,5412 0,8556 -0,709 -2,0419 0,6505 -3,861* 0,8506 -1,4683 -1,9918* 4 -1,7040 1,7689* 2,5638* 1,489 0,1038 1,774 -2,540 2,2036* 0,8304 -0,7793 5 -0,6840 -1,6540 -1,3441 -1,571 -1,4967 -3,0362* 1,973 -2,1237* -0,9393 -1,1133 7 0,2491 1,7800 2,7126* 2,5353 1,4422 0,1734 5,548* 0,4780 1,3657 -0,8738 8 -0,3715 2,2409* 3,9026* 1,248 1,6102 0,6637 1,134 0,5461 2,3681* -0,7868 Table 4.10 mean comparison of profile 6

Table 4.10 presents the mean differences of profile 6 compared to the other profiles. It is noteworthy that profile 6 has the ultimate lowest score on flexibility. This suggest that these lower educated men from an unsuccessful region do not react easily on changes in the environment. Combined with a rather low score on creativity, this is can be a pitfal for these entrepreneurs. All female profiles have a lower need for power than profile 6 but all other male profiles do have a higher score on this. The same thing counts for the factors self-belief and creativity. This suggests that the lower educated men from an unsuccessful region will outperform all other entrepreneurs in their region on these factors expect their higher educated counterpart. 4.9 Profile 7 (N=160)

Profile 7 has the following characteristics. “Skewed worn Uggs-women” Lower educated

Woman

Successful region

Comparisons of means between profiles Factor/ Profile Need for autonomy Need for achievement Need for power Social orientation

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Table 4.11 presents the mean differences of profile 7 compared to the other profiles. It striking that profile 7 has the lowest score on the riskt taking propensity, social orientation and need for autonomy. The score on risk taking is extreme low, these are the only differences greater than 10. Even the smalles difference on risk taking, with profile 5, is still rather big. Probably, these lower educated women from a successful region are afraid of risks or have a lower need to encounter them because they live in a successful region. Furthermore, the social orientation is the lowest compared to the other profiles. This suggests that these women will have difficulties in retrieving a good netwerk because of their lack of social orientation. The need for autonomy is also the lowest compared to the other profiles. This is interesting because the highly educated women from the same (successful) region, have the highest need for autonomy. Apparantly, the lower educated female entrepreneur in this region is not highly ambitious regarding this factor.

Next to these lowest scores, profile 7 also scores low on most other factors: the need for power, the need for achievement, self-belief and creativity. Only on the factors endurance and flexibility profile 7 scores moderate.

4.10 Profile 8 (N=316)

Profile 8 has the following characteristics. “Potentially surprising woman” Lower educated

Woman

Unsuccessful region

Comparisons of means between profiles Factor/ Profile Need for autonomy Need for achievement Need for power Social orientation Self belief Endurance Risk taking Market awareness Creativity Flexibility Pr of ile 8 1 -1,4635 -4,5300* -5,8629* -1,701 -5,1266* -1,3666 -5,759* -0,3553 -6,9025* -0,8338 2 -0,4187 -2,2193* -4,3517* -1,220 -3,6520* 0,5800 -6,282* 0,9281 -4,2445* -0,0398 3 -1,8400 -1,6997 -3,0470 -1,957 -3,6521* -0,0168 -4,995* 0,3046 -3,8364* -1,2050 4 -1,3325 -0,4720 -1,3388 0,241 -1,5064 1,1101 -3,674* 1,676 -1,15377 0,0075 5 -0,3125 -3,8949* -5,2467* -2,819* -3,1069* -3,7035* 0,839 -2,6698* -3,3075* -0,3265 6 0,3715 -2,2409 -3,9026* -1,248 -1,6102 -0,6673 -1,134 -0,5461 -2,3681* 0,7868 7 0,6206 -0,4610 -1,1900 1,287 -0,1680 -0,4939 4,414* -0,681 -1,0025 -0,871 Table 4.12 mean comparison of profile 8

Entrepreneurial Profiles