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The effect of regional culture on the
entrepreneurial profile; are people in
cities more entrepreneurial?
Msc Business Administration: Small Business and Entrepreneurship
Rijksuniversiteit Groningen 2019-2020
Master Thesis
By: Rodney Bouwmeester
S2713284
due date: 20/01/2020
Supervisor: Dr. M. Wyrwich
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ndcorrector: S. Murtinu
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Table of Contents.
1. Abstract……….………...………3
2. Introduction……….………...…….…4
3. Literature review ………..…..5
3.1 Literature ……….…….5
3.2 Hypotheses & conceptual model ……….…….8
4. Methodology ………..….9
4.1 Data description ……….………...9
4.2 Variable description………11
5. Results
……….…...13
5.1 Descriptive Statistics ………..……13
5.2 hypotheses testing……….…………...18
6. Discussion & Conclusion……….……….….20
6.1 Discussion……….………20
6.2 Conclusion………..…………..23
7. References……….….…24
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1. Abstract
This paper aims to provide insight in the spread of entrepreneurial capabilities in the population of the Netherlands. It does so based on survey data centered around personality profiles. The data gathered represents respondents between 2001 and 2015 who have filled out questions that aim to provide a score on different personality trades. The results of these questions are then weighed and used to create a score on each of the Big Five personality measures. In order to determine if an individual is suitable to be an entrepreneur the results of these scores were combined to create a variable measuring how fitting someone’s combination of characteristics is to be an entrepreneur. This is referred to as the Entrepreneurial Personality Fit and is used as the main measure of this paper. Various groups based on age and geographical location are compared in terms of mean Entrepreneurial personality fit scores, examining the difference between city and rural areas. In this paper 53 large cities in the Netherlands are identified and compared to the population outside these cities to answer the question; are people in cities more entrepreneurial than those who do not live in cities? This is tested through three hypotheses. First to test if people in a city score significantly better in terms of mean entrepreneurial fit score than the other group. The second hypothesis tests if the presence of a University is linked to higher mean scores on entrepreneurial personality fit. The hypothesis is divided in H2a and H2b comparing means of university cities to non-university cities as well as comparing them to the whole population.
In order to test if there are truly entrepreneurial individuals in the sample and to see if there is something that sets them apart from the rest of the population the third hypothesis focusses on High-potential entrepreneurs. This group consists of the top 5% best scoring individuals and tests if they are more likely to live in cities.
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2. Introduction
In the field of entrepreneurship there is quite some knowledge available regarding factors that lead to successful entrepreneurial ventures, but most of this is centered around the ventures and companies itself and the environment they are in. Less attention is paid to the entrepreneur himself as a person. However, recent literature has expanded into exploring the personality traits of the individuals behind entrepreneurial ventures instead of only the circumstances in which new ventures are started. Nevertheless, there is still a gap in research between the personal characteristics and personality traits of an individual and how these relate to chances of successful entrepreneurship. In other words, does who you are matter in terms of your chances to become a successful entrepreneur? This paper will aim to shed some light on this relationship and narrow the gap in academical knowledge between personality traits and entrepreneurship in a regional context.
Fritsch, Obschonka & Wyrwich (2019) studied the effects of regional entrepreneurial culture in different German municipalities making comparisons between regions based on patents per individual in a region. They delve deeper by making a historical comparison between regions pre and post war to see if there are long lasting entrepreneurial tendencies, which they believe to be linked to personality traits and culture of individuals residing in that region. This paper will attempt to further on this research by adopting a similar approach using data gathered in the Netherlands. More precisely, this paper will compare the individual personality profiles in various regions to examine if there is a difference between individuals residing in large cities or rural areas.
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Morris et. Al, (2018) have created four categories in which entrepreneurial ventures can be categorized based. This paper believes that, except for necessity entrepreneurship, the likelihood of an individual opting to pursue entrepreneurial activities is strongly linked to his or her personality traits. As well as the entrepreneurial culture in his or her environment. By comparing regions and municipalities based on entrepreneurial activity as well as innovation and known big five personality traits this paper hopes to uncover new insights with regards to the drivers of entrepreneurship and whether or not municipalities can do something to predict or stimulate this. Which brings forth this paper’s research question; Does living in a city make people more entrepreneurial than those who do not live in cities?
Furthermore, does proximity to university education have an influence on this?
Because the data is gathered on an individual level, it is not possible to simply say one region is more entrepreneurial than another. However, it is possible to determine the likelihood of people becoming successful entrepreneurs by examining the profiles of individuals per region and comparing average scores over all categories. In order to measure likeliness of a fostering environment we use the measure of high-potential entrepreneurs per region, these being the individuals that score highest on the Big Five characteristics that are associated with a predisposition for entrepreneurial qualities as defined by Huggins & Thompson(2017). Further assuming that areas with relatively many high-potentials are differentiated by underlying factors either practical, such as infrastructure or economical, or perhaps psychological such as shared culture or mentality.
An average score of entrepreneurship for a region is calculated based on the average scores in the individual-level Entrepreneurial personality fit (Obschonka & Stuetzer, 2017). Finally, this paper strives to shed light on the possible connection between the degree of entrepreneurial characteristics in a region and its population. By identifying cities in which the population has higher scores on certain personality characteristics the door is opened for further, more area specific research which can take a more in depth look at the environment, cultural and economic qualities of specific regions.
3. Literature review
3.1 Literature
Regional Culture
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between. Whilst Hayton & Cacciotti (2013) argue that there is an impact of national culture on factors affecting entrepreneurial activity they also state that more nuanced views are slowly emerging. However, culture is also affected by personal preferences, selective migration and other social influences (Rentfrow et. Al,2008). This could imply that in a small country such as the Netherlands, where distances between areas are small and easily overcome due to modern infrastructure, there is a lower rate of fixed culture per region because individuals move around easier. This moving might make it less likely for individuals to settle down in a certain culture long enough to fully embrace its norms and values, as it is a deeply embedded and very slow to change process to adopt a different culture. (Williamson, 2000)
Regional Innovation
It is argued that entrepreneurship per region is influenced by the perceived success of other entrepreneurs in that region. In regions where successful entrepreneurship is present the legitimacy of pursuing entrepreneurial opportunities is believed to be higher. Seeing others succeed in entrepreneurial activities supposedly boosts confidence of others to adopt behavior more closely related to entrepreneurship and become more accepting of the entrepreneurial lifestyle (Arenius & Minniti, 2005). In the early works of Joseph Schumpeter (1934), also known as Schumpeter Mark 1 it is argued that entrepreneurial individuals are the driving force behind the invention of new ideas and stimulation of innovation and registered patents. Thus meaning a region in which there are many entrepreneurs should have a higher rate of start-ups and also registered patents (Beugelsdijk, 2007)
Regional Entrepreneurship
It is argued that entrepreneurship in a region acts in a self-perpetuating way, meaning the more entrepreneurs there are, the faster entrepreneurship also starts to grow. This is caused by an increase in legitimacy and entrepreneurship (Kibler, Kautonen & Fink, 2014). They explain the role of an accepting attitude towards entrepreneurs in a region which is linked to increased desirability and appropriateness of entrepreneurial activity. This in turns leads to more individuals attempting entrepreneurship, thus also developing a more entrepreneurial personality.
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Entrepreneurship is also seen as a growing necessity in modern economic times due to the diminishing job security and tendency of lifelong employment (Audretsch 2007). The survival chances of new firms are strongly influenced by regional characteristics (Sternberg, 2009).
The role of regions is further explained by Huggins &Thompson (2017). They argue that the combination of personal characteristic of inhabitants of a region together with the nature and distribution of institutions and power leads to certain psychocultural behavior patterns. These patterns in turn influence a regions potential for economic and entrepreneurial growth and are part of the reason why discrepancies exist between regions. These discrepancies go beyond institutional and infrastructural factors but are more concerned with local cultural aspects. This supposedly influences the decision of individuals to partake in entrepreneurship and can have long lasting imprinted effects on the entrepreneurial tendencies displayed in certain regions (Stuetzer et. Al, 2016)
Personality Traits
Being or becoming an entrepreneur is not perceived as something that suits everybody for multiple studies found that people who poses certain traits are often better entrepreneurs. One study also found that being an entrepreneur and engaging with entrepreneurs changes and develops certain individual traits often towards becoming a better entrepreneur (Littunen, 2000). Another study focusing on social entrepreneurship found that certain characteristics were also linked to the entrepreneurial intentions, especially concerning the social role of entrepreneurship (Nga & Shamuganathan, 2010). Past research concerning entrepreneurship often incorporate characteristics linked to the big five personality traits which are all overarching concepts of various specific traits.
Extraversion is linked to one’s social abilities and relates to their openness in social situations and the
level of optimism displayed. Agreeableness is related to how well individuals get along with others and if they are perceived as likeable people, meaning others tend to agree with them.
Conscientiousness is related to how well individuals abide by rules and regulations and whether they
are considered dependable or reliable. Openness to experience represents how open minded a person is to new ideas and experiences, this often translates to an openness to new ideas which helps in developing new and creative solutions. Neuroticism describes how anxious or relaxed individuals are as well as take into account their emotional stability and proneness to mood swings or even depressions (Llewellyn & Wilson, 2003).
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Research conducted over the years has brought forward an ideal profile of personality traits to which most entrepreneurial activity is linked as well as determine the chances of survival, performance and entrepreneurial intention (Brandstätter, 2011). High or low scores on characteristics such as openness to new experiences or risk aversion are found to influence the decision of an individual to become self-employed (entrepreneurship) or choosing not to(Caliendo, Fossen & Kritikos, 2014). Furthermore, the Big Five provides five categories on which an individual can be scored; Extraversion,
Conscientiousness, Openness to experience, Agreeableness and Neuroticism (John, Naumann & Soto,
2008). The combination of this is linked to the likelihood of an individual possessing character traits that predict their potential of successful entrepreneurship and sets them apart from mainstream managers (Zhao & Seibert, 2006). Although not every individual who fits this description will automatically become an entrepreneur, it is still possible to identify them even if they have chosen to become employees (Engle, Mah & Sadri, 1997). Their study is focused on firms finding internal sources for innovation by resorting to these entrepreneurial employees. Therefor it can be argued that these individuals should serve as role models for entrepreneurial and innovative activities. The concept of role models for entrepreneurship finds further support in academic literature (Arenius & Minniti, 2005; Nanda & Sørenson, 2010).
Education
research suggests that formal education can be a positive contributor towards successful entrepreneurship. One paper by Robinson and Sexton (1994) states that those who are self-employed have more years of formal education than employees. Additionally, this paper reported that an increase in years of formal education increases chances of self-employment per year. Different results obtained through a meta-analysis performed in another paper by Van Der Sluis, Van Praag and Vijverberg (2008) state that formal education does not significantly impact the decision to opt into entrepreneurship but they find that the performance of educated entrepreneurs is often better.
3.2 Hypotheses
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H1: Individuals that inhabit a big city score better on entrepreneurial fit than those who do not inhabit
a city region.
Further studies have proven that education is linked to greater prowess in entrepreneurship and therefor it stands to reason that an educated population is potentially better suited to participate in entrepreneurial activities. Combined with the previous argument of self-perpetuating development it stands to reason that cities with University level education present should perform better due to a better educated population. This brings forth the second hypothesis:
H2a: The population of cities in which a University is present will have better scores on entrepreneurial
fit than cities where there Is no University.
H2b: The population of cities in which a University is present will have better scores on entrepreneurial
fit than areas where there Is no University.
Existing literature on entrepreneurship has also sought for a connection between economic or industrial clusters and entrepreneurial activity in this region. Sternberg and Litzenberger (2004) found evidence that entrepreneurial attitudes were better developed in clusters. As clusters often exist near larger settlements and cities, this paper assumes to find a similar effect for cities. Together with assumption that city populations have higher entrepreneurial personality fit scores this forms a third hypothesis;
H3: The share of high-potential entrepreneurs will be higher in cities than in rural regions.
To provide a quick overview of the relationships and their expected effects on the dependent variables the following conceptual model was constructed containing the variables that will be used to test the various hypotheses.
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4. Methodology
The aim of this paper is to verify if empirical results are similar to what expectations based on previously conducted research in order to strengthen theory and further the understanding of the effect of personality characteristics on entrepreneurship. Findings can contribute to academic knowledge in the fields of psychology and entrepreneurship. Additionally, it can provide insights for certain cities and municipalities with respect to entrepreneurship in their area.
4.1 Data Description
Access to the data used for this thesis is provided through the supervisor and originates from the Global Gossling-Potter internet Projects. More specifically, the Data used for this paper are those of the Netherlands in response to the Big Five Personality traits survey(appendix 6) created by Potter(Rentfrow et. Al, 2008). Respondents answered 44 statements regarding their behavior on 5-point Likert scales ranging from strongly disagree to strongly agree. E.g. “tends to be quiet”, “is systematic, likes to keep things in order”. These answers combined result in a score on each of the Big Five personality traits. Further background questions were asked regarding age, gender, place of residence, the place where they grew up and the highest completed formal education of their parents.
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respondents below the age of 14 and above the age of 67 were left out. These parameters were chosen because they define the Dutch working population, 14 being the age at which it becomes legal to have a job and 67 is the age at which you are entitled to your pension/retirement.
The individuals are grouped per postal code, this allows to check for their province and municipality and city. This is done because different municipalities are able to implement their own policies, making it possible for one municipality to have a set of policies or rules that either positively or negatively influence entrepreneurial activity. These rules or differences per region could also potentially affect the development of personality traits due to creation of certain habits as a consequence of policies and influencing overall culture and mentality. This study will not go so deep but aims to prepare the data in a way that future research can build upon this. Furthermore, individuals are placed into the category of city or non-city. This is again based on the postal code of the area they are residing in. The division for a city is any one town with over 50.000 inhabitants, as well as the so called ‘capital cities’ for each of the twelve provinces. The assortment of postal codes per municipality and city was done by combining data from the Dutch government statistics as provided by Central Bureau of Statistics(CBS,2019) and postocdebijadress.nl(2019) and postcode.site(2019) data for inhabitants per region was also obtained through CBS. (appendix 3.1) In total 53 cities in 53 different municipalities were identified and labeled according to their city code and name as well as all postal codes registered within city limits. Any postal codes not found within any of these 53 cities was left unlabeled and referred to as non-city or rural areas. PO boxes were excluded because these do not accurately represent whether the owner actually resides in the city.
In order to account for the influence of education it was factored in whether there as a University present. For this the Dutch definition of University was used which does not include universities of applied sciences (HBO). Furthermore, private universities and theological universities as well as those with under 1500 students were excluded because they are often directed to a certain educational niche and therefor don’t necessarily contribute to overall education of the population.
4.2 Variable Description
Personality traits
Each of the big five characteristics has their own corresponding variable: Extraversion,
Conscientiousness, Openness to experience, Agreeableness and Neuroticism. These contain a value
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corresponding to each of the 5 traits. The values of the answers were added up in order to determine a final score of each trait.
Entrepreneurial personality fit
In order to measure the potential entrepreneurial capabilities of an individual this compounded score of all of the big five traits is made. The variable Entrepreneurial personality fit takes the scores from 1 to 5 of all five traits and compares them to their ideal values: high on extraversion,
conscientiousness and openness to experience, as well as low on agreeableness and neuroticism
(Brandstätter,2011). It then combines them to determine a value of fit. For every deviation of the perfect value a negative value is assigned leading to a range of 0 to -80, zero being perfectly suited as an entrepreneur and -80 deviating the furthest possible from having suitable characteristics for entrepreneurship. This variable is used to measure entrepreneurship at an individual level and serves as the main dependent variable in this research. It is important to realize that entrepreneurial
personality fit is developed to test for the individual level, not with a regional scale in mind. In this
paper the variable serves as a means to an end for lack of existence of better measures.
Postal code of current residence
The 4-digit postal code system used in the Netherlands to identify which township or city someone resides in at the time or participation in the survey. The two letters usually following the digits were dropped because it made comparative research impossible due to software issues and did not provide relevant information for this study. However, the original variable still exists within the dataset for future reference. This variable forms the basis for the dummy variables city/non-city and is used to attribute each individual to a municipality and potentially a city. The average of inhabitants scores of an area will be used as a score for that city/municipality. In the same way this postal code also serves as input for the dummy variable University city.
Age
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Education
The presence of education will also be used as a control variable. In the form of testing whether the presence of a university in a city makes significant impact on the average entrepreneurial fit of the population, as opposed to those who live in a city were no university is present.
High-potential entrepreneurs
The way suitable entrepreneurs are identified was by looking at their final Entrepreneurial fit score. The total possible range would be 0 (perfect) to -80( least suitable). In the sample population participants score ranged between -2 to -61 with an average score of -19,35. The threshold of an entrepreneurial personality fit score higher than -12 was chosen to categorize high-potential
entrepreneurs. Compiling the top 5.1% of the total sample with 5605 individuals. This subgroup is
of special interest to test if they vary significantly in some ways from less suitable entrepreneurs. The reason a score cutoff of -11 was used rather than a percentage is because the personality fit scores are rounded. Meaning 5% would exclude some individuals who scored the same as those that would be included.
In order to test hypotheses a combination of simple regression analysis will be conducted on Entrepreneurial personality fit to determine if there is a statistical correlation between variables. Further information about samples and subgroups of the population will be obtained through comparative means analysis and T-tests and frequency tables. This should provide valuable new insights in terms of the spread of potential entrepreneurial capabilities throughout the Netherlands and further discussion about their possible implication in the real world will follow.
5. Results
5.1 Descriptive Statistics
Geographical distributions
14 Table 1.2 10 highest scoring cities Mea n EP fit score Number of respondent s Std. Deviatio n 10 Lowest scoring cities Mea n EP fit score Number of respondent s Std. Deviatio n Gooische meren (Naarden/Bussum)(NH ) -18,1 6 225 6,197 Gouda(SH) -19,7 2 494 5,932 Amsterdam(NH) -18,4 7 4.992 5,951 Nijmegen(GE) -19,7 4 2.443 5,840 Den Helder(NH) -18,5 0
214 6,141 Alphen aan den
Rijn(SH) -19,7 5 424 6,252 Den Haag(SH) -18,5 1 2.476 5,939 Apeldoorn(GE) -19,7 6 968 6,280 Rotterdam(SH) -18,5 3 3.546 6,178 Ede(GE) -20,0 7 887 5,513 Lelystad(FL) -18,6 9 337 6,238 Veenendaal(UT) -20,1 1 624 6,765 Breda(NB) -18,8 1 1.628 5,834 Leidschendam-Voorburg(SH) -20,1 9 204 7,084 Amstelveen(NH) -18,8 2 523 6,045 Venlo(LI) -20,2 1 398 6,839 Delft(SH) -18,8 8 1.507 5,645 Katwijk(SH) -20,3 8 321 6,572 Almere(FL) -18,9 7 1033 6,475 Wageningen(GE ) -20,3 9 354 6,484 Table 1.2
Out of the 10 highest scoring cities 4 are located in the province of North-Holland mostly in close proximity to the ‘Randstad’. The others lay in South-Holland, Flevoland and North-Brabant. Out of the 10 lowest scoring cities 4 are located in the province of Gelderland and 4 in South-Holland. The other two are found in Utrecht and Limburg. This is in line with data found in Table 5.1, appendix 5, which provides average scores per province. Listing North-Holland as the most entrepreneurial province with an average entrepreneurial profile fit score of -18,99 and Gelderland the least entrepreneurial with a score of -19,74.
15 Table 1.3 City and Univerisity name Mean EP fit score Number of respondents Std. Deviation City and Univerisity name Mean EP fit score Number of respondents Std. Deviation Amsterdam (Vrije Universiteit/UVA) -18,47 4.992 5,951 Leiden (Leiden University) -19,20 985 6,219 Rotterdam (Erasmus University) -18,53 3.546 6,178 Maastricht (Maastricht University) -19,32 948 5,966 Delft(Technical University Delft) -18,88 1.507 5,645 Groningen (Rijksuniversiteit Groningen -19,36 2.538 6,040 Tilburg (Tilburg University) -18,99 1.523 6,065 Eindhoven (Technical University Eindhoven) -19,38 1.640 6,300 Utrecht (Utrecht University) -19,01 3162 5,934 Nijmegen (Radboud University) -19,74 2.443 5,840 Enschede (University of Twente) -19,16 1.076 6,049 Wageningen( Wageningen University -20,39 354 6,484 Table 1.3
Table 1.3 shows similar results to table 1.2 in terms of geographical locations of the highest and lowest scoring cities with University cities in the Randstad making up the top of the board and University cities towards the rural areas scoring lower. Again, cities in Gelderland scoring lowest.
Age Distributions
city
Non-city
Total sample
Age group N Mean
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It becomes evident that age group Teenagers make up the majority of the sample, representing 60.316 individuals (55.01% of the total sample). Interestingly they also have the lowest average entrepreneurial personality fit score of all age groups (-19,79) which is below average for the entire population. 51.8% of teenagers reside in non-city areas and 48.2% lives in a city. In both these categories teenagers score below average on entrepreneurial personality fit scores. This is in line with research that shows that personality traits positively associated with entrepreneurial capabilities are further developed at a later age (Donnellan & Lucas ,2011). The share of suitable entrepreneurs in the Teenagers category is 43.62% which is considerably lower than their representation of the sample in terms of size at 55.01%.
In the category Young adults it is observed that 20.056 members make up 18.29% of the total sample. The average entrepreneurial personality fit score for young adults is -19,11 which is above average for the total sample. At 59.14% the majority of young adults resides in a city, also making this the only age group who are more represented in cities than in non-city territories. Young adults represent 22.02% of all suitable entrepreneurs which is relatively high for their representation in the sample
Adults have the largest range of ages of out of the four groups, twice that of teenagers and young
adults. However, they only make up 24.69% of the total sample. Adults score the best on entrepreneurial personality fit with a mean of -18,59. They represent 32% of all high-potential entrepreneurs relative to their total size this is much higher than any other age group. 44.56% of adults resides in a city.
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The following table provides an overview of the distribution of the age groups and high-potential entrepreneurs split between University cities and other areas of residence.
group Lives in University city
Lives in other TOTAL
HIGH-POTENTIALS PER AGE GROUP: N High-potentials N High-potentials Teenagers 13.456 668 (27.3%) 46.860 1.777 (72.7%) 2.445 (100%) Young_adult 5.609 357 (28.9%) 14.447 877 (71.1%) 1.234 (100%) Adult 4.323 298 (16.6%) 22.750 1.496 (83.4%) 1.794 (100%) Older 341 19 (14.5%) 1.852 113 (85.5%) 131 (100%) Totals: 23.729 1.342 85.909 4.263 5.605 Table 5
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Group statistics of each individual variable used to measure Entrepreneurial personality fit
city_or_rural_current N Mean Std. Deviation Std. Error Mean
Extraversion City 53910 3,53 0,741 0,003 Non-city 55728 3,52 0,736 0,003 Agreeableness City 53910 3,65 0,593 0,003 Non-city 55728 3,70 0,584 0,002 Consciousness City 53910 3,45 0,696 0,003 Non-city 55728 3,51 0,677 0,003 Neuroticism City 53910 2,80 0,806 0,003 Non-city 55728 2,78 0,794 0,003 Openness City 53910 3,63 0,633 0,003 Non-city 55728 3,46 0,627 0,003 Table 6
all variables provide statistically significant differences(sig. ,000) in mean except extraversion(sig. ,575). However, none of the mean differences are strong to a degree where they become worth examining at this point. Openness to experience has a noticeably higher mean difference than the other groups, yet it is still only ,165. The standard deviations per group for each variable are very close together warranting no further investigation at this point.
5.2 hypotheses testing
H1: Individuals that inhabit a big city score better on entrepreneurial fit than those who do not inhabit a city region. In order to test whether it is true that city residents have an overall higher score on
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entrepreneurial personality fit score of the population is 0,39 lower in cities than in urban areas. To further test for a relationship between city and Entrepreneurial personality fit a linear regression analysis is conducted. This analysis provides a B of ,387 and a significance of ,000 at α=.05, proving a positive correlation between city and Entrepreneurial personality fit. However, R square for the model is only ,001.
H2a: The population of cities in which a University is present will have better scores on entrepreneurial fit than cities where there Is no University. When testing only for the inhabitants of city n=53.190 it becomes
clear that there are 23.729 members of the population that reside in cities with a university. The remaining 30.181 live in cities where there is no university. The mean entrepreneurial fit score of the non-university-city group is -19,28 whereas the mean in university cities is -18.99. Levene’s test provides evidence at sig.,000 to assume unequal variances. This results in a mean difference of -0,287 at a 2tailed significance of ,000.(appendix 2.2.1) Thereby it is possible to conclude that H0 is rejected and H2 holds meaning that the population of cities in which a university is present score significantly better than cities without universities. To further test for a relationship between University city and Entrepreneurial
personality fit a linear regression analysis is conducted on the sample of city inhabitants. With R2=,001 it
is determined that B=,287 with a significance of ,000 at α=.05. This confirms that there is a positive correlation between living in a University city and Entrepreneurial fit scores.
H2b: The population of cities in which a University is present will have better scores on entrepreneurial fit than areas where there Is no University. If the same test is run with the entire population, meaning
population of university cities compared to non-university cities and non-cities alike, results vary even stronger. Where the university city group mean remains -18.99, the non-university group score a mean of -19,45. Levene’s test again assumes unequal variances at sig.,001 resulting in a 2-tailed significance of ,000 (appendix 2.2.2) this time with a mean difference of -,455. This means H0 is rejected and H2b holds which supports the idea that education in a region has a positive influence on entrepreneurship and in creating an environment in which entrepreneurial characteristics are fostered. A regression analysis between University city and Entrepreneurial personality fit is conducted on the entire population. With R2=,001 it is determined that B=,455 with a significance of ,000 at α=.05. This confirms that there is a positive correlation between living in a University city and Entrepreneurial fit scores.
H3: the share of high-potential entrepreneurs will be higher in cities than in rural regions. This was
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Whereas out of the remaining 104.033 member of the population that are not classified as high-potential, 53.112(51.05%) reside in non-city and 50.912(48.95%) in a city. This means that it is 6.7% more likely for a high-potential entrepreneur to reside in a city. Additionally, the chance of finding a high-potential entrepreneur is 2.4% higher when searching in a city.
Notable is that in the first subpopulation of high-potential entrepreneurs the mean scores on the entrepreneurial personality fit are -9,3 for both the city and non-city groups. An independent samples test (appendix 2.3) reveals that, equal variances assumed sig.,858, the difference in means is only 0,002 between the city/non-city groups. This would indicate that for the group of high-potential
entrepreneurs it does not seem to make a difference where they reside, even though the majority of
this group lives in a city
For the remaining 95% of the sample who are not classified as high-potential entrepreneurs results are different. When the top 5% is filtered out the mean scores on entrepreneurial personality fit drop for both groups. For the 53.112 individuals that live outside of a city their mean score has dropped to -20,04 compared to -19,54 when including high-potentials, a drop of -0.5, whereas the city inhabitants’ average has lowered from to -19.73 where it was -19,15 for the entire sample, a drop of 0,58.
6. Discussion & Conclusion
6.1 Discussion
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no groups examined scored means below -18 or above -21 at any point which is very consistent on a scale of 80 points.
At this point it is good to keep in mind that the measure of combined entrepreneurial personality fit score was never developed for regional comparisons in the first place. This study uses available data to create averages in hopes of finding new connections which could open the door to further research and understanding. In order to better test in future research more regional information should be constructed in order to create a better scoring method. The need for a better concept in order to measure regional entrepreneurial profiles would therefore be the most important implication in the field of academics to be drawn from this paper.
The very limited range of outcomes does make it more difficult to pinpoint potential factors that are of a strong influence. The only clear geographical trends that emerge from this research is that territories that are closer to the Randstad area tend to score the highest when compared to other cities. The data allows for an even more accurate view on where exactly entrepreneurial personality fit is the highest on the basis of municipality level or NUTS region but that Is beyond the scope of this research.
When the data is examined per different age category some insights are revealed. It becomes clear that the age distribution of the sample is quite positively skewed with the bulk of individuals being at the start of the spectrum under 25 years old. From there on out each older age group has decreasing representation in this research. This could potentially point to a very important limitation of this research as well as the existing Gosling-Potter project of sample bias. It is very possible that, due to the data being gather through an online survey the response is biased towards younger people and those who are more familiar and active with the internet. This could also lead to an inaccurate picture of reality because the possibility exist that there are older, well suited individuals that are not represented in the study.
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through schooling or seminars for example. This would also lead to a biased sample and should be taken into consideration for future research.
When considering the differences between university cities and other locations, it becomes clear that over half of the sample that inhabits a university city is part of the age group teenagers. This would make sense because this would be the age at which they attend university and therefor usually reside in the area. One downside is that the dataset does not provide information about current occupation at the time of participation in the study. Therefor it is not possible to say whether this large portion of teenagers are in fact students. Knowing this would help further compare entrepreneurial fit scores between students of a university and that city population excluding students. After testing the second hypotheses this paper finds that the sample of individuals that reside in a university city have the highest fit for entrepreneurial characteristics but only by a thin margin. The average score of university city inhabitants is -18,99. However the top 10 cities all score equally as good or better on average over their total population and only 3 of them have universities. Additionally, the worst scoring city is Wageningen which has a university, closely followed by Nijmegen which also has a university. It seems that having a university present is not necessarily going to ensure high entrepreneurial fit.
That being said, this study only focused on the 53 largest cities and everything else was considered as a non-city /rural area. In order to better test the effect, it would be possible to revisit the study on a municipality level and see if these low scoring cities are an accurate representation of their area or if they score higher or lower than the surrounding municipality averages.
23
possible that this has already happened over time on a national scale which could partially explain the similar averages.
Research suggests that exposure to entrepreneurship can foster the entrepreneurial culture and mindset and with the Netherlands being so densely populated, individuals are never more than an hour or so away from a hub of entrepreneurial activities. Combined with the small size, complete infrastructure and easy mobility throughout the country for all inhabitants, knowledge and influences are quick to share which might also explain just why the deviations from the national average in terms of Entrepreneurial personality fit are so small.
One further limitation of this study which is especially applicable in the Netherlands more so than in some other countries is the general trend of commuting. It is considered fairly normal for people to live in one town and work or run a business in another town. Many residents that are categorized as rural in this research might have a job in a city nearby, or have their own business located in a city, thus being influenced by its culture on a daily basis. Furthermore, what is considered a town or village in the Netherlands might be considered a suburb in e.g. the United-States in terms of size and amenities, which might explain why differences in outcomes of this research in the Netherlands vary so little in comparison to those from other countries.
6.2 Conclusion
24
7. References
-Arenius, P. & Minniti, M (2005). Perceptual variables and nascent entrepreneurship. Small
Business Economics, 24, 233-247
-Audretsch, D. B. (2007). The entrepreneurial society. New York: Oxford University Press.
-Beugelsdijk, S. (2007). Entrepreneurial culture, regional innovativeness and economic growth.
Journal of Evolutionary Economics, 17(2), 187-210.
-Brandstätter, H. (2011). Personality aspects of entrepreneurship: A look at five meta-analyses. Personality and individual differences, 51(3), 222-230.
-Caliendo, M., Fossen, F., & Kritikos, A. (2014). Personality characteristics and the decision to become and stay self-employed. Small Business Economics, 42, 787–814.
-Donnellan, M. B. & Lucas, R. E. (2011). Age differences in the Big Five across the life span: Evidence from two national samples. Psychology and Aging, 23(3), 558–566.
-Engle, D. E., Mah, J. J., & Sadri, G. (1997). An empirical comparison of entrepreneurs and employees: Implications for innovation. Creativity Research Journal, 10, 45-49.
-Fritsch, M., Obschonka, M. & Wyrwich, M. (2019). Historical roots of entrepreneurship- facilitating culture and innovation activity: an analysis for German regions, Regional Studies,
-Gemeenten Nederland. (POSTCODE.SITE). Retrieved January 7, 2020, from https://postcode.site/nl/gemeenten.
-Hayton, J. C., & Cacciotti, G. (2013). Is there an entrepreneurship culture? A review of empirical research. Entrepreneurship and regional Development, 25(9-10), 708-731.
-John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the integrative Big Five trait taxonomy: History, measurement, and conceptual issues. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 114–158). New York: Guilford.
25
-Littunen, H. (2000). Entrepreneurship and the characteristics of the entrepreneurial personality. International Journal of Entrepreneurial Behavior & Research, 6(6), 295-310. -Llewellyn, D. J., & Wilson, K. M. (2003). The controversial role of personality traits in entrepreneurial psychology. Education+ Training, 45(6), 341-345.
-Morris, M.H., Neumeyer, X., Jang, Y. and D.F. Kuratko (2018), Distinguishing Types of
Entrepreneurial Ventures; an Identity-Based Perspective, Journal of Small Business Management,
56(3), 453-474.
-Nanda, R., & Sørenson, J. B. (2010). Workplace peers and entrepreneurship. Management science,
56, 1116-1126.
-Nga, J. K. H., & Shamuganathan, G. (2010). The influence of personality traits and demographic factors on social entrepreneurship start up intentions. Journal of business ethics, 95(2), 259-282.
-Obschonka, M., & Stuetzer, M. (2017). Integrating psychological approaches to
entrepreneurship: The entrepreneurial personality system (EPS). Small Business Economics. -Regionale Kerncijfers Nederland. (CBS.nl). Retrieved from
https://opendata.cbs.nl/statline/#/CBS/nl/dataset/70072NED/table?dl=2096B
-Rentfrow, P. J., Gosling, S. D., & Potter, J. (2008). A theory of the emergence, persistence, and the expression of geographic variation in psychological characteristics. Perspectives on Psychological
Science, 3(5), 339-369
-Robinson, P. B., & Sexton, E. A. (1994). The effect of education and experience on self-employment success. Journal of business Venturing, 9(2), 141-156.
-Schumpeter, Joseph A., and Redvers Opie. The Theory of Economic Development; an Inquiry into
Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press, 1934.
-Sternberg, R., & Litzenberger, T. (2004). Regional clusters in Germany--their geography and their relevance for entrepreneurial activities. European Planning Studies, 12(6), 767-791.
-Sternberg, R. (2009). Regional dimensions of entrepreneurship. Foundations and Trends in Entrepreneurship, 5(4), 211-340
26
-Van der Sluis, J., Van Praag, M., & Vijverberg, W. (2008). Education and entrepreneurship selection and performance: A review of the empirical literature. Journal of economic surveys, 22(5), 795-841.
- Williamson, O. (2000). The new institutional economics: Taking stock, looking ahead. Journal of
Economic Literature, 38, 595-613.
-Westlund, H., Larsson, J. P., & Olsson, A. R. (2014). Start-ups and local entrepreneurial social capital in the Municipalities of Sweden. Regional Studies, 48, 974–994.
-Zhao, H., & Seibert, S. E. (2006). The big five personality dimensions and entrepreneurial status: A meta-analytical review. Journal of applied psychology, 91(2), 259.
8. Appendices
Appendix 1.1
Overview of Mean entrepreneurial personality profile fit scores for all city areas.
TABLE 1: OVERVIEW MEANS CITY ENTREPRENEURIAL PERSONALITY FIT
NAME OF CITY Mean
27
APELDOORN -19,76 968 6,280
APLHEN AAN DEN RIJN -19,75 424 6,252
ARHEM -19,54 1.854 5,743
ASSEN -19,24 430 5,623
BREDA -18,81 1.628 5,834
CAPELLE AAN DEN IJSSEL -19,66 393 6,245
29 T-Test results: GROUP STATISTICS CITY_OR_RURAL_CURRENT N Mean Std. Deviation Std. Error Mean ENTREPRENEURIAL PERSONALITY FIT rural 55.728 -19,54 6,152 0,026 city 53.910 -19,15 6,151 0,026
INDEPENDENT SAMPLES TEST
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ENTREPRENEURIAL PERSONALITY FIT Equal variances assumed 0,460 0,498 -10,401 109636 0,000 -0,387 0,037 -0,459 -0,314 Equal variances not assumed -10,401 ######### 0,000 -0,387 0,037 -0,459 -0,314
Appendix 2.2.1: H2a SPSS output
T-Test results: GROUP STATISTICSA UNIVERSITY_IN_CITY N Mean Std. Deviation Std. Error Mean ENTREPRENEURIAL PERSONALITY FIT No university 30181 -19,28 6,246 0,036 university 23729 -18,99 6,024 0,039
A. CITY_OR_RURAL_CURRENT = 1,00(CITY SAMPLE ONLY)
INDEPENDENT SAMPLES TESTA
Levene's Test for Equality of Variances
30 F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ENTREPRENEURIAL PERSONALITY FIT Equal variances assumed 19,509 0,000 -5,371 53908 0,000 -0,287 0,053 -0,391 -0,182 Equal variances not assumed -5,394 51731,989 0,000 -0,287 0,053 -0,391 -0,182
A. CITY_OR_RURAL_CURRENT = 1,00(CITY SAMPLE ONLY)
Appendix 2.2.2: H2B SPSS output T-Test results: GROUP STATISTICS UNIVERSITY_IN_CITY N Mean Std. Deviation Std. Error Mean ENTREPRENEURIAL PERSONALITY FIT No university 85.909 -19,45 6,186 0,021 university 23.729 -18,99 6,024 0,039
INDEPENDENT SAMPLES TEST
Levene's Test for Equality of Variances
t-test for Equality of Means
31
frequency percent Valid Percent Cumulative
percent ‘high-potential entrepreneurs’ 5605 5,1 100 100 other 104033 94,9 total 109638 100
Representation of the share of ‘high-potential entrepreneurs’ of the sample.
GROUP STATISTICSA CITY_OR_RURAL_CURRENT N Mean Std. Deviation Std. Error Mean ENTREPRENEURIAL PERSONALITY FIT Non city 2616 -9,30 1,463 0,029 city 2989 -9,30 1,451 0,027
A. SUITABLE_ENTREPRENEUR = 1 (HIGH-POTENTIAL ENTREPRENEURS ONLY)
INDEPENDENT SAMPLES TESTA
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ENTREPRENEURIAL PERSONALITY FIT Equal variances assumed 0,032 0,858 0,062 5603 0,950 0,002 0,039 -0,074 0,079 Equal variances not assumed 0,062 5493,471 0,951 0,002 0,039 -0,074 0,079
32 GROUP STATISTICSA CITY_OR_RURAL_CURRENT N Mean Std. Deviation Std. Error Mean ENTREPRENEURIAL PERSONALITY FIT Non city 53112 -20,04 5,847 0,025 city 50921 -19,73 5,822 0,026
A. SUITABLE_ENTREPRENEUR = 2 (HIGH-POTENTIALS EXCLUDED)
INDEPENDENT SAMPLES TESTA
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ENTREPRENEURIAL PERSONALITY FIT Equal variances assumed 0,112 0,738 -8,639 104031 0,000 -0,313 0,036 -0,384 -0,242 Equal variances not assumed -8,640 ######### 0,000 -0,313 0,036 -0,384 -0,242
A. SUITABLE_ENTREPRENEUR = 2(HIGH-POTENTIALS EXCLUDED)
Appendix 3.1: table of cities, municipality codes and corresponding postal codes used to assign all members of ‘city’ or ‘non-city’ groups.
Table 3.1: postal code assignment
33 CODE Alkmaar 361 1801,1802,1810,1811,1812,1813,1814,1815,1816,1817,1821,1822,1823,1824,1825,1826,1827, Almelo 141 7601,7602,7603,7604,7605,7606,7607,7608,7609,7610, Almere 34 1301,1302,1303,1305,1309,1311,1312,1313,1314,1315,1316, 1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327, 1328,1329,1331,1332,1333,1334,1335,1336,1338,1339,1341, 1343,1349,1351,1352,1353,1354,1355,1356,1357,1358,1359, 1361,1362,1363, Amersfoort 307 3802,3811,3812,3813,3814,3815,3816,3817,3818,3819,3821, 3822,3823,3824,3825,3826, Amstelveen 362 1181,1182,1183,1184,1185,1186,1187,1188,1189,1422,1432, Amsterdam 363 1001,1002,1003,1005,1006,1007,1008,1009,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022, 1023,1024,1025,1026,1027,1028,1030,1031,1032,1033,1034,1035,1036,1037,1040,1041,1042,1043,1044,1045, 1046,1047,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068 ,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1086,1087,1090,1091,1092, 1093,1094,1095,1096,1097,1098,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1181,1183, VU/ Universiteit amsterdam Apeldoorn 200 7301,7302,7303,7311,7312,7313,7314,7315,7316,7317,7320, 7321,7322,7323,7324,7325,7326,7327,7328,7329,7331,7332, 7333,7334,7335,7336,
aplhen aan den Rijn 484 2401,2402,2403,2404,2405,2406,2407,2408,2409,
Arhem 202 6801,6802,6803,6811,6812,6813,6814,6815,6816,6821,6822,
6823,6824,6825,6826,6827,6828,6831,6832,6833,6834,6835, 6836,6841,6842,6843,6844,6845,6846,
Assen 60 9401,9402,9403,9404,9405,9406,9407,9408,9489,
Breda 758 4801,4802,4803,4811,4812,4813,4814,4815,4816,4817,4818,
4819,4820,4822,4823,4824,4825,4826,4827,4834,4835,4836, 4837,4838,4839,
Capelle aan den Ijssel 502 2901,2902,2903,2904,2905,2906,2907,2908,2909,
34
Appendix 4.1.1: AGE related demographics in sample
CITY NON-CITY TOTAL SAMPLE AGE GROUP N Mean EPF score N of High-potentials N Mean EPF score N of High-potentials N Mean EPF score N of High-potentials Hilversum 402 1201,1202,1211,1212,1213,1214,1215,1216,1217,1218,1221,1222,1223,1403, Hoorn 405 1621,1622,1623,1624,1625,1627,1628, Katwijk 537 2221,2222,2223,2224,2225,2242, Lansingerand (Blijswijk) 1621 2661,2665, Leeuwarden 80 8901,8902,8903,8911,8912,8913,8914,8915,8916,8917,8918,8919,8921,8922,8923,8924,8925,8926,8927,8931,893 2,8933,8934,8935,8936,8937,8938,8939,8941,
Leiden 546 2301,2302,2303,2311,2312,2313,2314,2315,2316,2317,2318,2321,2322,2323,2324,2331,2332,2333,2334, Universiteit Leiden
Leidschendam-voorburg 1916 2271,2272,2273,2274,2275, Lelystad 995 8202,8203,8211,8212,8218,8219,8221,8222,8223,8224,8225,8226,8231,8232,8233,8239,8241,8242,8243,8244,824 5, Maastricht 935 6211,6212,6213,6214,6215,6216,6217,6218,6219,6221,6222,6223,6224,6225,6226,6227,6228,6229 MSM Middelburg 687 4331,4332,4333,4334,4335,4336,4337,4338, Nieuwegein 356 3431,3432,3433,3434,3435,3436,3437,3438,3439, Nijmegen 268 6501,6503,6504,6511,6512,6515,6521,6522,6523,6524,6525,6531,6532,6533,6534,6535,6536,6537,6538,6541,654 2,6543,6544,6545,6546,6564,6574, Radboud Nissewaard (Spijkenisse) 1930 3199,3200,3201,3202,3203,3204,3205,3206,3207,3208, Purmerend 439 1441,1442,1443,1444,1445,1446,1447,1448,1481, Rijswijk 603 2281,2282,2283,2284,2285,2286,2287,2288,2289, Rotterdam 599 3001,3002,3003,3004,3005,3006,3007,3008,3009,3011,3012,3013,3014,3015,3016,3021,3022,3023,3024,3025,302 6,3027,3028,3029,3031,3032,3033,3034,3035,3036,3037,3038,3039,3041,3042,3043,3044,3045,3046,3047,3050,30 51,3052,3053,3054,3055,3056,3059,3061,3062,3063,3064,3065,3066,3067,3068,3069,3071,3072,3073,3074,3075,3 076,3077,3078,3079,3081,3082,3083,3084,3085,3086,3087,3088,3089 Erasmus University Schiedam 606 3101,3102,3109,3111,3112,3113,3114,3115,3116,3117,3118,3119,3121,3122,3123,3124,3125, Sittard-Geleen 1883 6121,6122,6123,6124,6125,6127,6131,6132,6133,6134,6135,6136,6137,6141,6142,6143,6151,6153,6161,6162,616 3,6164,6165,6166,6167 Tilburg 855 5001,5002,5003,5004,5011,5012,5013,5014,5015,5017,5018,5021,5022,5025,5026,5032,5035,5036,5037,5038,504 1,5042,5043,5044,5045,5046,5047,5048,5049, Tilburg University Utrecht 344 3451,3501,3502,3503,3504,3505,3506,3507,3508,3509,3511,3512,3513,3514,3515,3521,3522,3523,3524,3525,352 6,3527,3528,3531,3532,3533,3534,3540,3541,3542,3543,3544,3545,3546,3551,3552,3553,3554,3555,3561,3562,35 63,3564,3565,3566,3571,3572,3573,3581,3582,3583,3584,3585,3737, Utrecht University Veenendaal 345 3901,3902,3903,3904,3905,3906,3907 Velsen (Ijmuiden) 435 1971,1972,1973,1974,1975,1976, Venlo 983 5901,5902,5911,5912,5913,5914,5915,5916,5921,5922,5923,5924,5925,5926,5927,5928, Vlaardingen 622 3131,3132,3133,3134,3135,3136,3137,3138, Zaanstad 479 1501,1502,1503,1504,1505,1506,1507,1508,1509 Zeist 355 3701,3702,3703,3704,3705,3706,3707,3708,3709, Zoetermeer 637 2700,2701,2702,2711,2712,2713,2715,2716,2717,2718,2719,2721,2722,2723,2724,2725,2726,2727,2728,2729, Zwolle 193 8001,8002,8003,8004,8007,8011,8012,8013,8014,8015,8016,8017,8019,8021,8022,8023,8024,8025,8026,8028,803 1,8032,8033,8034,8035,8041,8042,8043,8044,8045,
35 TEENAGERS (14-25) 29.057 -19,59 1.371 31.259 -19,97 1.074 60.316 -19,79 2.445 YOUNG ADULTS(26-35) 11.861 -18,84 746 8.195 -19,49 488 20.056 -19,11 1.234 ADULTS(36-55) 12.065 -18,42 816 15.008 -18,73 978 27.073 -18,59 1.794 OLDER(55-67) 927 -18,90 56 1266 -18,66 76 2.193 -18,76 132 TOTALS 53.910 -19,15 2.989 55.728 -19,54 2.616 109.638 -19,35 5.605
Appendix 4.2: AGE GROUPS X UNIVERSITIES
Group Statistics
University_in_city N Mean
Entrepreneurial Personality Fit University 23.729 -18,99
No university 85.909 -19,45 Older University 341 4,0000 No university 1.852 4,0000 Adults University 4.323 3,0000 No university 22.750 3,0000 Teenagers University 13.456 1,0000 No university 46.860 1,0000 Young_adults University 5.609 2,0000 No university 14.447 2,0000 High_Potential_entrepreneurs University 1.342 1,0000 No university 4.263 1,0000
Appendix 4.3: high-potential x non-university x age group
36
age group outside of university cities
High_Potential_entrepreneurs N Mean Entrepreneurial Personality Fit Non-university 4263 -9,31 - 0b Older Non-university 113 4,0000 - 0b Adults Non-university 1496 3,0000 - 0b Teenagers Non-university 1777 1,0000 - 0b Young_adults Non-university 877 2,0000 - 0b a. University_in_city = ,00
b. t cannot be computed because at least one of the groups is empty.
Appendix 4.4: high-potential x university x age group Group Statistics: distribution of high-potentials per age group in university cities
37
Appendix 4.5: overview of population x high-potentials x area
group Lives in University city Lives in other N High-potentials N High-potentials Teenagers 13.456 668 46.860 1.777 Young_adult 5.609 357 14.447 877 Adult 4.323 298 22.750 1.496 Older 341 19 1.852 113 Totals: 23.729 1.342 85.909 4.263
Appendix 5.1: Province data
Means per province based on “What state province do you live in” as answered in survey:
Table 5.1
In what state/province do you currently live?
Entrepreneurial Personality Fit
