regional innovation activity in the Netherlands.
“Is the entrepreneurship-prone personality profile regionally clustered per NUTS-3 region in the Netherlands and how does this profile affect the innovation activity per NUTS-3
region in the Netherlands?”
By
MARRIT ANNA RIENKS S3469700
University of Groningen
Faculty of Economics and Business MSc BA - Small Business & Entrepreneurship
January 2020
Supervisor: dr. M Wyrwich Co-assessor: S. Murtinu
Abstract
Previous studies found that the entrepreneurship-prone personality profile is regionally clustered in the United States, Germany, and in the United Kingdom. Moreover, previous studies found that a high share of people with an entrepreneurship-prone personality profile in a region positively affects the innovation activity in that region. This study complemented earlier research by examining beforementioned relationships in the Netherlands. Analyses were done using data from the global Gosling-Potter Internet project, the OECD RegPat database, and ‘Centraal Bureau voor de Statistiek’ (CBS). Results show that in contrast to the results of previous studies, no sufficient evidence is found to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands. This study did provide evidence for the regional clustering of the entrepreneurship-prone personality profile per NUTS-3 region in the Netherlands. Future research should incorporate different kind of industries and additional kind of measures of innovation activity to see the full distribution of innovation activity in a region and to be able to measure the full innovation activity of a region. Furthermore, future research should look for a more accurate reference profile at the ‘Big Five’ level (e.g. constellation in which one or more traits have moderate values). Finally, future research should investigate the single ‘Big Five’ personality traits and their relationship with innovation activity in a deeper matter.
Keywords: ‘Big Five’ personality traits, entrepreneurship-prone personality profile, innovation
Introduction
In recent years the topic entrepreneurship has become a major focus in the social sciences, with renewed interest in the links between personality and entrepreneurship (Obschonka, Schmitt-Rodermund, Silbereisen, Gosling, & Potter, 2013). One robust finding to emerge from entrepreneurship research is that individuals differ in their tendency toward entrepreneurial behaviour (Blanchflower, Oswald, & Stutzer, 2001; Obschonka et al., 2013). Scholars of entrepreneurship have long been interested in what factors might account for such variation and found that individual personality traits are associated with entrepreneurial behaviour (Obschonka et al., 2013; Rauch & Frese, 2000; Rauch & Frese, 2007). Moreover, there is growing evidence for what is called an entrepreneurship-prone personality profile (an entrepreneurial constellation of ‘Big Five’ traits within the person) that is particularly predictive of entrepreneurial behaviour (Fritsch, Obschonka, & Wyrwich, 2019; Obschonka, Silbereisen, & Schmitt-Rodermund, 2010; Obschonka, Silbereisen, & Rodermund, 2011; Obschonka, Silbereisen, & Schmitt-Rodermund, 2012; Obschonka et al., 2013; Obschonka & Stuetzer, 2017; Schmitt-Schmitt-Rodermund, 2004; Schmitt-Rodermund, 2007).
The contribution I aim to make with this study is confined to an empirical attempt to complement existing literature (Fritsch et al., 2019; Obschonka et al., 2013) on the regional clustering of the entrepreneurship-prone personality profile and its relationship with regional innovation activity. With this study the missing empirical evidence of the regional clustering of the entrepreneurship-prone personality profile and its impact on the regional innovation activity in the Netherlands will be addressed. The empirical perspective of the issue is also important from the viewpoint of policymakers, who should take into account personality differences per region when designing policy concerning entrepreneurs.
To fill the gap in the literature, the following research question has been formulated: “Is the entrepreneurship-prone personality profile regionally clustered per NUTS-3 region in the Netherlands and how does this profile affect the innovation activity per NUTS-3 region in the Netherlands?”.
The level of analysis in this study is NUTS-3. The reason to analyse the data on this regional level is that NUTS-3 allows for specific diagnoses in small regions (Eurostat, 2018). Moreover, the NUTS-3 classification has been implemented over entire Europe making regional statistics across the European Union easily comparable (RegioAtlas, 2019).
The overall structure of this paper takes the form of four consecutive sections. The first section includes theoretical background about the trait theory, the entrepreneurship-prone personality profile and the relationship of this profile with innovation activity. The second section provides a detailed overview of the methodology of this study. The third section includes the results that are found in this study. The paper ends with a discussion and conclusion. In this chapter the answer to the research question is given, followed by the implications and limitations of this study and suggestions for further research.
Literature Review
In this section, the relevant literature of this study is presented. The literature review is divided into two subsections. The first section includes the trait theory and theory about the independent variable entrepreneurship-prone personality profile. Second, theory about the entrepreneurship-prone personality profile and its link with innovation activity is discussed. In other words, the relationship between the independent variable and the dependent variable is discussed.
Entrepreneurship-prone personality profile
Gordon Allport was among the firsts to come up with a personality trait theory (Zuroff, 1986). He defines a personality trait as follows: “A trait is a dynamic trend of behaviour which results from the integration of numerous specific habits of adjustment, and which expresses a characteristic mode of the individual’s reaction to his surroundings” (Allport, 1927, p.288). In later papers this personality trait theory has been linked to entrepreneurial behaviour (Obschonka et al., 2013; Rauch & Frese, 2007). Moreover, there is growing evidence for what is called an entrepreneurship-prone personality profile that is particularly predictive of entrepreneurial behaviour (Fritsch et al., 2019; Obschonka et al., 2010; Obschonka et al., 2011; Obschonka et al., 2012; Obschonka et al., 2013; Obschonka & Stuetzer, 2017; Rodermund, 2004; Schmitt-Rodermund, 2007). An entrepreneurship-prone personality profile can be defined as: “An entrepreneurial constellation of ‘Big Five’ Traits within the person” (Obschonka et al., 2013, p.105).
Schmitt-Rodermund, 2007). Moreover, at the regional level this entrepreneurship-prone personality profile has shown to be clustered in the United States, Germany, and in the United Kingdom. (Obschonka et al., 2013). Results from the study of Fritsch et al. (2019) confirm this again for Germany.
In the paper by Fritsch et al. (2019), the todays’ clustering of the entrepreneurship-prone personality profile is explained by historical self-employment rates. “Areas with high historical self-employment levels are marked by cultural attitudes in favour of entrepreneurship today.” (Fritsch et al., 2019, p.1). Moreover, Renfrow, Gosling and potter (2008) argue that causes underlying the geographic variation in personality are due to first, selective migration. Individuals selectively migrate to regions where certain psychological and behavioural tendencies are common (Rentfrow et al., 2008). Thus, it can be concluded that people with an entrepreneurial mindset may tend to migrate to places where the local population has similar entrepreneurial personality characteristics (Fritsch et al., 2019; Obschonka et al., 2013; Rentfrow et al., 2008). Second, also social influence plays a role in the geographic clustering of personality (Renfrow et al., 2008). According to the dynamic social-impact theory, clustering of personality and attitudes can occur when individuals engage in repeated social interaction with others (Bourgeois & Bowen, 2001; Rentfrow et al., 2008). As a result, personality and attitudes become geographically clustered (Obschonka et al., 2013; Rentfrow et al., 2008). Third, it is shown that environmental influence plays a role in the geographic clustering of personality (Rentfrow et al., 2008). As in the case of social influence, characteristics of the physical environment could affect the personality and attitudes of individuals within a region (Rentfrow et al., 2008).
In summary, historical self-employment, selective migration, social influence, and environmental influence are four mechanisms that cause geographic differences in personality and therefore also in the entrepreneurship-prone personality profile. This regional clustering has proven to be true for the United States, Germany, and in the United Kingdom. (Obschonka et al., 2013). Therefore, it is most likely that this entrepreneurship-prone personality profile is also regionally clustered in the Netherlands.
Entrepreneurship-prone personality profile and Innovation activity
(Fritsch et al., 2019; Obschonka et al., 2013). An explanation for this can be found in the study of Fritsch et al. (2019). They argue that “entrepreneurship in its core includes behaviours such as creativity, recognition of opportunities, taking initiative, readiness to assume risk and introducing new ideas, products and services to the market. These behavioural elements are not only conducive to setting up one’s own business but also should be particularly relevant for innovation activity.” (Fritsch et al., 2019, p.3). Innovation is commonly acknowledged to be a principal means by which regions foster economic growth and competitiveness (Huggins & Thompson, 2015). The innovation approach to economic growth of Schumpeter (1934) suggests that markets tend towards disequilibrium as entrepreneurs contribute to the market’s process of ‘creative destruction’ with new innovations replacing old technologies (Huggins & Thompson, 2015; Schumpeter 1934). Therefore, successful regional economies are characterized as those associated with efficient innovation systems resulting from high levels of entrepreneurship, while weaker economies are those with failing innovation systems and lower levels of entrepreneurship (Huggins & Thompson, 2014; Huggins & Thompson, 2015). In this study, innovation activity can be defined as: “The process of transforming new ideas and knowledge into concrete products and services that are accepted in the marketplace (Fritsch et al., 2019, p.3).
As mentioned, the entrepreneurship-prone personality profile can be defined as “An entrepreneurial constellation of ‘Big Five’ traits within the person” (Obschonka et al., 2013, p.105). The ‘Big Five’ personality traits are agreeableness, neuroticism, extraversion, conscientiousness, and openness. The following paragraphs define above mentioned traits of the entrepreneurship-prone personality profile and their link with innovation activity.
increase the likelihood of becoming an entrepreneur. Putting this in line with theory from Schumpeter (1934), one could assume that lower levels of agreeableness have a positive impact on innovation activity since entrepreneurs contribute to innovations.
“Neuroticism represents individual differences in adjustment and emotional stability.” (Zhao & Seibert, 2006, p.260). Individuals high on neuroticism tend to experience a number of negative emotions including anxiety, hostility, depression, self-consciousness, impulsiveness, and vulnerability. Individuals who score low on neuroticism can be characterized as self-confident, calm, even tempered, and relaxed (Zhao & Seibert, 2006). Zhao and Seibert (2006) argue that neuroticism reduces the risk-taking behaviour associated with entrepreneurship. This, because entrepreneurs need to tackle phases of high uncertainty, failures, strong risks, and work stress. Those tasks demand lower levels of neuroticism (Obschonka et al., 2013). The same argument can be made about innovation (Lee, 2017).
“Extraversion describes the extent to which people are assertive, dominant, energetic, active, talkative, and enthusiastic.” (Zhao & Seibert, 2006, p.260). Individuals who score high on extraversion tend to be cheerful, like people and large groups, and seek excitement and stimulation. Individuals who score low on extraversion prefer to spend more time alone and are characterized as reserved, quiet, and independent (Zhao & Seibert, 2006). Lee (2017) argues that when populations are assertive, talkative, and enthusiastic, they may be more likely to engage in joint projects which results in higher rates of innovation. Moreover, people who have higher levels of extraversion are more likely to have greater social networks which is also particularly predictive of innovation activity (Lee, 2017).
“Conscientiousness indicates an individual’s degree of organization, persistence, hard work, and motivation in the pursuit of goal accomplishment.” (Zhao & Seibert, 2006, p.261). In the management literature, Drucker (1998) argued that hard work, an important characteristic of conscientiousness, is important for innovation. “Innovation often relied on seemingly obvious solutions which were put together through hard work, commitment and discipline rather than sudden moment of creativity.” (Lee, 2017, p.9).
narrow in interests, and unanalytical (Zhao & Seibert, 2006). In management literature, Judge and Zapata (2014) argued that people scoring high on openness performed better in occupations involving innovation. In economic geography literature, Richard (2002) argued that open geographic areas would attract open and creative workers that are most likely to produce new innovation. Moreover, those areas may be more tolerant and welcoming to others, take new ideas in and so be more innovative (Lee, 2017). Open individuals are also seen as people who like to try new things and therefore are more likely to engage in innovation activity (Lee, 2017).
In summary, low levels of Neuroticism and high levels of extraversion, conscientiousness, and openness have a positive relationship with innovation activity. The relationship between agreeableness and innovation activity stays rather ambiguous. However, in this study it is assumed that lower levels of agreeableness have a positive relationship with innovation activity since most studies state that lower levels of agreeableness have a positive relationship with innovation activity (Fritsch et al., 2019; Obschonka et al., 2010; Obschonka et al., 2011; Obschonka et al., 2012; Obschonka et al., 2013; Obschonka & Stuetzer, 2017; Rauch & Frese, 2007; Schmitt-Rodermund, 2004; Schmitt-Rodermund, 2007). The theoretical mechanisms outlined above reflect the relationship between the individual traits and innovation activity. However, as mentioned in the paragraph on the entrepreneurship-prone personality profile, the entrepreneurship-prone personality profile (which includes those individual traits) is regionally clustered (Obschonka et al., 2013; Fritsch et al., 2019). Therefore, it can be assumed that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity. This relationship has proven to be true in the United States, Germany, and in the United Kingdom (Fritsch et al., 2019; Obschonka et al., 2013). Therefore, it is most likely that this relationship also holds in the Netherlands.
Based on the beforementioned literature, the hypothesis is:
Methodology
This study follows a theory testing approach. Van Aken, Berends, and Van der Bij (2012) argue that the theory testing approach shall be used if the literature streams are already elaborated, which is the case for the trait theory. However, a literature gap can be identified in the explanation of the theory on the regional clustering of the entrepreneurship-prone personality profile and its relationship with regional innovation activity in the Netherlands.
The steps associated with the theory testing approach are followed, but with slight modification due to the fact that existing datasets are used. Data collection therefore has not been necessary. The steps in the theory testing approach, and the ones that have been followed in this study are first, definition of the business phenomenon and identification of the literature gap. Second, generation of conceptual model and hypotheses. Third, data analysis. Fourth, interpretation of results, reaching conclusions, and providing implications (Aken, Berends, & Van der Bij, 2012).
Regarding the methodology of this study, the remaining part of this chapter provides detailed information about the data collection and the sample of this study. Moreover, it provides information about the measures that are used in this study, and the analyses that are executed in order to investigate the research question.
Data collection and sample
et al., 2019). The third and fourth datasets are taken from ‘Centraal Bureau voor de Statistiek’ (CBS, 2019). These datasets contain data on the control variables population density and economic conditions.
The data of the beforementioned datasets are collected over multiple years. In this study, the average of those years is used in the analysis. This, because taking only one year could lead to biased results since that year could represent above average results.
Regarding the sample of this study, 159636 people participated in the online survey on personality data in the Netherlands. When filtered for useful cases, 108403 people were left in the sample. Participants who filled in erroneous zip-codes and participants who did not fill in their zip-code were removed from the sample. This, because the analysis is performed on a regional level and therefore zip-codes are a requisite. Additionally, only people between the age of 15 and 75 were left in the sample. People between the age of 15 and 75 are defined as the working population in the Netherlands (CBS, 2019). The sample of this study can be regarded as representative for the Dutch population. A correlation analysis including the population share of individual regions in the total population of the Netherlands and the regional shares of the sample in the total selection population resulted in a significant correlation of r = 0.956. In order to run a simple linear regression on the regional level, the dataset has been aggregated to a dataset of 40 cases, representing the 40 NUTS-3 regions.
Measurements
Regional share of people with an entrepreneurship-prone personality profile
The indicator for an entrepreneurship-prone personality profile based on the ‘Big Five’ personality traits measures the deviation from the statistical reference profile of an entrepreneurship-prone personality profile. This statistical reference profile consists out of highest scores on extraversion, conscientiousness and openness and lowest scores on agreeableness and neuroticism (Fritsch et al., 2019; Obschonka & Stuetzer, 2017). The individual level entrepreneurship-prone personality profile is the sum of the squared deviations of the individual ‘Big Five’ scores from this reference profile (Cronbach & Gleser’s D2, 1953). To achieve the regional value of the entrepreneurship-prone personality profile, average scores are taken based on the respondents’ current residence (zip-code) (Fritsch et al., 2019).
As mentioned before, the analysis is done on a regional level. More specifically, the level of analysis in this study is NUTS-3. NUTS-3 stands for the COROP-areas in the Netherlands. The reason to analyse the data on this regional level is that NUTS-3 allows for specific diagnoses in small regions (Eurostat, 2018). Moreover, the NUTS-3 classification has been implemented over entire Europe making regional statistics across the European Union easily comparable (RegioAtlas, 2019). Additionally, Lee (2017) argues that regional approaches to innovation helps explain patterns of innovation better. The regional innovation approach is based on the idea that innovation is determined by localized capabilities such as specialized resources, institutions, skills, and share of common cultural and social values (Lee, 2017). Studies on a national level would have missed these nuances and avoid the regional distribution of innovation.
An overview of the 40 NUTS-3 regions in the Netherlands can be found in appendix A.
Regional innovation activity
To measure the regional innovation activity, the number of patents per population in working age (in 10.000s) is used. If a patent has more than one inventor, the count is divided by the number of inventors, and each inventor is assigned his or her share of the patent. Patents are taken from the OECD RegPat database and will be assigned to the region in which the inventor claims his or her residence (zip-code) (Fritsch et al., 2019).
Control variables
The relationship suggested can also be influenced by factors not accounted for in the hypothesis. Therefore, control variables are included in order to test the relative relationship between the dependent variable and the independent variable. The control variables are selected based on theory. First, literature suggest that population density has an influence on the regional innovation activity (Frisch et al., 2019; Soo, 2018). Population density has been calculated by dividing the population by the area size. Second, literature suggest that economic conditions (GDP) has an influence on the regional innovation activity (Obschonka et al., 2013; Soo, 2018). Regions with a high density of population and economic activity may have higher start-up rates than rural regions and less prosperous regions due to better access to large and differentiated markets for input factors such as capital, labour, and services (Fritsch & Mueller, 2007; Obschonka et al., 2013). Third, dummies for the 12 provinces are included to control for their influence. This, because provinces are an important level of policymaking (Fritsch et al., 2019).
Analysis
After preparing the dataset, pre-analyses and hypothesis testing are performed in SPSS. The pre-analysis includes descriptive statistics, a correlation matrix, and a check for multicollinearity of the variables used in the analysis. If the correlation exceeded the threshold level (> 0.500), the variance inflation factor (VIF) scores are checked. Those scores should not exceed the critical level of five (Rogerson, 2001).
The hypothesis is tested by using a simple linear regression. The dependent variable in this simple linear regression is the number of patents per population in working age (in 10.000s) (innovation activity) and the independent variable is the regional share of people with an entrepreneurship-prone personality profile. Population density, economic conditions (GDP), and province dummies are included as control variables in the simple linear regression analysis in order to control for their confounding effects.
variable is normally distributed in some population. The hypothesis can be rejected if p < 0.05. Second, a log-transformation is used to deal with the skewed data and to generally improve the linear fit. Subsequently, a simple linear regression is performed.
Additional tests in the form of robustness checks are performed to test the structural validity of the simple linear regression analysis (Lu & White, 2014). First, a robustness check is performed by means of running a simple linear regression with different age groups. Instead of leaving people with all ages in the sample, people above the age of 45 are taken out of the sample. This, because this age is seen as the maximum probability age to become an entrepreneur (Gielnik, Wang, & Zacher, 2018; Kautonen, Down, & Minniti, 2014). Second, a robustness check is performed with NUTS-3 regions that has some duplicates in the NUTS-3-zip-code classification. In the main analysis those duplicates are dropped. In the robustness check those zip-codes that were dropped in the main analysis are included.
Results
In this section, the findings of this study are presented. First, the descriptive statistics, the test results of the correlation analysis and the multicollinearity check are presented. Second, the results of the simple linear regression are presented to confirm or reject the hypothesis as explained in the theory section. Finally, robustness tests results are presented, confirming (or not) the findings of previous tests.
Descriptive statistics and correlation analysis
Figure 1: distribution of the entrepreneurship-prone personality profile per NUTS-3 region in the Netherlands.
The results of the correlation matrix (table 2) indicates that there is first, a significant positive correlation between economic conditions and innovation activity (r = 0.522, p = 0.001). Second, a significant positive correlation between economic conditions and the entrepreneurship-prone personality profile is found (r = 0.311, p = 0.050). Third, a significant positive correlation between population density and innovation activity is found (r = 0.487, p = 0.002). Fourth, significant positive correlation between population density and the entrepreneurship-prone
personality profile is found (r = 0.536, p = 0.000). Finally, a significant positive correlation between population density and economic conditions is found (r = 0.542, p = 0.000). However, no significant correlation is found between the variables entrepreneurship-prone personality profile and innovation activity (r = 0.143, p = 0.386). Therefore, no significant relationship between the entrepreneurship-prone personality profile and innovation activity can be determined.
Table 2: Correlation matrix
Variables 1 2 3 4
1. Innovation activity -
2. Entrepreneurship-prone personality profile 0.143 -
3. Economic conditions 0.522*** 0.311** -
4. Population density 0.487*** 0.536*** 0.542*** -
* p < 0.1, ** p < 0.05, *** p < 0.01
Furthermore, the variables used in the analysis are tested on multicollinearity. There are three variables exceeding the threshold of 0.500. First, the correlation between economic conditions and innovation activity. Second, the correlation between population density and the entrepreneurship-prone personality profile. Third, the correlation between population density and economic conditions. However, the VIF scores are 1.403, 1.417, and 1.795 respectively. This does not exceed the critical level of five (Rogerson, 2001). Therefore, all variables are kept in the model.
Hypothesis testing
Table 3 shows the results of the simple linear regression analysis. The first model shows the results of the effect of the entrepreneurship-prone personality profile on innovation activity per NUTS-3 region. The second model shows the results of the effect of the entrepreneurship-prone personality profile on innovation activity per NUTS-3 region when the province dummies are included. The third model shows the results of the effect of the entrepreneurship-prone personality profile on innovation activity per NUTS-3 region when the control variables economic conditions and population density are included. The fourth model is the same as model 3, but here province dummies are also included.
Model 1 shows that the relationship between the regional share of people with an entrepreneurship-prone personality profile and the regional innovation activity cannot be determined, since f = 7.68 and p = 0.386. The model also shows a negative R² adjusted (-0.006) implicating insignificance of explanatory variables. This finding is not in line with findings documented in previous research (Fritsch et al., 2019; Obschonka et al., 2013). Those studies found that the regional share of people with an entrepreneurship-prone personality profile positively influences the regional innovation activity in the United States, Germany, and in the United Kingdom. Also, when no controls were added into the model. However, according to model 1 this cannot be confirmed for the Netherlands.
Model 2 shows that when the province dummies are included in the simple linear regression, the overall model becomes significant (f = 2.347, p = 0.035). The model has an R² of 0.489, implicating that model 2 explains 48.9% of the variance in the data. Even though that the model explains a significant amount of variance in innovation activity, no relationship can be determined between the regional share of people with an entrepreneurship-prone personality profile and the regional innovation activity (B = 0.197, p = 0.625).
an entrepreneurship-prone personality profile and the regional innovation activity (B = -0.391, p = 0.281).
Finally, model 4 shows that when all control variables (province dummies, economic conditions, and population density) are included in the simple linear regression, the overall model still is significant (f = 2.213, p = 0.043). The model has an R² of 0.535, implicating that model 3 explains 53,5% of the variance in the data. In this model, the relationship between the regional share of people with an entrepreneurship-prone personality profile and the regional innovation activity still cannot be determined (B = -0.111, p = 0.803). Besides, the significant positive relationship between population density and innovation activity, and economic conditions and innovation activity that has been found in model 3 is no longer present in model 4. This might be due to that the effect of population density and economic conditions on innovation activity is masked by the province dummies.
In summary, the sample of this study provides no sufficient evidence to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands. Hence, no evidence is found to support hypothesis 1.
Table 3: Simple linear regression
Model 1 B Model 2 B Model 3 B Model 4 B Constant 6.487 (7.005) 4.025 (7.611) -11.610 (7.420) -4.793 (9.588) Entrepreneurship-prone personality profile 0.316
(0.361) 0.197 (0.398) -0.391 (0.357) -0.111 (0.440) Economic conditions 0.275** (0.120) 0.119 (0.150) Population density 0.296** (0.142) 0.296 (0.249) Observations 40 40 40 40
Province dummies included No Yes No Yes
R² 0.020 0.489 0.353 0.535
R² adjusted -0.006 0.281 0.298 0.293
F-Value 0.768 2.347** 6.377*** 2.213*
Robustness checks
Robustness checks are preformed to test the structural validity of the simple linear regression analysis (Lu & White, 2014). First, a robustness check is performed by means of running a simple linear regression with different age groups. All people above the age of 45 are taken out of the sample. Descriptive statistics indicate that the mean of the entrepreneurship-prone personality profile in this sample is -19.50 (standard deviation = 0.321). The minimum of the profile in this sample is -20.10, and the maximum of the profile in this sample is -18.72. Comparing abovementioned results with the descriptive statistics of the overall sample indicates that the mean of the entrepreneurship-prone personality profile becomes even lower when all people above the age of 45 are excluded from the sample. Additionally, when running the simple linear regression for the four models, the overall models become slightly less significant. An explanation for this might be that the group of older people were selective and that they might have been more entrepreneurial. However, the results of the tests are still insignificant. This implies that the test results from the main analysis are robust.
Second, a robustness check is performed concerning the duplicates in the NUTS-3-zip-code classification. Similar results are found here, indicating that the issue concerning the duplicates did not have a significant impact on the simple linear regression analysis.
Discussion and conclusion
In this section, the findings of this study are discussed. First, a brief summary of the findings is given. Subsequently, the findings of this study are discussed. Second, the theoretical and policy implications of this study are given. Third, the limitations of this study are discussed and finally the overall conclusion of this study is presented.
Discussion
This study provides evidence for the regional clustering of the entrepreneurship-prone personality profile per NUTS-3 region in the Netherlands. This is in line with results from previous studies (Fritsch et al., 2019; Obschonka et al., 2013). However, no sufficient evidence is found to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands. This is not in line with results from previous studies. Results from the study of Fritsch et al. (2019) and Obschonka et al. (2013) shows that a high share of people with an entrepreneurship-prone personality profile in a region positively affects the innovation activity in that region. This result holds for the United States, Germany, and the United Kingdom.
businesses. Patents are expensive and time consuming. This discourages patent activity amongst smaller businesses, which lack the resources of larger businesses. (Storey & Greene, 2010). Arundel and Kable (1998) also show that patent activity in Europe is more likely among certain sectors (e.g. pharmaceuticals, chemicals, machinery, and precision instruments). Since there are more industries than beforementioned, innovation activity in those industries might not have been captured in this study. Additionally, Lee (2017) also argues that patenting is most likely to be associated with science and technologically focused industries whereas other types of ‘soft’ innovation may be more important in other service focused activities.
Second, even though that the entrepreneurship-prone personality profile is a widely studied phenomenon, the definition and therefore the measure of the entrepreneurship-prone personality profile in this study might have been too broad. The entrepreneurship-prone personality profile refers to extreme scores on the single ‘Big Five’ personality traits (low scores on the traits agreeableness and neuroticism and high scores on the traits extraversion, conscientiousness, and openness). However, it might have been the case that for some personality traits the level should have been medium instead of low or high. This thought is strengthened by the theoretical finding that the relationship between agreeableness and innovation activity is rather ambiguous. Literature suggests that on the one hand trust, an important characteristic of agreeableness, improves cooperation between partners and this is highly important for knowledge sourcing and so innovation (Huber, 2012; Lee, 2017). This implies that higher levels of agreeableness have a positive impact on innovation activity. On the other hand, Antoncic et al. (2015) argue that lower levels of agreeableness (which is characterized by manipulative, self-centred, suspicious, and ruthless (Zhao & Seibert, 2006)) increase the likelihood of becoming an entrepreneur. Putting this in line with theory from Schumpeter (1934), one could assume that lower levels of agreeableness have a positive impact on innovation activity since entrepreneurs contribute to innovations. In the end, it might be the case that medium levels of agreeableness have the most significant impact on innovation activity.
start-up rates than rural regions and less prosperous regions due to better access to large and differentiated markets for input factors such as capital, labour, and services (Fritsch & Mueller, 2007; Obschonka et al., 2013).
Theoretical and policy implications
As stated in the introduction, this research was aimed to address the missing empirical evidence of the regional clustering of the entrepreneurship-prone personality profile and its impact on the regional innovation activity in the Netherlands. This study was able to fill this gap by investigating this relationship in the Netherlands. Even though, this study did not find sufficient evidence to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands, as stated in the theory section of this study and as supposed by studies who investigated this particular relationship in other countries (Fritsch et al., 2019; Obschonka et al., 2013), this study contributed to the literature on geography on personality and economic geography by stating that no sufficient evidence is found to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands.
means of government steps that stabilize the financial market. By easing the pro-cyclicality within small and medium sized businesses’ loans, governments could stabilize the financial market of most OECD countries (the Netherlands included) (Seo, 2017). According to Seo (2017) this type of financial support contributes most to improved economic conditions. Moreover, small and medium sized businesses are seen as the main agent of employment (Seo, 2017). So, targeting small and medium sized businesses will most likely guarantee improvement of the economic conditions.
Limitations and further research
This study has a number of limitations. First of all, one of the biggest limitations of this study is concerned with measuring the dependent variable innovation activity. The regional innovation activity has been measured by using the number of patents per population in working age (in 10.000s). However, as mentioned in the discussion, taking only patent data as a measure of innovation activity might have missed the full innovation activity of that region. Moreover, the sample size of patent data might have been too small. Future research should take into account additional measures in order to capture the full innovation activity of a region. Additional measures of innovation activity should take into account innovation input measures, intermediate output measures, and direct measures of innovation output. Input measures include the number of knowledge workers (e.g. scientists or engineers) or the amount a business spends on research and development (Storey & Greene, 2010). Intermediate output measures include design rights, copyright rights, and trademark rights (Storey & Greene, 2010). Those intermediate output measures will measure the ‘soft’ innovation in service focused activities and innovation activity in other industries which according to Lee (2017) are missed when only measuring the number of patents per region. Finally, future research should take into account direct output measures. Direct output measures include directly surveying businesses about their innovative activity (Storey & Greene, 2010). This direct output measure of innovation activity avoids some of the size biases evident in both innovation input measures and intermediate output measures (Storey & Greene, 2010).
In this study no data was available, in the dataset on personality, on different kind of industries. Therefore, it has not been possible to check whether the data on innovation activity only has been captured in science and technologically focused industries. It is most likely that the innovation activity captured in this study is from those industries since patenting is most likely to be associated with those industries (Arundel & Kable, 1998; Lee, 2017). However, it would also have been interesting to see the type of innovation activity among other industries. Future research should incorporate the different kind of industries and the additional kind of measures of innovation activity to see the full distribution of innovation activity in a region and to be able to measure the full innovation activity of a region.
A second limitation of this study is concerned with measuring the independent variable the entrepreneurship-prone personality profile. As mentioned in the discussion, the definition and therefore the measure of the entrepreneurship-prone personality profile in this study might have been too broad. The entrepreneurship-prone personality profile refers to extreme scores on the single ‘Big Five’ personality traits (low scores on the traits agreeableness and neuroticism and high scores on the traits extraversion, conscientiousness, and openness). However, it might have been the case that for some personality traits the level should have been medium instead of low or high. Unfortunately, today’s theory and literature offer no clear basis for constructing an entrepreneurship-prone personality profile consisting of less extreme scores. Therefore, future research should look for a more accurate reference profile at the ‘Big Five’ level (e.g. constellation in which one or more traits have moderate values).
A third limitation of this study relates to the possibility to measure the relationship between the single ‘Big Five’ personality traits and innovation activity in the Netherlands. Lee (2017) argues that some personality traits of the ‘Big Five’ personality traits are seen as more important for innovation activity than other personality traits. In particular, the main personality trait associated with innovation activity is conscientiousness (Lee, 2017). This study tried to investigate the relationship between the single ‘Big Five’ personality traits and innovation activity. However, due to the low number of observations (N = 40) and the low variance in the single ‘Big Five’ personality traits, no relationship could be found. Future research should investigate the single ‘Big Five’ personality traits and their relationship with innovation activity in a deeper matter.
number of prior studies on regional personality differences (Fritsch et al., 2019; Obschonka et al., 2013), the results remain an important and meaningful first attempt to map the entrepreneurship-prone personality profile per the NUTS-3 regions in the Netherlands.
Overall conclusion
The research question of this study was “Is the entrepreneurship-prone personality profile regionally clustered per NUTS-3 region in the Netherlands and how does this profile affect the innovation activity per NUTS-3 region in the Netherlands?”. This study provides evidence for the regional clustering of the entrepreneurship-prone personality profile per NUTS-3 region in the Netherlands. However, contrary to the expectations, no sufficient evidence is found to conclude that a high regional share of people with an entrepreneurship-prone personality profile has a positive relationship with regional innovation activity in the Netherlands. Therefore, this research question could only be partly answered.
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Appendix A
Overview of the 40 NUTS-3 regions in the Netherlands
Table 1: Overview of the 40 NUTS-3 regions and their corresponding NUTS-3 code
NUTS-3 code NUTS-3 region
NL225 Achterhoek NL332 ‘s-Gravenhage NL324 Haarlem NL337 Leiden en Bollenstreek NL322 Alkmaar NL226 Arnhem/Nijmegen NL333 Delft en Westland NL112 Delfzijl en omgeving NL230 Flevoland NL326 Groot-Amsterdam NL339 Groot-Rijnmond
NL327 Het Gooi en Vechtstreek
NL323 IJmond
NL321 Kop van Noord-Holland
