Msc. Thesis
Small Business and Entrepreneurship
“The role of the environment in the relationship between
human capital and firm performance”
Paul Holtkamp
University of Groningen Faculty of Economics and Business
18 January 2015 Supervisor: A.J. Rauch Co-supervisor: F. Noseleit
p.e.holtkamp@student.rug.nl Student number 1995081
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Abstract
In this paper I try to indicate the moderating effect of the environment in terms of munificence, dynamism and complexity on the relationship between human capital and firm performance. With a sample 99 German and 19 Dutch firms, I found that education is not related to firm performance. Experience however seems to be related. Regarding the moderating effect of the environment, a high degree of munificence in the environment is associated with a weak relationship between management experience and growth. A high degree of dynamism is associated with a weak relationship between industry experience and profit margin. Complexity does not seem to have a moderating effect.
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1. Introduction
Entrepreneurs play an important role in the daily economy. They provide jobs, economic growth and innovation (Minniti & Lévesque, 2008). According to Van Praag (2007), entrepreneurs produce higher quality innovations and grow faster in terms of job creation compared to bigger companies. It is therefore beneficial for society when the economy contains healthy and profitable entrepreneurial firms. As long as the economy is not fully controllable, understanding entrepreneurship and the reasons why some firms are more profitable than others, is the most reasonable activity to undertake to navigate these entrepreneurial firms and therefore the economy towards high performance. To understand entrepreneurship in terms of the choice of becoming an entrepreneur and the economic outcomes of entrepreneurial activities, several determinants of entrepreneurship are defined. One of these determinants is human capital. Parker (2009) divides human capital in age, experience and formal education. Le (1999) divides it in education and labor market experience.
To understand why some entrepreneurs are more successful than others, it is interesting to relate the determinants of entrepreneurship to the success of the entrepreneurs, the performance of the firm. Many studies tried to find the relationship between human capital and success. For example, Ganotakis (2012) state that the management’s type and level of human capital, defined as education and experience, partly explains the performance of firms. According to Unger et al. (2011) human capital includes education, experience, knowledge and skills. In their meta-analysis they found a small but significant relationship between human capital and firm performance. The success of the firm depends partly on the human capital of the entrepreneur.
Education is the factor in human capital that can be stimulated by the government in terms of investments and regulations. If education increases entrepreneurial performance, investments in education is justified (Van der Sluis et al. 2008). Several studies examine the effect of education on firm performance, for example Lin et al. (2013) and Van der Sluis et al. (2008). Both studies report higher firm performance when the entrepreneurs are highly educated. Based on these studies, investments in education can be justified.
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human capital is more likely to be successful than an entrepreneur with a low level of human capital. However, human capital is not the only underlying factor of performance. The environment also affects firm performance. When the firm has no control over the changing environment, it faces uncertainty (Mohamad et al. 2011). The disability of a firm to monitor and to collect relevant information for decision making regarding the environment, will negatively affect its performance. (Duncan, 1972). Other studies however, stated that the environment’s uncertainty could be favorable to the firm. A changing environment forces the firm to change its strategy and this could increase its performance (Bratnicka & Bratnicki, 2013; Pett & Wolff, 2009).
Since the environment has a major effect on the performance of a firm, one could argue that the environment influences the relationship between human capital and firm performance. For example, in an environment that has low economic growth rates, it is probably useful to possess a high level of human capital. It is arguable that the manager with much experience is better able to handle the tough environment that faces low growth rates. However, in an environment with high economic growth rates, it might not be necessary to possess a high level of human capital since it is more likely to be successful than unsuccessful in such an environment.
Existing literature fails to provide information about this moderating effect of the environment on the relationship between human capital and firm performance. There are however many studies that try to explain the relationships between human capital and firm performance and between the environment and firm performance. Though, it can be very useful to know if the environment moderates the relation between human capital and firm performance. If so, the entrepreneur who owns limited human capital, should choose a different environment to start a business than the entrepreneur who owns significant human capital. This could also explain why some businesses in a particular industry fail and why others succeed.
In this paper I try to verify the moderating effect of the environment on the relationship between the human capital of the entrepreneur and firm performance. Therefore, the aim of this paper is to answer the next research question:
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I raised two sub questions to structure the paper and to help giving a clear answer on the research question.
What is meant by human capital and how does it influence firm performance?
In which way can the environment influence the relationship between education and performance?
In the next section of this paper (the literature review), I will elaborate on existing literature, including definitions and a conceptual model of the study. After that, I will explain the methodology of this research. I will continue with an analysis of the results. Finally, I will discuss the findings and give the implications and limitations of the study.
2. Literature review
In this section I will review the existing and related literature about topics presented in this paper. First, the concept human capital will be discussed. Also a relationship between human capital and firm performance will be presented and I will raise two hypotheses. Second, the moderating role of the environment on that relationship will be evaluated. Also here I will raise several hypotheses. Third, the performance aspect will be discussed. A conceptual model including all the hypotheses ends the literature review.
2.1 Human capital
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(Baum & Silverman, 2004). Before evaluating the role of human capital in firm performance, it is important to clarify the construct and to give a definition of human capital.
2.1.1 Definition
Human capital will be defined as:
The invested human capital in the form of education and experience (Davidson & Honig, 2003)
This definition includes two terms that also have to be defined. Education will be defined as: The total years and the level of prior education (Van der Sluis et al. 2008)
Experience is more difficult to define. In general, it will be defined as:
The number of years an individual has been able to work after completing his or her education (Robinson & Sexton, 1994)
In addition, in this paper I will make a distinction between industry experience, managerial experience and start-up experience. This because not every work experience may contribute equally to the relationship between human capital and performance. For example, a male entrepreneur starts a business in the fashion industry. He worked in this industry for 20 years. This seems like a high level of experience. However, it is not necessary that he performed a management job (or another function that requires some leadership). It is possible that he had a function of salesman in a clothing store. This off course is also experience, but is possibly less valuable in starting his own business. Also, industry-specific experience has in particular a positive effect on firm performance (Feeser & Willard, 1990; Bruderl & Preisendorfer, 2000). Returning to the example, imagine a second entrepreneur who also wants to start a business in the fashion industry. He also has 20 years of experience. But he didn’t perform a function in the fashion industry, he worked in the car industry. This experience is less valuable to his new business. Therefore, I will make a distinction between the different types of experience.
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experience that may or may not lead to knowledge and skills. The outcomes of these human capital investments are acquired knowledge and skills (Unger et al. 2011).
However, in this paper I will focus on the investments in human capital rather than on the outcomes of the investments. This because it is very hard to measure the outcomes of investments in human capital correctly. It is difficult to test all the knowledge and skills an individual has. An intelligence test can give an indication, but this will not cover all the aspects of the knowledge and skills of the person involved. To explain this, imagine for example a student that has to make a test at a University. He has studied very hard and knows almost everything. There are however 5 of the 100 subjects he understands wrongly. In the test that consist out of 10 questions, all the 5 subjects that the student doesn’t know are covered. This means the student who knows almost everything, will not receive a high grade. This example is a nice reflection of the problem of measuring the outcomes of investments in human capital.
2.1.2 Relationship education-performance
Becker (1964) states that people attempt to receive a compensation for their investments in human capital. Individuals that invested much in their human capital, for example in the form of education, strive in theory for higher economic benefits than individuals that invested less in their human capital (Cassar, 2006). This is not only the case for wage employment, but also for entrepreneurship. Entrepreneurs who invested significant in their education, strive for higher firm performance in terms of growth and profits than entrepreneurs who invested less in their education.
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Concluding from the meta-analysis of Van der Sluis et al. (2008), there is a positive relationship between prior education and performance. Both the number of years of education that have been pursued and the level of schooling show positive significant relationships with firm performance. The higher the education, the higher the chances that performance is good: the earnings are higher, survival changes are better and growth is more likely.
Also Robinson and Sexton (1994) suggest a positive relationship between education and the success of self-employed individuals in terms of earnings. According to Jo and Lee (1996) education is correlated with profitability, but not with growth. They define education as the degree to which an entrepreneur is educated (the level of education) and left the years of education out of account. They also state that the degree of relatedness between the education subject and the product of the new venture does not affect profitability.
Summarizing, existing literature shows a tendency regarding a positive relationship between education and firm performance. I will test this relationship by the next hypothesis:
H1: There is a positive relationship between the education of the entrepreneur and firm performance
2.1.3 Relationship experience-performance
According to Robinson and Sexton (1994), there is a positive relationship between experience and firm performance. They state however that experience has a weaker impact on firm performance than education. This is probably the result of the choice of the authors not to make a distinction between the types of experience of the entrepreneurs. The weaker impact could arise from the possible variation in experience.
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This vision can be confirmed by the work of Ganotakis (2012) and Lerner and Almor (2002) who state that industry-specific experience has in particular a positive effect on firm performance. Shane (2000) argues that entrepreneurs who have industry experience will have a better knowledge of any underdeveloped technological or marketing opportunities in that specific industry that provide a good potential for market exploitation. Also, because of entrepreneurial industry experience, the firm can benefit from past relationships with suppliers or customers (Marvel & Lumpkin, 2007).
Literature also shows a tendency regarding a positive relationship between experience and firm performance. Therefore,
H2: There is a positive relationship between the experience of the entrepreneur and firm performance
2.2 Environment
Another factor that influences the performance of a firm is the environment. The definition of environment that I use in this paper is:
All elements that actively and directly cooperated and competed with an organization (Dess & Beard, 1984)
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2.2.1 Environment and knowledge
To my knowledge, in existing literature no attention is paid on the moderating role of the environment on the relationship between human capital and firm performance. Previous research however does investigate the moderating role of education on the relationship between the environment and firm performance. It reflects the importance of the level of knowledge of the management (outcome of human capital investment), which reflects the capacity of changing a firm’s strategy when the environment forces the firm to do so.
The point of view that knowledge plays an important role in changing a firm’s strategy when the environment forces such action, is consistent with the resource-based view perspective of Barney (1991), which suggests that resources (like knowledge) contributes to competitive advantages. Boeker (1997) states that a manager’s experience and knowledge play an important role in gaining a competitive advantage.
However, no attention is paid on the moderating role of the environment on the relationship between human capital and firm performance. Since this paper does, it is first important to understand in which ways the environment can influence the firm. This is explained below.
2.2.2 Environment dimensions
As said, the environment can put pressure on the firm in different ways that will influence the performance of the firm. These ways are transferred into three dimensions, by Dess and Beard (1984). They distinguish three dimensions regarding the environment: munificence, dynamism and complexity.
Environmental munificence
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“how rich the environment is” in terms of resources. If a certain environment offers plentiful resources and competition is weak, the expectation is that firm performance is high.
Environmental dynamism
Environmental dynamism is based on the level of stability and predictability of the environment (Dess & Beard, 1984). Organizations prefer stability and predictability and seek therefore for homogeneous elements in their environments. Examples of strategies to increase stability and predictability are buffering, long-term contracts and vertical integration. According to Haleblian and Finkelstein (1993), the greater the environmental dynamism, the greater the difficulty in decision making and the greater the information-processing requirements. These information-processing requirements call for a certain level of knowledge.
Environmental complexity
Environmental complexity refers to the extent of different inputs and outputs of the environment (Dess & Beard, 1984). It concerns the number and dissimilarity of external elements relevant to an organization’s operations (Daft, 2001). Managers facing a more complex environment have greater information-processing requirements than managers facing a simple environment. Because of the many different inputs and outputs, firms in these environments should find resource acquisition or disposal of output more complex than firms in simple environments.
2.2.3 Moderating effects
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of the firm. Since the education of the entrepreneur proved to affect firm performance, what is the role of the environment in this relationship?
Munificence
13 H3a: The level of munificence in an environment, will moderate the relationship between
education and firm performance: a higher level and/or number of years of education will be more strongly associated with high firm performance when environmental munificence is low than when it is high
H3b: The level of munificence in an environment, will moderate the relationship between experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental munificence is low than when it is high
Dynamism
In a highly dynamic environment, the instability and unpredictability of the market makes it hard for entrepreneurs to start a business or to perform well. An investment that at first seems a great opportunity, can straight away change into a big disappointment. Therefore, entrepreneurs should read the market and handle the information that is available with care. According to Luo and Peng (1999), human capital increases the ability to scan the external environment, analyze changes and size opportunities. For example, if the entrepreneur runs a business in a dynamic environment and he has experience in that environment (industry specific experience), he is more likely to deal with the fast-changing environment and lead the firm to higher performance. It makes it easier to make decisions for the entrepreneur when he is able to process the information in a good way (Haleblian & Finkelstein, 1993). When the entrepreneur is not able to monitor the environment, which is possible when lacking human captial, performance will decrease (Duncan, 1972).
14 H4b: The level of dynamism in an environment, will moderate the relationship between
experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental dynamism is high than when it is low
Complexity
According to Thomas and Mengel (2008), education is an important tool for managers to successfully handle a complex environment. Managers and entrepreneurs in complex environments should possess competences like communication, organizational political skills and should understand the importance of visions, values and beliefs to run their business successfully. According to the authors, these competences are an important part of (entrepreneurial) education. This education “creates” students capable of governing rather than simply being governed. In simple environments, these competences are less important for high firm performance. As Dess and Beard (1984) stated that a complex environment goes with increased uncertainty, the entrepreneur in a complex environment, faces more uncertainty than an entrepreneur in a simple environment. Not everyone is able to deal with this uncertainty and according to Gibb (2002), this can be increased by education. He states that human capital increases the ability to deal with uncertainty through judgment. When lacking the human capital, the ability to deal with uncertainty by judgment is low. The entrepreneur will react on the basis of feelings, insecure expectations and incomplete argumented assumptions. Therefore, in complex environments, the human capital of the entrepreneur is an important factor leading to high firm performance because of the learned competences and the ability to handle uncertainty successfully. In simple environments however, human capital is a less important when it comes to firm performance. Therefore,
15 H5b: The level of complexity in an environment, will moderate the relationship between
experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental complexity is high than when it is low
2.3 Performance
The dependent variable of this study is performance. Accurate and appropriate measurement of performance is critical in entrepreneurship research. Without an adequate definition of performance and its measurement, it becomes difficult to develop useful prescriptions for entrepreneurs (Murphy et al. 1996). Therefore, for a research to make sense, performance should be defined properly. Strategic management research has tried to find the “right” measure of firm performance for decades (Steigenberger, 2014), but there is no truly right measure, because measures has its strong and weak aspects.
This paper uses a definition based on the definition of performance as given by Richard et al. (2009):
Performance encompasses three specific areas of firm outcomes: financial performance (profits, returns on assets, etc.), product market performance (sales, market share, etc.) and shareholder return (total shareholder return, economic value added, etc.)
However, this study uses only the first two outcomes: financial performance and product market performance. The reason for this is that shareholder return is more applicable to larger firms, instead of entrepreneurial firms. Besides, this study also takes the respondents’
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2.4 Conceptual model
To clarify the idea of this study and to give a quick overview of the constructs, I present a conceptual model:
The model shows all the hypotheses and the expected effects of the factors. In the next session, the methodology of the study will be presented.
3. Methodology
The methodology will discuss the data collection, the measurements of the constructs and the analyses used in this research.
3.1 Sample
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business himself/herself. This because entrepreneurship is often defined in terms of creation of new businesses (Rumelt, 1987; Low & MacMillan, 1988; Gartner, 1988; Morris, 1998). Third, the participant had to manage the business on a daily basis. This criteria is raised to make sure that the owner is the actual manager of the firm and makes the important decisions inside the business. Fourth, the firm had to have at least 1 permanent employee, next to the owner. This criteria was necessary to analyze the firm performance as growth correctly. For a business without any employees, the step to hire new personnel is much higher than for businesses that already have employees. The whole structure of the firm changes when it grows from 0 to 1 employee. This could influence the results of the study. Therefore, the firm had to have at least 1 permanent employee. At last, the business had to be at least 1 year old and up to 8 years old. More than 1 year because when a firm is less than 1 year old, it is hard to evaluate growth and an increase in profit margin. Less than 8 years because in the first few years, firms tend to grow faster (Storey & Greene, 2010). When comparing younger firms and older firms with each other, the age of the firm could influence the firm performance.
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3.2 Measurement
Here the measurements of the different variables (education, experience, firm performance and the moderating environmental effects [munificence, dynamism and complexity]) are explained.
3.2.1 Education
Since education is defined as the total years and the level of prior education, the questionnaire contained one question that asked for the highest achieved education (no education, primary school, lower-level applied education, preparatory middle-level applied education, secondary education, higher-level secondary education, university of applied sciences, university or PhD) and one question regarding the total years of education. The total years of education is a numerical variable and the level of education was coded ascending: no education received a 1, PhD received a 9 (Van der Sluis et al. 2008).
3.2.2 Experience
Whereas the factor education was measured quantitatively, experience was measured qualitatively. This because experience was not included in the database of the study of Rauch. Therefore only the 19 Dutch participants answered questions regarding their experience. Since a low sample decreases the reliability of a study (Van Aken et al. 2012), I chose to measure experience qualitatively. No statistical tests were used to accept or reject the hypotheses. Instead, I transformed the qualitative results into quantitative results. This was possible because the questions regarding experience asked for facultative information. Then, I used the analyzing method of Eisenhardt (1989) and did within-case analyses and cross-sectional analyses to accept or reject the hypotheses.
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concerning industry (coded as 1 for no and 2 for yes). To determine managerial experience, I asked whether he or she ever fulfilled a management function before (coded as 1 for no and 2 for yes). To determine start-up experience, I asked whether he or she ever raised a company before (coded as 1 for no and 2 for yes).
3.2.3 Performance
According to Murphy et al. (1996), performance is measured in dimensions. Each dimension can be measured in different ways. An example of a dimension is profit, which can be measured by (for example) return on sales, net profit margin or net profit level. Murphy et al. (1996) evaluated the performance dimensions of 71 studies after performance. The results show that a large share of the dimensions used existed out of efficiency (30), growth (29), profit (26) and size (15). Only 5 studies used market share as a dimension for performance. Murphy et al. (1996) state studies should include multiple dimensions of performance, if that is possible. This aligns with the definition of performance that I use in this paper, where 3 different dimensions are taken into account. First, the dimension profit was measured by profit margin (net profit of 2013 divided by the sales of 2013). In the interview I asked the entrepreneur for this financial data. Second, the dimension growth was measured by the growth of employees. I also asked this data during the interview. The growth was measured absolutely between 2013 and 2014. I chose for an absolute measurement instead of a relative measurement because the database consist primarily out of small firms (less than 100 employees). Only one firm in the database of 118 firms had more than 100 employees. For small firms, relative growth of employees can give extraordinary rates. When, for example a firm with 3 employees hires 3 new employees in one year, it has a growth rate of 100%. In comparison, when a firm with 60 employees hires 3 new employees, this is only a growth rate of 5%. This is in line with Delmar et al. (2003), who state that small firms can reach impressive relative growth. Finally, the success/fail dimension was measured by subjective satisfaction perception of the respondents (respondent satisfaction about business performance on a scale of 1 to 7 where 1 is the lowest amount of satisfaction and 7 the highest, scale source: Wiklund & Shepherd, 2003). This question was captured in the questionnaire.
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Because I needed sales and profit for the measurement of the profit margin, the sample for the performance measurement profit margin, reduced to 64.
3.2.4 Moderating factors
The moderating factors were measured by several questions in the questionnaire. Environmental munificence was measured by 3 questions (EH1 – EH3) regarding the abilities to invest and the threats of closure and whether the company is able to control and manipulate the environment to its own advantage (scale source: Khandwalla, 1976). Environmental dynamism was measured by 5 questions (SPS1 – SPS5)regarding the importance to adapt frequently to the environment and the predictability of the market (scale source: Miller & Friesen, 1982). Finally, environmental complexity was measured by 2 questions (CPL1 & CPL2) regarding the technological complexity and the importance of R&D activities (scale source: Khandwalla, 1976). The importance of R&D activities is a parameter for environmental complexity because complexity is characterized by many different inputs and outputs. It is reasonable to say that a firm in an environment that contains many different inputs and outputs requires R&D activities to invent new products that differ from existing products (from competitors). When there are none or only a few different inputs and outputs, the firm requires low R&D activities because the outputs of firms in the market are more or less the same.
21 Table 1: Chronbach’s Alpha
Variable Chronbach’s Alpha
Munificence 0,731
Dynamism 0,807
Complexity 0,791
3.2.5 Control variables
I included two additional variables to control for the possible effects of other variables than the dependent variables. The control variables were gender and country. Several studies have shown that firms owned by women underperform compared to firms owned by man (Brush et al. 2006; Fairlie & Robb, 2009). To prevent the results from differences between males and females, gender is one of the control variables. Gender was coded as 1 for male and 2 for female. The variable country was coded the same way. Germany was 1 and The Netherlands were 2. This to prevent cross-country differences.
3.3 Statistical analyses
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Multicollinearity occurs when the independent variables of the study are highly correlated with each other (Donald & Robert, 1967).
Then, several hierarchical regression analyses were performed to test the hypotheses. This analyses consisted out of 4 models. Model 1 included the control variables, model 2 included the independent variables education level and education in years (to test H1), model 3 included the variables munificence, dynamism and complexity and model 4 included the moderating variables (to test H3a, H4a and H5a). In model 4, the independent and moderating variables were mean centralized and multiplied by each other (Cohen, 1977).
3.4 Qualitative analysis
Experience was tested qualitatively. Here, only the Dutch firms were involved, since they answered questions regarding experience in the interview. In this paragraph I will explain how I did this. As in the quantitative analysis, the measurements of performance were the same. Of the 19 firms, 9 were willing to give their sales, investments and profit. Therefore, for the performance measurement profit, a sample of 9 firms was used. Each of the 19 firms were willing to give their amount of employees and the degree of satisfaction they had with their business. Therefore, for the performance measurements growth and satisfaction, the sample consisted out of all 19 firms.
In the analysis, each performance measurement was done separately. The performance values were divided in 3 groups: high performance values, average performance values and low performance values. The aim was to produce equally large groups. For example, for the performance measurement profit margin (with a sample of 9), each group contained 3 firms. The high performance group contained 3 firms with a profit margin larger than 0,1, the average performance group contained 3 firms with a profit margin of 0,1 and the low performance group contained 3 firms with a profit margin lower than 0,1.
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an entrepreneur without experience. However, this was never the case. It also happened that there were entrepreneurs in the low performance group who had experience or that entrepreneurs in the high performance group had no experience. I tried to find the explanation for these cases by the moderating environmental factors. As I did for performance, I also grouped the firms by their mean levels of munificence, dynamism and complexity. Returning to the example earlier this paragraph, imagine that of the high performance firms, 2 experience high complexity, of the average performance firms, 1 experiences high complexity and of the low performance firms, 2 experience high complexity. The next table (table 2) will clarify this example.
Table 2: Example qualitative analysis
Firm Performance group Management experience Complexity group
A High Yes High
B High Yes High
C High No Low
D Average Yes Low
E Average No Average
F Average Yes Average
G Low No High
H Low No High
I Low Yes Low
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complexity, experience is positively associated with performance and the absence of experience is negatively associated with performance. This suggest that a high level of complexity strengthened the relationship between experience and performance, whereas a low level of complexity weakens the relationship between experience and performance. Therefore, I would accept H5b.
This was an example, but I conducted the qualitative analysis the same way. I did this for all 3 performance measurements, for all 3 types of experience and for all 3 environmental moderating variables. That means a total of 9 tests per moderating variable. Table 3 shows all the value ranges of each variable for each group. To explain, for the performance measure growth, the high performance group contained firms with a growth of employees (growth of employees more than 0). The average level growth performance group contained firms that did not grow (growth of employees of 0). And the low level growth performance group contained firms that shrank (growth of employees less than 0). For the performance measurement profit margin, the high level munificence group contained firms with a value higher than 5. For the performance measure growth, the high level munificence group contained firms with a value higher than 3,5.
Table 3: Value ranges of each variable for each group Group
Variable Low Average High
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4. Data analysis
In this section, I will present the results of the study. Because the study consists out of a quantitative and a qualitative part, the results section will be split into two parts. First, I will review the quantitative results of the factor education and after that, I will review the qualitative results of the factor experience.
4.1 Results quantitative part
First, I present a descriptive analysis of the variables used in the study. This is shown in table 4. I want to make two short notices. First, profit margin is the only variable with a sample lower than 118. Only 64 respondents were willing to give their sales and profit. Second, the highest value of profit margin is 0,9. This means that at least for one firm 90% of its sales is profit. That is a remarkable high percentage.
Table 4: Descriptive analysis
Variable N Minimum Maximum Mean Std. Deviation
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Table 5 shows the correlations of the variables used in the regression analysis. Several correlation values are significant. Education in years, munificence and complexity are correlated with the level of education (all at the 0,05 level). Dynamism and satisfaction are both correlated with munificence (at the 0,01) level. Complexity, growth and satisfaction are correlated with dynamism. Remarkable is that profit margin is negatively correlated with the level of education and the years of education. However, both are not significant.
Table 5: Intercorrelation table
Variable 1 2 3 4 5 6 7 8 1 Education level 2 Education years N 0,24* 118 3 Munificence N -0,19* 118 0,01 118 4 Dynamism N 0,04 118 -0,03 118 0,31** 118 5 Complexity N 0,21* 118 0,01 118 0,03 118 0,43** 118 6 Profit margin N -0,11 64 -0,04 64 -0,10 64 -0,14 64 -0,02 64 7 Employee growth N -0,06 118 0,09 118 0,18 118 0,20* 118 0,08 118 -0,17 64 8 Satisfaction N 0,01 118 -0,01 118 -0,27** 118 -0,24** 118 -0,10 118 0,19 64 0,14 118 * P < 0,05 ** P < 0,01
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multicollinearity exists. The value of 5 is often used as a threshold (Kutner et al. 2004). As is shown in table 6, none of the VIF values exceed 5, which means that multicollinearity is not a problem in the regression analysis.
Table 6: VIF values
Variable VIF value
Education level VIF value Education years Education 1,544 1,222 Education x munificence 1,251 1,140 Education x dynamism 1,309 1,342 Education x complexity 1,080 1,261 Gender 1,096 1,102 Country 1,433 1,096
4.1.1 Performance measure profit margin
With the knowledge that multicollinearity is not a problem, I continue with the regression analysis. Since I use three different performance measures, three regression analyses are performed. The first, of which the results are show in table 7, uses the performance measure profit margin.
Table 7 shows that neither the control variable gender, nor country is significant. Thus, gender and country do not seem to create significant differences in profit margin. Also, the level of education and the years of education are not significant. Education therefore does not explain the profit margin of the firm. The results also do not show significant results for the moderating factors in model 4.
28 Table 7: Regression analysis with performance measure profit margin
Model 1 Model 2 Model 3 Model 4
Variable β β β β Model 1 Gender 0,108 0,116* 0,149* 0,153* Country -0,012 0,021 0,059 -0,044 Model 2 Education level -0,020 -0,033 -0,008 Education years 0,000 0,001 -0,005 Model 3 Munificence -0,015 0,024 Dynamism -0,024 -0,047 Complexity 0,027 0,009 Model 4 Edu L x Mun 0,029 Edu L x Dyn -0,004 Edu L x Com -0,021 Edu Y x Mun -0,034 Edu Y x Dyn 0,015 Edu Y x Com 0,024 Total variance (R²) 0,056 0,075 0,126 0,278 Change in R² 0,056 0,019 0,051 0,152 Significance of change 0,177 0,555 0,370 0,135 Df = 62 * = P < 0,05
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In table 8, I consider the growth of employees as performance measurement for the regression analysis. Also here, the control variables are not significant. The independent variables of education are also not significant. The same holds for the moderating factors. Besides, also in this table, none of the models are significant.
Table 8: Regression analysis with performance measure employee growth
Model 1 Model 2 Model 3 Model 4
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4.1.3 Performance measure satisfaction
Table 9 shows the results for the performance measurement satisfaction. Remarkable is that in these results, the control variable gender shows a significant positive effect in all three models. This suggest that female entrepreneurs are more satisfied than male entrepreneurs. Unfortunately no other results are meaningfully significant for this performance measurement. Model 1 and 3 in these results are both significant. Model 2 and 4 however are not.
Table 9: Regression analysis with performance measurement satisfaction
Model 1 Model 2 Model 3 Model 4
31 Change in R² 0,064 0,003 0,087 0,013 Significance of change 0,026* 0,837 0,015* 0,950 Df = 113 * = P < 0,05 ** = P < 0,01
4.2 Results qualitative part
Also the results of the qualitative part of the study are divided in three paragraphs, each with its own performance measure.
4.2.1 Performance measure profit margin
First, there seems to be a positive relationship between industry experience and profit margin. The firms of entrepreneurs with more industry experience, achieve higher profit margins. All 3 entrepreneurs that manage firms that achieve a “high” for profit margin, have industry experience (100%). Of the three entrepreneurs who manage “average” profit margin firms, also all 3 have industry experience (100%). The other 3 entrepreneurs who manage “low” profit margin firms, only 1 has industry experience (33%). Table 10 shows the results.
Table 10: Qualitative results with performance measure profit margin % of firms in the concerning group that have an
entrepreneur with the concerning experience
High Average Low
Management experience 33% 100% 67%
Industry experience 100% 100% 33%
Start-up experience 33% 67% 33%
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Now the relationship between industry experience and performance is established, I will search for a moderating effect. The data shows that the degree of dynamism in the environment has a negative moderate effect on the relationship. When the degree of dynamism in the environment is high, industry experience does not necessarily lead to high performance. However, when the degree of dynamism is low, industry experience is positively associated with high performance.
4.2.2 Performance measure growth (of employees)
With the performance measure growth, none of the experience types seem to be related to performance. This is shown in table 11. However, because the group “low” contains just 2 firms, it is worth it to look at the moderating factors.
Table 11: Qualitative results with performance measure growth (of employees) % of firms in the concerning group that have an
entrepreneur with the concerning experience
High Average Low
Management experience 75% 44% 50%
Industry experience 56% 75% 50%
Start-up experience 56% 63% 50%
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4.2.3 Performance measure satisfaction
Finally the performance measure satisfaction. Both management experience and start-up experience seem to be positively related to satisfaction. Table 12 shows that 100 % of the entrepreneurs that are highly satisfied of their business, have management experience and start-up experience. For average satisfied entrepreneurs this is 56% and for low satisfied entrepreneurs this is also 56%.
Table 12: Qualitative result with performance measurement satisfaction Satisfaction
High Average Low
Management experience 100% 56% 56%
Industry experience 100% 56% 67%
Start-up experience 100% 56% 56%
For this performance measurement, I did not find any moderating factors.
4.3 Summary results
To clarify the results, I will give a summary. The structure is based on the hypotheses. H1: There is a positive relationship between the education of the entrepreneur and firm
performance
34 H2: There is a positive relationship between the experience of the entrepreneur and firm
performance
Experience is partly positively related to firm performance. Industry experience is positively related to profit margin and management experience and start-up experience are both positively related to satisfaction. Therefore, I accept H2.
H3a: The level of munificence in an environment, will moderate the relationship between education and firm performance: a higher level and/or number of years of education will be more strongly associated with high firm performance when environmental munificence is low than when it is high
The level of munificence does not moderate the relationship between education and performance. Therefore, I reject H3a.
H3b: The level of munificence in an environment, will moderate the relationship between experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental munificence is low than when it is high
Munificence negatively affects the relationship between management experience and growth. However, it does not affect the relationships between any form of experience and profit margin or satisfaction. It also does not affect the relationships between industry experience and growth and start-up experience and growth. This means munificence only has an effect on 1 of the 9 relationships. Therefore, I reject H3b.
H4a: The level of dynamism in an environment, will moderate the relationship between education and firm performance: a higher level and/or number of years of education will be more strongly associated with high firm performance when environmental dynamism is high than when it is low
35 H4b: The level of dynamism in an environment, will moderate the relationship between
experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental dynamism is high than when it is low
Like munificence, dynamism has a moderating effect on 1 of the 9 relationships. There seems to be a negatively moderate effect between industry experience and profit margin. Because this is the only moderating effect, I reject H4b.
H5a: The level of complexity in an environment, will moderate the relationship between education and firm performance: a higher level and/or number of years of education will be more strongly associated with high firm performance when environmental complexity is high than when it is low
Complexity seems not to be a moderator for the relationship between education and firm performance. Therefore, I reject H5a.
H5b: The level of complexity in an environment, will moderate the relationship between experience and firm performance: a higher level of experience will be more strongly associated with high firm performance when environmental complexity is high than when it is low
The same occurs for the relationship between experience and firm performance. Therefore, I reject H5b.
5. Discussion
36
and start-up experience. Firm performance is operationalized in 3 ways: profit margin (profit divided by sales), growth (of employees) and satisfaction of the entrepreneur about his business.
I conducted interviews and questionnaires to gather data. 99 German and 19 Dutch firms were willing to participate. To test the hypotheses, I did a quantitative research for education and a qualitative research for experience. The results of this study suggest that there is partly evidence for the relationship between human capital and firm performance. The data shows that education (both the level and the years of education) is not related to firm performance. However, experience seems to be related to firm performance.
5.1 Answer on the research question
The research question of this study was:
What is the moderating role of the environment on the relationship between human capital and entrepreneurial firm performance?
Based on the results of my study, the answer on this question would be that the environment barely seems to have a moderating effect on the relationship. For education, none of the environmental dimensions seem to moderate the relationship. For experience, I found 2 moderating effects. The degree of munificence in the environment negatively effects the relationship between management experience and growth. The degree of dynamism in the environment negatively affects the relationship between industry experience and profit margin.
5.2 Comparing the results with existing theory
37
significant coefficients. This meta-analysis shows that in general, the higher the type of schooling, the higher the chances of better performance. The research consisted largely out of firms in the USA, which have higher positive coefficients for both the level and the years of education than other countries. The reason for this according to the authors is that the educational environment in the USA is more conducive for entrepreneurship. This could also be the reason for the insignificant negative results in this study. Perhaps the Dutch and the German educational environments are less conducive towards entrepreneurship. In 1992, Dana (1992) concluded that the USA indeed place more emphasis on entrepreneurship in education than Europe. Nowadays, this difference is minimized and also the education in Europe focusses more on entrepreneurship (Klandt, 2004). Assuming that the participants of my research went to school several years ago, it is imaginable that their education did not put much emphasis on entrepreneurship and that could be the reason why the results of the study regarding education and firm performance are negative and insignificant.
The results show that experience is positively related to firm performance. This is the case for as well industry experience, as management experience as start-up experience. In the meta-analysis from Song et al. (2008) industry experience is also positively related to experience. They however did not find any relationship between start-up experience and performance. Contrary, Delmar and Shane (2006) did find this relationship and stated that it was positive. Wise and Valliere (2014) found that management experience also was positively related to firm performance. Also Storey and Greene (2010) state that experience facilitates higher firm performance. My results match these findings and emphasize that experience is positively related to firm performance.
The environmental dimensions barely seem to have a moderating effect on the relationship between human capital and entrepreneurship. Only the degree of munificence and the degree of dynamism seem to moderate the relationship between experience and performance. Both munificence and dynamism seem to negatively moderate the relationship. The negative moderating effect of munificence matches H3b. However, because this only is the case for the relationship between management experience and growth, H3b is rejected.
38
market understanding is important regarding a dynamic environment. Liu (2012) agrees and states that entrepreneurs that lack experience are passively waiting for governmental solutions to their problems and trying adjustments in the wrong way. This decreases their performance. On the other hand, one could argue that dynamism should have a negative moderate effect. In a highly dynamic environment, with many inputs and outputs, it may be easier to find valuable opportunities. Accordingly, in a less dynamic environment, it is more important to possess the human capital in order to find opportunities easier. In a highly dynamic environment however, human capital is not very necessary because the environment creates enough opportunities itself. This reasoning is then in line with the results of this study.
5.3 Theoretical implications
Contrary to most existing research, the results of this study show that education is not related to firm performance. One could therefore suggest that education, contrary to much existing research, is not that important when it comes to firm performance, like Baum and Silverman (2004). I suggest there are two reasons why education may not be that important. First, other factors than education are more important to achieve high firm performance. This could for example, as confirmed by this research, be experience. People gain skills like mathematics and communication by education, but also the ability to discuss and understand different social and economic perspectives. However, this may not always be very relevant for running a business. There are of course skills and abilities needed when being an entrepreneur that are taught in any form of education, but maybe the important skills and abilities to achieve high performance are obtained with experience.
Second, as already discussed, education in Germany and the Netherlands has lack of entrepreneurial focus. The current types of education pay too little attention to entrepreneurship and do not cover the important aspects of running an own business. Preparing students better in terms of (for example) practical courses, internships and specific market related knowledge, could lead to higher firm performances.
39
could imply that for entrepreneurs in The Netherlands, education is more important than for entrepreneurs in India.
5.4 Practical implications
Although most environmental dimensions do not seem to have a moderating role on the relationship between human capital and firm performance, munificence and dynamism have a negative moderating effect on the relationship between experience and firm performance. This result could have an influence on the choice of an entrepreneur in what market he or she wants to set up his or her business. For example, a potential entrepreneur has two passions in which he wants to start a business: accounting services and the food industry. He doubts between setting up an accounting office or a new factory that produces bread on a very large scale. The accounting industry is characterized as a high munificent industry because of its high potential growth rates (Farrell, 2008). The food industry (especially bread) is characterized as a low munificent industry because of the low potential growth rates (Farrell, 2008). The entrepreneur hasn’t got any experience in either of the industries. The negative moderating effect of munificence, on the relationship between experience and firm performance could influence the choice of this entrepreneur. Because setting up a factory to produce bread is associated with low munificence, experience in this industry is positively related to firm performance. The entrepreneur however has no experience and would therefore choose for setting up a new accounting office. If he however had experience in the food industry, he should choose for setting up a factory to produce the bread because this would increase his changes to be successful.
5.2 Limitations and further research
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quantitative study was not even possible with a sample of 19. The qualitative study was a solution, but a quantitative study would be more reliable.
Another limitation is the subjectivity of the environmental dimensions. The munificence, dynamism and complexity of the environment of the businesses were based on answers by the respondents. These were subjective questions based on a scale from 1 to 7. Although this seems a good measurement, 2 entrepreneurs in the same environment can have very different feelings and perceptions about the environment and give different answers to the same questions. Also, some entrepreneurs may not be aware of the environment in which they operate. Their answers on the questions about the environment could therefore be less relevant and reliable.
Therefore, for future research (and to extend this study), I would suggest a larger sample for both education and experience. This would increase the significance and reliability of the outcomes. Another suggestion would be to rate the degrees of munificence, dynamism and complexity externally. Based on existing information (for example government information) the degrees of the dimension could be established beforehand per branch. Then, the subjectivity of the answers is eliminated.
Also, in future research the roles of munificence and dynamism as moderating factors should be further discussed. In this research, both munificence and dynamism seem to have a negative moderating effect on the relationship between experience and performance. Especially for dynamism, it would in future research be interesting to examine the direction of the moderating effect of dynamism, positive or negative. This because existing literature suggest a positive effect, but the results of my study are negative.
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Appendix 1: Interview questions
1. Have you ever fulfilled a management function before?
2. Have you ever worked in this industry before?
3. Have you ever started a company before?
4. Can you indicate the firm’s profit and sales of the year 2013?
