Master Thesis in Entrepreneurship
Predictors of the Willingness to Work
for a Startup
Matthijs de Vroome
2509536
Vrije Universiteit & Universiteit van Amsterdam, The Netherlands
Supervisor: Bram Kuijken Second corrector: Yuval Engel Academic year: 2015-2016 Date: 31/12/2015
Statement of Originality
This document is written by Student Matthijs de Vroome who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Acknowledgements
I would like to gratefully acknowledge and thank Dr. Bram Kuijken, who has been my supervisor during the writing of this thesis. He has helped me by reviewing this thesis and has given me many useful suggestions, which I have benefited from. I would also like to thank my girlfriend for reviewing this thesis as well and thank my family and friends for helping me find many respondents for my survey in a short amount of time. I hope this thesis will be of use for its readers.
Table of Contents
1. Introduction ... 5 2. Theoretical framework ... 9 2.1 Recruitment ... 9 2.2.1 Environment startup: ambiguity ... 10 2.2.2 Characteristic employee: Ambiguity tolerance ... 11 2.3 Similarities entrepreneurs ... 12 2.4 Innovativeness ... 14 2.5 The desire to take risks ... 15 2.6 Locus of control ... 16 2.7 Socio-demographic characteristics: parental entrepreneurship ... 18 3. Method ... 20 3.1 Data and Sample ... 20 3.2 Definition Startup ... 21 3.3 Control Variables ... 21 3.4 Independent Variables ... 23 3.5 Dependent Variable ... 24 3.6 Reliability analysis ... 25 4. Results ... 26 4.1 The correlation ... 26 4.2 Multicollinearity ... 26 4.3 Hypothesis testing ... 27 5. Discussion ... 32 5.1 Main findings ... 32 5.2 Theoretical implications ... 35 5.3 Managerial implications ... 35 5.4 Limitations and future research ... 36 5.5 Conclusion ... 38 6. Literature ... 39 7. Appendix ... 471. Introduction
Startups often have difficulties identifying and attracting competent employees that fit their firm well. Literature related to HRM is mostly written about bigger firms, while literature concerning the HRM of startups is lacking. It is still unclear what attracts employees to a startup. In this article we develop and test a number of theoretical concepts to predict what influences an individual to work for a startup. Using data from a varied number of people, the results suggest that there are indeed some predictors of the willingness to work for a startup. A person’s innovativeness and level of risk-seeking seem to have an influence on this. Other characteristics and socio-demographics will also be discussed, as well as some control variables. Doing so this study hopes to shed some more light on the underexplored topic that is the HRM of a startup.
Keywords: startup, startup employment, HRM, tolerance of ambiguity, innovativeness, risk-seeking, locus of control, parental
entrepreneurship.
Many scholars have written about the importance of entrepreneurs, specifically the importance of startups that they create. “New firm formation has been recognized for a long time as an important source of economic growth and labor demand (Praag & Ophem, 1995). Besides job creation, new firms also have an effect on innovation, contribute to competition and economic efficiency, open up chances for upward social mobility and stimulate industrial reorganization (Piore and Sabel, 1984; Acs, 1999; Birch 1979 & Rainnie, 1989).
This is why the topic of entrepreneurship has gained interest among scholars and policymakers. Nowadays governments are actively promoting entrepreneurship (Storey, 2006). Governments are continuously working to increase their understanding of entrepreneurship. Studies have been done to gain more insight in how to create opportunities
for entrepreneurs and how to increase their willingness to form a startup. Antecedents of entrepreneurship have been identified, which are used by governments to influence the formation of startups to create a healthier economy and stimulate the growth of markets (Davidsson, Lindmark, & Olofsson, 1998; Praag & Ophem, 1995).
Although entrepreneurship can benefit the economy, many startups are not able to survive and die an early death (Scott & Bruce, 1987). How does a startup overcome this obstacle? One of the key challenges for startups is to locate and attract highly talented employees (Aldricht, 2002). Development of human resource has become a huge aspect of professionalization, even more so for high-technology sectors where human capital is the most important factor (Hellman & Puri, 2002). Organizations have to continuously develop and provide products and services that are based on strategies that are created by employees, therefore employees are extremely valuable (Meaghan et al., 2002). Organizations are now recognizing that human capital is one of the key sources of competitive advantage in a competitive business environment (Ployhart & Moliterno, 2011).
However there has not yet been a study conducted that looked further into reasons why employees are willing to work for these startups. This is a current research gap. Zingales (2000) states that human capital is central to the development of new firms. Having competent employees will result in a higher firm performance (Wright, Dunford & Snell, 2001), so it is relevant to have an understanding as to what influences the willingness to work for startups for employees. Research that has been done concerning HRM is almost solely focused on large firms (Barber, 1998; Williamson, 2000). Even though locating and hiring new qualified employees is one of the most difficult goals for startups, which has been cited as the biggest threat to business growth for small firms (Williamson et al, 2002). This leads us to the following research question:
What are the predictors of the willingness of individuals to work for a startup?
A huge aspect that makes finding the right employee so important is that it can diminish the employee turnover. Businesses invest a lot of money in their employees, in terms of training, developing, maintaining and retaining them in the organization (Kevin et al. 2004). Many scholars agree that high turnover rates have a negative effect on the profitability of businesses (Hogan, 1992; Wasmuth and Davis, 1993; Barrows, 1990). Hiring and training a new employee to replace the vacant possession costs a business approximately 50% of the annual salary of the employee (Johnson et al. 2000).
Only it does not stop there, according to Phillips (1990) turnover has many hidden costs, such as co-workers having to adjust to a new employee and positions having to be filled temporarily while vacant. Costs such as a loss of sales and productivity will also play a part (Gustafson, 2002). The productivity will drop because of the learning curve involved when starting a new job within an organization (Meaghan et al., 2002). Besides this, Maeghan et al. (2002) argue that organizations will also lose their intellectual and relational property. Meanwhile competitors could gain these precious assets.
Thus, employee turnover brings forth a lot of negative consequences. This turnover is mostly caused by job dissatisfaction (Firth et al. 2004). Employees are not satisfied with their job, which is a result of poor recruitment policies. “The process of building up the internal organization, and, in particular, the employee base of a company, begins with the recruitment process” (Hellman and Puri, 2002). So as a business you have to know what type of employee you have to recruit that will fit your organization well. It could be devastating for a startup if an employee, who is attracted to the startup, eventually turns out to not be so interested in working there after all.
We believe this paper addresses questions that have not received much attention in the literature. Even though research has been done to establish the importance of human capital, little research has been done examining the willingness of employees to work for a startup. As a startup, how to find the most highly talented employees, that fit your business best, to work for you? To find an answer to this question, we will look deeper into the characteristics of a startup, which seem to demand a tolerance of ambiguity of its employees. Moreover, employees of a startup and entrepreneurs seem to share some personality traits. We will investigate if these personality traits are predictors of the willingness of an employee to work for a startup. Finally we will study if a socio-demographic could also play a role.
The remainder of this paper is structured as follows. We start by making our theoretical framework and formulating our hypotheses, where we will look at the ambiguity within a startup, and what role it plays for the employees. Within this framework we will also examine the similarities an employee of a startup might have with an entrepreneur, where we will discuss the following personality traits; innovativeness, the desire to take risks and locus of control. Then we will take a look at the role of a socio-demographic characteristic. Next we will rapport our method, results and discussion. Finally we will give our limitations and conclude.
2. Theoretical framework
In order to have an idea what attracts employees to work for a startup, firstly we will take a look at how the current recruitment of a startup works. Secondly we will establish a deeper understanding what the characteristics of a startup are and see if there are certain characteristics of employees that could predict if they would want to work for a startup. Lastly we will discuss a certain socio-demographic.
2.1 Recruitment
Many startups have difficulties recruiting employees (Williamson et al., 2002) and lack formal human resource policies (Markman & Baron, 2002). Most startups do not have personnel departments, or even a person that handles human resource issues (Bartram et al., 1995). This in is contrast to bigger firms that usually have formal human resource departments with human resource professionals working for them. This benefits the big firms into having developed internal labor markets, integrated human resource practices and formal career development systems (Aldrich & Auster, 1986; Guthrie & Olian, 1991).
Startups lack all of this, which makes for a “muddle-through” human resource approach, “adopting idiosyncratic practices as needed and lacking a clear overarching-strategy” (Windolf, 1986). This is why this paper could be of use to startups; make them realize they need to develop a strategy regarding HRM, since the right selection may be the “key component of overall effective management of a firm’s human resources” (Heneman & Berkley, 1999).
2.2.1 Environment startup: ambiguity
According to Budner (1962) an ambiguous situation can be defined as “a situation, which cannot be adequately structured or categorized by the individual because of the lack of sufficient cues, situations like these can be characterized as novelty, complexity, or insolubility.”
There are several reasons that make startups ambiguous. First of all there is a great ambiguity concerning the startup’s future, because of their lower survival rates and shorter life cycles (Katz et al., 2000). Secondly employees of startups have fewer boundaries within their functional roles compared to those in more established firms (Chen, 2013). The reason for this is that an employee of a big firm has specific functions, while an employee of a startup seems to have more roles to fulfill in certain cases, so he has to be more versatile (Sørensen and Stuart, 2000). Bigger firms have more specific functions because as firms grow, they become increasingly bureaucratic and that brings forth a more institutionalized division of labor (Sørensen and Stuart 2000). Moreover, these firms become more reliant on established routines through the adoption of standard operating procedures. These aspects make for more job stability (Bendix 1956; Blau and Schoenherr 1971). However these routines limit their flexibility by restricting the range of organizational action (Nelson & Winter, 1982). Bamford et al. (2004) agree by stating that new firms have the ability to change their resources and decisions dramatically, since they are not locked into policies, processes and procedures that limit their flexibility, like bigger firms. Bygrave et al. (2007) go even further and state that an entrepreneur does not even need to write a business plan as a startup changes so much in the starting phase. Levitt & March (1988) give a reason for this; as a firm gets older they are less likely to change, as the competencies of the firm will improve when they accumulate experience in their domain of activity.
These situations where employees have more flexibility, lesser-specified functional roles, and are less reliant on established routines fit the description of an ambiguous situation. Additionally, these changes a startup goes through, create novel situations that cannot be adequately structured or categorized by the individual because of the lack of sufficient cues. This makes us presume that a startup is characterized by ambiguity.
2.2.2 Characteristic employee: Ambiguity tolerance
According to Budner (1962) tolerance of ambiguity of an individual can be described as “the tendency to perceive ambiguous situations as desirable", while intolerance of ambiguity can be described as "the tendency to perceive ambiguous situations as sources of threat". MacDonald (1970) confirms this and argues that individuals with a high ambiguity tolerance are purposely seeking to find unstructured situations.
This is interesting for us to know, because according to Cable & Judge (1994) and Judge & Brentz (1992) the personality traits of an individual have a great influence on their job choice. They want to work for a firm that has attributes that align well with their own characteristics. If the attributes of the firm align well with their own characteristics, it is seen as desirable, making the firm a desirable entity to work for (Suchman, 1995). Since we presume that a startup is characterized by ambiguity, we expect the following:
Hypothesis 1: Ambiguity tolerance has a positive effect on an individual’s willingness to work for a startup.
2.3 Similarities entrepreneurs
As mentioned, the personality traits of an individual have a great influence on their job choice; they want to work for a firm that has attributes that align well with their own characteristics (Cable & Judge 1996; Turban & Keon, 1993). That makes it interesting for us to look at other personality traits too. There are scholars who see possible similarities between the characteristics of entrepreneurs and the employees of startups, as we will discuss below.
Sørensen (2007) argues that individuals with innate entrepreneurial characteristics are more likely to work for startups and would rather avoid working for a bureaucratic firm. The works of many scholars can support this claim, as we will show. Firstly, Gompers, Lerner and Scharfstein (2005) state that “exposure to the entrepreneurial process is an important determinant of the propensity of employees to enter entrepreneurship; it is in these environments that employees learn from their co-workers about what it takes to start a new firm and are exposed to a network of suppliers and customers who are used to dealing with start-up companies.” Sørensen (2007) confirms this by stating: “Employees of entrepreneurial firms are presumed to be exposed to more entrepreneurial opportunities and to be in a position to acquire more entrepreneurially relevant skills.”
Secondly, Wagner (2004) found out that people working for young and small firms are more likely to identify themselves as a person who will start a new venture themselves in the future. This is also in line with the works of other scholars who found that people working for older and bigger firms are less likely to found a venture (Eriksson & Kuhn 2006, Dobrev & Barnett 2005).
Moreover, bigger and more bureaucratized firms are assumed to be accountable for creating “timid and conforming workers who are unlikely to challenge the existing order by pursuing entrepreneurial opportunities” (Whyte, 1956). Another scholar, Merton (1968)
agrees by arguing that bureaucracies make rigidly defined roles for their employees, which in combination with the elaborated hierarchy and an emphasis on rules and routines creates timid and conserved employees.
These firms create an environment where entrepreneurial skills cannot be developed well. According to Lazear (2005), a successful entrepreneur must be able to fulfill a wide variety of roles, and so individuals with a wide variety of work experience will find entrepreneurial opportunities more attractive. An employee in a bureaucratic firm will be more narrow-minded, focusing on the depth of a particular skill. In a firm without an elaborated division of labor, employees will learn a wider variety of skills and therefore are able to develop their entrepreneurial skills (Lazear, 2005).
Furthermore, the formalization of roles within a bigger and more bureaucratized firm make employees dependent of personal relationships to further continue their career prospects within the firm. This gives them some safety, however it does increases their opportunity cost to leave such a job to launch a risky entrepreneurial venture. Employees that have been working for a smaller, less formalized firm such as a startup might be more inclined to do so as their opportunity cost is lower (Sørensen, 2007).
However, the question may rise why these individuals with innate entrepreneurial attributes would want to work as an employee of a startup instead of starting their own business. The International Social Survey Programme of 1989 has done random samples in various countries to test if individuals have a preference for being an employee or an entrepreneur and it turned out that a staggering 63% of the Americans, 48% of the Britons and 49% of the Germans would like to be self-employed. Even though the actual proportion of self-employed people in these countries is around 15%. A key factor is that most of these people do not have sufficient capital to start a business themselves (Evans and Leighton, 1989; Evans and Jovanovic, 1989). That means that working for a startup might be an
interesting alternative, especially knowing that they will gain entrepreneurial experience and will more able to launch a venture themselves in the future.
This could be a possible explanation why many employees working for startups would have quite similar characteristics as entrepreneurs as Sørensen (2007) claimed. However Sørensen did not work out what specific characteristics of employees makes them more willing to work for a startup, and this has not been tested.
2.4 Innovativeness
To describe an entrepreneur, different scholars name different attributes of entrepreneurs. One of these attributes is innovativeness (Casson, 1982; Caird, 1989; McClelland, 1961). There are many different types of definitions of innovativeness. Feaster (1968) argues that innovativeness is an awareness of the need to innovate or a positive attitude towards change. Hurt (1977) summarizes this orientation by stating: “Innovativeness is a normally distributed, underlying personality construct, which may be interpreted as the willingness to change.” This definition would allow us to be able to measure and predict the innovativeness of an individual. In this paper we will use the definition, that is in line with these thoughts, given by The Jackson Personality Inventory Manual (1994), who define innovativeness as: “A tendency to be creative in thought and action.”
According to Casson (1982) and Caird (1989) the degree of innovativeness is a determinant for individuals to become an entrepreneur. We would like to know if this could also be a determinant for an individual’s willingness to work for a startup. We would expect this relationship to exist, as startups face challenges that they have to overcome by being innovative. Startups face certain burdens because of their youth and small size at inception. Challenges, such as their liabilities of newness, and the challenges of entering a new and unknown industry. “They must find ways to gain legitimacy in the industry without the track
record that experience and performance often provide” (Hannan & Freeman, 1984; Stinchcombe, 1965). This means finding new ways to do things by being innovative. Knowing that employees play a big part in the development of new firms (Zingalas, 2000), we expect that employees of startups have to be innovative. Moreover, as we presume that an individual wants to work for a firm which attributes align well with their own characteristics (Cable & Judge 1996; Turban & Keon, 1993), we expect the following:
Hypothesis 2: An individual’s level of innovativeness has a positive effect on their willingness to work for a startup.
2.5 The desire to take risks
Another attribute scholars use to describe entrepreneurs is the desire to take risks (Casson, 1982; Caird, 1989; Brockhaus, 1982). We will define risk as “the perceived probability of receiving the rewards associated with success of a proposed situation, which is required by an individual before he will subject himself to the consequences associated with failure, the alternative situation providing less reward as well as less severe consequences than the proposed situation” (Brockhaus, 1980).
It is widely recognized that risk is definitely an aspect that plays a big role for many startups and a lot has to do with startup-survival. Many startups have difficulties staying in the market and die an early death. Most startups fail within five years (Scott & Bruce, 1987), which implies that working for a startup brings forth much risk. There is uncertainty whether the startup will fail and consequently that means losing your job as an employee.
Furthermore, benefits to an employee that offer long-term security, such as company-funded pension plans or life insurances, may never become realized because of the high fail rates among startups. The retirement that is one the most costly benefits will often be funded
by a profit-sharing plan, where the funding is dependent of organizational success so that employee’s retirement’s funds will only be realized when the target profits of the firm are realized (Gomez-Mejia & Balkin, 1989). This is a very risky aspect of a startup.
Additionally, the pay-mix of a startup will often be more emphasized on the variable at-risk pay, because of the uncertain conditions a startup faces (Balkin & Gomez-Mejia, 1984; Barringer, Jones & Lewis, 1988; Milkovic, Gerhart & Hannon, 1991). According to Balkin & Logan (1988) small firms put a significant portion of their earnings (10-50% more than large firms) at risk by making pay incentives.
Yet individuals working for startups are willing to bear this risk, so apparently the perceived probability of receiving the rewards associated with success are sufficient to take this risk by working for a startup. At least that is what the theory would suggest (Brockhaus, 1980). The potential pay-offs of the employment at a startup is deemed rewarding enough to take the risk of gambling on an innovation, invention or business prospect (Graham et al., 2002). Therefore we expect that the desire to take risks has a positive relationship with an individual’s willingness to work for a startup, which leads us to the following hypothesis:
Hypothesis 3: An individual’s level of risk-seeking has a positive effect on their willingness to work for a startup.
2.6 Locus of control
Locus of control is another returning attribute that scholars use to describe entrepreneurs (Rotter, 1966; Brockhaus, 1982; McClelland, 1967). Actually it is the most studied psychological trait in research about entrepreneurship (Perry 1990). According to Rotter (1990) locus of control refers to an individual’s perception towards the question: Who or what controls life’s events?
A person’s locus of control can develop early in their childhood (Lefcourt, 1980). Locus of control can be defined as a generalized expectancy; it can be divided into internal and external locus of control. Individuals with an internal locus of control believe that life is controlled by their own actions, and individuals with an external locus of control believe that other external forces control their life (Specter, 1988).
Research has shown that individuals with an internal locus of control are more motivated and more willing to change their behaviors than individuals with an external locus of control. The reason for this is that when an individual has an internal locus of control, he will believe that his personal actions have an impact on the desired outcome (Neeman, 1995; Rotter et al., 1962). These individuals will also be better able to cope with change (Judge et al., 1999).
For entrepreneurs there is a clear relationship between locus of control and becoming an entrepreneur as scholars have researched (Rotter, 1966; Brockhaus, 1982; McClelland, 1967). The relationship between an individual’s locus of control and their willingness to work for a startup might be less evident, but theory suggests that it could also be an influential factor as we will explain. Since individuals with an internal locus of control are better able to cope with change and are more willing to change their behaviors, this could be in line with the characteristic of a startup that changes drastically in their beginning phases (Hannan & Freeman, 1984).
On the contrary of startups, large firms are often very bureaucratic where the individual has very little influence or participation in the realization of the businesses strategic orientation. In startups however there are far less formal structures which offers employees the possibility to become actively involved in creating the direction of the firm’s future and also have a say in the firm’s decision-making (Ahmadi and Helms, 1997; Williamson, 2000; Williamson et al., 2002).
Furthermore, employees in large firms often have a specialized job and have a narrow task-set, whereas employees of startups usually have to fulfill a much broader range of tasks and responsibilities. Their job role usually does not consist of clear boundaries (Cardon & Stevens, 2004; Cardon & Tolchinsky, 2006; Heneman & Berkeley, 1999). This would also match the description tasks with a higher degree of locus of control, thus leading us to the following hypothesis:
Hypothesis 4: An individual’s level of locus of control has a positive effect on the willingness to work for a startup.
2.7 Socio-demographic characteristics: parental entrepreneurship
Besides personality traits, there is also a socio-demographic characteristic that could play a part in the decision-making of an individual to want to work for a startup, namely parental entrepreneurship.
Literature has been done measuring the relationship between parental entrepreneurship and own entrepreneurship. According to Lindquist et al. (2012) “parental entrepreneurship is one of the strongest, probably the strongest, determinant of own entrepreneurship. Parental entrepreneurship increases the probability of children’s entrepreneurship by about 60%.” This statement is consistent with the research of other scholars, who argue that parental entrepreneurship increases children’s entrepreneurship with a factor of 1.3 to 3 (Arum and Mueller 2009, Colombier and Masclet 2008 and Sørensen 2007). Lindquist et al. (2012) argue that the main underlying reason of the parental influence on own entrepreneurship is role modeling. Parents have a huge impact on the aspirations and job values of their children (Halaby 2003).
As parental entrepreneurship is such an influence on their children’s choice to become an entrepreneur, could it also affect a child of an entrepreneur to be more willing to work for a startup? We would expect this since Sørensen (2007) argued that individuals with innate entrepreneurial attributes are more likely to work for startups. This leads us to the following hypothesis:
Hypothesis 5: An individual with entrepreneurial parents is more willing to work for a startup than an individual without entrepreneurial parents.
3. Method
3.1 Data and Sample
This study used a survey to gather quantitative data. A sample of random individuals was used. The individuals had to be eligible to work for a startup. The data is collected by the use of an online questionnaire. Existing scales in combination with new scales were used to measure the different constructs. In total there were 177 respondents who participated in the survey, of which 168 respondents filled in all the questions and completed the survey. The 9 respondents who did not complete the survey were excluded from the original survey. Of the remaining 168 respondents the descriptive data can be found in Table 1. The full questionnaire can be found in the appendix.
Table 1
Descriptive Statistics of Key Variables
Minimum Maximum M SD Gender 1,00 2,00 1,34 ,48 Age 19,00 64,00 35,19 13,66 Paid employment 1,00 2,00 1,40 ,49 Employment at startup 1,00 2,00 1,95 ,21 Experience as entrepreneur 1,00 3,00 2,34 ,91 Ambiguity tolerance 1,00 4,25 2,41 ,57 Innovativeness 1,43 3,86 2,53 ,55 Risk-seeking 1,50 7,00 4,37 1,21 Locus of control 1,00 3,25 1,87 ,41 Parents entrepreneur 1,00 3,00 2,43 ,67
Willingness to work for startup 1,00 4,67 2,51 ,77
3.2 Definition Startup
In our review of the literature concerning startups the definition of a startup was not established very well or clear. This problem is also mentioned in another article by Cordon & Stevens (2004). Many articles are discussing emerging and small ventures, without making any distinctions. They often use the term SME, which includes firms ranging from 1 to 250 employees. However the management needs for firms of 1, 10, 50 and 250 employees are significantly different as is noted by Cordon & Stevens (2004). In this paper we want to be clear on what we mean of a startup and define it as follows: A startup is a firm that is not
older than 5 years and does not have more than 10 employees. We make this clear
demarcation to focus on the very early stages of a small startup.
3.3 Control Variables
In our study we will control for the following variables:
Gender; we control for this variable because we suspect that there could be gender inequalities regarding the willingness to work for a startup. According to the Global Entrepreneurship Monitor of the Netherlands (2014) the male-female ratio involved with entrepreneurialism was 64-36% in 2014 in the Netherlands. We expect that this ratio could give us an indication for the male-female ratio of the people that are willing to work for a startup, since employees working for a startup have an overlap with entrepreneurs as we discussed earlier in our theoretical framework. We gave reasons for this such as the following: “Employees of entrepreneurial firms are presumed to be exposed to more entrepreneurial opportunities and to be in a position to acquire more entrepreneurially relevant skills” Sørensen (2007).
Age; we want to control for age as it also plays an influence in entrepreneurial activity. According to Levesque and Minniti (2006) there is an inverted U-shaped relationship between
age and entrepreneurial activity. We want to control if age plays a part in influencing an individual’s willingness to work for a startup too.
Paid employment; we expect that a paid employment could be a barrier for someone to be willing to work for a startup. The reason for this is that bigger firms are likely to offer greater benefits to their employees than small firms such as startups (Gomez-Mejia & Balkin, 1989). An individual with paid employment at a bigger firm has to give up these benefits, to want to work for a startup. Therefore, individuals without paid employment are expected to be more willing to work for a startup than individuals with paid employment.
Experience as an entrepreneur; as stated earlier entrepreneurs have an overlap with employees working for a startup. That is why we want to control for a person’s experience as an entrepreneur. We expect that individuals with an experience as an entrepreneur will be more willing to work for a startup than individuals without an experience as an entrepreneur.
Our last control variable is Employment at a startup. We expect that this variable will be similar to our dependent variable willingness to work for a startup. However we do expect that the variables will differ slightly as an individual who is working at a startup might not be satisfied with his job and not so willing to work there, as the attributes of the startup might not align well with his own characteristics. Therefore, it is interesting for us to control for this variable.
Some specifications; paid employment could be answered with “1: Yes” or “2: No”. Employment at a startup was tested by the questions if someone was working for a firm, and if so how long this firm existed when they started working there and how many employees the firm then counted. Based on this we could conclude if this individual started working for a startup. Experience as an entrepreneur could be answered with “1: Yes, I still am an entrepreneur”, “2: Yes, but I am not an entrepreneur anymore” and “3: No, I have never been
one”. All questions were translated to Dutch, just as all the other questions in the questionnaire.
3.4 Independent Variables
In our paper we have 5 independent variables:
Ambiguity tolerance; to measure this construct we used an existing scale. The intolerance of ambiguity scale of Budner (1962) was used to measure the tolerance of ambiguity. Moreover we used the shortened version comprising of 7 questions, tested by
Kirton, M. J. (1981), to limit the total number of questions of our own survey. The ambiguity scale of Budner (1962) is a scale that is often cited by other scholars and appears to be a reliable measure of an individual’s tolerance of ambiguity. The shortened version has an internal reliability of 0.65. An example of the statements: “A good job is one where what is to be done and how it is done is always clear.” Answers could be given according to a 5-point Likert scale, ranging from “1: Totally agree” to “5: Totally disagree”.
Innovativeness; for this construct we used the existing scale from the Jackson Personality Inventory (1994), shortened by Mueller & Thomas (2001) to 8 questions. The JPI scale is also a well-used scale, and the shortened version was used for the same reason as given above for the ambiguity tolerance scale. An example statement: “I often surprise people with my novel ideas.” The internal reliability of the scale was ranging from 0.82 (Canada) to 0.66 (China). Answers could be given according to a 5-point Likert scale, ranging from “1: Totally agree” to “5: Totally disagree”.
Risk-seeking; for the construct risk-seeking we used the scale of Blais & Weber (2009). We used the 4 questions regarding investing for our scale. The whole scale consists of more parts of different types of risk-seeking, namely ethical, gambling, health/safety,
recreational and social. We feel however that the part regarding investing fits our study best. Therefore we chose to only use the questions related to this scale. An example statement: “Investing 10% of your annual income in a new business venture.” Answers could be given according to a 7-point Likert scale, ranging from “1: Extremely likely” to “7: Extremely unlikely”. It had an internal reliability of 0.78.
Locus of control; to measure this construct we used the shortened scale of Mueller & Thomas (2001), adapted from Rotter (1966). Rotter (1966) appears to be a respected authority regarding the construct locus of control. We chose to use the shortened version, for the same reasons as mentioned earlier. In addition to this, we shortened it the scale even more to 6 items. The shortened version of Mueller & Thomas (2001) originally consisted of 10 items. We pre-tested this new scale for internal reliability, which was a sufficient 0.71. An example of the statements: “My life is determined by my own actions.” Answers could be given according to a 5-point Likert scale, ranging from “1: Totally agree” to “5: Totally disagree”.
Parents entrepreneur; finally to measure this construct we asked the question if someone’s parents were an entrepreneur. The choices were “1: Yes, both parents”, “2: Yes, one of my parents” and “3: No”.
3.5 Dependent Variable
In our paper we only have one dependent variable: the willingness to work for a startup. As noted our definition of a startup is a firm that does not exist longer than 5 years and does not have more than 10 employees. There are no existing scales that can be found to measure this construct, so we had to make our own. We have pre-tested a scale that consisted of three questions and the internal reliability indicated that this scale was viable, with an internal
reliability of 0.86. An example of one of the statements: “I would like / I like to work for a starting firm.”
3.6 Reliability analysis
A reliability analysis was done to measure the reliability of the different scales. In order to do this the means of the sets of items were computed and also some of the variables of the different scales had to be recoded. An example of a scale that had to be recoded is the following statement regarding the locus of control scale: “Success in business is mostly a matter of luck.” When computing the analysis, some items were deleted to increase the cronbach’s alpha and consequently the reliability of the scale. Almost all of the cronbach’s alphas of the different scales were sufficient (cronbach’s alpha > 0.65), only the ambiguity tolerance scale cronbach’s alpha was questionable with a score of 0.56. Moreover, the
corrected item-total correlation was computed, and all of the items scored above the 0.30 that is needed to be reliable. The complete set of information regarding the reliability analysis can be found in Table 2 below.
4. Results
4.1 The correlation There are 20 significant relationships among the different key variables when testing for correlation, with an alpha of 0.10. However most of these relationships that are found significant have a low correlation. There are only 2 relationships that are worth noting, namely Age with Experience as an entrepreneur (-0.55, p<0.01) and Paid employment with Experience as an entrepreneur (-0.62, p<0.01). Meaning that older people more often have experience as an entrepreneur than younger people, and people working in paid employment have less experience as an entrepreneur than people that are not working in paid employment. 4.2 MulticollinearityA correlation analysis showed that no correlation between one of the pairs of the independent variables was too high (r=0.70 or higher). We also performed a multicollinearity test to analyze the VIF scores and these were also all in order, as the tolerance is much higher than
0.2 and the VIF scores are much lower than 10 (Field, 2009). The multicollinearity matrix is shown below in Table 4.
4.3 Hypothesis testing
Control Variables
Linear regression was used to test the effects of the control variables on the willingness to work for a startup, excluding the independent variables. Gender, Age, Paid employment,
Employment at a startup and Experience as an entrepreneur are the control variables. The
effects are shown in Table 5. This model has enough explanatory power to be valuable (F=4.12, p<0.01), explaining 11% of the variance of our dependent variable. The results show that some control variables influence the Willingness to work for a startup significantly, namely Gender (t=2.00, p<0.05), Age (t=2.56, p<0.05) and Employment at a startup (t=2.19, p<0.05).
Ambiguity tolerance
To test the hypothesized effects we used a linear regression. We tested hypothesis 1 in Model 1 of Table 6. In this model we controlled again for our control variables, but now the independent variable Ambiguity tolerance was added. This model also explains 11% of the variance of our dependent variable. Even though it appears to explain the same amount of variance as the model excluding the independent variable, the model still has enough explanatory power to be valuable (F=3.41, p<0.01). In this model Gender (t=1.99, p<0.05),
Age (t=2.56, p<0.05) and Employment at a startup (t=2.18, p<0.05) were significant.
However there is no supporting evidence that an individual’s Ambiguity tolerance has a positive effect on their Willingness to work for a startup (t=-0.10, p=0.92). Therefore, we do not find support for our first hypothesis.
Innovativeness
The second hypothesis was tested in Model 2 of Table 6. We controlled for the control variables and added the independent variable Innovativeness to the model. This model explains 14% of the variance of the Willingness to work for a startup (F=4.51, p<0.01).
Consequently meaning that this model has enough explanatory power. Of the control variables Age (t=3.07, p<0.01) and Employment at a startup (t=2.32, p<0.05) are significant. Moreover, an individual’s level of innovativeness has a positive effect on their willingness to work for a startup (t=2.49, p<0.05), which means that our second hypothesis is supported.
Risk-seeking
The third hypothesis was tested in Model 3 of Table 6. We controlled for the control variables and added the independent variable Risk-seeking to the model. This model explains 13% of the variance of our dependent variable (F=3.93, p<0.01). Consequently meaning that this model has enough explanatory power. Of the control variables Age (t=2.20, p<0.05) and
Employment at a startup (t=2.37, p<0.05) are significant. Furthermore, an individual’s level of Risk-seeking has a positive effect on their willingness to work for a startup (t=1.66, p<0.10), which means that our third hypothesis is supported.
Locus of control
Our fourth hypothesis was tested in Model 4 of Table 6. We controlled for the control variables and added the independent variable Locus of control to the model. This model explains 12% of the variance of our dependent variable (F=3.63, p<0.01), which means that the model has sufficient explanatory power. Gender (t=2.11, p<0.05), Age (t=2.60, p<0.05) and Employment at a startup (t=2.20, p<0.05) are significant control variables. Nonetheless, there is no supporting evidence that an individual’s Locus of control has a positive effect on their Willingness to work for a startup (t=-1.08, p=0.28). Therefore, we do not find support for our fourth hypothesis.
Parents entrepreneur
Our fifth and final hypothesis was tested in Model 5 of Table 6. We controlled for the control variables and added the independent variable Parents entrepreneur to the model. This model explains 12% of the variance of our dependent variable (F=3.47, p<0.01). This means that our model has sufficient explanatory power. Gender (t=2.02, p<0.05), Age (t=2.37 p<0.05) and
Employment at a startup (t=2.23, p<0.05) are significant control variables. There is no
supporting evidence that an individual with Entrepreneurial parents is more willing to work
for a startup than an individual without Entrepreneurial parents (t=0.69, p=0.49). We tried recoding Entrepreneurial parents in just “Yes” or “No” instead of “Yes, both parents”, “Yes, one of my parents” and “No” to test if this difference made the effect significant, but it did not. Hypothesis 5 is not supported.
5. Discussion
5.1 Main findings
The first hypothesis of this paper is the one regarding ambiguity tolerance. We hypothesized that an individual’s ambiguity tolerance has a positive effect on their willingness to work for a startup. We had reasons to suspect that a startup can be seen as an ambiguous entity. Lower survival rates and shorter life cycles (Katz et al., 2000), fewer boundaries for employees (Chen, 2013) and less routines (Bendix 1956; Blau and Schoenherr, 1971) were reasons for it. Consequently we expected individuals with a high tolerance of ambiguity to be more willing to work for a startup than individuals with low tolerance of ambiguity. Nevertheless, the findings do not support this hypothesis. We believe that there is a reason to explain this lack of support; a startup is possibly not so ambiguous as we thought. It might be slightly ambiguous compared to a bigger firm, but perhaps it is seen as much less ambiguous compared to launching your own venture as an entrepreneur. This could presumably mean that individuals with a high tolerance of ambiguity would rather start their own venture, and not be so interested in working for a startup.
The second hypothesis of this paper is the expectation that an individual’s level of innovativeness has a positive effect on their willingness to work for a startup. This hypothesis is supported by the results. Sørensen (2007) argued that individuals with innate entrepreneurial characteristics are more likely to work for startups. Since our hypothesis is supported we can confirm this statement and also specify the precise characteristic. Innovativeness is one of the key determinants of entrepreneurs (Casson, 1982; Caird, 1989) and now we can also say that it is a key determinant for an individual willing to work for a
startup. We believe this has to do with a startup’s liability of newness and the challenges of entering a new and unknown industry. This forces startups to find new ways of doing things (Hannan & Freeman, 1984; Stinchcombe, 1965) and thus having to think innovative. We believe that this attracts innovative individuals and consequently is the reason why an individual’s level of innovativeness has a positive effect on their willingness to work for a startup.
In our third hypothesis we state that an individual’s level of risk-seeking has a positive effect on their willingness to work for a startup. This hypothesis is supported by the results as well. We have provided many reasons to argue that a startup is a risky entity. A lot has to do with a startup’s chances of survival (Scott & Bruce, 1987). Furthermore, the desire to take risks is another key determinant of an entrepreneur, so the support of this hypothesis is also in line with Sørensen’s (2007) claim that individuals with innate entrepreneurial characteristics are more likely to work for startups.
The fourth hypothesis is not supported. We wrongfully expected that an individual’s level of locus of control has a positive effect on their willingness to work for a startup. One of the main reasons of this expectation is that startups offer employees possibilities to be actively involved in the decision making of the firm (Ahmadi and Helms, 1997; Williamson, 2000; Williamson et al., 2002), which would presumably fit a person with a high level of locus of control well. Our findings are in contrast with the statement of Sørensen (2007) who argued that individuals with innate entrepreneurial characteristics are more likely to work for startups, as locus of control is a returning attribute that scholars use to describe entrepreneurs (Rotter, 1966; Brockhaus, 1982; McClelland, 1967). This demonstrates that Sørensen’s (2007) statement needs specifications as to what entrepreneurial characteristics it applies to. A possible reason for the lack of support from this hypothesis is that individual’s with a high
degree of locus of control might rather launch their own startup instead of working for a startup as it allows them to be even more in control of the decision making.
Our fifth and final hypothesis is not supported. The results show that an individual with entrepreneurial parents is not more willing to work for a startup than an individual without entrepreneurial parents. We expected this relationship to exist, because Sørensen (2007) argued that individuals with innate entrepreneurial attributes are more likely to work for startups. And since parents have a great impact on the aspirations and job values of their children (Halaby, 2003), entrepreneurial parents could possibly influence their child’s attributes to resemble their own entrepreneurial characteristics, which in turn would make them more willing to work for a startup. We do have a probable answer that explains why the results do not support our hypothesis. When an individual has entrepreneurial parents, it might aspire him to become an entrepreneur just like his parents. However the parental influence might not affect the child’s individual attributes to resemble the attributes of a typical entrepreneur, since these entrepreneurial attributes are given at birth according to Sørensen (2007). This means that a child of an entrepreneur might be more likely to become an entrepreneur, to resemble the entrepreneurial behavior of his parents but perhaps his underlying innate characteristics do not resemble the entrepreneurial characteristics of his parents. And it is these innate entrepreneurial characteristics that make individuals more likely to work for a startup according to Sørensen (2007). If children of entrepreneurs lack these innate entrepreneurial characteristics, it could explain why children of entrepreneurs are not more willing to work for a startup than children without entrepreneurial parents.
5.2 Theoretical implications
The HRM of startups is still an underexplored topic in the existing literature. This is a current research gap. Even though studies have been conducted concerning human resources, most of these studies are written about large firms (Katz et al., 2000). The knowledge that has been gathered from large firm regarding HRM is often applied to startups, as if their HRM policies would be similar. This is a mistake, as startups differ from large firms in many aspects
(Aldrich & Auster, 1986; Guthrie & Olian, 1991). In this study the HRM of a startup is examined, specifically of the employee’s perspective. We have gained some insights on what characteristics could increase their willingness to work for a startup. The findings of this study fill a part of the current research gap. Innovativeness and risk-seeking are characteristics that positively influence an individual’s willingness to work for a startup. This is in accordance with Sørensen (2007) who argued that individuals with innate entrepreneurial characteristics are more likely to work for startups.
5.3 Managerial implications
Founders of startups can benefit from these new findings, as they can improve their recruitment. Currently many startups are using a “muddle-through” human resource approach (Windolf, 1986), lacking any strategy. We hope that this study will give the founders of startups some clarification how they can improve their search for new employees. It appears that individuals with a high level of innovativeness and risk-seeking will be more willing to work for startups and might also be more fitting than employees with a low level of innovativeness and risk-seeking. This contribution to the existing literature offers founders of startups the possibility to improve their recruitment as mentioned. Improved recruitment will lead to more satisfied employees, and less turnover for startups (Firth et al., 2004) as the
firm’s attributes will have a better alignment with the employees’ characteristics (Suchman, 1995). This will diminish many costs for startups, as they have to replace fewer employees, which in turn increases their profitability (Hogan, 1992; Wasmuth and Davis, 1993; Barrows, 1990.
Moreover, we hope that for startups the improved efficiency in finding the right employees and the resulting improved profitability will increase their chances of survival. Since startups are an important source of economic growth and labor demand (Praag & Ophem, 1995), the contribution of our study could consequently lead to an improvement of our economy.
In addition to this, employees in search of a job will also be able to benefit from our findings, as they will have a better idea if working for a startup aligns well with their own characteristics. This will satisfy their needs better, since they are better able to make the right choice where they would want to work (Suchman, 1995). The increased satisfaction will consequently give them less reason to change their job (Firth et al., 2004), giving the employees more job stability.
5.4 Limitations and future research
Despite of the strengths of these findings, it should be noted that there are also a number of limitations and the results should be interpreted with the following limitations held in mind.
First, our data may not be drawn completely random as gender and age correlate, meaning that there were significantly more old men than old women. This could have influenced the results slightly. In our sample there were also somewhat more men than women in total. Future research could attempt to find a slightly more representative sample to gather their data from.
Second, the scale to measure the tolerance of ambiguity scored a little low on the reliability analysis. When Kirton, M.J. (1981) tested this scale, the cronbach alpha scored a sufficient 0.65. However in our research the cronbach alpha scored an insufficient 0.56, though the corrected inter-item correlation was in order. Future research could attempt to test the effect of tolerance of ambiguity again with a different scale. We argue though that it is not probable that an individual’s ambiguity tolerance has a significant effect on their willingness to work for a startup, as the tested p-value of our sample was far from significant (t=-0.10, p=0.92). Furthermore, the cronbach alpha of our scale did not differ that substantially from the cronbach alpha of the same scale tested by Kirton, M.J. (1981).
Third, due to time constraints we chose to only test the effects of 5 different variables on the willingness to work for a startup. However we believe that there are more factors that influence a person’s willingness to work for a startup. It can be demonstrated by the R² of our models. The R² is moderately low, which means that there are many other possible variables influencing an individual’s willingness to work for a startup. This could be interesting for further research. We suggest that other entrepreneurial characteristics will be investigated as Sørensen (2007) argued that individuals with these characteristics are more likely to work for startups.
And fourth, in our study we limited ourselves to startups that are not older than 5 years and do not have more than 10 employees. We wanted to make a clear demarcation to focus on the very early stages of a small startup. Nonetheless, future studies could investigate other stages of a startup as well.
5.5 Conclusion
The HRM of a startup is an underexplored topic; little research of it has been done so far. This study has attempted to begin to explore the topic by answering the following research question: What are the predictors of the willingness of individuals to work for a startup? We found that an individual’s level of innovativeness and risk-seeking have a positive effect on their willingness to work for a startup. Hopefully founders of startups will benefit from our contribution to the knowledge of a startup’s HRM to improve their search for new employees. We also hope that further research will continue clarifying and improving this search, as there is still much to be explored.
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