Small Business Economics

, Volume 45, Issue 1, pp 85–101 | Cite as

ADHD-like behavior and entrepreneurial intentions

  • Ingrid Verheul
  • Joern Block
  • Katrin Burmeister-Lamp
  • Roy Thurik
  • Henning Tiemeier
  • Roxana Turturea
Open Access
Article

Abstract

Little is known about the relation between entrepreneurship and the extent of psychiatric symptoms. Validated psychiatric symptom scores are seldom used for non-clinical reasons. One prevalent symptom that deserves our interest is Attention Deficit and Hyperactivity Disorder (ADHD). ADHD is a developmental disorder characterized by inattentiveness and hyperactivity that has been linked to occupational choice and performance. Building on the person–environment fit literature, we hypothesize that individuals who exhibit behavior associated with ADHD are more likely to have entrepreneurial intentions. Using a sample of 10,104 students enrolled in higher education, we can confirm our prediction that students with a higher level of ADHD-like behavior are more likely to have entrepreneurial intentions. Additionally, we show that risk taking propensity is a mediator that partly explains this positive effect. Our study points to the importance of behavioral tendencies associated with developmental disorders, when making entrepreneurship decisions. Our study contributes to the literature on the determinants of entrepreneurship, which so far has largely neglected the effects of psychiatric symptoms on entrepreneurship.

Keywords

Entrepreneurial intentions ADHD-like behavior Risk propensity Psychiatric symptoms Diagnostic and Statistical Manual of mental disorders (DSM) 

JEL Classifications

J24 L26 

1 Introduction

Entrepreneurs are commonly characterized as individuals who have high energy levels (Kets de Vries 1985), who dare to pursue risky activities and who show resilience in times of adversity (Markman et al. 2005). At the same time, there is anecdotal evidence of successful entrepreneurs with ADHD (Attention Deficit and Hyperactivity Disorder) such as David Neeleman (founder of JetBlue Airlines) and Paul Orfalea (founder of Kinko’s) (The Economist 2012). As a clinical condition, according to the American Psychiatric Association (2013), ADHD is a developmental disorder characterized by ample energy in the form of severe and persistent hyperactivity and distractibility that is essentially driven by behavioral “disinhibition” or a lack of restraint (Barkley 1997; Nigg 1999). Allegedly, there is similarity in the characteristics associated with entrepreneurship and those present in individuals with ADHD. However, there is only a small literature linking behaviors commonly associated with ADHD to well-known entrepreneurial characteristics such as risk taking (Mäntylä et al. 2012), creativity (White and Shah 2011), and action orientation (Barkley 1997), and thus far research has not systematically studied the link between behaviors associated with ADHD and a career in entrepreneurship (Mannuzza et al. 1993). In this study, we examine the relation between individual behaviors associated with ADHD and the intention to pursue an entrepreneurial career. Our paper links the literature on the consequences of ADHD to research about the determinants of entrepreneurship (Block et al. 2013; Verheul et al. 2012). So far, this literature has remained largely silent on the effects of psychiatric symptoms on entrepreneurship. Prior research has taken a psychological perspective and investigated the effects of different personality characteristics on entrepreneurship intentions (Lee et al. 2011; Nyock Ilouga et al. 2014) as well as the decision to become and stay self-employed (Beugelsdijk and Noorderhaven 2005; Caliendo et al. 2014), but has not taken an explicit psychiatric symptom perspective, which we do in our study.

Though the exact causes of clinical ADHD are not known, medical studies find consistent evidence that the disorder has a neurobiological origin (Mathis et al. 2014) and is genetically determined (Thapar et al. 1999; Mathis et al. 2014) with genetics contributing to about 60–75 % of cases (Cortese 2012; Faraone et al. 2005). Despite the fact that ADHD appears quite stable and the majority of adolescents continue to experience its symptoms in (young) adulthood (Biederman et al. 2007; Kan et al. 2013; Saviouk et al. 2011), most of what we know about its consequences for individual behavior is derived from research with children. Far less attention is paid to adult decision making and behavior (Young 2000). Nevertheless, it is recognized that high levels of attention deficit and hyperactivity have negative consequences within the work context. For example, adults who experience such behaviors tend to show substandard job performance (de Graaf et al. 2008; Halbesleben et al. 2013; Nadeau 2005) and have a higher chance of becoming unemployed (Barkley et al. 2006; Kessler et al. 2005). Even when equipped with higher levels of intelligence, few of them are found in higher-ranked occupational positions (de Graaf et al. 2008). At the same time, however, they may have specific talents. Recently, The Economist (2012) praised such “disorganization men” for their gift of breaking through business routines and inertia because of their ability to envision and create new realities. When they manage to develop “resilience” mechanisms to cope with their “weaknesses,” individuals who exhibit behavior associated with ADHD may even outperform others in particular work environments, for example, in jobs that require fast decision making or creativity (Bozionelos and Bozionelos 2013).

Building on the person–environment (P–E) fit literature (Cable and Edwards 2004; Edwards et al. 2006; Kristof-Brown et al. 2005), we explore the person–entrepreneurship (career) fit of individuals who find themselves at the start of their careers and who report varying levels of ADHD-like behavior measured as the average symptom score on a validated ADHD screening scale. Whereas research has mainly taken a pathological perspective (i.e., studying the consequences of ADHD as a disorder that is typically diagnosed during childhood), we take a different approach and examine ADHD as a behavioral tendency that varies across individuals. Hence, the aim of this study is not to diagnose individuals with ADHD and examine the career interests among those clinical cases. Instead, we hypothesize that individuals who exhibit higher levels of ADHD-like behavior—but who are not necessarily screened positive on ADHD in a clinical sense—have a relative good fit with entrepreneurship (compared to wage-employment), in turn boosting their entrepreneurial intentions. Drawing on evidence of behavioral disinhibition as the central “deficiency” of ADHD that triggers the experience of “under-arousal” and the need to seek incoming stimuli via engagement in excessive or extreme activities (Barkley 1997), we introduce risk taking propensity as a possible driver of the relationship between ADHD-like behavior and entrepreneurship.

Our study has several contributions. First, we test the relationship between behaviors associated with ADHD and entrepreneurial intentions in a large-scale quantitative study. So far, reliable evidence was lacking; the link between ADHD and entrepreneurship has been mainly described in the popular press (The Economist 2012; Hartmann 2002) based on anecdotal evidence from renowned entrepreneurs and small-scale studies such as that of Kirby and Honeywood (2007). By establishing that ADHD-like behavior predicts entrepreneurial intentions, we extend the literature on the determinants of entrepreneurial intentions, which so far has only taken a psychological (Douglas and Fitzsimmons 2013; Nyock Ilouga et al. 2014), but not a psychiatric symptom perspective.

Furthermore, we contribute to the emerging literature that takes a clinical perspective to explaining entrepreneurial intentions. By linking (“distal”) psychiatric symptom scores via (more “proximal”) psychological tendencies (such as risk taking propensity) to entrepreneurship, scholars may be able to create a better understanding of entrepreneurial intentions and the personality of future entrepreneurs (Epstein and O’Brian 1985; Mathieu and St-Jean 2013). Recent research provides evidence of relationships between entrepreneurship (measured as entrepreneurial intentions, activity or orientation) and continuous scores on other (initial) clinical constructs such as narcissism (Wales et al. 2013; Mathieu and St-Jean 2013), psychopathy (Akhtar et al. 2013), or the Dark Triad (narcissism, psychopathy, Machiavellianism) (Hmieleski and Lerner 2013). By examining the mediating role of risk taking propensity in the relation between ADHD-like behavior—that shows a highly genetic predisposition—and entrepreneurial intentions, our study also adds to the understanding of the genetic basis of entrepreneurship (van der Loos et al. 2013; Nicolaou et al. 2008; White et al. 2006).

Finally, research on the role of ADHD in the workplace generally focuses on the implications for working in large established, and often heavily regulated, organizations (Kessler et al. 2009). Following Markman and Baron (2003, p. 282) who argue that: “While much research … has focused on important components of fit with respect to existing, well-established organizations and routines, far less attention has been directed to person–organization fit in the context of new venture formation,” we contribute by applying the person–environment fit literature to examine the fit with an entrepreneurial career of individuals who report varying levels of ADHD-like behavior. This is important given that individuals who exhibit higher levels of such behavior often have difficulties committing to a career decision (Painter et al. 2008) and exhibit below average performance in regular wage-employment (Nadeau 2005). Our findings can help create awareness of what inspires and motivates these individuals in a (future) profession and support them in deciding upon a career that is aligned with their wishes and abilities.

2 Theoretical background and hypotheses

2.1 Person–career fit

The person–environment (P–E) fit literature emphasizes the role of both individual and environmental (or organizational) factors in determining career decisions and outcomes (Kristof 1996; Kristof-Brown et al. 2005; Oh et al. 2013). The idea of P–E fit draws on principles of Interactional Psychology, asserting that neither personal nor environmental factors alone are able to explain individual behavior (Lewin 1951). The underlying premise is that of the compatibility between people and their environment—the latter of which can refer, for example, to an organization, job, or supervisor (Kristof-Brown et al. 2005). For example, personorganization fit may refer to the congruence of personal and organizational values, and personjob fit to that between the skills and/or knowledge of an employee and what the job requires (Cable and DeRue 2002). P–E fit has been linked to several outcome variables, such as job satisfaction, organizational commitment, and citizenship behavior (Cable and Edwards 2004; Cable and DeRue 2002; Kristof-Brown et al. 2005), but also to job or career transitions. For example, Kristof-Brown et al. (2005) show that the perceived person–job fit leads to lower intentions to quit a job, and Carless (2005) finds evidence for a direct link between a perceived person–job fit and the intention to accept a job offer.

Within the context of entrepreneurship, Markman and Baron (2003, p. 286) argue that some people are “better suited to exploit commercial opportunities or create new companies than others”. Despite contradictory findings, there is a large literature indicating that entrepreneurs differ from non-entrepreneurs on a range of characteristics including cognitive biases (Baron 1998; Busenitz and Barney 1997), intuition (Allinson et al. 2000), risk taking propensity (Stewart and Roth 2001) and taste for variety (Åstebro and Thompson 2011). In addition, the theoretical classics of Schumpeter (1934), Kirzner (1979) and Knight (1921) emphasize innovation, opportunity perception, and handling uncertainty, respectively, as defining characteristics of entrepreneurs. The higher individuals score on these distinctive characteristics, the better will be their P–E fit (Markman and Baron 2003). Building on the P–E fit literature, Lee et al. (2011) focus on innovation orientation as a distinctive individual characteristic and find that a misfit between an employee’s innovation orientation and an organization’s (lack of an) innovative climate leads to higher entrepreneurial intentions via lower satisfaction in the current job. Hence, if organizational conditions are not favorable, i.e., show a relatively poor fit with individuals’ needs, skills, and characteristics, it is likely that they become dissatisfied and start exploring alternative career paths.

In the present study, we examine the perceived relative fit of individuals who exhibit different levels of ADHD-like behavior with entrepreneurial intentions versus intentions to work in a wage job. In considering fit, we examine it relative to the individual (i.e., whether entrepreneurial intentions are perceived to fit the individual’s characteristics better than intentions to work in a wage job). Thus, we are not suggesting that an individual scoring higher on ADHD-like behavior would be necessarily good at entrepreneurship (relative to other individuals or relative to some particular standard), but simply that those individuals perceive an entrepreneurial career as a relatively good (i.e., better) fit compared with wage-employment. In sum, we suggest that the choice for an entrepreneurial career can be determined by a relative good fit between individuals’ characteristics and the benefits and the requirements of entrepreneurship as compared to a relative poor fit with the work environment in wage-employment. No claim is being made that the fit with entrepreneurship is inherently “good”—but that for the particular individual the fit is better relative to wage-employment.

2.2 ADHD-like behavior and the work environment

Given that ADHD-like behavior is associated with “deficiencies” such as acting before thinking, a short attention span, and lack of persistence when facing routine tasks, (Barkley 1997), individuals who display such behavior may find it difficult to meet the requirements of a regular work environment (Barkley and Murphy 2010). They generally seek activities that do not require close supervision and that allow them to work independently (Mannuzza et al. 1993). Their impulsive nature makes them more prone to acting without thinking about the consequences, thereby risking offending their supervisors or other co-workers. Their distractibility, stemming from a lower inhibitory control, may prevent them from engaging successfully in activities that require sustained attention (Barkley 1997). Even when they are capable of working in a regular wage job, adults who exhibit ADHD-like behavior may prefer to work independently because of a desire for self-determination (Mannuzza et al. 1993). Their strong strive to maintain control to counteract an often chaotic lifestyle (Toner et al. 2006) contributes to their preference for a work environment that allows and promotes independent behavior. Thus, irrespective of whether adults with ADHD-like behavior are more independent out of necessity or because of a clear preference, they are more likely to be attracted to occupations in which they can work independently, in their own pace and without having to report to someone higher in hierarchy. At the same time, a high level of freedom and autonomy is generally seen as a universal reason for entrepreneurial intentions and new venture creation (Shane et al. 1991) and among the most cited factors for preferring to found an own venture over working for a boss in wage-employment (Douglas and Shepherd 2002; Kolvereid 1996).

Entrepreneurship does not only fit well with the behavior associated with ADHD because of the absence of a rigid and formally structured work environment, it also requires characteristics and skills commonly attributed to individuals who exhibit ADHD-like behavior. For example, prior research shows evidence of a positive relation between ADHD and individual creativity (Abraham et al. 2006; Shaw and Brown 1991; White and Shah 2006). The lower inhibitory control associated with ADHD (Barkley 1997; Clark et al. 2007) has multiple behavioral consequences including a difficulty focusing attention on a given task, mind-wandering and a lower ability to distinguish irrelevant from relevant stimuli. Though this may hinder productivity in a formal work environment, in particular in terms of “in-role performance” (Halbesleben et al. 2013), an “uninhibited imagination” has been found conducive to creative thinking (Carson et al. 2003). In fact, adults who exhibit behaviors associated with ADHD perform better at tasks that require divergent thinking (White and Shah 2006), demonstrate higher originality in performing tasks, and have a higher preference for generating ideas compared with idea clarification or idea implementation (White and Shah 2011). The APA (2000, p. 86/7) notes that adults with ADHD are easily distracted when fulfilling “boring, repetitive” tasks and tend to perform better when working in novel settings or engaging in activities that they are passionate about. Because they seem to be more creative and prefer to engage in non-repetitive, idea-generating tasks, adults who exhibit ADHD-like behavior are more likely to pursue occupational activities that will enable them to exploit their creativity. At the same time, creating something new is a common motive for having entrepreneurial intentions and pursuing an entrepreneurial career (Carter et al. 2003; Cassar 2007), and it also distinguishes entrepreneurs from non-entrepreneurs (Carland et al. 1984).

Furthermore, adults who show ADHD-like behavior generally have to deal with a greater number of adverse events (e.g., poor performance in school, unemployment) originating from their lower inhibitory control. While adversity is often negatively related to well-being (Breslau et al. 1999; Turner and Lloyd 1995), recent evidence suggests that adversity may also foster resilience, i.e., individuals who experience moderate adversity may be better able to cope with stressful situations or failure and, therefore, report higher well-being (Seery et al. 2010; Seery, et al. 2013). By experiencing the negative consequences of ADHD from early childhood, those individuals may develop a higher resistance to failure as well as ways to cope with adversity and achieve success against significant odds (Wilmshurst et al. 2011). In particular, high-functioning adults who show ADHD-like behavior may exhibit greater resilience to disappointments. Resilience to disappointments and the ability to “bounce back” by continually (re)assessing and adapting to changing and stressful situations is not only common among individuals who exhibit ADHD (Young 2005), it is also a prerequisite for entrepreneurs who need to persevere in the face of high risk and resource constraints (Markman et al. 2005). Consequently, adults with ADHD-like behavior may perceive themselves as better equipped than their peers to work in environments that are stressful, uncertain and where setbacks are frequent. To summarize, a career in entrepreneurship appears to show a relative good fit with individuals who exhibit higher levels of ADHD-like behavior. We therefore assume that they are more likely to have entrepreneurial intentions. We derive the following hypothesis:

H1:

ADHD-like behavior is positively related to entrepreneurial intentions.

2.3 The mediating role of risk taking propensity

ADHD is generally associated with a low activity level in the behavioral inhibition system (BIS) (Quay 1988, 1997), and according to Barkley (1997), poor response inhibition can be seen as the central deficiency in ADHD. It leads to an impairment of the executive functions including working memory, self-regulation of affect-motivation-arousal, internalization of speech, and reconstitution. The purpose of BIS is to withhold an initial response to an event, inhibit ongoing behavior and resist distraction by competing happenings (Barkley 1997). In addition, it motivates risk assessment behavior and behavioral caution (McNaughton and Gray 2000). Response inhibition essentially facilitates the self-regulation of arousal. Individuals who experience ADHD-like behavior, such as restlessness and hyperactivity, tend to experience a chronic state of “under-arousal” (Shaw and Giambra 1993; White 1999). According to the optimal stimulation theory (Zentall and Zentall 1983), individuals who are exposed to ongoing low levels of incoming sensory stimulation have the habit to respond by showing “deviating” behavior aimed at increasing the level of sensory inputs. Loo et al. (2009) show that adults who exhibit ADHD are in need for continuously high levels of arousal (“cortical activation”) to sustain their attention. They may therefore seek self-stimulation by way of engaging in excessive activity or, alternatively, in activities that induce higher arousal levels. And when they find themselves in a situation characterized by high levels of stimulation, the lower inhibitory control associated with ADHD-like behavior makes those individuals more likely to (re)act on the presented stimulus (White 1999). Applying Damasio’s (1996) somatic marker hypothesis to individuals who exhibit ADHD, it is seen that they experience weaker physical signals to guide risky decisions (Bechara et al. 1997; Mäntylä et al. 2012; Toplak et al. 2005) which makes them relatively tolerant of risk. As a consequence, children and adolescents with ADHD may be more likely to engage in risky behavior than others. Shaw and Brown (1999) report that students who show higher levels of ADHD-like behavior indicate to have more interest in searching for stimulating and “risky” types of activities. Other studies provide evidence of a positive relationship between the occurrence of ADHD in childhood and the level of sensation seeking as a college student (Shaw and Giambra 1993) and the level of risk taking in adulthood (Olazagasti et al. 2013). This may lead (young) adults who exhibit ADHD-like behavior to be attracted to more risky jobs such as sales, stock brokerage and entrepreneurship (Weiss and Murray 2003). Therefore, we formulate the following hypothesis:

H2:

ADHD-like behavior is positively related to risk taking propensity.

Traditionally, risk taking has been associated with entrepreneurship. Knight (1921) already pointed out that, unlike managers, entrepreneurs make business decisions in uncertain situations, thereby risking the loss of their investment. Yet, empirical research reveals conflicting findings, with some studies reporting a higher risk taking propensity of entrepreneurs as compared to the general population or managers (Stewart et al. 1998; Caliendo et al. 2009), while others report no significant differences (Brockhaus 1980). Recently, however, Niess and Biemann (2014) reported that high risk propensity predicts the self-employment decision. In addition, the meta-analysis by Zhao et al. (2010) provides further evidence that risk propensity is positively associated with entrepreneurial intentions. This leads us to hypothesize the following:

H3:

Risk taking propensity is positively related to entrepreneurial intentions.

Given that ADHD is linked to an interest in risky professions and risk taking propensity has been associated with entrepreneurship, we expect risk taking propensity to mediate the relationship between ADHD-like behavior and entrepreneurial intentions. Selecting risk taking propensity as a mediating factor is also in line with other studies examining the effects of genetically determined attributes on entrepreneurship. For example, White et al. (2006) find that the (biological) effect of testosterone levels on new venture creation is partly mediated by risk taking propensity, while Nicolaou et al. (2008) find that sensation seeking (which involved taking risk) mediates the effect of genetics on the pursuit of an entrepreneurial career. We thus formulate the following mediation hypotheses:

H4:

The relationship between ADHD-like behavior and entrepreneurial intentions is mediated by risk taking propensity.

3 Methods

3.1 Data collection

We test our hypotheses in a sample of students who did not yet embark on a career path. Specifically, we use the Global University Entrepreneurial Spirit Students’ Survey (GUESSS) for 2011—a data set collected by an international research consortium aimed at examining career aspirations of students in higher education.

For the present study, we rely on data collected among 13,121 students at 14 universities and 24 universities of applied sciences in the Netherlands. Students received a link to the online survey via direct mailing, a newsletter, or the Intranet. A reminder was sent out after 1 month and two iPads 2.0 were raffled. The final response rate for universities that systematically collected data among their students amounts to 7.4 %.1 To prevent self-selection of students who have entrepreneurial intentions, the survey was introduced as focusing on future career paths in general, without explicitly stating its focus on entrepreneurship.

Our final sample amounts to 10,104 students, which excludes students who do not yet know what they want to do after their studies (N = 2,752) and those who want to take over a (family) business (N = 191), the latter which cannot be considered intentional founders. Furthermore, 74 respondents are excluded because of missing values on one or more of the variables included in our analysis.

3.2 Measures2

3.2.1 Dependent variable

To measure our dependent variable entrepreneurial intentions students were asked to answer the following question: “Which career path do you intend to pursue right after completion of your studies?” We create a dichotomous variable where “1” represents entrepreneurial intentions as a prospective founder, and “0” denotes prospective employees.

3.2.2 Independent variable

To measure the level of ADHD-like behavior we use the six-item ADHD Self-Report Screener (ASRS-6) of the World Health Organization (WHO). The ASRS-6 is a short form of the 18-item patient-reported ASRS-v1.1 questionnaire, assessing the frequency of all 18 DSM-IV symptoms of ADHD. The ASRS-v1.1 scale has been proven effective in screening for adult ADHD (Kessler et al. 2005, 2007; Matza et al. 2011). The six-item screener shows a strong concordance with clinical diagnoses and outperforms a longer 18-item ADHD scale in terms of sensitivity, specificity and total classification accuracy (Kessler et al. 2005; Das et al. 2014). To capture the level of ADHD-like behavior, we calculate the average score of the ASRS-6 screener. The Cronbach’s α for the ASRS-6 scale amounts to 0.58, which is relatively low but still close to the lower bound of reported alphas for the ASRS screener questions in Kessler et al. (2007).

3.2.3 Mediator

Risk taking propensity (i.e., the willingness to take risks) is measured with the single-item experimentally validated scale proposed by Dohmen et al. (2011): “How do you see yourself: Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks?” with response categories “0” (risk averse) to “10” (fully prepared to take risk). This measure is highly correlated with economic measures on risk taking behavior with real money at stake (Dohmen et al. 2011).

3.2.4 Control variables

Prior research has shown that entrepreneurial intentions depend on age (Birley 2002; Matthews and Moser 1996), gender (Carter et al. 2003; Verheul et al. 2012), marital status (Amit et al. 1995), nationality (Bosma et al. 2008), the presence of self-employed parents (Laspita et al. 2012; Hoffmann et al. 2015), study level (Laspita et al. 2012), and study field (Zellweger et al. 2011). We therefore control for these factors. Moreover, we control for the particular university (20 dummies) and a self-reported grade [on a scale from 1 (bad) to 10 (excellent)] to consider that students who exhibit ADHD-like behavior may have unequal access to the job market, leading them toward founding their own venture. Furthermore, we include variables related to the Theory of Planned Behavior (Ajzen 1991): attitude toward entrepreneurship, social norms, compliance motivation and entrepreneurial self-efficacy (Linan and Chen 2009) as well as locus of control. Finally, we control for individuals’ perceived risk of entrepreneurship by including the score on the question: “How risky do you perceive starting your own company?” (0 = not risky … 10 = very risky) because the more risky an entrepreneurial career is perceived, the less likely individuals intend to found a venture (Simon et al. 2000).

3.3 Common method bias

Given that both our dependent and independent variables are measured in the same sample and at the same point in time, our results might be subject to common method bias (Podsakoff et al. 2003). To diagnose the extent of common method variance, we performed several tests. Our first test was Harman’s one-factor test which is based on an exploratory factor analysis across all variables included in our regression analysis. The unrotated one-factor solution yields a factor with an eigenvalue of 2.52 accounting for 11.81 % of inter-item covariance. The extent of common method variance seems to be comparatively low. Next to the Harman’s one-factor test, we performed partial correlation procedures (Podsakoff et al. 2003) to control for common method variance in our regression models. We used exploratory factor analysis to identify a latent common method factor and inserted the corresponding factor values into our regression models. The latent common method factor did not show a significant relation with the dependent variable; the relations between the independent variables were similar when compared to those in the main regression analyses. The (directly measured) latent method factor technique is another option to investigate the magnitude of possible common method variance (Podsakoff et al. 2012). We use AMOS and estimate a structural equation model (SEM) with the goal to find a common latent factor (common latent factor) that determines the common variance shared among all observed items in the model (Podsakoff et al. 2012). If this common variance is large, common method bias can occur. In our case, the results show a common variance of about 0.07 %. Thus, we observe a relatively low level of common method variance, which is unlikely to lead to a severe case of common method bias. The results for our main independent variables were also not strongly affected by the inclusion of the common latent factor. We conclude that common method bias seems not to be of major concern for our statistical analyses.

4 Results

4.1 Descriptives and correlation analysis

In our sample, 9,025 students (i.e., 89.3 %) intend to work in wage-employment and 1,079 students (10.7 %) aim to found a venture directly after their study. This percentage of intentional founders is comparable to the average level of start-up intentions in the Netherlands according to the Global Entrepreneurship Monitor (GEM), which is reported to be about 10 % (van der Zwan et al. 2012). Our sample contains 55 % female students and about 85 % have the Dutch nationality. Furthermore, 68 % of the students are undergraduates, 30 % are graduates, and 2 % are doctoral and MBA students. The majority of students are from Management (18 %), Medicine and Health Science (14 %), and Economics (10 %). Cultural Studies and Social Sciences together account for about 13 % of students, whereas the study fields Pedagogy, Engineering, and Law each account for about 5 % of the students.

Correlation analysis and the variance inflation factors (VIF) for our measures show that problems of multicollinearity are unlikely (see Table 1). The maximum VIF score is 2.08 for “Attitude,” which is well below the recommended level of 10 (Neter et al. 1990).
Table 1

Correlation table

  

Mean

SD

VIF

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1

Entrepreneurial intentions

0.11

0.31

1.15

                

2

ADHD-like behavior

2.57

0.60

1.17

0.06

               

3

Risk taking propensity

5.88

2.07

1.25

0.16

0.01

              

4

Risk perception

6.24

2.05

1.06

−0.11

0.05

−0.05

             

5

Female

0.55

0.50

1.14

−0.08

−0.10

−0.08

0.06

            

6

Age (years)

23.18

4.93

1.66

0.08

−0.02

0.05

−0.04

−0.08

           

7

Single

0.93

0.26

1.51

−0.06

0.02

−0.005

0.05

0.01

−0.57

          

8

Self-employed

parents

0.28

0.45

1.04

0.05

0.004

0.10

−0.05

0.02

−0.07

0.06

         

9

Nationality = Dutch

0.84

0.37

1.08

0.01

0.02

−0.07

−0.01

−0.02

−0.02

−0.02

−0.04

        

10

Attitude

4.23

1.57

2.08

0.22

0.06

0.34

−0.12

−0.25

0.03

−0.01

0.14

−0.15

       

11

Social norms

5.47

0.98

1.43

0.07

−0.03

0.20

−0.06

−0.06

−0.05

0.03

0.13

−0.08

0.47

      

12

Compliance

5.18

1.08

1.11

−0.05

0.01

−0.03

−0.04

0.11

−0.10

0.08

0.04

−0.05

0.03

0.23

     

13

Self-efficacy

4.48

0.87

2.03

0.19

−0.12

0.40

−0.13

−0.26

0.04

−0.02

0.13

−0.13

0.63

0.38

0.01

    

14

Locus of control

3.08

0.72

1.18

0.01

0.30

−0.12

0.03

0.01

−0.07

0.02

−0.02

0.03

−0.02

−0.13

0.05

−0.18

   

15

Study grade

7.29

0.70

1.10

−0.04

−0.18

−0.03

0.05

0.04

0.05

−0.03

0.003

−0.14

−0.04

−0.02

0.005

0.06

−0.15

  

16

Study field = Management

0.19

0.39

1.10

0.02

−0.005

0.05

0.03

−0.12

−0.001

0.01

0.05

−0.17

0.22

0.13

0.03

0.23

−0.03

0.04

 

17

Study level = Bachelor

0.68

0.47

1.15

0.06

0.05

0.05

−0.05

0.01

−0.28

0.08

0.03

0.07

0.04

0.05

−0.004

0.01

0.08

−0.17

−0.03

N = 10,104; all correlations ≥0.02 are significant at 5 % significance level

SD standard deviation, VIF variance inflation factor

4.2 Hypothesis testing

To test our first hypothesis, stating that the level of ADHD-like behavior is positively related to entrepreneurial intentions, we estimate stepwise binary logistic regressions (see Table 2). Control variables and mediator (risk propensity) are entered in Model 1 and ADHD-like behavior is entered in Model 2. We find that students who exhibit a higher level of ADHD-like behavior are significantly more likely to have entrepreneurial intentions (B = 0.244; p < 0.001). This provides support for hypothesis H1. The unstandardized coefficients explaining entrepreneurial intentions correspond with an odds ratio of 1.28. In total, 89.42 % of all observations were correctly classified in the model.
Table 2

Binary logistic regression explaining entrepreneurial intentions

 

Model 1

Model 2

 

Coeff. (SE)

Coeff. (SE)

ADHD-like behavior

 

0.244 (0.060)***

Risk taking propensity

0.139 (0.021)***

0.136 (0.021)***

Risk perception

−0.126 (0.017)***

−0.129 (0.017)***

Female

−0.202 (0.078)**

−0.171 (0.079)*

Age (years)

0.025 (0.008)**

0.026 (0.008)**

Single

−0.291 (0.146)*

−0.293 (0.146)*

Self-employed parents

0.197 (0.075)**

0.194 (0.075)**

Nationality (5 dummies)

Included

Included

Attitude

0.422 (0.036)***

0.406 (0.036)***

Social norms

−0.193 (0.044)***

−0.186 (0.044)***

Compliance

−0.096 (0.032)**

−0.098 (0.032)**

Self-efficacy

0.321 (0.059)***

0.356 (0.060)***

Locus of control

0.151 (0.050)**

0.101 (0.051)*

Study grade

−0.153 (0.052)**

−0.131 (0.052)*

Study field (14 dummies)

Included

Included

Study level (4 dummies)

Included

Included

University (20 dummies)

Included

Included

Constant

−2.884 (0.752)***

−3.663 (0.778)***

N (observations)

10,104

10,104

Log Pseudolikelihood

−2,865.27

−2,857.11

McFadden Pseudo R2

0.1653

0.1677

SE robust standard errors

*** p < 0.001; ** p < 0.01; * p < 0.05 (two-sided tests)

To test hypotheses H2, H3, and H4 we follow Hayes (2013) and report the results of the mediation analyses in Table 3. The Stata program binary_mediation is used to conduct the analyses since our dependent variable (entrepreneurial intentions) is a binary variable. Standard errors and significance levels for the direct and indirect effects are calculated using bootstrapping (500 replications).
Table 3

Mediation analyses

 

Risk taking propensity

Entrepreneurial intentions

Model 1

Model 2

Model 3

ADHD-like behavior

0.185*** (0.033)

0.261*** (0.060)

0.246*** (0.060)

Risk taking propensity

  

0.136*** (0.021)

Controls

Included

Included

Included

Likelihood ratio test

 

p < 0.001

p < 0.001

F test

p < 0.001

  

Log Pseudolikelihood

 

−2,879.98

−2,858.14

McFadden Pseudo R2

 

0.1610

0.1674

Direct effect

 

0.0798 (0.0204)***

 

Indirect effect

 

0.0083 (0.0021)***

 

Total effect

 

0.0881 (0.0205)***

 

N = 10,104. This table shows coefficients and standard errors in parentheses; the Stata program binary mediation was used

Standard errors and significance values for direct and indirect effects are calculated using bootstrapping (500 replications)

*** p < 0.001; ** p < 0.01; * p < 0.05 (two-sided tests)

First, we regressed the mediator (risk taking propensity) on the predictor (ADHD-like behavior) and the controls in Model 1, then the dependent variable entrepreneurial intentions on the predictor and the controls in Model 2, and finally the dependent variable on the predictor and controls and the mediator in Model 3. First, we find that ADHD-like behavior is significantly related to risk taking propensity (Model 1: B = 0.185; p < 0.001), thereby supporting hypothesis H2. Second, ADHD-like behavior is significantly related to entrepreneurial intentions (Model 2: B = 0.261; p < 0.001). Finally, risk taking propensity is positively linked to entrepreneurial intentions after controlling for ADHD-like behavior (Model 3: B = 0.136; p < 0.001 and Model 5: B = 0.114; p < 0.01). Thus, hypothesis H3 is supported. Testing the indirect effect with bootstrapping confirms that risk taking propensity significantly mediates the relationship between ADHD-like behavior and entrepreneurial intentions (observed coefficient = 0.0083; bootstrap SE = 0.0021; 95 % CI from 0.0041 to 0.0124). This provides support for H4.

5 Discussion

The aim of this study is to examine the attraction of an entrepreneurial career among young adults who exhibit ADHD-like behavior. Adults who show this behavior tend to experience difficulties committing to a career choice, and when they finally commit to one, they often exhibit substandard performance and rarely attain higher-ranked occupational positions in salaried employment (de Graaf et al. 2008). Investigating the career intentions of over 10,000 university students, we find that ADHD-like behavior is positively related to entrepreneurial intentions. These findings convey two main messages. First, students with ADHD-like behavior seem to prefer an entrepreneurial career over one in wage-employment. Second, the preference for an entrepreneurial career of students who show ADHD-like behavior may be explained on the basis of the P–E fit theory. In other words, students base their career choices, at least to some extent, on their perceived fit with the work environment and the demands of entrepreneurship relative to wage-employment.

In our attempt to explain the relationship between ADHD-like behavior and entrepreneurship we also investigate whether risk taking propensity, a recurring theme in both entrepreneurship and ADHD research, mediates the relationship between ADHD-like behavior and entrepreneurial intentions. We find evidence to support our prediction. Thus, it seems that one underlying factor explaining the preference for entrepreneurship among students with ADHD-like behavior is the tendency to search for, and engage in, stimulating activities to compensate for their experienced under-arousal. Because of their willingness to take risks in general, they are also more likely to prefer an entrepreneurial career instead of one in wage-employment.

By establishing that ADHD-like behavior predicts entrepreneurial intentions, we extend the literature on the determinants of entrepreneurial intentions, which so far has only taken a psychological (Douglas and Fitzsimmons 2013; Nyock Ilouga et al. 2014) but not a psychiatric symptom perspective.

Our findings have implications for (entrepreneurship) educators as well as individuals with ADHD-like behavior who have to decide on a career. Considering the potential fit with an entrepreneurial career, it is important that individuals who show ADHD-like behavior carefully reflect on what motivates them in a (future) profession and create awareness that the identified preferred work characteristics may offer them guidance when deciding upon a career. Furthermore, educators should not only be aware of the challenges ADHD-like behavior poses, but also understand and facilitate its “blessings.” Because an entrepreneurial career appears to fit with the risk taking propensity of young adults who demonstrate ADHD-like behavior, educators may want to help those “energetic” youngsters to develop skills and the persistence to found their own venture.

6 Limitations and future research

Our study has several limitations. First, we acknowledge that the effect size of our measure of ADHD-like behavior in explaining entrepreneurial intentions is relatively small. Calculating marginal effects for the model predicting entrepreneurial intentions, we find that a one unit change in the scale measuring ADHD-like behavior increases the likelihood of having entrepreneurial intentions by 3.85 %. To be able to compare the marginal effects of ADHD-like behavior with differently scaled independent variables (gender and self-employed parents are binary variables), we calculated full elasticities (instead of semi-elasticities). Here we find that a 1 % increase in ADHD-like behavior leads to a 1.5 % increase in entrepreneurial intentions, which is comparable to the 1.1 % increase in entrepreneurial intentions when someone has a 1 % higher chance of having entrepreneurial parents or when the likelihood of being male increases by 1 %.

Second, we are aware of the relatively low reliability of our scale measuring the level of ADHD-like behavior measured by the Cronbach’s α (0.58), which may be attributed to the fact that the scale captures two different types of behaviors; i.e., inattention and hyperactivity (Hesse 2011). Future research may yield interesting results when disentangling inattentive and hyperactive behaviors and examining their separate relations with entrepreneurship instead of using an average score capturing the full spectrum of ADHD-like behaviors.

There are several other avenues for researchers to pursue that advance our understanding of the relationship between ADHD-like behavior and entrepreneurship. First, more research is needed assessing this relationship in non-student samples, including employed and unemployed individuals. Although students are an appropriate population for studying (future) career decisions, we acknowledge that students who pursue a university education are a distinct group that may exhibit more efficient coping mechanisms and may therefore include more “success” cases and exclude more extreme cases. Second, future research is also warranted on how different levels of ADHD-like behavior impact an individual’s capacity to advance in the entrepreneurial process. Assuming that a relative good person–career fit leads to more work satisfaction and better performance, the strengths of individuals who display ADHD-like behavior may lead them to outperform other entrepreneurs in certain domains, while their weaknesses may lead them to underperform elsewhere. The question arises whether adults who demonstrate ADHD-like behavior are also persistent, i.e., do they start their own venture and do they survive the ups and downs of the entrepreneurial journey in the long run? Third, further research should investigate how well-equipped individuals with ADHD-like behavior are to start, manage, and grow successful new ventures. We also expect that the relationships between ADHD-like behavior and actual founding behavior, and between ADHD-like behavior and firm performance are subject to contextual effects. Thus, the effect of ADHD-like behavior on founding behavior should be greater when accompanied by a higher level of human capital, or a supportive environment (e.g., availability of institutions that foster entrepreneurship, support from friends and family). Fourth, the effect of ADHD-like behavior on firm performance may vary depending on the industry where the firm operates, and the entrepreneurial team composition. Moreover, entrepreneurs with ADHD-like behavior may perform better, when integrated in entrepreneurial teams with a high complementarity in skills and functions. This is due to the fact that other team members may compensate for the behavioral “deficiencies” associated with ADHD, while at the same time benefiting from their unusual “blessings.” Finally, another avenue for future research is the identification of other mediators in the relationship between ADHD-like behavior and entrepreneurial intentions. For example, taste for variety may drive individuals who exhibit ADHD-like behavior to pursue an entrepreneurial career because they often search for varied sensory input to satisfy their chronic under-arousal, and taste for variety is also related to entrepreneurship (Åstebro and Thompson 2011). Another potential mediator is adversity resilience because individuals with ADHD-like behavior are likely to develop ways to cope with adversity and are therefore relatively resilient (Wilmshurst et al. 2011), while at the same time, adversity resilience has been related to entrepreneurship (Holland and Shepherd 2013; Markman and Baron 2003; Markman et al. 2005; Patel and Thatcher 2014; van Gelderen 2012).

The use of validated psychiatric symptom scores, originally developed to assess the extent of psychiatric symptoms, for non-clinical reasons is still in its infancy. Associating ADHD with occupational decisions such as the intention to become an entrepreneur appears successful. The rich world of these symptom scores (e.g., in the framework of Diagnostic and Statistical Manual of mental disorders; DSM-5) offers many opportunities for investigating the effect of addiction (such as reward-driven decision making), of hypomania (such as creativity), or of psychopathy (such as fearlessness) on entrepreneurship outcomes.

Footnotes

  1. 1.

    For the calculation of the response rate universities with no systematic data collection and/or those with less than twenty respondents were excluded. In the analysis these observations are combined in the category ‘other’ (N = 247).

  2. 2.

    More details about our measures can be found in the electronic supplementary material.

Notes

Acknowledgments

The authors are grateful for participating in the international GUESSS project initiated and coordinated by Philipp Sieger and his colleagues of University of St. Gallen, Switzerland. We acknowledge the financial support of Panteia BV Zoetermeer and the Erasmus Centre for Entrepreneurship (ECE). We thank Henk Schmidt, Rector Magnificus of Erasmus University Rotterdam at the time of starting the project, for supporting this research endeavor. We also are grateful for the efforts of all representatives of the Higher Educational Institutions that participated in the Dutch data collection, in particular Marta Berent (Nyenrode University), Maryse Brand (University of Groningen), Martin Carree (Maastricht University), Frans Donders (Hanze University of Applied Sciences, Groningen), Geert Duysters, Jessica van den Bosch and Joop Vianen (University of Tilburg), Roelof Eleveld (InHolland University of Applied Sciences), Aard Groen (University of Twente), Erik Stam (Utrecht University), Lex van Teeffelen (University of Applied Sciences, Utrecht), and Karen Verduijn (VU University Amsterdam). The supporting organizations are not responsible for the current report. We thank Shailesh Gadjiedas, Hendrik Halbe, and Kim de Rotte for their support and Wim Rietdijk and Daan van Knippenberg for their comments on an earlier version of this paper. Earlier versions of the present paper have been presented at the University of Groningen (April 11, 2013), at the Technical University Eindhoven (February 27, 2013), at the University of Potsdam (June 14, 2012) and at the Technical University of Munich (September 26, 2011).

Supplementary material

11187_2015_9642_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 18 kb)

References

  1. Abraham, A., Windmann, S., Siefen, R., Daum, I., & Güntürkün, O. (2006). Creative thinking in adolescents with attention deficit hyperactivity disorder (ADHD). Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence, 12(2), 111–123. doi:10.1080/09297040500320691 Google Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T Google Scholar
  3. Akhtar, R., Ahmetoglu, G., & Chamorro-Premuzic, T. (2013). Greed is good? Assessing the relationship between entrepreneurship and subclinical psychopathy. Personality and Individual Differences, 54, 420–425. doi:10.1016/j.paid.2012.10.013 Google Scholar
  4. Allinson, C. W., Chell, E., & Hayes, J. (2000). Intuition and entrepreneurial behavior. European Journal of Work and Organizational Psychology, 9(1), 31–43. doi:10.1080/135943200398049 Google Scholar
  5. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR (4th ed.). Washington, DC: American Psychiatric Press.Google Scholar
  6. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM V (5th ed.). Washington, DC: American Psychiatric Press.Google Scholar
  7. Amit, R., Muller, E., & Cockburn, I. (1995). Opportunity costs and entrepreneurial activity. Journal of Business Venturing, 10(2), 95–106. doi:10.1016/0883-9026(94)00017-O Google Scholar
  8. Åstebro, T., & Thompson, P. (2011). Entrepreneurs, Jack of all trades or Hobos? Research Policy, 40(5), 637–649. doi:10.1016/j.respol.2011.01.010 Google Scholar
  9. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of AD/HD. Psychological Bulletin, 121(1), 65–94. doi:10.1037/0033-2909.121.1.65 Google Scholar
  10. Barkley, R. A., Fischer, M., Smallish, L., & Fletcher, K. (2006). Young adult outcome of hyperactive children: Adaptive functioning in major life activities. Journal of the American Academy of Child and Adolescent Psychiatry, 45(2), 192–202. doi:10.1097/01.chi.0000189134.97436.e2 Google Scholar
  11. Barkley, R. A., & Murphy, K. (2010). Impairment in occupational functioning and adult AD/HD: The predictive utility of executive function (EF) ratings versus EF tests. Archives of Clinical Neuropsychology, 25, 157–173. doi:10.1093/arclin/acq014 Google Scholar
  12. Baron, R. A. (1998). Cognitive mechanisms in entrepreneurship: Why and when entrepreneurs think differently than other people. Journal of Business Venturing, 13(4), 275–294. doi:10.1016/S0883-9026(97)00031-1 Google Scholar
  13. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 272, 1293–1295. http://www.jstor.org/stable/2892390
  14. Beugelsdijk, S., & Noorderhaven, N. (2005). Personality characteristics of self-employed; an empirical study. Small Business Economics, 24(2), 159–167. doi:10.1007/s11187-003-3806-3 Google Scholar
  15. Biederman, J., Petty, C. R., Fried, R., Doyle, A. E., Spencer, T., Seidman, L. J., et al. (2007). Stability of executive function deficits into young adult years: A prospective longitudinal follow-up study of grown up males with ADHD. Acta Psychiatrica Scandinavica, 116, 129–136. doi:10.1111/j.1600-0447.2007.01008.x Google Scholar
  16. Birley, S. (2002). Attitudes of owner-managers’ children towards family and business issues. Entrepreneurship Theory and Practice, 26(3), 5–19. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=6749006&site=ehost-live
  17. Block, J., Hoogerheide, L., & Thurik, R. (2013). Education and entrepreneurial choice: an instrumental variables analysis. International Small Business Journal, 31(1), 23–33. doi:10.1177/0266242611400470 Google Scholar
  18. Bosma, N., Acs, Z. J., Autio, E., Coduras, A., & Levie, L. (2008). Global entrepreneurship monitor executive report. Santiago, London: Babson Park.Google Scholar
  19. Bozionelos, N., & Bozionelos, G. (2013). Attention deficit/hyperactivity disorder at work: Does it impact job performance? Academy of Management Perspectives,. doi:10.5465/amp.2013.0107 Google Scholar
  20. Breslau, N., Chilcoat, H. D., Kessler, R. C., & Davis, G. C. (1999). Previous exposure to trauma and PTSD effects of subsequent trauma: Results from the Detroit Area Survey of Trauma. American Journal of Psychiatry, 156, 902–907. http://search.proquest.com/docview/220479016?accountid=13598
  21. Brockhaus, R. H. (1980). Risk-taking propensity of entrepreneurs. Academy of Management Journal 23, 509–520. http://www.jstor.org/stable/255515
  22. Busenitz, L. W., & Barney, J. B. (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of Business Venturing, 12, 9–30. doi:10.1016/S0883-9026(96)00003-1 Google Scholar
  23. Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87(5), 875–884. doi:10.1037//0021-9010.87.5.875 Google Scholar
  24. Cable, D. M., & Edwards, J. R. (2004). Complementary and supplementary fit: A theoretical and empirical integration. Journal of Applied Psychology, 89(5), 822–834. doi:10.1037/0021-9010.89.5.822 Google Scholar
  25. Caliendo, M., Fossen, F., & Kritikos, A. S. (2009). Risk attitudes of nascent entrepreneurs - new evidence from an experimentally-validated survey. Small Business Economics, 32(2), 153–167. doi:10.1007/s11187-007-9078-6 Google Scholar
  26. Caliendo, M., Fossen, F., & Kritikos, A. S. (2014). Personality characteristics and the decision to become and stay self-employed. Small Business Economics, 42(4), 787–814. doi:10.1007/s11187-013-9514-8 Google Scholar
  27. Carland, J. W., Hoy, F., Boulton, W. R., & Carland, J. C. (1984). Differentiating entrepreneurs from small business owners: A conceptualization. Academy of Management Review, 9(2), 354–359. doi:10.5465/AMR.1984.4277721 Google Scholar
  28. Carless, S. A. (2005). Person–job fit versus person–organization fit as predictors of organizational attraction and job acceptance intentions: A longitudinal study. Journal of Occupational and Organizational Psychology, 78, 411–429. doi:10.1348/096317905X25995 Google Scholar
  29. Carson, S. H., Peterson, J. B., & Higgins, D. M. (2003). Decreased latent inhibition is associated with increased creative achievement in high-functioning individuals. Journal of Personality and Social Psychology, 85, 499–506. doi:10.1037/0022-3514.85.3.499 Google Scholar
  30. Carter, N. M., Gartner, W. B., Shaver, K. G., & Gatewood, E. J. (2003). The career reasons of nascent entrepreneurs. Journal of Business Venturing, 18, 13–39. doi:10.1016/S0883-9026(02)00078-2 Google Scholar
  31. Cassar, G. (2007). Money, money, money? A longitudinal investigation of entrepreneur career reasons, growth preferences and achieved growth. Entrepreneurship and Regional Development, 19(1), 89–107. doi:10.1080/08985620601002246 Google Scholar
  32. Clark, L., Blackwell, A. D., Aron, A. R., Turner, D. C., Dowson, J., Robbins, T. W., & Sahakian, B. J. (2007). Association between response inhibition and working memory in adult ADHD: A link to right frontal cortex pathology? Biological Psychiatry, 61, 1395–1401. doi:10.1016/j.biopsych.2006.07.020 Google Scholar
  33. Cortese, S. (2012). The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): what every clinician should know. European Journal of Pediatric Neurology, 16(5), 422–433. doi:10.1016/j.ejpn.2012.01.009 Google Scholar
  34. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, B-351, 1413–1420. http://www.jstor.org/stable/3069187
  35. Das, D., Cherbuin, N., Anstey, K. J., Abhayaratna, W., & Easteal, S. (2014). Regional brain volumes and ADHD symptoms in middle-aged adults: The PATH through life study. Journal of Attention Disorders,. doi:10.1177/1087054714523316 Google Scholar
  36. de Graaf, R., Kessler, R. C., Fayyad, J., ten Have, M., Alonso, J., Angermeyer, M., et al. (2008). The prevalence and effects of adult attention-deficit/hyperactivity disorder (AD/HD) on the performance of workers: results from the WHO World Mental Health Survey Initiative. Occupational and Environmental Medicine, 65(12), 835–842. doi:10.1136/oem.2007.038448 Google Scholar
  37. Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: measurement, determinants and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550. doi:10.1111/j.1542-4774.2011.01015.x Google Scholar
  38. Douglas, E. J., & Fitzsimmons, J. R. (2013). Intrapreneurial intentions versus entrepreneurial intentions: distinct constructs with different antecedents. Small Business Economics, 41(1), 115–132. doi:10.1007/s11187-012-9419-y Google Scholar
  39. Douglas, E. J., & Shepherd, D. A. (2002). Self-employment as a career choice: Attitudes, entrepreneurial intentions, and utility maximization. Entrepreneurship Theory and Practice, 26(3), 81–90. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=6749030&site=ehost-live
  40. Edwards, J. R., Cable, D. M., Williamson, I. O., Lambert, L. S., & Shipp, A. J. (2006). The phenomenology of fit: Linking the person and environment to the subjective experience of person–environment fit. Journal of Applied Psychology, 91(4), 802–827. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=21711352&site=ehost-live
  41. Epstein, S., & O’Brian, E. J. (1985). The person–situation debate in historical and current perspective. Psychological Bulletin, 98, 513–537. doi:10.1037/0033-2909.98.3.513 Google Scholar
  42. Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005). Molecular genetics of attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 1313–1323. doi:10.1016/j.biopsych.2004.11.024 Google Scholar
  43. Halbesleben, J. R. B., Wheeler, A. R., & Shanine, K. K. (2013). The moderating role of Attention-Deficit/Hyperactivity Disorder in the work engagement-performance process. Journal of Occupational Health Psychology, 18(2), 132–143. doi:10.1037/a0031978 Google Scholar
  44. Hartmann, T. (2002). ADHD secrets of success. New York: SelectBooks Inc.Google Scholar
  45. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis—A regression-based approach. New York: Guilford Press.Google Scholar
  46. Hesse, M. (2011). The ASRS-6 has two latent factors: Attention deficit and hyperactivity. Journal of Attention Disorders, 17(3), 203–207. doi:10.1177/1087054711430330 Google Scholar
  47. Hmieleski, K. M., & Lerner, D. A. (2013). The dark triad: narcissism, psychopathy, and Machiavellianism as predictors of entrepreneurial entry. Frontiers of Entrepreneurship Research, 33(4). http://digitalknowledge.babson.edu/fer/vol33/iss4/6
  48. Hoffmann, A., Junge, M., & Malchow-Moller, N. (2015). Running in the family: parental role models in entrepreneurship. Small Business Economics, 44(1), 79–104.Google Scholar
  49. Holland, D. V., & Shepherd, D. A. (2013). Deciding to persist: Adversity, values, and entrepreneurs’ decision policies. Entrepreneurship Theory and Practice, 37, 331–358. doi:10.1111/j.1540-6520.2011.00468.x Google Scholar
  50. Kan, K.-J., Dolan, C. V., Nivard, M. G., Middeldorp, C. M., van Beijsterveldt, C. E. M., Willemsen, G., & Boomsma, D. I. (2013). Genetic and environmental stability in attention problems across the life span: Evidence from the Netherlands Twin Register. Journal of the American Academy of Child and Adolescent Psychiatry, 52(1), 12–25. doi:10.1016/j.jaac.2012.10.009 Google Scholar
  51. Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., et al. (2005). The World Health Organization adult AD/HD self-report scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35, 245–256. doi:10.1017/S0033291704002892 Google Scholar
  52. Kessler, R. C., Adler, L. A., Gruber, M. J., Sarawate, C. A., Spencer, T., & van Brunt, D. L. (2007). Validity of the World Health Organization Adult AD/HD Self-Report Scale (ASRS) in a representative sample of health plan members. International Journal of Methods in Psychiatric Research, 16(2), 52–65. doi:10.1002/mpr.208 Google Scholar
  53. Kessler, R. C., Lane, M., Stang, P. E., & van Brunt, D. L. (2009). The prevalence and workplace costs of adult attention deficit hyperactivity disorder in a large manufacturing firm. Psychological Medicine, 39, 137–147. doi:10.1017/S0033291708003309 Google Scholar
  54. Kets de Vries, M. F. R. (1985). The dark side of entrepreneurship. Harvard Business Review, 160–167. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=3921348&site=ehost-live
  55. Kirby, D. A., & Honeywood, D. (2007). Graduate entrepreneurship, AD/HD and the creation of young entrepreneurs: Is there a need to rethink? International Journal of Entrepreneurship Education, 5, 79–92.Google Scholar
  56. Kirzner, I. M. (1979). Perception, opportunity and profit: studies in the theory of entrepreneurship. Chicago and London: University of Chicago Press.Google Scholar
  57. Knight, F. H. (1921). Risk, uncertainty and profit. New York: Kelly Millman.Google Scholar
  58. Kolvereid, L. (1996). Organizational employment versus self-employment: Reasons for career choice intentions. Entrepreneurship Theory and Practice, 20(3), 23–31.Google Scholar
  59. Kristof, A. L. (1996). Person–organization fit: An integrative review of its conceptualizations, measurement, and implications. Personnel Psychology, 49, 1–49. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=9604081490&site=ehost-live
  60. Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work: A meta-analysis of person–job, person–organization, person–group, and person–supervisor fit. Personnel Psychology, 58, 281–342. doi:10.1111/j.1744-6570.2005.00672.x Google Scholar
  61. Laspita, S., Breugst, N., Heblich, S., & Patzelt, H. (2012). Intergenerational transmission of entrepreneurial intentions. Journal of Business Venturing, 27(4), 414–435. doi:10.1016/j.jbusvent.2011.11.006 Google Scholar
  62. Lee, L., Wong, P. K., Foo, M. D., & Leung, A. (2011). Entrepreneurial intentions: The influence of organizational and individual factors. Journal of Business Venturing,. doi:10.1016/j.jbusvent.2009.04.003 Google Scholar
  63. Lewin, K. (1951). Formalization and progress in psychology. In D. Cartwright (Ed.), Field theory in social science (pp. 1–29). New York: Harper.Google Scholar
  64. Linan, F., & Chen, Y.-W. (2009). Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship: Theory and Practice, 33(3), 593–617. doi:10.1111/j.1540-6520.2009.00318.x Google Scholar
  65. Loo, S. K., Hale, T. S., Macion, J., Hanada, G., & McGough, J. J. (2009). Cortical activity patterns in ADHD during arousal, activation and sustained attention. Neuropsychologica, 47, 2114–2119. doi:10.1016/j.neuropsychologia.2009.04.013 Google Scholar
  66. Mannuzza, S., Klein, R. G., Bessler, A., Malloy, P., & LaPadula, M. (1993). Adult outcome of hyperactive boys: Education achievement, occupational rank, and psychiatric status. Archives of General Psychiatry, 49, 565–576. doi:10.1001/archpsyc.1993.01820190067007 Google Scholar
  67. Mäntylä, T., Still, J., Gullberg, S., & del Missier, F. (2012). Decision making in adults with AD/HD. Journal of Attention Disorders, 16(2), 164–173. doi:10.1177/1087054709360494 Google Scholar
  68. Markman, G. D., & Baron, R. A. (2003). Person–entrepreneurship fit: why some people are more successful as entrepreneurs than others. Human Resource Management Review, 13, 281–301. doi:10.1016/S1053-4822(03)00018-4 Google Scholar
  69. Markman, G. D., Baron, R. A., & Balkin, D. B. (2005). Are perseverance and self-efficacy costless? Assessing entrepreneurs’ regretful thinking. Journal of Organizational Behavior, 26, 1–19. http://www.jstor.org/stable/4093843
  70. Mathieu, C., & St-Jean, E. (2013). Entrepreneurial personality: The role of narcissism. Personality and Individual Differences, 55, 527–531. doi:10.1016/j.paid.2013.04.026 Google Scholar
  71. Mathis, C., Savier, E., Bott, J. B., Clesse, D., Bevins, N., Sage-Ciocca, D., et al. (2014). Defective response inhibition and collicular noradrenaline enrichment in mice with duplicated retinotopic map in the superior colliculus. Brain Structure and Function, 1–12. doi:10.1007/s00429-014-0745-5
  72. Matthews, C. H., & Moser, S. B. (1996). A longitudinal investigation of the impact of family background and gender on interest in small firm ownership. Journal of Small Business Management, 34(2), 29–43.Google Scholar
  73. Matza, L. S., van Brunt, D. L., Cates, C., & Murray, L. T. (2011). Test-retest reliability of two patient-report measures for use in adults with AD/HD. Journal of Attention Disorders, 15(7), 557–563. doi:10.1177/1087054710372488 Google Scholar
  74. McNaughton, N., & Gray, J. A. (2000). Anxiolytic action on the behavioral inhibition system implies multiple types of arousal contribute to anxiety. Journal of Affective Disorders, 61, 161–176. doi:10.1016/S0165-0327(00)00344-X Google Scholar
  75. Nadeau, K. G. (2005). Career choices and workplace challenges for individuals with AD/HD. Journal of Clinical Psychology, 61, 549–563. doi:10.1002/jclp.20119 Google Scholar
  76. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied statistical models. Boca Raton, FL: CRC Lewis Publishers.Google Scholar
  77. Nicolaou, N., Shane, S., Cherkas, L., Hunkin, J., & Spector, T. D. (2008). Is the tendency to engage in entrepreneurship genetic? Management Science, 54(1), 167–179. doi:10.1287/mnsc.1070.0761 Google Scholar
  78. Niess, C., & Biemann, T. (2014). The role of risk propensity in predicting self-employment. Journal of Applied Psychology, 99(5), 1000–1009. doi:10.1037/a0035992 Google Scholar
  79. Nigg, J. T. (1999). The ADHD response inhibition deficit as measured by the stop task: Replication with the DSM-IV combined type, extension, and qualification. Journal of Abnormal Child Psychology, 27, 393–402. doi:10.1023/A:1021980002473 Google Scholar
  80. Nyock Ilouga, S., Nyock Mouloungni, A. C., & Sahut, J. M. (2014). Entrepreneurial intention and career choices: The role of volition. Small Business Economics, 42(4), 717–728. doi:10.1007/s11187-013-9524-6 Google Scholar
  81. Oh, I.-S., Guay, R. P., Kim, K., Harold, C. M., Lee, J.-H., Heo, C.-G., & Shin, K.-H. (2013). Fit happens globally: A meta-analytic comparison of the relationships of person–environment fit dimensions with work attitudes and performance across East Asia, Europe, and North America. Personnel Psychology, 67, 99–152. doi:10.1111/peps.12026 Google Scholar
  82. Olazagasti, M. A. R., Klein, R. G., Mannuzza, S., Belsky, E. R., Hutchison, J. A., Lashua-Shriftman, E. C., & Castellanos, F. X. (2013). Does childhood attention-deficit/hyperactivity disorder predict risk-taking and medical illnesses in adulthood? Journal of the American Academy of Child and Adolescent Psychiatry, 52(2), 153–162. doi:10.1016/j.jaac.2012.11.012 Google Scholar
  83. Painter, C., Prevatt, F., & Welles, T. (2008). Career beliefs and job satisfaction in adults with symptoms of attention-deficit/hyperactivity disorder. Journal of Employment Counseling, 45, 178–188. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=35751339&site=ehost-live
  84. Patel, P. C., & Thatcher, S. M. B. (2014). Sticking it out: Individual attributes and persistence in self-employment. Journal of Management, 40(7), 1932–1979. doi:10.1177/0149206312446643 Google Scholar
  85. Podsakoff, P. M., MacKenzie, S. B, Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=10986397&site=ehost-live
  86. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. doi:10.1146/annurev-psych-120710-100452 Google Scholar
  87. Quay, H. C. (1988). The behavioral reward and inhibition systems in childhood behavior disorders. In L. K. Bloomingdale (Ed.), Attention deficit disorder (Vol. 3, pp. 176–186). Oxford: Pergamon.Google Scholar
  88. Quay, H. C. (1997). Inhibition and attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 25, 7–13. doi:10.1023/A:1025799122529 Google Scholar
  89. Saviouk, V., Hottenga, J.-J., Slagboom, E. P., Distel, M. A., de Geus, E. J. C., Willemsen, G., & Boomsma, D. I. (2011). Adhd in Dutch adults: Heritability and linkage study. American Journal of Medical Genetics Part B Neuropsychiatric Genetics, 156(3), 352–362. doi:10.1002/ajmg.b.31170 Google Scholar
  90. Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest and the business cycle. Cambridge, MA: Harvard University Press.Google Scholar
  91. Seery, M. D., Holman, E. A., & Silver, R. C. (2010). Whatever does not kill us: Cumulative lifetime adversity, vulnerability, and resilience. Journal of Personality and Social Psychology, 99, 1025–1041. doi:10.1037/a0021344 Google Scholar
  92. Seery, M. D., Leo, R. J., Lupien, S. P., Kondrak, C. L., & Almonte, J. L. (2013). An upside to adversity? Moderate cumulative lifetime adversity is associated with resilient responses in the face of controlled stressors. Psychological Science, 24(7), 1181–1189. doi:10.1177/0956797612469210 Google Scholar
  93. Shane, S., Kolvereid, L., & Westhead, P. (1991). An exploratory examination of the reasons leading to new firm formation across country and gender. Journal of Business Venturing, 6, 431–446. doi:10.1016/0883-9026(91)90029-D Google Scholar
  94. Shaw, G. A., & Brown, G. (1991). Laterality, implicit memory and attention disorder. Educational Studies, 17, 15–23. doi:10.1080/0305569910170102 Google Scholar
  95. Shaw, G., & Brown, G. (1999). Arousal, time estimation, and time use in attention-disordered children. Developmental Neuropsychology, 16, 227–242. doi:10.1207/S15326942DN1602_6 Google Scholar
  96. Shaw, G. A., & Giambra, L. M. (1993). Task unrelated thoughts of college students diagnosed as hyperactive in childhood. Developmental Neuropsychology, 9, 17–30. doi:10.1080/87565649309540541 Google Scholar
  97. Simon, M., Houghton, S. M., & Aquino, K. (2000). Cognitive biases, risk perception, and venture formation: How individuals decide to start companies. Journal of Business Venturing, 15(2), 113–134. doi:10.1016/S0883-9026(98)00003-2 Google Scholar
  98. Stewart, W. H., & Roth, P. L. (2001). Risk propensity differences between entrepreneurs and managers: A meta-analytic review. Journal of Applied Psychology, 86(1), 145–153. doi:10.1037//0021-9010.86.1.145 Google Scholar
  99. Stewart, W. H., Watson, W. E., Carland, J. C., & Carland, J. W. (1998). A proclivity for entrepreneurship: A comparison of entrepreneurs, small business owners, and corporate managers. Journal of Business Venturing, 14, 189–214. doi:10.1016/S0883-9026(97)00070-0 Google Scholar
  100. Thapar, A., Holmes, J., Poulton, K., & Harrington, R. L. (1999). Genetic basis of attention deficit and hyperactivity. British Journal of Psychiatry, 174, 105–111. doi:10.1192/bjp.174.2.105 Google Scholar
  101. van der Loos, M. J. H. M., Rietveld, C. A., Eklund, N., Koellinger, P. D., Rivadeneira, F., et al. (2013). The molecular genetic architecture of self-employment. PLoS ONE, 8(4), e60542. doi:10.1371/journal.pone.0060542 Google Scholar
  102. The Economist. (2012). In praise of misfits. Why business needs people with Asperger’s syndrome, attention-deficit disorder and dyslexia. http://www.economist.com/node/21556230
  103. Toner, M., O’Donoghue, T., & Houghton, S. (2006). Living in chaos and striving for control: How adults with attention deficit hyperactivity disorder deal with their disorder. International Journal of Disability, Development and Education, 53, 247–261. doi:10.1080/10349120600716190 Google Scholar
  104. Toplak, M. E., Jain, U., & Tannock, R. (2005). Executive and motivational processes in adolescents with attention-deficit-hyperactivity-disorder (AD/HD). Behavioral and Brain Functions, 1(8), 1–12. doi:10.1186/1744-9081-1-8 Google Scholar
  105. Turner, R. J., & Lloyd, D. A. (1995). Lifetime traumas and mental health: The significance of cumulative adversity. Journal of Health and Social Behavior, 36, 360–376. http://www.jstor.org/stable/2137325
  106. van der Zwan, P., Hessels, J., van Stel, A., & Wennekers, S. (2012). Global entrepreneurship monitor 2011, the Netherlands. Zoetermeer: Panteia.Google Scholar
  107. van Gelderen, M. (2012). Perseverance strategies for enterprising individuals. International Journal of Entrepreneurial Behavior and Research, 18(6), 630–648. doi:10.1108/13552551211268102 Google Scholar
  108. Verheul, I., Thurik, A. R., Grilo, I., & van der Zwan, P. W. (2012). Explaining preferences and actual involvement in self-employment: new insights into the role of gender. Journal of Economic Psychology, 33(2), 325–341. doi:10.1016/j.joep.2011.02.009 Google Scholar
  109. Wales, W. J., Patel, P. C., & Lumpkin, G. T. (2013). In pursuit of greatness: CEO narcissism, entrepreneurial orientation, and firm performance variance. Journal of Management Studies, 50(6), 1041–1069. doi:10.1111/joms.12034 Google Scholar
  110. Weiss, M., & Murray, C. (2003). Assessment and management of attention-deficit hyperactivity disorder in adults. Canadian Medical Association Journal, 168, 715–722.Google Scholar
  111. White, J. D. (1999). Personality, temperament and ADHD: a review of the literature. Personality and Individual Differences, 27, 589–598. doi:10.1016/S0191-8869(98)00273-6 Google Scholar
  112. White, H. A., & Shah, P. (2006). Uninhibited imaginations: Creativity in adults with attention-deficit/hyperactivity disorder. Personality and Individual Differences, 40, 1121–1131. doi:10.1016/j.paid.2005.11.007 Google Scholar
  113. White, H. A., & Shah, P. (2011). Creative style and achievement in adults with attention-deficit/hyperactivity disorder. Personality and Individual Differences, 50, 673–677. doi:10.1016/j.paid.2010.12.015 Google Scholar
  114. White, R. E., Thornhill, S., & Hampson, E. (2006). Entrepreneurs and evolutionary biology: The relationship between testosterone and new venture creation. Organizational Behavior and Human Decision Processes, 100, 21–34. doi:10.1016/j.obhdp.2005.11.001 Google Scholar
  115. Wilmshurst, L., Peele, M., & Wilmshurst, L. (2011). Resilience and well-being in college students with and without a diagnosis of ADHD. Journal of Attention Disorders, 15(1), 11–17. doi:10.1177/1087054709347261 Google Scholar
  116. Young, S. (2000). AD/HD children grown up: An empirical review. Counseling Psychology Quarterly, 13(2), 190–200. doi:10.1080/095150700411728 Google Scholar
  117. Young, S. (2005). Coping strategies used by adults with AD/HD. Personality and Individual Differences, 38, 809–816. doi:10.1016/j.paid.2004.06.005 Google Scholar
  118. Zellweger, T., Sieger, P., & Halter, F. (2011). Should I stay or should I go? Career choice intentions of students with family business background. Journal of Business Venturing, 26(5), 521–536. doi:10.1016/j.jbusvent.2010.04.001 Google Scholar
  119. Zentall, S. S., & Zentall, T. R. (1983). Optimal stimulation: A model of disordered activity and performance in normal and deviant children. Psychological Bulletin, 94(3), 446–471. doi:10.1037/0033-2909.94.3.446 Google Scholar
  120. Zhao, H., Seibert, S. E., & Lumpkin, G. T. (2010). The relationship of personality to entrepreneurial intentions and performance: A meta-analytic review. Journal of Management, 36(2), 381–404. doi:10.1177/0149206309335187 Google Scholar

Copyright information

© The Author(s) 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Ingrid Verheul
    • 1
  • Joern Block
    • 2
    • 5
  • Katrin Burmeister-Lamp
    • 1
  • Roy Thurik
    • 3
    • 6
    • 7
  • Henning Tiemeier
    • 4
  • Roxana Turturea
    • 1
  1. 1.Department of Strategic Management and Entrepreneurship, Rotterdam School of ManagementErasmus University RotterdamRotterdamThe Netherlands
  2. 2.Universität TrierTrierGermany
  3. 3.Department of Applied Economics, Erasmus School of EconomicsErasmus University RotterdamRotterdamThe Netherlands
  4. 4.Departments of Child and Adolescent Psychiatry and Epidemiology, Erasmus Medical CenterErasmus University RotterdamRotterdamThe Netherlands
  5. 5.Erasmus Institute of ManagementErasmus University RotterdamRotterdamThe Netherlands
  6. 6.Panteia BVZoetermeerThe Netherlands
  7. 7.Montpellier Business SchoolMontpellierFrance

Personalised recommendations