Intentions to reenter venture creation: The effect of entrepreneurial experience and organizational climate

Abstract

We identify and examine an important but overlooked group of entrepreneurs – individuals who have started one or more business ventures in the past but are currently employed in established organizations. We label these individuals as ‘organizationally employed former entrepreneurs’ (OEFEs). Our goal is to provide insight into the factors affecting their serial entrepreneurship intentions, that is, their intentions to re-enter entrepreneurship by re-engaging in venture creation. Applying Schneider’s attraction-selection-attrition theory, we identify factors that are likely to affect serial entrepreneurship intentions of OEFEs: the length of their venture creation experience and the climate of their employing organizations. Using survey data from 196 OEFEs, we show that the length of OEFE venture creation experience is positively related to their serial entrepreneurial intentions and that this relationship is negatively moderated by the organizational structure and entrepreneurial orientation of the organization in which the OEFEs are employed.

Keywords

entrepreneurial intention Schneider’s attraction-selection-attrition theory serial entrepreneurs

Introduction

Entrepreneurship is a career choice, and movements back and forth between entrepreneurship and paid employment are quite common (Burton et al., 2016; Manso, 2016). When entrepreneurs exit their venture – for whatever reason – they can pursue alternative career paths. One option is to start new ventures and become serial entrepreneurs, that is, ‘the individuals who have sold/closed a business in which they had a minority or majority ownership stake, and they currently have a minority or majority ownership stake in a single independent business that is either new, purchased or inherited’ (Westhead et al., 2005: 73). Another option, which is the focus of this study, is to pursue organizational employment and to become organizational employees (Baù et al., 2016). We examine organizationally employed former entrepreneurs (OEFEs), because many serial entrepreneurs do not move directly from one new venture to the next (Hyytinen and Ilmakunnas, 2007), and many serial entrepreneurs were once OEFEs (Westhead and Wright, 1998). Understanding why some OEFEs become serial entrepreneurs while others do not is particularly important. To this end, we study the formation of serial entrepreneurship intentions of OEFEs, that is, their intentions to re-engage in venture creation (Simmons et al., 2016).

Two useful streams of literature offer insights to the phenomenon we are studying. The first suggests that learning from prior experience (Dawson and Henley, 2012; Haber and Reichel, 2007; Westhead et al., 2005) predicts entrepreneurial intentions. However, the formation of individual intentions to start businesses – entrepreneurial intentions – could be very different from that of entrepreneur intentions to start another business – serial entrepreneurship intentions – because contextual factors largely affect the latter (Simmons et al., 2016). The second research stream focuses on organizational employees and examines organizational influences on their entrepreneurial intentions (Douglas and Shepherd, 2002; Lee et al., 2011; Muñoz-Bullón and Cueto, 2010; Sørensen, 2007). While certainly valuable, this second perspective often overlooks an employee’s prior venture creation experiences.

We seek to bridge these gaps by integrating the two research streams to provide insights into the serial entrepreneurship intentions of OEFEs. Drawing on Schneider’s (1987) attraction-selection-attrition (ASA) theory, we examine the impact of work experience (in the form of prior venture creation experience) and the impact of organizational climate. We argue that the ASA mechanism can predict the effects of both venture experience and organizational factors on serial entrepreneurship intentions of OEFEs. A central tenet of ASA theory is that employees choose to remain in a particular organization as a result of numerous environmental factors (Schneider, 1987). Applying this lens to OEFEs, we propose a two-step ASA mechanism. Having more venture creation experience causes OEFEs to have stronger serial entrepreneurship intentions (i.e. adaption to entrepreneurship), but the climate of their employing organization, such as its entrepreneurial orientation (Lumpkin and Dess, 1996) and organic structure (Burns and Stalker, 1961; Mintzberg, 1979), can alter the impact of venture creation experience on serial entrepreneurship intentions as the result of OEFE adaption to the employing organization.

This study is one of the first, to our knowledge, that clearly identifies the understudied population of OEFEs. We introduce a new terminology, OEFEs, to describe this unique group and study their serial entrepreneurship intentions. In doing so, we contribute to both serial entrepreneurship and corporate entrepreneurship literatures. Many studies have shown that prior entrepreneurial experience (Zhao et al., 2005) or exposure to entrepreneurship (Krueger, 1993) fosters entrepreneurial intentions. From the human capital perspective (Dimov, 2010), the learning perspective (Politis, 2005), and the self-efficacy perspective (Hsu et al., 2015; Zhao et al., 2005), the more venture creation experience that individuals have, the stronger their entrepreneurial intentions. This finding was confirmed in our study. However, we found that the relationship between the length of venture creation experience and serial entrepreneurship intentions became negative for OEFEs employed in organizations with specific organizational climates. That is, the more venture creation experience the OEFEs have, the lower their entrepreneurial intentions when they are employed in organizations with a more entrepreneurial climate. This finding is important as it provides guidance for established organizations on ways to retain employees who have venture creation experience, that is, organizations should foster an entrepreneurial climate by promoting higher levels of entrepreneurial orientation and an organic structure in order to retain OEFEs.

In addition, our article extends the application of the ASA model (Schneider et al., 1995) to the entrepreneurship context. Specifically, the two-step mechanism we propose, which is based on ASA theory, suggests that adaptation to the organization offsets the impact of adaptation to entrepreneurship. Whereas previous studies applying ASA theory have examined adaptation either in the organizational context (De Cooman et al., 2009; Ostroff and Rothausen, 1997) or in the entrepreneurship context (Lee et al., 2011), our study makes a theoretical contribution to ASA theory by considering the joint effect. In examining the unique context of former entrepreneurs now employed in established organizations, our study is designed to examine the joint effect of entrepreneurship adaptation and organization adaptation.

A challenge in seeking to learn more about OEFEs lies in finding them. For this particular study, Amazon Mechanichal Turk (MTurk), a crowdsourcing (Aguinis and Lawal, 2012) website offered by Amazon.com, is uniquely valuable as it enabled us to identify, access, and survey a unique group – OEFEs – across different organizations. In our view, data sources should be evaluated not in isolation but in comparison to alternatives and with respect to the importance and nature of the research questions they help to answer. While data collection via MTurk certainly has limitations – as do all methods of data collection, its strength lies in its ability to access underrepresented samples (Smith et al., 2015) that are otherwise hard to access. We, thus, sought to contribute to the research on OEFEs, who are underrepresented in the entrepreneurship literature, not only by examining the research question pertaining to them but also by pioneering the use of a novel method for collecting data from them.

In the sections which follow, we anchor our proposals regarding understudied OEFEs in the literature on serial entrepreneurship intentions and on ASA theory. Finding and surveying OEFEs can be challenging. We describe the innovative approach used to identify the sample and to obtain survey data. Next, we present our analysis and discuss our findings. We end by discussing future research, implications, and limitations of our research.

Theoretical development

Serial entrepreneurship intentions

Rooted in the theory of planned behavior (TPB) (Ajzen, 1991) and the model of the entrepreneurial event (Shapero and Sokol, 1982), entrepreneurial intentions have been shown to be a good predictor of entrepreneurial activity (Krueger and Dickson, 1994). Additionally, entrepreneurial self-efficacy (Douglas, 2013; Liñán and Chen, 2009; Zhao et al., 2005), personality traits (Zhao and Seibert, 2006), risk tolerance (Douglas, 2013), and regulatory focus theory (Fitzsimmons and Douglas, 2011) have been shown to affect entrepreneurial intentions. While this line of literature places emphasis on the individual’s personal attributes, other empirical evidence has shown the importance of the environment and how it interacts with, or fits, the individual’s personal attributes to shape entrepreneurial intentions (Lee et al., 2011). In the context of former entrepreneurs, Simmons et al. (2016) also showed that fit between a former entrepreneur’s past venture creation experience and personal attributes predicted serial entrepreneurship intentions. These findings are consistent with the growing literature on person–environment fit (Kristof, 1996) in that intentions are strongest when individuals perceive fit between their personal attributes and their environment (Kristof-Brown et al., 2005).

Former entrepreneurs are often found among employees in organizations (Westhead and Wright, 1998) and may develop intentions to leave the organization to start another business. The decision to leave may be shaped by factors related to the fit that OEFEs perceive with the employing organization. For example, an OEFE working in a bureaucratic organization with rigid structures and formal hierarchy mechanisms may perceive poor fit and choose to leave. Indeed, frequently cited motivations for becoming self-employed are independence and freedom to pursue one’s own ideas (Feldman and Bolino, 2000). In contrast, OEFEs working in an entrepreneurial organization may experience sufficient independence and autonomy, such that motivations to leave and start a new venture would diminish. For instance, the concept of ‘organizational slack’ (Finkle, 2011: 879) evident at Google could be seen as a retention tool. By encouraging employees to pursue unique interests and by designing an organizational climate that is supportive of entrepreneurial capabilities, Google reinforces the retention of entrepreneurially oriented employees.

Among various theories related to person–environment fit, Schneider’s (1987) (Schneider et al., 1995) ASA theory has become increasingly prominent and has been applied to topics such as career choice (Bretz et al., 1989) and job turnover (De Cooman et al., 2009; Schaubroeck et al., 1998). These behaviors are particularly relevant to our research question. Thus, we build on the fit literature (Lee et al., 2011) and ASA theory to develop the model of entrepreneurial intentions presented in Figure 1.

Figure 1.

Theoretical model.

ASA theory

Drawing on the person–environment fit perspective, Schneider’s ASA theory (Schneider, 1987; Schneider et al., 1995; Schneider et al., 2001) describes why people choose to join, remain with, or exit a certain organization. The theory describes the attraction, selection, and attrition processes that are fundamental to behavior within organizations. People choose to join organizations that appear attractive to them (attraction). Organizations select and retain those people who fit or are able to adapt well to their organizational context (selection). The people selected who do not fit, or cannot adapt, choose to leave the organizations (attrition). From the individual’s perspective, the ASA cycle continues if the individual is attracted to another organization.

We contend that people will attempt to adapt not only to organizations but also to work environments. Entrepreneurship is a unique work environment that requires a unique set of personal attributes; to succeed, people need to have or develop the attributes required by an entrepreneurial environment (Markman and Baron, 2003). ASA theory is particularly relevant to entrepreneurship. When applied to individuals who were once entrepreneurs and now work in an organization (OEFEs), ASA theory suggests a two-step mechanism: one step taking place in the entrepreneurial context and the second, taking place in the organizational context. In the first step, individuals adapt to the environment of venture creation because people tend to adapt to their environments (Cooper-Thomas et al., 2004; Robinson and O’Leary-Kelly, 1998; Schneider et al., 1995). Similarly, in the second step, OEFEs – like all employees – attempt to adapt to the organizational environment after joining organizations.

According to ASA theory, people attempt to adapt to the entrepreneurial environment when they engage in entrepreneurial activities. Individuals who perceive a poor fit choose to leave the entrepreneurial environment, while the individuals who perceive a strong fit choose to stay. This process may, therefore, provide an explanation of the intense fit experienced by habitual entrepreneurs ‘addicted’ to entrepreneurship (Spivack et al., 2014). In general, this process and the fit reasoning supporting it suggest that entrepreneurs experience a stronger fit with entrepreneurship the longer they stay in the entrepreneurial context, thereby decreasing their intentions to exit entrepreneurship (Lee et al., 2011).

Although entrepreneurs leave their ventures for various reasons, such as conflict among partners (Kelly et al., 2002) or low monetary returns (Gimeno et al., 1997; Wennberg et al., 2010), the intensity of their entrepreneurial intentions prior to business exit is likely to carry over to the post-exit stages because of the carryover effect of attitudinal outcomes (Bolton and Drew, 1991; Johnson et al., 2006) or intentions in this case. Indeed, Zhao et al. (2005) found that previous entrepreneurial intentions are good predictors of subsequent entrepreneurial intentions. Similarly, Kautonen et al. (2010) showed that having founded one or more businesses in the past has a positive impact on future entrepreneurial intentions. Therefore, we argue that when entrepreneurs leave their ventures, join organizations, and become OEFEs, the intensity of their entrepreneurial intentions remains strong initially due to the carryover effect. Given that OEFEs may have adapted to entrepreneurship as explained by the first step of the ASA mechanism, namely, the entrepreneurship-adaption process, the entrepreneurial intentions of the OEFEs should be highly influenced by their length of experience in their prior ventures. Therefore, we propose that

Hypothesis 1. The length of venture creation experience of the OEFEs prior to organizational employment is positively related to their serial entrepreneurial intentions.

The relationship between the length of venture creation experience and serial entrepreneurial intentions may change after the former entrepreneur has been organizationally employed for a period of time. The second step of the ASA mechanism, the organization-adaption process, suggests that when former entrepreneurs enter an organization and become OEFEs, the organizational context presents a new work environment. According to the ASA theory, OEFEs endeavor to adapt to the organizational context. If they cannot adapt, they experience poor fit (Furnham and Schaeffer, 1984; Lauver and Kristof-Brown, 2001; Resick et al., 2007) and are likely to exit the organization – as explained by the attrition process. These individuals may perceive better fit with the entrepreneurial context than with the organizational context and may return to venture creation as a result (Lee et al., 2011). In contrast, OEFEs who find or develop strong fit within their organizational context may choose to stay as described in the selection process of ASA (Furnham and Schaeffer, 1984; Lauver and Kristof-Brown, 2001; Resick et al., 2007). Hence, we argue that the relationship between prior venture experience and subsequent entrepreneurial intentions depends on OEFE adaptation to their organizational contexts.

How well OEFEs adapt to organizational contexts can be shaped by the ‘organizational personality’ (Schneider et al., 1998), which is often conceptualized as organizational climate (Kristof, 1996; Ostroff and Rothausen, 1997) or culture (Davies et al., 2001; Yahyagil, 2006). It is argued that an organization’s ‘personality’ or climate will shape the degree to which organizational adaptation occurs (Schneider et al., 1998). Since ASA theory suggests that an organization has a distinct climate or ‘organizational personality’ as a result of the ASA process (Schneider et al., 1998), we examine two characteristics of organizational climate – organizational structure and entrepreneurial orientation – as both characteristics matter in shaping an entrepreneurial climate (Miller, 1983).

Organizational structure and entrepreneurial orientation

Organizational theorists have found that one useful way to characterize organizational structures is by describing them on a continuum from mechanistic to organic (Burns and Stalker, 1961; Mintzberg, 1979). Organizations with a more mechanistic structure tend to have strictly defined job tasks, hierarchical management systems, and centralized decision-making policies. Organizations with a more organic structure tend to be more flexible in defining job tasks, to have a horizontal communication system and to deploy a decentralized decision-making policy (Burns and Stalker, 1961; Covin and Slevin, 1988; Mintzberg, 1979; Sine et al., 2006). Researchers have suggested that many new ventures have organic structures (Sine et al., 2006), which foster the adoption of an entrepreneurial strategy (Russell and Russell, 1992) and promote entrepreneurial activities within an organization (Morris et al., 2001). Therefore, organizations with organic structures may share climates or personalities with new ventures.

OEFE entrepreneurial intentions might initially be strong as a result of the adaptation to venture creation, which should be greater with longer venture experience prior to joining the organization. However, after they join an organization with an organic structure, which shares a similar climate with new ventures, the organization offers an environment similar to the context of venture creation and makes it easier for OEFEs to fit or adapt to it. For instance, research has shown that having employees participate in decision-making, a component of organic structure, helps them adapt to the employing organization (Ostroff and Rothausen, 1997). Thus, we propose that the organization-adaptation process, facilitated by an organization’s organic structure, weakens the relationship between an OEFE’s prior entrepreneurship-adaptation and their serial entrepreneurship intentions. Furthermore, we suggest that at some point, this negative moderation will reverse the relationship between the length of venture creation experience and serial entrepreneurship intentions, that is, change it from positive to negative. When the employing organization’s structure is highly organic, OEFEs may experience strong fit, become comfortable with the employing organization, and thus have lower serial entrepreneurship intentions. We therefore propose that

Hypothesis 2a. The organic structure of the employing organization negatively moderates the positive relationship between the OEFE’s length of venture creation experience and serial entrepreneurship intentions.

Beyond organizational structure, the second factor shaping entrepreneurial climate is the degree of entrepreneurial orientation (Lumpkin and Dess, 1996). Entrepreneurial orientation relates to an organization’s processes, practices, and decision-making styles, regarding how the organization operates (Lumpkin and Dess, 1996). A strong entrepreneurial orientation improves organizational performance in more dynamic contexts, such as emerging industries or markets (Lumpkin and Dess, 2001; Rauch et al., 2009; Wiklund and Shepherd, 2003, 2005). Most new ventures rank high on entrepreneurial orientation because activities and processes in a start-up context are generally unstable and dynamic (Lumpkin and Dess, 1996). Some established organizations exhibit high entrepreneurial orientation as well (Covin and Slevin, 1988; Dess and Lumpkin, 2005; Lumpkin et al., 2009).

Established organizations with a high degree of entrepreneurial orientation are likely to share properties with new ventures. Thus, similar to the process described with regard to organic structure, the extent of entrepreneurial orientation within an employing organization facilitates the organization-adaptation process experienced by an OEFE. This consequently weakens the relationship between the OEFE’s prior entrepreneurship-adaptation and serial entrepreneurship intentions. Specifically, we propose that the positive relationship between the length of the OEFE’s venture creation experience and their serial entrepreneurship intentions will be weakened by the degree of entrepreneurial orientation of the employing organization. We further propose that, at some point, the negative moderation will eventually reverse the relationship between the length of venture creation experience and serial entrepreneurship intentions of the OEFEs. That is, if the employing organization exhibits a high entrepreneurial orientation, OEFEs may fit in well, having adapted to entrepreneurship from their venture creation experiences. OEFEs become more comfortable with the entrepreneurial orientation of the employing organization, and as a result have lower intentions to leave the organization in pursuit of entrepreneurship. Thus, the stronger the OEFE’s adaptation to entrepreneurship, the more comfortable they are with an employing organization that is very entrepreneurial and the weaker their serial entrepreneurship intentions. In fact, these OEFEs may feel that working for the employing organization is similar to working in a new venture, with the only difference being that the risk is assumed by the employing organization rather than the entrepreneurs themselves. That is, the relationship between the length of venture creation experience and serial entrepreneurship intentions would become negative when the organizational climate becomes very entrepreneurial. Building on the discussion, we propose that

Hypothesis 2b. The entrepreneurial orientation of the employing organization negatively moderates the positive relationship between the OEFE’s length of venture creation experience and serial entrepreneurship intentions.

Methodology

Sample

Our goal was to identify a sample of US-based former entrepreneurs currently employed by organizations (OEFEs). We considered several data collection options. One would be to examine a single organization and study the former entrepreneurs employed by that organization, but this approach would limit variation in the employment conditions experienced by former entrepreneurs. While there might be some variation across departments and units within an organization, this variation would be far less than that expected across different organizations. Another option might be to use snowball sampling to identify former entrepreneurs in multiple organizations. Even if snowball sampling generated a sufficient sample, the potential for bias in this non-random sample would be high (Atkinson and Flint, 2001).

In this study, we decided to use Amazon Mechanical Turk (MTurk), a marketplace offered by Amazon.com for sellers to find workers willing to perform tasks for pay. Critics of samples generated from MTurk argue that these are not representative of the general population of working professionals in the United States. While this concern is worth considering, we argue that for this particular study, MTurk is a better option than sourcing through a single organization and snowball sampling for three reasons. First, methodologists argue that MTurk workers are relatively representative of the population of Internet users in the United States (Ross et al., 2010) and have demonstrated that ‘the population of Mechanical Turk is at least as representative of the U.S. population as traditional subject pools’ (Paolacci et al., 2010: 411). Second, Paolacci et al. (2010) showed that the primary motive for the majority of MTurk workers is to find ‘a fruitful way to spend free time’ (p. 413) rather than for supplemental income generation. Third, MTurk is increasingly being used for behavioral research, including entrepreneurship (Aguinis and Lawal, 2012; Gupta et al., 2014). Recent research has demonstrated that data collected through well-designed psychology studies using MTurk are as reliable as data collected using traditional survey methods (Buhrmester et al., 2011; Kittur et al., 2008). Considering all the pros and cons, we concluded that MTurk was the best available data source for this particular study, as it enabled us to sample OEFEs across different organizations in the United States, thereby increasing generalizability.

We posted a survey invitation in MTurk that was written in American English requesting participation from anyone meeting three criteria. Participants were required to (1) have previously had their own venture, (2) have exited a venture and be currently employed by an organization, and (3) be willing to spend approximately 30 minutes to complete the survey. MTurk can record Internet protocol (IP) addresses of the respondents. We used this function to screen respondents by location in order to ensure the US-based sample and to reduce the potential for language issues. As an incentive, we offered US$1.50 for completion of the survey, an amount consistent with recommendations of other researchers using MTurk to conduct surveys (Paolacci et al., 2010). MTurk workers meeting the criteria to participate in the study were provided a link to the online Qualtrics survey. As a reminder, the three criteria were shown on the front page of the survey.

Our online survey required respondents to answer all questions in the randomized order presented. Upon completion of the survey, respondents received a code that could be used to obtain payment via MTurk. Because prior venture experience was an important focus of our study, the following question was inserted as a post-survey questionnaire: ‘How many businesses have you started before?’ We used responses to this question to confirm that participants did indeed meet the requirement of having had prior venture creation experience. The participants who responded ‘none’ on this reliability check were excluded from the study. We obtained 200 survey responses between 13 and 19 May 2013, of which 196 were useable.

We took precautions to assure confidence that respondents to the survey were OEFEs. We followed recommendations of other researchers who have used MTurk as a data source for entrepreneurship studies (Aguinis and Lawal, 2012; Gupta et al., 2014) and behavioral research. In designing our survey, we (1) announced participant qualifications in the invitation posted on MTurk, (2) reminded respondents by posting the screening questions on the front page of the Qualtrics survey, and (3) performed a reliability check on all participants. Given the number and variety of tasks available on MTurk and the modest compensation offered, respondents had little incentive to pretend to be OEFEs.

Survey design

To minimize the potential for common method bias, we followed several procedural approaches suggested by research methodologists (Chang et al., 2010; Conway and Lance, 2010; Podsakoff et al., 2003). In our survey, we assured respondents of anonymity and confidentiality in order to reduce bias due to social desirability of responses (Chang et al., 2010; Podsakoff et al., 2003). Also, MTurk does not release information that can be linked to a respondent’s identity. To reduce the priming effect (Podsakoff et al., 2003) or order effect (Conway and Lance, 2010), we randomized the order, in which we obtained data for independent variables and dependent variables. To create psychological space and thereby, reduce carryover effect (Chang et al., 2010; Podsakoff et al., 2003), we inserted demographic questions between independent variables and dependent variables. Finally, we used established scales with different endpoints and formats for independent variables and dependent variables in order to minimize bias due to an anchor effect (Chang et al., 2010; Podsakoff et al., 2003).

Measures

Serial entrepreneurship intentions, our dependent variable, was measured using Hsu et al.’s (2015) 6-item Likert scale with strong reliability (alpha = 0.924). Items are shown in Appendix 1. Our two-step model focuses on organizational adaptation. Researchers have suggested that organizational tenure may capture organizational adaptation. Rollag (2004) argued that

The concept of organizational tenure is also implicit in most theory and research into socialization processes and outcomes. Since the transition from newcomer to insider is seen as largely a function of learning, and learning takes effort and time, socialization requires a certain amount of tenure in the organization to occur. Members with greater tenure are often seen as more socialized than members of lesser tenure because they have had more time to observe, accept, and adopt predominant norms and values. (p. 854)

Therefore, we measured the length of venture creation experience using the total number of years working as an entrepreneur rather than the number of start-ups founded. The length of experience should be a better indicator of the ASA process (Lee et al., 2011).

We used two variables to assess the entrepreneurial climate of the employing organizations: organizational structure that is organic (i.e. organicity, with a higher score indicating a more organic structure and a lower score indicating a more mechanistic structure) and entrepreneurial orientation. The degree to which a respondent’s employing organizations had organic structures was measured using a 7-item Likert scale (Covin et al., 2006; Green et al., 2008), which showed very high reliability (alpha = 0.895). To measure entrepreneurial orientation, we used Covin and Slevin’s (1989) well-established 9-item Likert scale (Covin and Slevin, 1989; Green et al., 2008) which also demonstrated high reliability (alpha = 0.883). Each multi-item scale was averaged before being entered into statistical analyses.

Control variables

Respondents were asked to report their sex, age, and years of education beyond high school. These variables were used as control variables – a procedure consistent with previous entrepreneurship studies (Block et al., 2011; Chen et al., 1998; Wilson et al., 2007; Zhao et al., 2005). The serial entrepreneurship literature suggests that past venture performance is an important predictor of subsequent start-ups (Hsu et al., 2015); we therefore included a dummy variable – past performance – as another control. We obtained data for this variable by asking the respondents, ‘How well did your last start-up perform?’ The respondents were prompted to choose between ‘Financially poorly’, coded as 1, and ‘Financially well’, coded as 2. Other studies have shown that industry experience matters in predicting the individual’s intention to enter (Krueger, 1993) as well as exit entrepreneurship (Wennberg et al., 2010). In the OEFE context, whether an OEFE’s prior industry experience can be applied to the current organization may affect his or her intention to leave or stay. Therefore, we included industry similarity as a control variable by asking ‘How similar was the industry or sector of your last start-up to that of your current organization?’ on a 7-point Likert scale, ranging from 1 = ‘Completely different’ to 7 = ‘Identical’.

Common method bias

Since we obtained the dependent and independent variables from same source self-reports, we conducted confirmatory factor analysis (CFA), a statistical technique to test for the risk of common method bias (Cheung and Lau, 2008). We also used procedural remedies (Chang et al., 2010) to reduce the risk of common method bias.

We used three steps to perform CFA. First, we specified a measurement model for the three independent and dependent variables (serial entrepreneurship intentions, organicity, and entrepreneurial orientation) that contained 22 items, for which all indicators loaded on their respective latent constructs. All items loaded significantly (p < 0.001) on their respective constructs. The three-factor model obtained an acceptable fit – χ² = 567, p < 0.001, comparative fit index (CFI) = 0.85, and root mean square error of approximation (RMSEA) = 0.04 (Hu and Bentler, 1999; Schreiber et al., 2006). Then, we compared these fit indices, constraining all covariances between latent variables, with the fit of alternative two- and one-factor models, where the number of factors was reduced by setting different combinations of covariances between latent variables equal to one. The fit indices for the three-factor model (CFI = 0.85, RMSEA = 0.04) were better than those of any alternative models. This showed that the three-factor model fits the data better than all alternative models. These tests also suggested that there was no single underlying factor (latent variable) that potentially represented bias resulting from the use of a single method (Malhotra et al., 2006). Hence, common method bias did not appear to be a concern.

Analyses and results

Table 1 shows the descriptive statistics and correlation matrix for the variables used in our analysis. Respondents spent an average of about 11 minutes to complete the survey. Given the length of the survey and the nature of the questions, 11 minutes suggests that respondents spent adequate time considering and responding to the survey questions. Our sample was 44% female. The average age of respondents was 36.5 years, which is consistent with findings from other studies. For example, the 2015/2016 Global Entrepreneurship Monitor (GEM) report (Kelley et al., 2016) found the highest entrepreneurship participation rates among people in the 25–34 and 35–44 age brackets. Wadhwa (2012) found that the average age of entrepreneurs who started successful in the United States technology businesses was 39 years. All respondents reported living in the United States and had an average of 17 years of professional work experience. Respondents averaged 4.4 years of education beyond high school, suggesting that most had the equivalent of a college degree or higher. Start-up experience averaged 5.4 years, which is more than the 3 years of self-employment reported by Chen (2013) and less than the 12.8 years of self-employment reported by Politis (2008), studies that examined samples including both novice and serial entrepreneurs.

Table 1.

Descriptive statistics and correlations (N = 196).

Variable		Min	Max	Mean	SD	1	2	3	4	5	6	7	8
1	Gender^a	0	1	0.44	0.50
2	Age	19	68	36.45	11.13	0.048
3	Education^b	0	10	4.38	2.51	−0.062	−0.053
4	Industry similarity	1	7	2.91	1.96	−0.097	−0.030	0.046
5	Past performance^c	1	2	1.36	0.48	−0.015	−0.086	−0.052	0.060
6	LE	0	38	5.37	5.22	0.066	0.535**	−0.004	0.032	0.090
7	Organicity	1	7	3.62	1.44	−0.084	−0.139*	−0.073	0.185**	0.165*	0.000
8	EO	1	7	3.65	1.33	−0.010	−0.165*	−0.101	0.005	0.193**	0.067	0.271**
9	SEI	1	7	5.02	1.40	0.088	−0.225**	0.002	0.085	0.169**	0.062	0.067	0.086

SD: standard deviation; LE: length of venture creation experience captured by years of experience in venture creation; EO: entrepreneurial orientation; SEI: serial entrepreneurship intentions.

Gender is coded as 0 = female and 1 = male.

Years of education beyond high school.

Past performance is coded as 1 = ‘Financially poorly’ and 2 = ‘Financially well’.

p < 0.05; **p < 0.01.

Time since business exit (from the most recent start-up) averaged 5 years prior to completing the survey. A majority (64.3%) reported that their last start-up was performing poorly in financial terms when they exited. This high rate of self-reported financial losses is consistent with statistics of business failure (Shane, 2008), and respondent forthrightness in such sensitive information might be due to the anonymity and confidentiality afforded by our MTurk survey. Surprisingly, only 41.8% indicated that the performance of their ventures had deteriorated since founding. For most (84.2%), the job held at the time of survey completion was either their first or second job since exiting their last venture. About half of the respondents (48.5%) were not in a supervisory position and about half (52.4%) were employed in organizations with fewer than 100 employees.

We used hierarchical linear regression to test the theoretical model depicted in Figure 1; this allowed us to test the effects of the independent variables while accounting for the five control variables (Warner, 2008). Length of venture creation experience, organicity, and entrepreneurial orientation were centered to compute the interaction terms and entered into the regression models. Table 2 presents our hypotheses tests. Model 1 specifies the model with the five control variables only. Model 2 examines the relationship between length of venture creation experience and serial entrepreneurship intentions while controlling for both organicity and entrepreneurial orientation. Model 3 adds the interaction term of length of venture creation experience and organicity. Model 4 examines the interaction term of length of venture creation experience and entrepreneurial orientation alone. Model 5 examines the two interaction terms together. To ease interpretation of the interacting effects, we centered each of the independent variables before entering them into regression.

Table 2.

Regression models of serial entrepreneurship intentions (N = 196).

	Standardized coefficients
	Model 1	Model 2	Model 3	Model 4	Model 5
Control variables
Gender^a	0.099	0.087	0.073	0.073	0.076
Age	−0.221**	−0.351**	−0.362**	−0.364**	−0.343**
Education^b	0.108	0.114	0.113	0.116	0.088
Industry similarity	0.058	0.047	0.037	0.035	0.055
Past performance^c	0.136*	0.108	0.124	0.123	0.136
Main effects
LE		0.230**	0.260**	0.263**	0.231**
Organicity		−0.003	0.008	0.002	0.070
EO		−0.17	−0.007	−0.002	−0.061
Interaction effect
LE × organicity			−0.215**		0.067
LE × EO				−0.213**	−0.083
R²	0.10**	0.13**	0.18**	0.13**	0.18**
ΔR²		0.03**	0.05**	0.03**	0.05**

LE: length of venture creation experience captured by years of experience in venture creation; EO: entrepreneurial orientation.

Gender is coded as 0 = female and 1 = male.

Years of education beyond high school.

Past performance is coded as 1 = ‘Financially poorly’ and 2 = ‘Financially well’.

p < 0.05; **p < 0.01.

Across all four models (Models 2–5), length of venture creation experience is positively related to serial entrepreneurship intentions (β ranges from 0.230 to 0.263; p < 0.01), thereby supporting hypothesis 1. The longer the individuals had engaged in venture creation in the past, the stronger their intentions are to start another venture. We reason that people become adapted to the context of venture creation as suggested by ASA theory. We found that the relationship between the length of venture creation experience and serial entrepreneurship intentions was negatively moderated by the level of organicity as well as by the level of entrepreneurial orientation of the employing organization because the interaction term in Model 3 was significant and negative (for organicity, β = −0.215; p < 0.01). In order to determine the nature of the interaction, we plotted serial entrepreneurship intentions as a function of the length of venture creation experience at high and low levels (at ±1 standard deviation (SD) from the mean as recommended in the literature Cohen et al., 2003) and organicity (Figure 2) and entrepreneurial orientation (Figure 3), each at high (−1 SD) and low levels (−1 SD). As shown in the figures, the relationships of serial entrepreneurship intentions with entrepreneurial orientation and organicity are positive when entrepreneurial orientation and organicity are low, but the relationships become negative when entrepreneurial orientation and organicity are high. These results provide support for hypotheses 2a and 2b by demonstrating that the entrepreneurial climate of the employing organization negatively moderates the relationship between length of venture creation experience and serial entrepreneurship intentions. We infer that an OEFE’s adaptation to their employing organizations was driven by properties of the organization’s entrepreneurial climate; namely, organicity and entrepreneurial orientation.

Figure 2.

Interaction effect of length of venture creation experience and organicity on serial entrepreneurship intentions.

Figure 3.

Interaction effect of length of venture creation experience and entrepreneurial orientation on serial entrepreneurship intentions.

While we examined the interaction of organicity with venture creation experience and the interaction of entrepreneurial orientation with venture creation experience in different regression models, we also included the interactions in the same regression model (Model 5, which had the same control variables as the other models and the independent variables of venture creation experience, organicity, and entrepreneurial orientation) to check robustness. In Model 5, the interaction terms of both organicity and venture creation experience and entrepreneurial orientation and venture creation experience were not statistically significant (organicity × venture creation experience, β = −2.373; p > 0.10; entrepreneurial orientation, β = 2.163; p > 0.10). This is likely attributed to the high correlation between the two interaction terms, organicity × length of venture creation and entrepreneurial orientation × length of venture creation (ρ = 0.691, p < 0.01). The high correlation between the two interactions terms was not surprising as they were artificially created with the same variable (length of venture creation experience) and thus shared explanation for the dependent variable (serial entrepreneurship intentions). Therefore, we concluded that with both interaction terms in the same regression (Model 5), both interaction terms became insignificant. This was due to multicollinearity resulting from the commonality of the two interaction terms, which removed relevant information that predicted serial entrepreneurship intentions. This result is consistent with suggestions of methodologists (Conger, 1974; Pandey and Elliott, 2010) that interaction terms should be entered into separate regression models to avoid suppression.

Post hoc analyses

In our study, we argue that individuals with more venture creation experience are more likely to adapt to an entrepreneurship context and thus, would have a stronger intention to reenter entrepreneurship. We examined the length of venture creation experience; however, we acknowledge that the number of past ventures started by individuals might be a factor in our model. Therefore, we ran bivariate correlations which showed the number of past ventures and the independent variable, length of entrepreneurial experience, were highly correlated (ρ = 0.567, p < 0.01). To avoid the multicollinearity issue, we did not report a model with the number of past ventures. Instead, we ran post hoc analyses to add this variable to all models. In doing so, all the significance levels and directions remained the same. Therefore, we concluded that the variable, number of past ventures, would not have affected our models or our results.

Discussion

Research has extensively examined the career paths of active entrepreneurs, both before they became entrepreneurs and while they are entrepreneurs. One stream of research examines serial entrepreneurs who start ventures consecutively (e.g. Hsu et al., 2015; Simmons et al., 2016; Westhead et al., 2005). Yet, much less is known about former entrepreneurs who have exited their ventures. While many former entrepreneurs in the United States may choose to retire, other former entrepreneurs (both successful and unsuccessful) may decide to pursue and gain employment in established organizations. It is this group of OEFEs that are of particular interest in this study.

OEFEs may remain with established organizations and contribute to firm performance (Stuart and Abetti, 1990), product innovation (Marvel and Lumpkin, 2007), and overall innovation performance (Camelo-Ordaz et al., 2011) of their employing organizations. Alternatively, OEFEs may decide to leave their employing organization in order to return to entrepreneurship. Understanding organizational factors that are likely to affect the serial entrepreneurship intentions of the OEFEs to re-engage in venture creation is important; however, a challenge in seeking to learn more about these former entrepreneurs lies in finding them.

In this study, we used an innovative way to survey OEFEs in order to examine the following research question: ‘Can the organizational conditions experienced by former entrepreneurs affect their intentions to return to entrepreneurship?’ To guide our efforts, we employed ASA theory. Because the dynamics posited by ASA theory affects former entrepreneur both while they are entrepreneurs and once they become OEFEs, we advanced a two-step model. In the first step, individuals who feel an affinity for entrepreneurship are attracted to it. Those who are a good fit for this career path tend to remain in it longer and thus, in effect, are selected for this career path; those who are not a fit exit earlier, which is a form of attrition. Our first hypothesis proposed that the longer that former entrepreneurs had been entrepreneurs, the greater would be their intentions to return to entrepreneurship once they became organizationally employed. We found support for this hypothesis, but we should acknowledge that our hypothesis must be interpreted carefully. The relationship between the length of venture creation experience and serial entrepreneurship intentions could be caused by the ASA mechanism that we proposed, but it could also be external factors that cause entrepreneurs to remain in venture creation longer or by conditioning. For example, entrepreneurs who experience more financial success as entrepreneurs may engage in venture creation longer and may seek to return to it because of that financial success – rather than because of perceived fit with this career path. Entrepreneurs may also become conditioned to or even – as Spivack et al. (2014) suggested – addicted to entrepreneurship. Such a conditioning mechanism is different from the fit mechanism that we posited based on ASA theory. Our analysis found support for the relationship between length of venture creation experience and serial entrepreneurship intentions that we hypothesized, but the fit mechanism based on ASA theory that we proposed is not the only explanation for our hypothesis and finding.

In the second step of the two-step model we advanced, former entrepreneurs are attracted to organizations with employment conditions similar to those of new ventures, and to the extent that this match is realized, former entrepreneurs tend to remain or be selected for continued employment. Attrition occurs as former entrepreneurs find their conditions of organizational employment too different from that of venture creation and they exit the organization. In examining conditions of organizational employment, we focused on two properties or factors – structural organicity and entrepreneurial orientation – in part because these two factors are well established in the entrepreneurship and related literatures. More specifically, we proposed in a two-part second hypothesis that organicity and entrepreneurial orientation would negatively moderate the effect of the prior venture creation experience on the intentions of the OEFEs to reenter venture creation. Our analysis confirmed these moderating relationships. More importantly, we found that the relationship between the length of venture creation experience and serial entrepreneurship intentions is negative when the employing organization is more entrepreneurial. This finding is particularly interesting as previous studies (Dimov, 2010) have generally shown a positive relationship between the length of venture creation experience and entrepreneurial intentions.

Had we not found significant moderating effects, we would not have been able to conclude that properties of organizational employment cannot moderate the impact of the length of venture creation experience on entrepreneurial intentions. Clearly, there could have been other moderators that could have been significant. We could have selected and studied the properties of organizational climate that might be irrelevant to our research question. However, by finding two significant moderators, our study has shown that properties of organizational climate can dampen the relationship between a former entrepreneur’s length of venture creation experience and their intentions to return to entrepreneurship. Future research should seek to identification additional properties of organizational climate that also act as moderators.

Our findings have obvious implications for organizations seeking to create environments that facilitate the retention of former entrepreneurs. They also suggest that entrepreneurship should be viewed as a career phase and not as a profession. We second Burton et al.’s (2016) suggestion that an individual’s work experiences and the organizational contexts in which those experiences were acquired can shape future actions, namely, can support or suppress the pursuit of entrepreneurial endeavors. To gain a more complete understanding of serial entrepreneurship, research needs to employ the kind of dual perspective applied in this study – to encompass both entrepreneurial experience and organizational employment experience.

Policy and practical implications

Our findings regarding an OEFE’s serial entrepreneurship intentions have implications for individuals, employers, and policymakers seeking to understand the factors that influence an entrepreneur’s career decisions. Researchers have emphasized the need to understand the behaviors of entrepreneurs in a variety of settings (Amaral et al., 2011). Since many entrepreneurs have intervening employment between undertaking venture creation endeavors (Amaral et al., 2011; Hyytinen and Ilmakunnas, 2007; Westhead and Wright, 1998), it is important to understand the dynamics involving their entrepreneurial intentions while they are employed in established organizations. For the employer, it is important to understand the impact of the organizational environment on all employees, including former entrepreneurs, as this could have an impact on human resource policies and procedures for attraction, selection, and retention. OEFEs could be especially effective recruits in situations that require entrepreneurial knowledge and skills in the employing organization. The nature of the organizations or their entrepreneurial climate could potentially play an important role in retaining former entrepreneurs and in the success of entrepreneurial endeavors undertaken by organizations. Indeed, Westhead and Birley (1993) found that habitual entrepreneurs have a more realistic assessment of the environment and are less optimistic than their novice counterparts, enhancing their potential contribution to the employing organization.

Organizations seeking to attract and retain former entrepreneurs should establish organizational structures that would be appealing to former entrepreneurs, thereby enticing them to stay and to contribute to the missions and objectives of organizations. Organizations operating in uncertain and fast-paced environments could benefit from retaining former entrepreneurs as leaders in building organizational climates that value collaboration and innovation. Our results suggest that organic organizational structures and stronger entrepreneurial orientation could serve that purpose.

Limitations

While we found support for our propositions, our analysis was not sufficiently fine grained to conclude that the mechanisms that we presented actually caused the results that we found. We developed and tested mechanisms based on ASA theory, and our findings are consistent with the mechanisms that we proposed. The fact that we found statistically significant relationships is interesting and important, but further study is needed to delve into and confirm the mechanisms that we proposed. For example, additional research is needed to measure entrepreneurial adaptation and organizational adaptation directly rather than assume these processes occur consistently across time and individuals. Longitudinal research examining adaptation and intentions to leave at key points during employment would be very beneficial but also very challenging to implement. More research is needed to complete our understanding of an OEFE’s serial entrepreneurship intentions.

We acknowledge that the motivations for OEFEs to exit their prior ventures and join organizations may vary. Some OEFEs may perceive poor fit with entrepreneurial environments and thus choose to exit venture creation and seek paid employment. This argument is consistent with ASA theory’s attraction prediction that people will be attracted to a work environment with which they perceive a fit (Schneider et al., 1995). For those who exit entrepreneurship, their venture creation experience would be shorter than those who perceive a better fit; they would be expected to have lower intentions to return to venture creation when organizationally employed. Other OEFEs may have undertaken business ventures and been ‘pushed out’ from venture creation against their will for reasons such as financial distress (Gimeno et al., 1997; Wennberg et al., 2010) or conflict among partners (Kelly et al., 2002). Such individuals may also seek employment for wages – even if only temporarily – so as to recover from a financial loss. Because their exit was not by choice, they may have strong serial entrepreneurship intentions. In contrast, there might be some OEFEs who were ‘pushed out’ early from entrepreneurship and thus engaged in venture creation for shorter periods of time. ASA theory suggests that these individuals would not have adapted fully to venture creation and would thus have lower intentions to start up again.

There are additional limitations in our study that future research could and should address. First, because this study was used to test ASA theory, only the variables related to ASA theory were included. We acknowledge that exogenous variables rooted in other theories, such as variables related to personality traits, may contribute to entrepreneurial intentions. Future research, such as experiments utilizing random assignments, might be able to rule out alternative explanations (Hsu et al., 2016). Also, data for this study were collected via MTurk. Although MTurk allowed us to reach the unique group of entrepreneurs, that is, OEFEs, there is some uncertainty regarding the generalizability of our data; research using data from other sources, if available, could be used to confirm the robustness of our findings. Finally, given that our sample consists of OEFEs based solely in the United States, our results may not be generalizable across institutional environments or national cultures. For example, Tan (2001) notes that the way in which failure is viewed can differ across cultures and can determine the degree to which individuals perceive a second start-up attempt as feasible or not. Differing attitudes toward entrepreneurial failure may impact serial entrepreneurship intentions among OEFEs outside the United States, a society known to respect risk-taking and celebrate those who persist in the face of failure (Maddock, 2013).

Conclusion

The organizational employment experience of former entrepreneurs is an increasingly important, yet neglected, domain of study. As internationalization and technological advancements hasten the rate of change, many organizations are pressed to operate more entrepreneurially. Recruiting individuals with entrepreneurial experience may help organizations become more entrepreneurial, but only if organizations are able to integrate and retain former entrepreneurs for sufficiently long periods of time. Retaining former entrepreneurs requires, among other things, a better understanding of an organization’s impact on intentions of former entrepreneurs to leave organizational employment in favor of re-engagement in venture creation. Viewed from the perspective of individuals, it is valuable for former entrepreneurs to have access to organizational roles for which they are well adapted. The research direction developed in our study holds the potential to help organizations retain former entrepreneurs as employees and to help former entrepreneurs to find better fitting and more rewarding employment opportunities.

Footnotes

Appendix 1 Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Dan K Hsu is an Assistant Professor in the Department of Management at the Walker College of Business, Appalachian State University. Dr Hsu holds a PhD in Entrepreneurship from Syracuse University. His research interests include venture capital, serial entrepreneurs, and entrepreneurs’ psychology around the business exit stage.

Rachel S Shinnar is a Professor at Appalachian State University’s Walker College of Business, Department of Management. Dr Shinnar holds a PhD and MS degrees in Hotel Administration from the University of Nevada, Las Vegas (UNLV) and a Bachelor of Arts in French Literature and General Humanities from the Tel Aviv University in Tel Aviv, Israel. Her research interests focus on entrepreneurial intentions, female and minority entrepreneurship, and entrepreneurship education.

Benjamin C Powell is an Associate Professor in the Department of Management at the Walker College of Business, Appalachian State University. Dr. Powell holds a PhD in Management from the Wharton School at the University of Pennsylvania. His research interests focus on task interdependence, structural inertia, organizational structures supporting innovation, and entrepreneurship education.

Betty S Coffey is a Professor in the Department of Management at the Walker College of Business, Appalachian State University. Dr Coffey holds a PhD in Business Administration (Strategic Management) from the University of Tennessee, Knoxville. Her research interests include small and medium enterprises, corporate governance, and strategy implementation.

References

Aguinis

Lawal

(2012) Conducting field experiments using eLancing’s natural environment. Journal of Business Venturing 27(4): 493–505.

Ajzen

(1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2): 179–211.

Amaral

Baptista

Lima

(2011) Serial entrepreneurship: Impact of human capital on time to re-entry. Small Business Economics 37(1): 1–21.

Atkinson

Flint

(2001) Accessing hidden and hard-to-reach populations: Snowball research strategies. Social Research Update 33: 1.

Baù

Sieger

Eddleston

et al . (2016) Fail but try again? The effects of age, gender, and multiple-owner experience on failed entrepreneurs’ reentry. Entrepreneurship Theory and Practice. Epub ahead of print 12 July. DOI: 10.1111/etap.12233.

Block

Hoogerheide

Thurik

(2011) Educational and entrepreneurial choice: An instrumental variables analysis. International Small Business Journal 31(1): 23–33.

Bolton

Drew

(1991) A longitudinal analysis of the impact of service changes on customer attitudes. Journal of Marketing 55(1): 1–9.

Bretz

Ash

Dreher

(1989) Do people make the place? An examination of the attraction–selection–attrition hypothesis. Personnel Psychology 42(3): 561–581.

Buhrmester

Kwang

Gosling

(2011) Amazon’s mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science 6(1): 3–5.

10.

Burns

Stalker

(1961) The Management of Innovation. London: Tavistock Publications.

11.

Burton

Sørensen

Dobrev

(2016) A careers perspective on entrepreneurship. Entrepreneurship Theory and Practice 40(2): 237–247.

12.

Camelo-Ordaz

Fernández-Alles

Ruiz-Navarro

et al . (2011) The intrapreneur and innovation in creative firms. International Small Business Journal 30(5): 513–535.

13.

Chang

Van Witteloostuijn

Eden

(2010) From the editors: Common method variance in international business research. Journal of International Business Studies 41(2): 178–184.

14.

Chen

(2013) Selection and serial entrepreneurs. Journal of Economics & Management Strategy 22(2): 281–311.

15.

Chen

S-FS

Monroe

Lou

(1998) The effects of framing price promotion messages on consumers’ perceptions and purchase intentions. Journal of Retailing 74(3): 353–372.

16.

Cheung

Lau

(2008) Testing mediation and suppression effects of latent variables. Organizational Research Methods 11: 296–325.

17.

Cohen

West

et al . (2003) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum.

18.

Conger

(1974) A revised definition for suppressor variables: A guide to their identification and interpretation. Educational and Psychological Measurement 34(1): 35–46.

19.

Conway

Lance

(2010) What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology 25(3): 325–334.

20.

Cooper-Thomas

Van Vianen

Anderson

(2004) Changes in person-organization fit: The impact of socialization tactics on perceived and actual P–O fit. European Journal of Work and Organizational Psychology 13(1): 52–78.

21.

Covin

Slevin

(1988) The influence of organization structure on the utility of an entrepreneurial top management style. Journal of Management Studies 25(3): 217–234.

22.

Covin

Slevin

(1989) Strategic management of small firms in hostile and benign environments. Strategic Management Journal 10(1): 75–87.

23.

Covin

Green

Slevin

(2006) Strategic process effects on the entrepreneurial orientation–sales growth rate relationship. Entrepreneurship Theory and Practice 30(1): 57–81.

24.

Davies

Chun

da Silva

et al . (2001) The personification metaphor as a measurement approach for corporate reputation. Corporate Reputation Review 4(2): 113–127.

25.

Dawson

Henley

(2012) Over-optimism and entry and exit from self-employment. International Small Business Journal 31(8): 938–954.

26.

De Cooman

Gieter

Pepermans

et al . (2009) Person-organization fit: Testing socialization and attraction–selection–attrition hypotheses. Journal of Vocational Behavior 74(1): 102–107.

27.

Dess

Lumpkin

(2005) The role of entrepreneurial orientation in stimulating effective corporate entrepreneurship. Academy of Management Executive 19(1): 147–156.

28.

Dimov

(2010) Nascent entrepreneurs and venture emergence: Opportunity confidence, human capital, and early planning. Journal of Management Studies 47(6): 1123–1153.

29.

Douglas

(2013) Reconstructing entrepreneurial intentions to identify predisposition for growth. Journal of Business Venturing 28(5): 633–651.

30.

Douglas

Shepherd

(2002) Self-employment as a career choice: Attitudes, entrepreneurial intentions, and utility maximization. Entrepreneurship Theory and Practice 26(3): 81–90.

31.

Feldman

Bolino

(2000) Career patterns of the self-employed: Career motivations and career outcomes. Journal of Small Business Management 38(3): 53–67.

32.

Finkle

(2011) Corporate entrepreneurship and innovation in Silicon Valley: The case of Google, Inc. Entrepreneurship Theory and Practice 36(4): 863–884.

33.

Fitzsimmons

Douglas

(2011) Interaction between feasibility and desirability in the formation of entrepreneurial intentions. Journal of Business Venturing 26(4): 431–440.

34.

Furnham

Schaeffer

(1984) Person–environment fit, job satisfaction and mental health. Journal of Occupational Psychology 57(4): 295–307.

35.

Gimeno

Folta

Cooper

et al . (1997) Survival of the fittest? Entrepreneurial human capital and the persistence of underperforming firms. Administrative Science Quarterly 42(4): 750–783.

36.

Green

Covin

Slevin

(2008) Exploring the relationship between strategic reactiveness and entrepreneurial orientation: The role of structure–style fit. Journal of Business Venturing 23(3): 356–383.

37.

Gupta

Goktan

Gunay

(2014) Gender differences in evaluation of new business opportunity: A stereotype threat perspective. Journal of Business Venturing 29(2): 273–288.

38.

Haber

Reichel

(2007) The cumulative nature of the entrepreneurial process: The contribution of human capital, planning and environment resources to small venture performance. Journal of Business Venturing 22(1): 119–145.

39.

Hsu

Wiklund

Cotton

(2015) Success, failure, and entrepreneurial reentry: An experimental assessment of the veracity of self-efficacy and prospect theory. Entrepreneurship Theory and Practice. Epub ahead of print 16 April. DOI: 10.1111/etap.12166.

40.

Hsu

Simmons

Wieland

(2016) Designing entrepreneurship experiments: A Review, Typology, and Research Agenda. Organizational Research Methods. Epub ahead of print 30 December. DOI: 10.1177/1094428116685613.

41.

Bentler

(1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6(1): 1–55.

42.

Hyytinen

Ilmakunnas

(2007) What distinguishes a serial entrepreneur? Industrial and Corporate Change 16(5): 793–821.

43.

Johnson

Herrmann

Huber

(2006) The evolution of loyalty intentions. Journal of Marketing 70(2): 1–132.

44.

Kautonen

Luoto

Tornikoski

(2010) Influence of work history on entrepreneurial intentions in ‘prime age’ and ‘third age’: A preliminary study. International Small Business Journal 28(6): 583–601.

45.

Kelley

Singer

Herrington

(2016) Global Entrepreneurship Monitor (GEM) 2015/2016 Global Report. Available at: http://www.gemconsortium.org/report/49480

46.

Kelly

Schaan

Joncas

(2002) Managing alliance relationships: Key challenges in the early stages of collaboration. Research and Development Management 32(1): 11–22.

47.

Kittur

Chi

Suh

(2008) Crowdsourcing user studies with mechanical Turk. In: Proceedings of the SIGCHI conference on human factors in computing systems, Florence, 5–10 April, pp.453–456. New York, NY: ACM.

48.

Kristof

(1996) Person-organization fit: An integrative review of its conceptualizations, measurement and implications. Personnel Psychology 49(1): 1–49.

49.

Kristof-Brown

Zimmerman

Johnson

(2005) Consequences of individuals’ fit at work: A meta-analysis of person-job, person-organization, person-group, and person-supervisor fit. Personnel Psychology 58(2): 281–342.

50.

Krueger

(1993) The impact of prior entrepreneurial exposure on perceptions of new venture feasibility and desirability. Entrepreneurship Theory and Practice 18(1): 5–22.

51.

Krueger

Dickson

(1994) How believing in ourselves increases risk taking: Perceived self-efficacy and opportunity recognition. Decision Sciences 25(3): 385–400.

52.

Lauver

Kristof-Brown

(2001) Distinguishing between employees’ perceptions of person-job and person-organization fit. Journal of Vocational Behavior 59(3): 454–470.

53.

Lee

Wong

Foo

et al . (2011) Entrepreneurial intentions: The influence of organizational and individual factors. Journal of Business Venturing 26(1): 124–136.

54.

Liñán

Chen

(2009) Development and cross cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice 33(3): 593–617.

55.

Lumpkin

Dess

(1996) Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review 12(1): 135–172.

56.

Lumpkin

Dess

(2001) Linking two dimensions of entrepreneurial orientation to firm performance: The moderating role of environment and industry life cycle. Journal of Business Venturing 16(5): 429–451.

57.

Lumpkin

Cogliser

Schneider

(2009) Understanding and measuring autonomy: An entrepreneurial orientation perspective. Entrepreneurship Theory and Practice 33(1): 47–69.

58.

Maddock

(2013) Why Americans make the best entrepreneurs … for now. Forbes. Available at: www.forbes.com/sites/mikemaddock/ (accessed 20 January 2016).

59.

Malhotra

Kim

Patil

(2006) Common method bias in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science 52(12): 1865–1883.

60.

Manso

(2016) Experimentation and the returns to entrepreneurship. Review of Financial Studies 29: 2319–2340.

61.

Markman

Baron

(2003) Person–entrepreneurship fit: Why some people are more successful as entrepreneurs than others. Human Resource Management Review 13(2): 281–301.

62.

Marvel

Lumpkin

(2007) Technology entrepreneurs’ human capital and its effects on innovation radicalness. Entrepreneurship Theory and Practice 31(6): 807–828.

63.

Miller

(1983) The correlates of entrepreneurship in three types of firms. Management Science 29(7): 770–791.

64.

Mintzberg

(1979) The Structuring of Organizations: A Synthesis of the Research. Englewood Cliffs, NJ: Prentice Hall.

65.

Morris

Zahra

Schindehutte

(2001) Understanding factors that trigger entrepreneurial behavior in established companies. Advances in the Study of Entrepreneurship, Innovation, and Economic Growth 12: 133–159.

66.

Muñoz-Bullón

Cueto

(2010) The sustainability of start-up firms among formerly wage-employed workers. International Small Business Journal 29(1): 78–102.

67.

Ostroff

Rothausen

(1997) The moderating effect of tenure in person – environment fit: A field study in educational organizations. Journal of Occupational and Organizational Psychology 70(2): 173–188.

68.

Pandey

Elliott

(2010) Suppressor variables in social work research: Ways to identify in multiple regression models. Journal of the Society for Social Work and Research 1(1): 28–40.

69.

Paolacci

Chandler

Ipeirotis

(2010) Running experiments on Amazon mechanical Turk. Judgment and Decision Making 5(5): 411–419.

70.

Podsakoff

MacKenzie

Lee

et al . (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88(5): 879–903.

71.

Politis

(2005) The process of entrepreneurial learning: A conceptual framework. Entrepreneurship Theory and Practice 29(4): 399–424.

72.

Politis

(2008) Does prior start-up experience matter for entrepreneurs’ learning? A comparison between novice and habitual entrepreneurs. Journal of Small Business and Enterprise Development 15(3): 472–489.

73.

Rauch

Wiklund

Lumpkin

et al . (2009) Entrepreneurial orientation and business performance: An assessment of past research and suggestions for the future. Entrepreneurship Theory and Practice 33(3): 761–787.

74.

Resick

Baltes

Shantz

(2007) Person-organization fit and work-related attitudes and decisions: Examining interactive effects with job fit and conscientiousness. Journal of Applied Psychology 92(5): 1446–1455.

75.

Robinson

O’Leary-Kelly

(1998) Monkey see, monkey do: The influence of work groups on the antisocial behavior of employees. Academy of Management Journal 41(6): 658–672.

76.

Rollag

(2004) The impact of relative tenure on newcomer socialization dynamics. Journal of Organizational Behavior 25(7): 853–872.

77.

Ross

Irani

Silberman

et al (2010) Who are the crowd workers? Shifting demographics in mechanical Turk. In: Extended abstracts of the conference on human factors in computing systems, Atlanta, GA, 5–10 April, pp.2863–2872. New York, NY: ACM.

78.

Russell

(1992) An examination of the effects of organizational norms, organizational structure, and environmental uncertainty on entrepreneurial strategy. Journal of Management 18(4): 639–656.

79.

Schaubroeck

Ganster

Jones

(1998) Organization and occupation influences in the attraction-selection-attrition process. Journal of Applied Psychology 83(6): 869–891.

80.

Schneider

(1987) The people make the place. Personnel Psychology 40(3): 437–453.

81.

Schneider

Goldstein

Smith

(1995) The ASA framework: An update. Personnel Psychology 48(4): 747–773.

82.

Schneider

Smith

Paul

(2001) PE fit and the attraction-selection-attrition model of organizational functioning: Introduction and overview. In: Erez

Thierry

Kleinbeck

(eds) Work Motivation in a Globalizing Economy. Hillsdale, NJ: Lawrence Erlbaum, pp.231–246.

83.

Schneider

Smith

Taylor

et al . (1998) Personality and organizations: A test of the homogeneity of personality hypothesis. Journal of Applied Psychology 83(3): 462–470.

84.

Schreiber

Nora

Stage

et al . (2006) Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Education Research 99(6): 323–338.

85.

Shane

(2008) The Illusions of Entrepreneurship: The Costly Myths That Entrepreneurs, Investors, and Policy Makers Live by. New Haven, CT: Yale University Press.

86.

Shapero

Sokol

(1982) The social dimensions of entrepreneurship. In: Kent

Sexton

Vesper

(eds) Encyclopedia of Entrepreneurship. Englewood Cliffs, NJ: Prentice Hall, pp.72–90.

87.

Simmons

Carr

Hsu

et al . (2016) The regulatory fit of serial entrepreneurship intentions. Applied Psychology: An International Review 65(3): 603–627.

88.

Sine

Mitsuhashi

Kirsch

(2006) Revisiting Burns and Stalker: Formal structure and new venture performance in emerging economic sectors. Academy of Management Journal 49(1): 121–132.

89.

Smith

Sabat

Martinez

et al . (2015) A convenient solution: Using MTurk to sample from hard-to-reach populations. Industrial and Organizational Psychology 8(2): 220–228.

90.

Sørensen

(2007) Bureaucracy and entrepreneurship: Workplace effects on entrepreneurial entry. Administrative Science Quarterly 52(3): 387–412.

91.

Spivack

McKelvie

Haynie

(2014) Habitual entrepreneurs: Possible cases of entrepreneurship addiction? Journal of Business Venturing 29(5): 651–667.

92.

Stuart

Abetti

(1990) Impact of entrepreneurial and management experience on early performance. Journal of Business Venturing 5(3): 151–162.

93.

Tan

(2001) Innovation and risk-taking in a transitional economy: A comparative study of Chinese managers and entrepreneurs. Journal of Business Venturing 16(4): 359–376.

94.

Wadhwa

(2012) Innovation without age limits. Technology Review 115(2): 66–67.

95.

Warner

(2008) Applied Statistics: From Bivariate through Multivariate Techniques. Thousand Oaks, CA: Sage Publications, Inc.

96.

Wennberg

Wiklund

DeTienne

et al . (2010) Reconceptualizing entrepreneurial exit: Divergent exit routes and their drivers. Journal of Business Venturing 25(4): 361–375.

97.

Westhead

Birley

(1993) A comparison of new business established by ‘novice’ and ‘habitual’ founders in Great Britain. International Small Business Journal 12(1): 38–60.

98.

Westhead

Wright

(1998) Novice, portfolio, and serial founders: Are they different? Journal of Business Venturing 13(3): 173–204.

99.

Westhead

Ucbasaran

Wright

(2005) Experience and cognition do novice, serial and portfolio entrepreneurs differ? International Small Business Journal 23(1): 72–98.

100.

Wiklund

Shepherd

(2003) Knowledge-based resources, entrepreneurial orientation, and the performance of small and medium-sized businesses. Strategic Management Journal 24(13): 1307–1314.

101.

Wiklund

Shepherd

(2005) Entrepreneurial orientation and small business performance: A configurational approach. Journal of Business Venturing 20(1): 71–91.

102.

Wilson

Kickul

Marlino

(2007) Gender, entrepreneurial self-efficacy, and entrepreneurial career intentions: Implications for entrepreneurship education. Entrepreneurship Theory and Practice 31(3): 387–406.

103.

Yahyagil

(2006) The fit between the concepts of organizational culture and climate. Journal of Organizational Culture, Communications and Conflict 10(2): 77–104.

104.

Zhao

Seibert

(2006) The big five personality dimensions and entrepreneurial status: A meta-analytical review. Journal of Applied Psychology 91(2): 259–271.

105.

Zhao

Seibert

Hills

(2005) The mediating role of self-efficacy in the development of entrepreneurial intentions. Journal of Applied Psychology 90(6): 1265–1272.