Gender Effects on Entrepreneurial Intention: A Meta-Analytical Structural Equation Model

Transcription

1 Gender Effects on Entrepreneurial Intention: A Meta-Analytical Structural Equation Model Inga Haus University of Giessen, Germany Inga.C.Haus@wirtschaft.uni-giessen.de Holger Steinmetz University of Giessen, Germany Holger.Steinmetz@web.de Rodrigo Isidor University of Giessen, Germany Rodrigo.Isidor@wirtschaft.uni-giessen.de Rüdiger Kabst University of Paderborn, Germany Kabst@upb.de

2 Inga Haus (University of Giessen, Germany) Inga Haus is a PhD student working at the University of Giessen, department of human resource management, small and medium sized enterprises, and entrepreneurship. Her research interests are gender differences and similarities in the area of entrepreneurship. Holger Steinmetz (University of Giessen, Germany) Holger Steinmetz is assistant professor at the University of Giessen, department of human resource management, small and medium sized enterprises, and entrepreneurship. His research interests are structural equation modeling, cross-cultural research, and work psychology. Rodrigo Isidor (University of Giessen, Germany) Rodrigo Isidor is assistant professor at the University of Giessen, department of human resource management, small and medium sized enterprises, and entrepreneurship. His research interests are contingent work and entrepreneurial behavior. Rüdiger Kabst (University of Paderborn, Germany) Rüdiger Kabst is Professor for International Business at the University of Paderborn, Faculty of Business Administration and Economics. His research interests include international comparative human resource management, expatriate management, human resource practices between market and hierarchy (e.g., outsourcing, downsizing, interim-management, working time flexibility, etc.), interfirm cooperations (e.g., joint ventures), trust between organizations, young technology start-ups, international entrepreneurship, and internationalization of medium sized enterprises.

3 Abstract Purpose Although the percentage of female entrepreneurs has increased over the past several years, it is far below the level of males. Drawing on the theory of planned behavior and role congruity theory, the purpose of this paper is to specify a model in which the relationship between gender and EI is mediated by three essential motivational constructs (i.e., attitude toward starting a business, subjective norm, and perceived behavioral control (PBC)). Design/methodology/approach The study specifies and tests a meta-analytical structural equation model. The study aggregates the results of 30 studies (N = 52,367). Findings The study reveals a higher average EI for men compared to women. However, although significant, the gender differences in EI and the motivational constructs were small and can not sufficiently explain the substantial differences in actually starting a business. Furthermore, moderator analyses show differences in the gender-ei relationship between Europe and the U.S. and between students and non-students. Originality/value The model helps to decompose the overall gender-ei relationship into specific pathways and, hence, helps understanding the reasons for this relationship. The metaanalytical approach leads to more precise estimates of relationships (i.e., with lower sampling error) and a higher statistical power. Furthermore, the moderator analysis helps understanding the role of contextual factors (i.e., region) for the gender-ei relationship. Future research Differences between men and women seem to be a consequence of differences in turning intentions into implementation. Researchers are called upon to investigate gender differences in hindrances as a potential explanation for different implementations and when and why women give up their entrepreneurial plans. Moreover, future research should investigate further motivational processes beyond those suggested by the theory of planned behavior. Keywords Gender, Entrepreneurship, Entrepreneurial Intention, Theory of Planned Behavior, Meta-Analysis, Social Role Theory Paper type Research paper

4 Introduction Although the number and importance of female entrepreneurs has consistently increased over the past several years (DeBruin et al., 2006; Hisrich and Brush, 1984; Scherer et al., 1990; Mattis, 2004), the bulk of entrepreneurs are still men (Allen et al., 2007; Verheul et al., 2005; Zhao et al., 2005). The increasing amount of research investigating gender differences in entrepreneurial processes underlines the growing interest in the role of gender in entrepreneurship (e.g., Zhang et al., 2009; Verheul et al., 2009, Langowitz and Minniti, 2007; Verheul, 2005; Bird and Brush, 2002). However, empirical studies investigating gender differences have resulted in mixed empirical findings (Langowitz and Minniti, 2007; Wilson et al., 2007). While some studies have found gender differences in entrepreneurial intention (EI) (Zhao et al., 2005; Moore and Buttner, 1997), others have failed to find such differences (Gupta et al., 2009; Kolvereid, 1996). Although researchers theorize about the processes that are responsible for building EI, the field has not empirically tested these theories. For instance, most studies draw on the theory of planned behavior (Engle et al., 2010; Shook and Bratianu, 2008; Krueger and Carsrud, 1993), but only a small number of empirical analyses connect the theory with gender differences (Díaz-García and Jiménez-Moreno, 2009). Our study has three aims. First, it examines the relationship between gender and EI using a meta-analysis, which aggregates and summarizes the existing research. Compared to narrative reviews, which may often be open to biases resulting from subjective interpretations, disregard sampling errors and power deficits due to small-scale studies (Rauch et al., 2009), meta-analyses help to detect the relationship between variables across studies in a quantitative and systematic way (Hunter and Schmidt, 2006). Second, this study analyzes the motivational processes that relate gender to EI. We draw on Ajzen s (1991) theory of planned behavior, which assumes three motivational constructs that underlie the formation of EI, and investigate the degree to which these three constructs explain potential gender differences in EI (Figure

5 1). To hypothesize the relationship between gender and the three constructs, we draw on social role theory (Eagly and Wood, 1991) and role congruity theory (Eagly and Karau, 2002). Third, we explore potential moderators (region and respondents) of the overall gender- EI relationship and the specific processes in the model Figure 1 about here Our study makes three major contributions to the gender and entrepreneurship literature. First, we clarify if gender differences in entrepreneurial activity are a result of gender differences in EI and whether these differences are strong enough to be responsible for the majority of entrepreneurs being male. Second, by specifying a theoretical model based on the theory of planned behavior, this study examines which motivational constructs mediate and explain a potential relationship between gender and EI. Third, we analyze if and how the relationship between gender and EI is contingent on study-specific moderators. Theoretical background and hypotheses Gender in Entrepreneurship Research Welter (2011) emphasized the importance of contexts for entrepreneurship to understand when, how and why people become entrepreneurs. In particular, social contexts (i.e., traditions and norms) are important as they help to explain gender specific behavior in entrepreneurship by determining gender roles (Welter, 2011). Especially, female entrepreneurship finds interest in many research questions. Sullivan and Meek (2012) summarized the literature on gender and entrepreneurship, concluding that gender is mainly used as an independent variable related to several variables such as discrimination in market access (Bates, 2002), success and profit (Buttner and Moore, 1997; Fasci and Valdez, 1998; Collins-Dodd et al., 2004), financial capital and capital structure (Buttner and Rosen, 1989; Lerner et al., 1997; Haynes and Haynes, 1999; Boden and Nucci, 2000; Coleman, 2000; Alsos

6 et al., 2006; Chaganti et al., 1995), and networking (Bruni et al., 2004; Kepler and Shane, 2007; Klyver and Grant, 2010). Although women use networking more intensely than men (Greve and Salaff, 2003), women s networks are less helpful in enhancing their possibilities (Kepler and Shane, 2007). The gender of the entrepreneur is also used as mediator or moderator (Gatewood et al., 1995; Parasuraman and Simmer, 2001; Manolova et al., 2007; Wilson et al., 2007; Sorenson et al., 2008; Cron et al., 2009). Sullivan and Meek (2012) only found one study investigating gender as a dependent variable (DeMartino and Barbato, 2003). Other studies have investigated the relationship between gender and constructs relevant to entrepreneurial behavior, such as self-efficacy (Verheul et al., 2005; Boyd and Vozikis, 1994), risk taking (Brindley, 2005), entrepreneurial education (Peterman and Kennedy, 2003; Kourilsky and Walstad, 1998), and entrepreneurial family background (Matthews and Moser, 1996). These studies indicated that men display higher self-efficacy and higher risk-taking behavior, but exhibit lower neediness of entrepreneurial education (Wilson et al. 2007; Sexton and Bowman-Upton, 1990). Men with an entrepreneurial family background show higher levels of EI than women with an entrepreneurial family background (Matthews and Moser, 1996). In contrast, no gender differences were found in financial support (Buttner and Rosen, 1989). Whereas these studies focused on gender, other studies have included gender as a control variable (e.g., Davidsson and Honig, 2003; Forbes, 2005a; Kolvereid and Isaksen, 2006). Furthermore, other studies have investigated the relationship between gender and EI (e.g., Lu and Tao, 2010; Malach-Pines and Schwartz, 2008; Diaz-Garcia and Jimenez-Moreno, 2009; Crant, 1996; Kolvereid, 1996; Wilson et al., 2007; Zellweger et al., 2011; Zhao et al., 2005). Some of these studies have found gender differences in EI (Langowitz and Minniti, 2007; Kourilsky and Walstadt, 1998; Lu and Tao, 2010; Wilson et al., 2007; Zellweger et al., 2011; Zhao et al., 2005). For example, Fagenson and Marcus (1991) confirmed that entrepreneurs are primarily associated with masculine activities. Additionally, women have

7 fewer possibilities of becoming self-employed due to perceived gender-specific barriers (Verheul et al., 2012). Other studies, in contrast, have emphasized that gender similarities are much higher than differences (Gupta et al., 2009; Diaz-Garcia and Jimenez-Moreno, 2009; Kolvereid, 1996, Buttner and Moore, 1997). Therefore, the research on gender in entrepreneurship remains inconclusive. Theory of planned behavior Previous research has drawn on a variety of theoretical perspectives when considering the motivation to start a business, most notably, adaption innovation theory (Stewart et al., 1999), learning theory (Dimov, 2007), and social and human capital theory (Langowitz and Minniti, 2007; Davidsson and Honig, 2003). In recent years, researchers (i.e., Díaz-García and Jiménez-Moreno, 2009; Tornikoski and Kautonen, 2009; Krueger et al., 2000) have tended to use Ajzen s (1991) theory of planned behavior as a framework for predicting EI. This theory was created to explain non-spontaneous behavior in particular (cf. Armitage and Conner, 2001, for a meta-analysis and review). As starting a business can be seen as the result of a non-spontaneous decision-making process, the theory offers a useful framework for research on EI (Linan and Chen, 2009; Shook and Bratianu, 2008; Engle et al., 2010; Autio et al., 2001). The theory proposes that intention is the most important determinant of an individual s behavior and is, in turn, determined by three motivational constructs: attitude toward the behavior, subjective norm, and perceived behavioral control (PBC). Applied to entrepreneurship, attitude toward behavior (hereafter, attitude) reflects whether an individual considers starting a business as positive or negative. The subjective norm indicates the degree of perceived social expectations of relevant others. Finally, PBC describes the quantity of perceived difficulties in starting a business. Hence, the theory proposes that EI results from positive attitude, positive perceived expectations of others, and feelings of control over the creation process.

8 A further benefit of the theory of planned behavior is that it allows hypothesizing on the role of background factors for the execution of behavior. Ajzen (2005) argues that personal characteristics, demographic factors, or environmental conditions do not directly affect behavior but are mediated by attitude, subjective norm, and PBC (Krueger and Carsrud, 1993). Therefore, this theory allows for the identification of the specific processes by which background factors influence behavior. Applied to entrepreneurship, it implies that gender differences in attitude, subjective norm, and PBC account for differences in EI; hence, gender effects will be mediated by attitude, subjective norm, and PBC (Figure 1). Gender differences in motivational constructs Based on Eagly and Karau s (2002) role-congruity theory, we expect gender differences in the attitude, subjective norm, and PBC. According to role-congruity theory (Eagly & Karau, 2002), men and women match their behavior (e.g., starting a business) with the perceived characteristics and attributes required for a successful behavior. Entrepreneurship is associated with characteristics such as aggressiveness, achievement orientation, dominance, independence, challenge, and high risk taking, which are more strongly linked to men than women (Ahl, 2006; Gupta et al., 2009; Stewart Jr. and Roth, 2001; Buttner and Moore, 1997; Hughes, 2006). For instance, women tend to avoid risk or, rather, become less involved in situations with vague success (Byrnes et al., 1999; Sexton and Bowman-Upton, 1990; Powell and Ansic, 1997; Orobia et al., 2011). Van Gelderen et al. (2008) found that men value challenge and autonomy more than women, while women value financial security and workload. Since men, thus, exhibit entrepreneurial characteristics to a higher degree, we suggest that women anticipate a misfit between their traits and behaviors and those required to become a successful entrepreneur. Hence, self-identification decreases, and cognitive dissonances occur (Eagly and Karau, 2002). Consequently, becoming an entrepreneur may be regarded as less attractive for women compared to men.

9 H1. Women exhibit a lower average attitude toward starting a business compared to men. We further expect a lower average subjective norm for women. In accordance with social role theory (Eagly and Wood, 1991), gender roles cause men and women to choose different career options. As mentioned above, starting a business is culturally associated with masculine characteristics (Ahl, 2006; Gupta et al., 2009). These gender stereotypes lead to family and occupational roles (Eagly, 1987; Eagly and Wood, 1991) and create a social environment in that men, rather than women, are seen as entrepreneurs (Baron et al., 2001). Thus, female entrepreneurship is considered as less desirable and lacks normative societal support (Baughn et al., 2006). As women are highly conditioned by societal norms and the roles ascribed to women (Welter et al., 2007), they continue to feel substandard and unable to escape negative stereotypes (Marlow and Patton, 2005). Hence, women exhibit a lower average subjective norm than men. H2. Women exhibit a lower average subjective norm to start a business than men. Finally, we expect a lower average PBC for women. PBC reflects the perceived capabilities to demonstrate a respective behavior (Ajzen, 2002). In accordance to rolecongruity theory (Eagly and Karau, 2002), men and women choose jobs with characteristics that comply with their individual characteristics. However, women perceive a mismatch between their characteristics and those characteristics necessary for being successful entrepreneurs. As a consequence, we suggest that women possess a lower self-confidence and perceive a higher number of barriers in starting a business. For instance, Wilson et al. (2007) showed that men demonstrate a higher degree of self-efficacy, which is similar to PBC

10 (Ajzen, 2002), when starting a business, whereas, women often lack self-assurance in their abilities to start a business. Men see themselves as more suitable for entrepreneurship (Malach-Pines and Schwartz, 2008). Compared to men, women show lower self-confidence and more often underrate their skills and abilities (Hyde and Kling, 2001). Additionally, women perceive greater hindrances than men in becoming an entrepreneur (Marlow, 2002). Cliff (1998) and Cooper et al. (1994) showed that women are faced with additional obstacles to becoming a successful entrepreneur in that their businesses are smaller and grow less quickly than men s businesses. Furthermore, perceived control depends on knowledge about different situations. In contrast to men, women show different behaviors depending on whether they know more or less about their upcoming tasks, and evaluate skill deficiencies better than men (Bandura, 1997; Bandura et al., 2001). Hence, we expect a lower level of PBC for women than for men. H3. Women show a lower average PBC toward starting a business than men. Moderators of the relationship between gender and EI We examined potential moderators of the gender-ei relationship. First, we investigated the moderating role of two theoretically based moderators, namely region and respondents. Region displays the country in which the study was conducted (United States vs. Europe) and respondents indicate the occupational status of the investigated respondents (i.e., students or non-students). Furthermore, we investigated the potential moderating role of study specific features, that is, average age of the respondents, percentage of female respondents, Gross Domestic Product per capita, and publication year. As we had no a priori expectations concerning the direction of the moderator effect, we only offer explicit hypotheses for the theoretically based moderators.

11 Theoretically based moderators Region. We coded the respective sample as originating from either the United States (hereafter, U.S.) or a European country. By using these categories, we were able to consider 20 studies. The remaining studies stemmed from diverse countries (e.g., Malaysia, South Africa, or cross-cultural) and were too culturally heterogeneous to form a meaningful additional category. The two coded regions show several distinct characteristics; hence, we expect differences in the relationship within the model. For instance, cross-cultural comparisons show that European countries appreciate certainty, whereas the U.S. shows greater tolerance for uncertainty and ambiguity (House et al., 2004; Hofstede, 1980). As starting a business implies risk and uncertainty, it is likely that relationships between motivational constructs and EI are moderated by uncertainty avoidance, and are thus lower in European countries. For instance, the motivational impact of viewing entrepreneurship as attractive could be reduced by the perceived risk implied by entrepreneurship. H4. The relationship between attitude and EI is moderated by region, and studies from European countries show a lower effect of attitude on EI compared to studies from the U.S. Respondents. We further distinguished between student and non-student samples. We expect the level of work experience to be larger in non-student (i.e., manager) samples. Work experience, in turn, could act as a moderator of the relationships between motivational constructs and EI. Because individuals with a high level of experience base their attitude and PBC on realistic assumptions and beliefs, the predictive validity of these constructs should increase. Furthermore, we expect that respondents (students and non-students) influence the relationship between subjective norm and EI because becoming an entrepreneur is a career choice process and students are still in the identification stage. Thus, students increasingly

12 draw on the opinions of parents, partners, friends, and other caregivers (van Gelderen et al., 2008). In contrast, non-students, such as managers, are less dependent of their caregivers opinions because they can rely on their personal view of career options. It is also expected that the average differences between males and females will be more pronounced in non-student samples. For instance, the means for attitude may differ less in the student sample because attitude may reflect wishful thinking rather than the result of an explicit reasoning process. H5. The relationships between PBC and EI, as well as subjective norm, EI and attitude and EI are moderated by respondents in that non-students show lower PBC, subjective norm and attitude towards starting a business than students. Study-specific moderators Average Age: Starting a business is driven by several motivators, the effects of which can vary according to the individual s age. Assuming that family state is related to age, younger individuals without family and children will care less about work-family conflict than elders with children (Brush, 1992). To analyze if age moderates the gender-ei relationship, we coded the average age of respondents. Percentage of female respondents in the included studies: To avoid a gender specific respondent bias, we coded the percentage of female respondents. Menzies and Tatroff (2006) concluded that a perceived misfit between entrepreneurship and personality may influence female respondents. Gross Domestic Product per capita: We coded the Gross Domestic Product per capita to investigate the possibility that in countries with high income there might be less need to start a business than in countries with low income and, hence, smaller relationships would occur.

13 Publication year: Over the past several years, the percentage of female entrepreneurs has increased (Hughes et al., 2012) indicating that the gender stereotypes might have changed. To investigate, hence, if this change could have affected the relationships between gender and the other model variable, we coded the publication year of the study. Method Sample The meta-analysis included 30 studies with an overall sample size of 52,367 individuals. To obtain these studies, we conducted a comprehensive literature search in EBSCO Host, ScienceDirect, PsycINFO, and SpringerLink. Keyword searches included entrepreneurial intention, theory of planned behavior, subjective norm, attitude, self-efficacy, perceived behavioral control, starting a business, creation new venture, motivation, male and female differences, and become entrepreneur in combination with the terms sex and gender. Moreover, we systematically browsed the most important journals in entrepreneurship research (Entrepreneurship Theory and Practice, Journal of Business Venturing, Journal of Small Business Management, Small Business Economics, Journal of Applied Psychology, International Journal of Gender and Entrepreneurship, International Journal of Entrepreneurship and Small Business, and Gender, Work & Organization). Additionally, we checked the references of published studies. Finally, we used Google Scholar to locate possible unpublished studies, such as dissertation abstracts or working papers. In the meta-analysis, we included studies that reported correlations between at least two of the five model variables (Figure 1). In addition, studies were only included if their measurement of the constructs reflected our construct definition (Table 1). The only exception was PBC, for which we observed a variety of measures and constructs, however, all of these tapped the issue of control over behavior (e.g., self-efficacy)

14 Table 1 about here We focused on studies published within the last fifteen years ( ), excluding studies that analyzed entrepreneurship in the context of unemployed persons. Table 2 provides an overview of the studies included in our meta-analysis and provides information about the respective studies Table 2 about here Meta-Analysis We tested the gender-ei relationship using the meta-analytical procedures of Hunter and Schmidt (2006). We calculated a weighted average correlation r by weighting each study s correlations with their sample size and calculating their average. Additionally, we calculated the 95 percent confidence interval (CI) around the weighted mean correlation r. A correlation was regarded as significant if the 95 percent CI excluded zero. We also tested the stability of the effects by calculating the Fail-Safe N, which estimates the number of hypothetical new, unpublished, or unretrieved insignificant (null-results) studies needed to change a statistically significant result to a statistically insignificant finding (Rosenthal, 1979). Meta-analytic results can be considered robust to publication bias if the effect sizes are greater than 5k+10, where k is the number of integrated studies (Rosenthal, 1984). To test for heterogeneity of the correlations, we applied the 75 percent rule recommended by Hunter and Schmidt (2006), according to which correlations are treated as homogenous if more than 75 percent of the observed variance in the correlations is due to sampling error. As this finding was not true in the gender-ei correlations, this study tested for the aforementioned moderators (region and respondents) by conducting a subgroup analysis. To test the significance of a moderator effect, we used a z-test to analyze the differences in the weighted correlations (Hunter and Schmidt, 2006).

15 Meta-Analytical Structural Equation Modeling To investigate the motivational processes underlying the gender-ei relationship, we used a meta-analytical structural equation model. In contrast to traditional meta-analyses (Hunter and Schmidt, 2006), which result in average bivariate correlations, a meta-analytical structural equation model is a complex causal model that allows for the testing of a causal structure and the estimating of regression effects of predictors while controlling for the presence of other predictors in the model. Furthermore, mediation effects can be investigated in a single step (Brown and Peterson, 1993; Viswesvaran and Ones, 1995). With this approach, it is possible to specify a theoretically postulated structure of relationships between variables (i.e., the theory of planned behavior in this case). A statistical test (i.e., the chi-square test) and diverse fit indices allow for the evaluation of the model s fit. The basis of this model is a metaanalytically derived correlation matrix that contains correlations among all relevant variables (i.e., gender, attitude, subjective norm, PBC, and EI). Our study used the most current, sophisticated approach (i.e., the two-step-approach, Cheung and Chan, 2005; Cheung, 2002), which overcomes disadvantages in traditional metaanalytical structural equation models. These problems concerned, for instance, the bias resulting from using a correlation matrix instead of a covariance matrix and the specification of an arbitrary sample size for the whole model because of an unequal number of studies analyzing the various bivariate correlations. In the first step of the approach, we tested the homogeneity of the 30 correlation matrices by specifying a multi-group-model in which each of the studies was treated as a separate group. The input for this model was the correlation matrices of each study that contained the particular correlations considered by the study. Given that the 30 studies had only estimated some of the correlations, these matrices were highly fragmented. By imposing equality constraints on the cells of the matrices across the studies, the homogeneity assumption that all

16 30 correlation matrices differed beyond chance was tested. The fit of this model allowed for this determination if the difference was statistically significant (i.e., by inspecting the chisquare statistic) and substantial (i.e., by inspecting the fit indices). The multi-group model tested in the first step resulted in a maximum likelihood estimated population correlation matrix, which served as the basis for the structural equation model in the second step. This step tested the theoretical model (cf. Figure 1). In addition, an asymptotic correlation matrix that retained the information about cross-study fluctuations in the correlations was provided. In the theoretical model, attitude, subjective norm, and PBC mediated the relationship between gender and EI. The regression effects linking gender with the three motivational variables denote the mean differences between men and women in the respective variable. A positive regression effect denoted a higher mean for women, while a negative regression effect showed a higher mean for men. To evaluate the model, we used the chi-square test, the root mean square error of approximation (RMSEA), and the comparative fit index (CFI), as they provide non-redundant information about the model s fit (Beauducel and Wittmann, 2005). The chi-square statistic tests if the correlation matrix implied by the model significantly differs from the empirical correlation matrix. A significant departure signifies a misspecification of the causal structure. The fit indices measure the degree of departure of both matrices. Following Hu and Bentler (1999), a CFI close to or above.95 and a RMSEA less than.06 indicates an acceptable fit. For statistical software, we used the open source software R (R Development Core Team, 2011) for data management and LISREL 8.70 (Jöreskog and Sörbom, 1997) for estimating and testing the model.

17 Results Meta-Analysis Table 3 presents the results of the meta-analysis. The correlation between gender and EI in the overall sample was The percentage of variance attributable to a sampling error was only 4 percent, which was less than the 75 percent needed to assume homogeneity (Hunter and Schmidt, 1990). Hence, it was reasonable to expect systematic differences between studies that affect the relationship between gender and EI Table 3 about here As shown in Table 3, region and respondents moderated the relationship between gender and EI. First, the relationship significantly differed between Europe and the U.S., with European studies showing a negative relationship ( = -.139), and American studies showing a positive relationship ( =.110). According to these results, American women show a higher EI to start a business than men, while European women show a lower EI. Second, the gender- EI relationship differed significantly for student and non-student samples, with female students showing a stronger EI than men ( =.088), and female non-students showing a weaker EI than men ( = -.086). Although the results showed that region and respondents significantly moderated the gender-ei relationship, in both cases, the remaining percentage of variance attributable to sampling variance in the subgroups was less than the 75 percent needed for assuming homogeneity. Thus, further unknown moderators are likely to influence the relationship between gender and EI. Table 4 presents the results of the study-specific moderators. None of the moderators significantly influence the gender-ei relationship Table 4 about here

18 Meta-Analytical Structural Equation Modeling The first step of the meta-analytical structural equation model was to test the homogeneity of the 32 correlation matrices within a multi-group-model. Analogous to the bivariate metaanalysis, the misfit of this model ( 2 (129) = , p <.01, RMSEA =.10, CFI =.72) indicates a significant and substantial heterogeneity of the correlation matrices. Hence, the coefficients in the theoretical model should be regarded as average effect sizes across a variety of potential moderators. As mentioned above, the first step also resulted in both an estimated population correlation matrix (see Table 5) and an asymptotic correlation matrix, which served as an empirical input for the theoretical model tested in the second step (see Figure 2) Table 5 about here Figure 2 about here The fit of the theoretical model was acceptable ( 2 (1) = 28.61, p <.01, RMSEA =.023, CFI =.99). Gender had a significant, albeit small, regression effect on all three motivational constructs ( = -.06, p <.001 on attitude, = -.09, p <.001 on subjective norm, and = -.08, p <.001 on PBC), thus confirming Hypotheses 1, 2, and 3. The negative sign of these effects indicates that women have a lower attitude, subjective norm, and PBC compared to men. As theoretically proposed by the theory of planned behavior, all three motivational constructs had a significant relationship with EI ( =.30 for attitude, =.14 for subjective norm, and =.18 for PBC). The explained variance in EI was 22 percent.

19 Moderator Analysis Whereas the moderator analysis in the bivariate meta-analysis allowed for determination of whether the overall relationship between gender and EI differed across the categories of the respective moderator, the meta-analytical structural equation model allowed for an examination of the three motivational processes that were moderated, thus providing an explanation for the moderator effect on the overall relationship. To this end, we repeated the two steps by testing the homogeneity and estimating the structural model separately for the categories of the region and respondents moderators. Region. The region moderator reflected whether studies were conducted in Europe or the U.S. The number of studies conducted in other countries was too small to form additional categories for the moderator. The result was a group of 12 European studies and a group of 8 U.S. studies. Homogeneity tests for these groups again resulted in significant heterogeneity ( 2 (67) = , RMSEA =.17, CFI =.45 for the European studies, and 2 (18) = , RMSEA =.092, CFI =.79 for the U.S. studies). To test the theoretical models, we estimated a multi-group structural equation model, which allowed for specifying the theoretically proposed structure, estimating group specific regression effects, and testing for significant differences in the regression effects across both groups. The fit of this model was unacceptable ( 2 (2) = , RMSEA=.089, CFI=.92). Given that the model was nearly saturated, the only source of a misspecification was a missed direct effect of gender on EI, which was fixed to zero in the baseline model. Estimating this effect, however, would have led to a consumption of the two degrees of freedom, which the model possesses, and, thus, to an inability to test the model. The solution was to specify an equality constraint on two nearly identical estimates: the effects of gender on attitude and PBC. Constraining both effects allowed for an estimation of the direct effect of gender and the testing of the model. The

20 resulting fit was excellent ( 2 (2) =.98, RMSEA = 0.0, CFI = 1.0). Table 6 shows the regression effects in this model Table 6 about here The test for significant differences across the groups was conducted within the framework of invariance testing of parameters in structural equation modeling (Byrne et al., 1989; Steenkamp and Baumgartner, 1998; Steinmetz et al., 2009). That is, we specified an overall constraint that all regression coefficients must be estimated as equal across the groups and tested if this constraint significantly worsens the model s fit. The significant decrease in fit ( 2 (6) = , p <.001) showed that some regression effects differed significantly. Following Byrne et al. (1989), we relaxed the equality constraints that had caused the decrease in fit for those regression effects that differed significantly. The result showed that all regression effects except for the effect of subjective norm on EI and of gender on subjective norm differed significantly. The U.S. samples had a relationship twice as high as the European samples between attitude and EI ( =.35, p <.01, vs. =.18, p <.01). Hence, H4 is supported. PBC, in contrast, had a significant effect only in the European samples ( =.16, p <.01). Gender was a significant predictor of subjective norm only in the U.S. samples, with women perceiving a higher expectation to start a business compared to men. The remaining effects of gender were small but significant (i.e., ranging from =.02 to.05). Finally, gender had a significant direct effect on EI but with opposing signs in both regions ( = -.13, p <.01, in Europe vs. =.09, p <.01, in the U.S.). When controlling for attitude, subjective norm, and PBC, this effect implies a lower average EI for women in Europe but a higher average EI in the U.S. The fit of the final model was excellent ( 2 (2) =.11, RMSEA = 0.0, CFI = 1.0).

21 Respondents. As a further moderator, we investigated the use of student vs. non-student samples. The group containing student samples included 23 studies (N = 21,557), and the group containing non-student samples included 9 studies (N = 30,810). Again, the homogeneity tests resulted in significant and substantial heterogeneity within these two groups ( 2 (88) = , RMSEA =.14, CFI =.75, for the student samples, and 2 (31) = , RMSEA =.056, CFI =.80 for the non-student samples). The multi-group structural equation model showed an adequate fit ( 2 (2) = 33.34, p <.001, RMSEA =.024, CFI =.99). The specification of equality constraints on the regression parameter resulted in a significant decrease of the chi-square statistic ( 2 (6) = , p <.001). Post-hoc analyses (cf. Byrne, et al., 1989) showed that all relationships, except for the effect of gender on subjective norm, were significantly different across the groups (see Table 7) Table 7 about here Most notably, students showed a lower effect of attitude on EI than did non-students ( =.21, p <.001 vs. =.32, p <.001, respectively) but a considerably higher effect of PBC on EI ( =.37, p <.001 vs. =.15, p <.001, respectively). Regarding gender differences, the attitudes were slightly but significantly lower for female students compared to male students ( = -.07, p <.001). However, the attitudes were higher for female non-students compared to male non-students ( =.10, p <.001). The subjective norm was significantly lower for female students compared to male students ( = -.12, p <.001), whereas no significant difference existed for female and male non-students. PBC was significantly lower for females compared to men; however, the gender differences were larger for student samples than for non-student samples ( = -.13, p <.001, vs. = -.06, p <.001). Hence, H5 is partially supported.

22 Discussion Using meta-analysis, our study aimed to summarize the research on the relationship between gender and EI, and to analyze the motivational processes linking gender to EI. The bivariate meta-analysis revealed an overall negative relationship between gender and EI, indicating a higher average EI for men compared to women. Beyond the gender-ei relationship, the meta-analytical structural equation model (cf. Figure 2) revealed significant negative relationships between gender and attitude, subjective norm and PBC. These negative relationships imply that women express lower attitudes, subjective norms, and perceptions of control toward starting a business. Furthermore, moderator analyses showed that the relationship significantly differed between Europe and the U.S., as well as students and nonstudents. The overall weak relationship between gender and EI indicate that the substantial differences in the number of male and female entrepreneurs cannot solely be explained by differences in motivation. Consequently, we suggest that differences arise while one is in the process of becoming an entrepreneur. We posit that women less frequently turn intention into implementation. One explanation for this discrepancy is that their perceived control may overestimate the amount of their actual control. That is, women may be motivated by perceived facilitators and minor hindrances, but may actually be confronted with a higher quantity of hindrances than men. In this case, a high EI may not result in starting a business due to unforeseen problems in implementing the intention. Therefore, we suggest that future research should focus more closely on the intention-behavior gap rather than on preintentional processes (Heckhausen & Gollwitzer, 1987; Johnson, Chang, & Lord, 2006). As theory and practice are important, investigating external barriers and internal factors (e.g., personality traits, such as uncertainty avoidance and achievement motivation) allows us to explain this differential implementation.

23 Entrepreneurial workshops should not attempt to increase motivation (as women are already equally motivated) but to increase knowledge regarding the starting process, gender specific facilitators and hindrances, improvement of self-regulatory skills to overcome hindrances and setbacks, and ways to increase persistence. This type of training has been successfully conducted with unemployed (e.g., Frese et al., 2002) and mixed samples of small business owners (Glaub, 2009) intending to start a business. One goal of the study was to use the theory of planned behavior as a theoretical framework to help explain motivational processes. The results showed that gender affects EI mediated by the theory s central concepts (i.e., attitude, subjective norm, and PBC). Contrary to the theory s proposition, we also found a direct effect of gender on EI. The scarce but still existing direct effect, however, indicates that other mediators must exist (e.g. support that is lacking in society). Hence, further research must take potential mediators into consideration. However, as the direct effect is only small ( =.05), the relevance of these mediators should not be overestimated. Testing the model separately for European vs. American studies, and for student samples vs. non-student samples, showed significant differences in all regression effects except for the effect of subjective norm on EI. These differences concerned the relationship between gender and the three motivational constructs (i.e., attitude, subjective norm, and PBC) as well as effects on EI. The most notable difference was that the effect of gender on EI was negative in Europe but positive in the U.S. This result implies a lower EI for European women and a higher EI for American women. Furthermore, the effect of attitude on EI was twice as high in the U.S. compared to Europe. As we had assumed, due to higher uncertainty avoidance in European countries, women in Europe have a lower EI than do women in the U.S. One possible reason is the differently perceived gender stereotypes in Europe and the U.S. Eagly (1987) distinguished between the domestic role (i.e., housework and childcare) and the role of breadwinner (i.e., work outside the home with high degree of decision making) to describe

24 traditional family roles, whereas the domestic role is associated with lower status than the role of breadwinner. We suggest that European countries have stronger traditional gender stereotypes where women are supposed to engage in the domestic role. In contrast, U.S. women also tend to engage in the role of breadwinner. For instance, the female employment rate in the U.S. is notably higher than in European countries (OECD, 2012). Moreover, the U.S. shows higher values of gender equality than European countries (except Scandinavian and Benelux countries) meaning that men and women are treated more equally in the U.S. (House et al., 2004). Klyver et al. (2012) found that gender equality of a country is associated with gender differences in starting a business; thus, we suggest that American women show higher EI. The different gender stereotypes and different cultural values might explain the higher number of female entrepreneurs in the U.S. Thus, further research should investigate cross-cultural gender differences in stereotypical behavior and the influence of entrepreneurial activity. Regarding the comparison between students and non-students, female students had a stronger EI than male students, whereas female non-students had a weaker EI compared to male non-students. In a sample of students, Franco et al. (2010) found that most of the students had not yet decided whether to start their own business or to enter into employment. According to our findings, we suggest that men are behind women in the planning process; therefore, female students show higher EI than male students at the time of interviewing. It is also possible that the proportion gradually changes after entering into employment. Hence, the number of men who want to start a business is rising, and male non-students show higher EI than female non-students. Regarding the effects on motivational constructs, female students had a lower attitude than male students. Furthermore, attitude had a higher effect on EI in the non-student sample compared to the student sample, whereas PBC had a substantially higher effect on the student sample. The lower effect of attitude matches the assumption that attitude in the student group reflects wishful thinking, with less relevance for developing an EI.

25 Contrary to our expectations, gender was not significantly related to subjective norm. The only exceptions were a slightly lower subjective norm for female students (vs. male students) and a slightly higher subjective norm for American women (vs. American men). Baughn et al. (2006) found that a country s gender equality is a predictor of normative support for female entrepreneurship; hence, the higher gender equality in the U.S. may support the slightly higher subjective norm for American women. The non-significant relationship between gender and subjective norm in Europe is incongruent with the expectation of stronger support for men within European societies, caused by conservative attitudes toward gender roles. In fact, European women do not feel less supported than men. Hence, these results imply that gender differences in creating a business are not due to differences in perceived social support. Conclusions Our study sought to analyse the research on the relationship between gender and EI. The results show a weak relationship between gender and EI which indicates that the higher number of male entrepreneurs cannot solely be explained by differences in motivation. Based on our findings, our study suggests various avenues for further research. For example, based on our findings, the differences identified between men and women seem to be a consequence of differences in turning intentions into implementation. Thus, we call upon researchers to investigate gender differences in various forms of hindrances as a potential explanation for different implementations and, thus, for differences in actual levels of female and male entrepreneurship. In addition, qualitative studies could be useful to identify precisely when and why in the implementation process women abandon their entrepreneurial plans.

26 The direct effect of gender on EI indicates the existence of other mediators of the gender- EI relationship. Thus, a second area for future research could involve investigating further motivational processes beyond those suggested by the theory of planned behavior. Finally, our findings in relation to entrepreneurial differences between Europe and the U.S. call for more research on different gender stereotypes and cultural values. Thus, a third valuable area for future research could focus on analyzing male and female occupational roles in crosscultural studies to help explain differences in entrepreneurial activity in the respective countries. Limitations Our study has some limitations that must be considered when assessing the implications of the meta-analysis. We only included studies that reported the necessary sample sizes and outcome statistics to conduct the meta-analysis. In line with Tihanyi, Griffith, and Russell (2005), we recommend that authors include basic statistics, such as means, standard deviations, and correlations, in published articles or submit missing statistics upon request. The small number of empirical research studies limited the opportunities to search and test for possible moderators. In addition, this analysis presumes that most of the bivariate correlations occur in the surveys to enable the aggregation of complete matrices, which was, unfortunately, not the case, as no further group aggregations led to matrices. Moreover, the problem of heterogeneity continues to exist. Although reduction of heterogeneity is possible with the moderator analysis, it was not possible to build entirely homogenous subgroups. This fact leads to the conclusion that the results for the model only allow for an interpretation in terms of average values. Although it is a limitation, it does not diminish the importance of this paper.

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