Impact of Attitude Towards Entrepreneurship Education and Role Models on Entrepreneurial Intention

In this paper, we investigate entrepreneurial intention by applying the Theory of Planned Behavior by Ajzen (1991). We specically examine the role of gender on entrepreneurial education and role models or parental self-employment, by carrying out a Multi-Group Analysis. We used a web-based questionnaire to collect information from 216 students at a Spanish university. Data are analysed with the help of Structural Equation Modelling (SEM) – Partial Least Square (PLS). We conducted a tripartite analysis on Complete, Male, and Female Models. Regarding the Complete and Male Models, all the primary hypotheses were accepted, compared with four for the Female Model. We recommend the institutionalization of traineeship, elective courses, conference and workshops on entrepreneurship to boost the entrepreneurial spirit of students. Though this study has conrmed the applicability of the TPB model to entrepreneurial intention, we did not nd a signicant relationship between Males and Females about their entrepreneurial intentions for some relationships. But this study suggests that the relationship between PSE and PBC is stronger for Males than Females Our results have implications for entrepreneurship education scholars, program evaluators, and policymakers.

From the foregoing, we advance some questions: What are the entrepreneurial intentions among university students? What is the relationship between PSE and ATE and PBC? What is the relationship between ATEE and ATE and PBC? To what extent do the relationships between Males and Females differ? Following Entrialgo and Iglesias (2017), we examine the indirect effect of PSE and ATEE on entrepreneurial intentions with the TPB and also analyse the role of gender in these relationships. Thus, the main objective of this study is to examine the role played by the attitude towards entrepreneurial education (ATEE) and Parental Self-Employment (PSE) in fostering entrepreneurial intention among students.
To test the validity of the model, samples were drawn from students from a university in Catalonia, Spain. According to Liñán, Urbano, and Guerrero (2011) Catalonia has a reputation for having a hard-working population, entrepreneurial spirit, and a dynamic economy.
To our best knowledge, this is a novel approach and may encourage future research in this area. A contribution of this paper is the provision of a better understanding of the role of Entrepreneurship Education and PSE and their impact on entrepreneurial intention. Moreover, the outcomes of this study could be bene cial to policymakers to understand not only the pattern of relationships among intention antecedents but also its implications for interventions and developing entrepreneurial intention. Our paper extends the studies of Trivedi (2016) by introducing Role Model or Parental Self-employment as an additional antecedent of the TPB and gender as a moderating variable.
The remainder of the paper is structured as follows. In the second part, the literature on entrepreneurial intention in line with TPB along with the university environment and support (which we operationalize as Attitude towards Entrepreneurship Education-ATEE) is outlined. The next section provides the methodology. Finally, the results of the study and their practical implications have been provided along with direction for future research and conclusion. Bird (1988, p. 442) de ned intention as 'a state of mind directing a person's attention toward a speci c object (goal) or path in order to achieve something (means)'. Entrepreneurial intention is considered to be the most critical aspect for the future formation of entrepreneurial ventures (Nguyen, Do, Vu, Dang, & Nguyen, 2019). According to Bae et al. (2014) entrepreneurial intentions are the willingness to own or venture into a business. The concept of intention and its antecedents have received immerse attention in entrepreneurship research for its importance in predicting entrepreneurial behavior.

Entrepreneurial Intention and the Theory of Planned Behaviour
The TPB (Ajzen, 1991(Ajzen, , 2002 is perhaps one of the most popular models that has caught the attention of researchers in these contemporary times. Thus among the many models (e.g. Shapero &Sokol, 1982 andBird, 1988) used to explain entrepreneurial intentions, none have had as much impact as Ajzen's theory of planned behavior (Ajzen, 1991;Krueger et al., 2000;Liñán & Chen, 2009). As of April 2020, the theory of planned behavior (Ajzen, 2012) has been subject to empirical analysis in more than 4,200 papers referenced in the Web of Science bibliographical database, making it one of the popular theories in the social and behavioral sciences (Bosnjak, Ajzen & Schmidt, 2020). They further revealed that the TPB has gained enormous attention in disciplines like health sciences, environmental science, business and management, and educational research. The model explains how the cultural and social environment affects human behavior. In this study, the TPB is used as a basic framework to understand students' entrepreneurial intentions. The TPB model has often been used to study the intention to start a venture in a couple of research setting (Krueger, 1993;Trivedi, 2016) and it has proven that Ajzen's TPB was an appropriate research framework for assessing intentions in the choice of employment (Kolvereid, 1996;Iakovleva & Kolvereid, 2009). According to the TPB, human behavior is guided by three kinds of re ections, beliefs about the likely consequences of the behavior (behavioural beliefs), beliefs about the normative expectations of others (normative beliefs), and beliefs about the presence of factors that may ease or impede performance of the behavior (control beliefs) (Bosnjak, Ajzen & Schmidt, 2020). Ajzen (1991) conceptualized attitude as the extent to which an individual has a positive or negative evaluation of the behavior in question. The attitude towards the behavior (entrepreneurship) is an important component concerning the perception of desirability that affects entrepreneurial intention. According to Santos, Roomi, and Liñán, 2016) and , attitude towards entrepreneurship has a positive impact on entrepreneurial intentions.

Subjective Norm (SN)
According to Ajzen (1991), the opinion of important reference groups such as parents, spouses, friends, and relatives may also in uence the behavior of a person to perform or not perform certain actions. Social norms refer to the perceived social pressure from family, friends, or signi cant others to perform an entrepreneurial behavior (Ajzen, 1991). Social norms tend to contribute more weakly to intention (Kolvereid & Isaksen, 2006) for individuals with a strong internal inner locus of control (Ajzen, 2002) compared to those with a strong action orientation (Bagozzi, 1992). Some studies did not establish any signi cant direct correlation between subjective norms and entrepreneurial intention (Krueger et al., 2000;Liñán & Chen, 2009;Santos et al., 2016). Most studies have established that subjective norms favorably affect ATE and the PBC ( Entrialgo & Iglesias, 2016;Liñán & Chen, 2009;Liñán & Santos, 2007;Trivedi, 2017). Some empirical studies (Scherer, Adams, Carley & Wiebe, 1989;Mathews & Moser, 1995); Trivedi, 2016; have asserted that social norms in uence attitude and perceived behavioural control and thus indirectly entrepreneurial intention.

Perceived behavioral control (PBC)
The third and most important determinant identi ed by Ajzen (1991) is the perceived behavioural control. PBC examines the perceived feasibility of performing behaviour and its closely related to the perception of self-e cacy (Krueger et al., 2000). PBC is the perceived easiness or di culty of becoming an entrepreneur (Ajzen, 1991). Although some researchers have considered PBC as similar to self-e cacy, Ajzen (2002) speci es that it is a wider construct, since it encompasses and perceived controllability of the behavior. According to Santos et al. (2016) and , PBC has a positive impact on entrepreneurial intentions. Generally, the more favorable the attitude and subjective norm, and the greater the perceived control, the stronger should be the individual's intention to perform the behavior in question (Bosnjak, Ajzen & Schmidt, 2020). The gure A below is a diagram illustration of the theory of planned behavior by Ajzen (2019).

Entrepreneurship Education and Support
Entrepreneurship education refers to education for entrepreneurial attitudes and skills (Bae et al., 2014). It consists of 'any pedagogical program or process of education for entrepreneurial attitudes and skills ( Research shows that participation in entrepreneurship courses increase the possibilities of a career in entrepreneurship (Valliere, 2016;Iakovleva & Kolvereid, 2009;Kolvereid & Moen, 1997). According to Upton, Sexton, and Moore (1995), 40% of those who pursued entrepreneurship courses started their own businesses. Liñán (2008) posits that entrepreneurship education can nurture a student's attitudes and intentions, as well as the establishment of a new rm. Previous studies suggest that certain university support policies and practices can promote entrepreneurial activities among students, for example, technology transfer o ces and faculty consultants (Mian, 1996); university incubators and physical resources (Mian, 1997); and university venture funds (Lerner, 2005) According to Urbano and Guerrero (2013), it is expedient to expand the scope of the university from the conventional or old-fashioned mode of knowledge to an entrepreneurial ecosystem leading to the concept of an entrepreneurial university.

Role Models/Parental Self-employment
Entrepreneurial family background refers to those people whose parent(s) or family member(s) is (are) involved in self-employment (Bae et al., 2014). According to Stephens (n.d.) parents play a major role in how their children turn out. Parents are powerful role models for children and they can in uence their children's entrepreneurial intentions. Zellweger, Sieger, and Halter (2011) argued that entrepreneurship education is less probable to promote entrepreneurial intentions of students who come from an entrepreneurial family background. According to Bae et al. (2014), entrepreneurship education may be less effective on entrepreneurial intentions for students from an entrepreneurial family compared to students without an entrepreneurial family background. In fact, they failed to support the hypothesis that, the positive link between entrepreneurship education and entrepreneurial intentions will be weaker in people from an entrepreneurial family background than for those who do not come from one.

The Role of Gender
Most studies claim that gender plays a major role in measuring entrepreneurial and self-employment career choice intentions ( From the foregoing, the following hypotheses (see Table 1) are proposed.

Methodology
The empirical analysis of this survey was carried out among university students in a Spanish university in the Catalonia region. Thus, the study is developed in a single country, a single institution, and a single culture. We used a structured on-line questionnaire. Convenience sampling technique was used because it is a popular tool in entrepreneurship research (Kolvereid, 1996

Measurement Variables
The questionnaire was divided into the following sections: demographic, independent (ATE, SN, and PBC), dependents variables (entrepreneurial intention), and Attitude towards Entrepreneurship Education and Parental Self-employment. The study adopted the Entrepreneurial Intention Questionnaire (EIQ) proposed by Liñán Trivedi (2016) was also used in this study. Eighteen items make up the ATEE Scale and are classi ed into two categories; General Education Support (check items 38-44 on Appendix) and Targeted Cognitive and Non-cognitive Support (check items 27-37 on Appendix). ATE, SN, PBC, and ATEE constructs were measured through re ective indicators. The other constructs were measured by nominal scales due to their qualitative nature: Parental Self-employed (PSE) and gender. For PSE, we asked the respondents if their mothers or fathers were entrepreneurs. It was a binary YES/NO variable. Regarding Role Models, we asked the students if, at least, one of their parents was an entrepreneur. It was a binary Yes/No variable.

Data Analysis
Structural equation modeling (SEM) was used to test the proposed model which hypothesizes a relationship between entrepreneurial intention, ATE, SN, PBC, and ATEE. Hypotheses H12 to H15 were tested using Multi-Group Analysis (MGA).

Pro le of Respondents
The number of respondents was 216, out of which 110 (50.9%) were males and 106 (49.1%) were females. Regarding Parental Selfemployment, 110 (50.9%) of the respondents' parents were business owners whereas 106 (49.1%) whereas 110 (50.9%) reported on the contrary. About 97.4% of the respondents were undergraduate students, 88.2% of whom were not in employment. The majority of the students fall within 20-24 ages (71.8%) category.

PLS-SEM Results
In this section, we present the results of the PLS-SEM analysis. According to Hair et al. (2010), a two-dimensional method can be applied for structural equation modelling (SEM); rst, a measurement model analysis and second, a structural model analysis. This two-step process guarantees scale validity and reliability.

Measurement Model Assessment
According to Roldán and Sanchez-Franco (2012), the rst stage of the measurement model assessment consists of observing the indicator loading values of the model (in our case, the three models: Complete, Male, and the Female). Table 2 depicts the parameters. It can be seen that Composite reliability, Cronbach's alpha, and Average Variance Extracted (AVE) exceed 0.7, 0.7, and 0.5, respectively, hence meeting the recommended values in literature (Fornell & Larcker, 1981). Though reliability analysis may be conducted using item loadings of above 0.707, Sánchez-Franco & Roldán, (2005) opined that for newly developed measures, a lower threshold of 0.6 may be accepted. Generally, the measurement model of this study was investigated following four criteria's, i.e. (a) Item reliability, (b) Internal consistency, (c) Convergent validity, and (d) Discriminant validity. As shown in Table 2, almost all the values support the convergent validity of the composite scales for the Male and Female models, but fully for the Complete model. Prior to this, the analysis of the measurement model for the full sample found low loadings (check Appendix) for some items and were removed, and the PLS algorithm was run again. Scores regarding item reliability, construct reliability and convergent, and discriminant validity is satisfactory (see Tables 2 and 4). The coe cient of determination is 0.712 for the EI endogenous latent variable for the Complete model. This implies that the three latent variables (ATE and PBC) explain 71.2% of the variance in EI as shown in Table 3. The coe cient of determination for Males and Females is also shown in Table 3. According to Höck and Ringle (2006) results above the cutoffs 0.67, 0.33, and 0.19 are 'substantial', 'moderate', and 'weak' respectively. Thus the results for the three models are 'substantial'. These ndings are consistent with the study by (Trivedi, 2016) who found 69% of the variance in the explanation of entrepreneurial intention.   Table 6. For the Male model, ve of the hypotheses are accepted and four are rejected (see Table 8). Whereas, four of the hypotheses associated with the Females are accepted and ve rejected as depicted in Table 7. Figure 5 shows the variance explained (R Square) in the dependent constructs and the path coe cients (b) for the complete model. Consistent with Chin (1998), bootstrapping (5000 re-samples) was used to generate standard errors and T-statistics. Bootstrap represents a non-parametric R 2 approach for estimating the accuracy of PLS estimation. This helps in the assessment of the statistical signi cance of the path coe cients. The Complete model, Male model, and Female model explain 71.2%, 72.3%, and 68.3% respectively of the variance in entrepreneurial intention based on SN, ATE, and PBC. These results are encouraging since most previous research typically explains less than 40%.

Collinearity Assessment
Collinearity is a potential issue in the structural model and that variance in ation factor (VIF) value of 5 or above typically indicates such a problem (Hair et al., 2011). The collinearity assessment results for the Combined Model are summarized in Tables 5. It can be observed that all VIF values are lower than 5, signifying that there is no indicative collinearity between each set of predictor variables.    'failure to establish data equivalence is a potential source of measurement error (i.e., discrepancies of what is intended to be measured and what is actually measured), which accentuates the precision of estimators, reduces the power of statistical tests of hypothesis, and provides misleading results'.
The MICOM procedure provides the method for studying the invariance before the multi-group analysis. After con rming the existence of invariance, the next is to apply the MGA, comparing the explained variance of each group. MICOM involves a three-step process: a) Con gural invariance, b) Compositional invariance and c) Scalar invariance (equality of composite means and variances).
According to Garson (2016), running MICOM in SmartPLS normally automatically establishes con gural invariance. Thus, since statistical output does not apply to the rst step, we did not show it. However, steps 2 and 3 are discussed below. It must be noted that in running the MICOM, outer loadings that were insigni cant were deleted. This accounts for the difference in the Algorithm gure for the MGA.

Compositional invariance
Compositional invariance is a test of the invariance of indicator weights for measurement (outer) paths between groups (Garson, 2016). According to Henseler, Ringle and Sarstedt (2016), if the results of MICOM's Steps 1 and 2 (but not step 3) show that there is lack of measurement invariance, partial measurement has been established. This result allows for the comparison of the standardized path coe cients across the groups by performing a multi-group analysis. If the analysis and tests on different required levels do not support full measurement invariance, applied research typically focusses on the least partial ful llment of measurement invariance (Hair et al., 2010). A result of non-signi cance means that compositional invariance may be assumed. This implies the correlations are not signi cantly lower than 1.0, as depicted in Table 9. Compositional invariance has been ful lled because the Original Correlation is equal or greater than 5% quantile.  We start by rst running the PLS Algorithm to determine whether the results for the group's speci c model estimation differ. Using the 'Use Relative Values', stronger path relationships have thicker lines and smaller path coe cients have thinner lines. As shown in the diagram below, we can apply this representation to compare the results for Males and Females. From the gure, we can see that the group speci c PLS coe cients differ (e.g., ATE-EI, SN-ATE, PBC-EI). Since there are differences in the group speci c PLS path model estimations, we need to nd out if these differences are signi cant by running the PLS-MGA. , we can see differences in the regression weights or beta coe cients. However, to ascertain whether the differences are signi cant we have to apply the bootstrap t-test in the output section on the con dence intervals. From Table 12, it can be seen that the path from ATE -EI, SN -ATE, and SN -PBC con dence intervals overlap. This implies that at the 0.05 signi cance level, there is no difference in path coe cients between Male and Female samples. Thus, the paths in the structural model (ATE-EI, SN-ATE, and SN-PBC) are signi cant for both Males and Females, as depicted in the p-Values columns. However, for the MGA, we focus on Hypotheses H12, H13, H14, and H15. From Table 11, it can be noted that there is signi cant relationship between PSE and PBC but no signi cant relationship between the other variables; hence hypotheses H13 is accepted but H12, H14 and H15 are rejected. These results are con rmed by the output from the Parametric Test in Table 12, the Welch-Satterthwait Test in Table 13, the Bootstrapping  Results on Table 14, and the Con dence intervals in Table 15.
According to H12, the relationship between PSE and ATE is stronger for men than women. However, there are no signi cant relationships between both groups, hence this hypothesis is rejected. According to H13, the relationship between PSE and PBC is stronger for men than women, hence this hypothesis is accepted. According to H14, 'The relationship between ATEE and ATE is stronger for Males than for Females'. From Table 14, it can be seen that the relationship is not signi cant for both groups, hence we reject this hypothesis. Regarding H15, the relationship between ATEE and PBC is stronger for Males than Females. However, results reveal that the relationship between the Male and Female groups was insigni cant. Hence we reject this hypothesis.

F Square
The f-square equation expresses how large a proportion of unexplained variance is accounted for by change (Hair et al., 2014). The effect size is assessed with a tool known as F Square indicated in Table 16

Mediation Analysis
According to Aguinis et al. (2017), mediation refers to the presence of an intermediate variable or mechanism that transmits the effect of an antecedent variable to an outcome. The framework (Fig. 1) for this study called for multiple mediation analysis. As shown in Table 17, there are three Total Indirect Effects. However, the Speci c Indirect Effects were six as depicted in Table 18. Tables

Discussion
The main claim of the TPB is that intention is in uenced by three variables, i.e. ATE, SNs, and PBC. This exposition of the Ajzen model lays the foundation for the hypotheses which tested the validity of the model in the present paper. Speci cally, we investigated the moderating effect of gender on ATEE and Role Models by applying the theory of planned behavior (1991). Though empirical studies in entrepreneurship have produced contradictory results, we proceeded to apply the TPB to examine students' entrepreneurial intention because it is probably one of the most tried and tested theories in entrepreneurial research. We explored the extent to which Parental Self-employment and entrepreneurship education impact entrepreneurial intentions. We formulated two categories of hypotheses; primary and secondary and conducted a tripartite analysis for Complete, Male and Female models.
This study underscored ATE as one of the important determinants of our framework, exhibiting a strong and highly signi cant relationship between ATE and entrepreneurial intention. This con rms the ndings of Krueger  With regards to the relationship between PSE/Role Models, the results points out that having a parent who is an entrepreneur positively in uence a student's PBC (for the Complete and Female models), most probably increasing one's knowledge, mastery, or general set of ability with regard to engaging in tasks required for becoming an entrepreneur (BarNir, Watson & Hutchins. 2011). Interestingly, there was an insigni cant relationship between PSE/Role Models and PBC for the male respondents.
According to this study the relationship between PSE and PBC is stronger for Males than Females, hence H13 is accepted. According to Wilson, Marlino and Kickul (2004) women tend to shy away from entrepreneurial activity more frequently than men due to a lower perception of perceived self-e cacy in carry out entrepreneurial tasks. Verheul, Uhlaner and Thurik (2003) buttress this by emphasizing that females less frequently perceive themselves as entrepreneurs.
However, this study fails to certain support certain aspects of previous studies on how exposure to entrepreneurial education and role models impact on Males and Females. Thus hypotheses H12, H14 and H15 were not supported hence there was no signi cant relationship between Males and Females. The in uence of ATEE on PBC was not signi cant. These ndings are consistent with those of Entrialgo and Iglesias (2017). We established non-signi cant impacts on gender and parental self-employment. These results are in line with a paper by Bae et al. (2014).
This study has con rmed the applicability of the TPB model to entrepreneurial intention and the moderating role of gender. However, we did not nd a signi cant relationship between Males and Females concerning their entrepreneurial intentions for H12, H14 and H15. Therefore gender had no signi cance on the path coe cients. That means the gender of a student doesn't affect the link between attitude towards entrepreneurship education and EI. The nding further revealed that gender has no in uence on the relationship between attitude and intention, which was supported by Nowinski et al. (2019) and (Jena, 2020). These results are inconsistent with those of Santos et al. (2016) who found that Males display higher entrepreneurial intentions than Females.

Implications And Direction For Future Research
This study has some interesting implications. First, ATE came out as the most important variable of the model and this implies that entrepreneurial attitudes may be in uenced by the relevant stakeholders in academic circles. Though we did not establish a positive correlation between PSE and ATE, in uential role models can support nascent entrepreneurs. We recommend the institutionalization of traineeship, elective courses, conference and workshops on entrepreneurship to boost the entrepreneurial spirit of students. Also, policy-makers can motivate students by providing some scal incentives to allow individual and business angel investments in the seed stage of their entrepreneurial activities (European Commission, 2020).
Our paper extends the studies of Trivedi (2016) by introducing Role Model or Parental Self-employment as an additional antecedent and gender as a moderating variable. This study also proximately mirrors the study by Entrialgo and Iglesias (2017), though our study used a Likert scale to measure entrepreneurial education instead of a dichotomous variable.
Though we found no signi cant relationship for ATEE on EI, we suggest that educators and the relevant stakeholders focus on how to stimulate entrepreneurial intentions through education.
Notwithstanding the importance of entrepreneurship education in the development of entrepreneurial intentions, this paper revealed that ATEE has no signi cant impact on ATE and PBC. This will probably call for early engagement of the students to expose them to entrepreneurial education (Entrialgo & Iglesias, 2017).
The ndings contribute to research on parental self-employment (PSE). The results indicate that role model or parental self-employment impact on PBC for the Complete and the Female models. However, there was an insigni cant relationship between parental self-employed and PBC for the Male model.

Limitations
In considering the generalizability of this paper, it is important to highlight some limitations. First, the respondents were sampled from a single university in Spain. It will be exciting to replicate the study with a multi-country sample to identify the dynamics of ATEE and Role Models in those countries.
Also, the majority of the students were from the Faculty of Law and Business Administration, leading to skewness of the sample characteristics.