The impact of entrepreneurship education on university students’ entrepreneurial skills: a family embeddedness perspective

To provide individuals with entrepreneurial skills and prepare them to engage in entrepreneurial activities, universities offer entrepreneurship education (EE) courses. However, the growing number of studies on EE impact offers mixed and apparently contradictory results. The present study contributes to this literature by indicating the type of EE (elective vs. compulsory) and the characteristics of students’ exposure to an enterprising family as two complementary boundary conditions that contribute to explain the outcomes of EE. To do so, the paper takes advantage of quasi-experimental research on a sample of 427 university students who participated to two consecutive waves of the Global University Entrepreneurial Spirit Students’ Survey (GUESSS). The study finds that both types of EE contribute to students’ entrepreneurial skills; however, the impact of EE in compulsory courses is contingent on students’ perceptions of parents’ performance as entrepreneurs.

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Notes

A full description of the GUESSS project is available at the website www.guesssurvey.org. Several works based on the GUESSS project have already been published in entrepreneurship journals: see, for example, Bergmann et al. (2016); Minola, Donina, and Meoli (2016); and Sieger and Minola (2017).

The inclusion of the lagged values of the dependent variables raises concerns related to the potential autocorrelation in the error terms. The Cumby and Huizinga (1992) statistic is applied to test for autocorrelation of order 1 in the residuals under the null hypothesis of no autocorrelation. The statistic rejects the null hypothesis of entrepreneurial skills being serially uncorrelated (χ 2 = 169.062, p < 0.001) and shows that serial correlation between the dependent variable and its lagged value is correctly specified at degree 1.

VIF values above 5 were found only for the dummy variables bachelor and master because together they cover 96% of the sample and are thus highly correlated (− 0.90). By dropping the variable bachelor and keeping undergraduate students as reference category, VIFs remained below 2 with results substantially unchanged.

In support to this idea, the model was also run with robust standard errors, not clustered by university. In this model, the coefficient of EE was positive and statistically significant (β = 0.203, p < 0.10). Moreover, we run a model without university fixed effects, and it was found that the coefficients of EE in elective (β = 0.262, p < 0.05) and compulsory (β = 0.224, p < 0.10) courses were both positive and statistically significant. To gain further insights into the different effects produced by EE in elective and in compulsory EE, we also separately focused on compulsory EE vs. no EE and on elective EE vs. no EE: in the first case, entrepreneurial skills were regressed on EE in compulsory courses, leaving students with no EE as baseline and excluding from the sample students who received EE in elective courses; in the latter case, entrepreneurial skills were regressed on EE in elective courses, leaving students with no EE as baseline and excluding from the sample students who received EE in compulsory courses. In both cases, the university fixed effects were included. In these specifications, the coefficient of EE in elective (β = 0.358, p < 0.05) remains positive and significant, while the coefficient of compulsory EE remains positive albeit not significant.

Applying the Oster (2017) procedure (cf. Lyons and Zhang 2018), support is found to the need to control for potential endogeneity due to unobservables (correlated both to selection into EE and to outcomes of EE). Additionally, both the Wu-Hausman test and Durbin-Wu-Hausman statistics reject the null hypothesis that the EE indicator is not exogenous.

To measure variables at the local level that might affect students’ entrepreneurial learning, periods of multiple years have already been used in empirical models aimed at quantitatively assessing the impact of EE (cf. Hahn et al. 2017).

In the first stage, using a probit estimator, the EE indicators on the instrument are regressed to obtain predicted values that contain the variation in the EE indicators uncorrelated with the error terms. Then, in the second stage, the dependent variable, entrepreneurial skills, is regressed on predicted values defined in the first stage, applying a two-stage least squares estimator. Concerning the instrument relevance, first-stage estimates suggest that the instrument and the EE indicators are negatively correlated and that the degree of correlation rises as university fixed effects are introduced in the first-stage regression.

As described in Section 3.1, the complete longitudinal GUESSS 2013–2016 dataset records the answers from 1383 students from 21 countries, but the present analysis includes only questionnaires of those students who in the 2013 survey reported not having attended EE before. The sample also excludes students who attended EE in both elective and compulsory EE, students from countries whose number of complete responses was below 5, and respondents for whom it was not possible to build the variables of interest.

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Acknowledgments

We would like to thank the organizers and participants of the Workshop Knowledge Frontiers and Knowledge Boundaries in Europe (Bozen, October 2017) where we received insightful comments that helped us to refine this paper. We are grateful also for the suggestions offered at the 28th Riunione Scientifica Annuale Associazione italiana di Ingegneria Gestionale (Bari, October 2017), the Technology Transfer Society Annual Conference (Washington, November 2017), and at the International Research Conference on Science and Technology Entrepreneurship Education (Toulouse, April 2017) where earlier versions of the manuscript have been presented.

Funding

Support for this research was provided by the “Campus Entrepreneurship” project, financed by the University of Bergamo through the “Excellence Initiative” funding scheme, and by the Italian Ministry of Education and Research through the “Contamination Lab” funding scheme.

Author information

Authors and Affiliations

1. Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7b, 24044, Dalmine, BG, Italy Davide Hahn, Tommaso Minola & Lucio Cassia
2. Center for Young and Family Enterprise, University of Bergamo, Via Salvecchio 19, 24127, Bergamo, BG, Italy Davide Hahn, Tommaso Minola, Giulio Bosio & Lucio Cassia
3. Department of Management, Economics and Quantitative Methods, University of Bergamo, Via dei Caniana 2, 24127, Bergamo, BG, Italy Giulio Bosio

1. Davide Hahn