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Individual entrepreneurial orientation and performance: the mediating role of international entrepreneurship

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Abstract

This paper analyses the role that individual entrepreneurial orientation (IEO) plays in the success of international entrepreneurship moves. We focus on the mediation effect of international entrepreneurship in the relationship between IEO and firm performance. We argue that entrepreneurial experience constitutes an important source of IEO and propose an objective measure of IEO. The hypotheses are empirically analysed using a 22-year panel of family SMEs. Our results confirm the hypotheses and provide a better understanding of the role of IEO in the success of corporate strategies such as internationalisation. Specifically, IEO is found to improve firm performance indirectly by increasing the speed of internationalisation, and this effect is non-linear. Our study contributes to the literature by extending international entrepreneurship literature by offering a more complete view of the causes and consequences of IEO. Finally, our results also contribute to the literature on family firm heterogeneity.

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Notes

  1. Habbershon and Williams (1999: 11) define familiness as ‘the unique bundle of resources a particular firm has because of the systems interaction between the family, its individual members, and the business’.

  2. Gómez-Mejía et al. (2007: 106) define SEW as the ‘nonfinancial aspects of the firm that meet the family’s

    affective needs’.

  3. We have estimated a specific production function for each sector (Levinsohn and Petrin 2003).

  4. Defined as a Herfindahl index: \( 1-{\sum}_{G=1}^{G=3}{P}_G^2 \) (Goerzen and Beamish 2003), divided by the number of years since foundation. PG is the percentage of exports to geographical area G. The database identifies three geographical areas: the European Union, the rest of the Organisation for Economic Co-operation and Development (OECD) countries, and the rest of the world.

  5. Following Pangarkar (2008), we first arrange the different regions by increasing physical distance: the European Union, the rest of the OECD countries and the rest of the world. Second, we compute the measure of distance as follows: Dit = (1 × PEuropean Union) + (2 × PRest of OCDE) + (1 × PRest of World). Third, we divide the distance by the number of years since foundation (SD).

  6. The factors loading of four dimensions have values equal to or greater than 0.7, the Kaiser-Meyer-Olkin (KMO) index estimates are high and the Balett’s sphericity test is significant, i.e. the four international dimensions are included properly in one unique factor (Table 1).

  7. A difference-in-differences (DID) estimation makes such a comparison possible, reduces the problem of self-selection of exporting firms and eliminates the unobservable differences between the treatment and control firms. This allows us to control for endogeneity and reverse causality (Bertrand et al. 2004; De Loecker 2007; Greenaway and Kneller 2008; Yang and Mallick 2010). Specifically, this method allows us to estimate the potential effect of a treatment on an outcome during the first ten years of export activity.

  8. The firm-level dummy variables allow us to control for systematic unobserved heterogeneity between firms (Armstrong and Shimizu 2007; Henderson and Cockburn 1994). If the sectoral mobility of firms is reduced or zero, the firm-level dummy variable includes the sectoral effect.

  9. The firm-level dummy variables allow us to control for systematic unobserved heterogeneity between firms (Armstrong and Shimizu 2007; Henderson and Cockburn 1994). If the sectoral mobility of firms is reduced or zero, the firm-level dummy variable includes the sectoral effect.

  10. All explanatory variables have variance inflation factors (VIFs) below 10 for the regression models (Kutner et al. 2004), and the condition number obtained is substantially below 30 (Pesaran 2015).

  11. In all models, the estimation of the modified Wald test reveals a potential problem of heteroscedasticity. Therefore, in all models we initially apply the ordinary least squares (OLS) method by incorporating the Huber Sandwich Estimator for controlling for the potential problem of heteroscedasticity. Furthermore, the Wooldridge test highlighted a problem of serial correlation. To solve this potential problem, we opt for a cluster-robust variance estimator (CRVE). To this end, we initially used the industry code to create the cluster, but because the number of industries considered in our study is low (19 industries), it fails to reach asymptotic behaviour. Accordingly, we use a wild cluster bootstrap and Rademacher weights to estimate the significance levels of the coefficients linked to the different explanatory variables included in the models (Cameron and Trivedi 2009). These estimates allow us to solve this potential problem of serial correlation.

  12. To solve the potential multicollinearity problem, we have excluded the variable LowSpeedit.

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  1. ESIC Business and Marketing School. Rey Juan Carlos University, Madrid, Spain

    Francisco Javier Forcadell

  2. Autonomous University, Madrid, Spain

    Fernando Úbeda

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Forcadell, F.J., Úbeda, F. Individual entrepreneurial orientation and performance: the mediating role of international entrepreneurship. Int Entrep Manag J 18, 875–900 (2022). https://doi.org/10.1007/s11365-020-00693-8

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