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Academics’ start-up intentions and knowledge filters: an individual perspective of the knowledge spillover theory of entrepreneurship

Small Business Economics volume 43, pages 57–74 (2014)Cite this article

Abstract

Previous studies suggest that entrepreneurial activity tends to be greater in contexts where investment in new knowledge is relatively high (e.g., entrepreneurial universities). However, in this specific knowledge context, only a few academics recognize opportunities and act on them through entrepreneurial activities (e.g., spin-offs). A plausible explanation could be the existence of several filters that limit the total conversion of knowledge into economically useful knowledge. The vehicle to knowledge transfer is entrepreneurship. Therefore, the main actor is the academic entrepreneur, but no empirical study has highlighted the knowledge spillover theory of entrepreneurship at the individual level. The purpose of this paper is to explore the role of academics’ start-up intentions and knowledge filters on the knowledge transfer process within entrepreneurial university. Adopting the knowledge spillover theory of entrepreneurship and the planned behavior theory, a proposed model was tested with a sample of 207 academics enrolled in entrepreneurial universities in Spain using structural equation modeling. Our findings could provide insights for policy-makers to design policies that bring further benefits to society and educational organizations as well as significant contributions to the literature.

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Notes

  1. This study is focused on entrepreneurial universities that are predominantly supported by public funds (i.e., European, national, regional governments).

  2. The respondents were asked to rate each item on a 10-point Likert scale (1 = low, 10 = high). Marketing studies recommend that, in behavioral analysis, a 7- to 10-point Likert scale should be adopted in order to avoid bias that represents a greater problem in 5-point scales (Felício et al. 2013). Also, it is more numerical, as a 5-point scale is usually worded.

  3. Based on the universities’ directories and websites, we identified 3,438 e-mails of academics involved in their business and engineering departments. The population estimate based on those e-mails coincides with the official statistics of those universities in the previous academic period published by the Spanish Institute of Statistics (INE). Therefore, the sample was not stratified or distributed; the criterion was to send the questionnaire to all those e-mails. We did three reminders.

  4. UPC (Technical University of Catalonia), UPV (Technical University of Valencia), UB (University of Barcelona), UAB (Autonomous University of Barcelona), USE (University of Seville), UAM (Autonomous University of Madrid), UMH (Miguel Hernandez University), USC (University of Santiago de Compostela), UCA (University of Cadiz University). These universities were selected according to the criteria used to identify entrepreneurial universities (Clark 1998; Di Gregorio and Shane 2003; Shane 2004; Audretsch and Lehmann 2005; O’Shea et al. 2005, 2007; Rothaermel et al. 2007; Wright et al. 2007; Guerrero and Urbano 2012): (1) promoting an entrepreneurial culture by strategic actions that allow for adaptation to environmental changes; (2) making self-instituting efforts to change its general character by developing entrepreneurial initiatives; and (3) being located in regions characterized by higher levels of entrepreneurship measured by the number of new enterprises.

  5. The response rate was integrated as follows: UPC (15 %), UPV (10 %), UB (15 %), UAB (13 %), USE (13 %), UAM (16 %), UMH (6 %), USC (12 %) and UCA (8 %). Unfortunately, based on this lower response rate, the main limitation of our results is that our data are not statistically representative at the university level. However, the aim of this paper is to explore the role of academics’ start-up intentions in the knowledge spillover theory of entrepreneurship; therefore, the unit of analysis is the academic enrolled in the business and engineering departments of those universities (characterized by implementing several mechanisms or policies that promote entrepreneurship, innovation, and knowledge transfer). Thus, this sample is not generalizable at the university level but will help us to explore our research question. A possible bias will be observed in the positive effect, instead of the negative effect, of the motivational factors on the academics’ start-up intentions. Nevertheless, the distribution of the sample by the control groups (i.e., type of university, gender or percent of time involved in research activities) could help to shape or understand the results.

  6. Previous studies showed that obtaining data on academics’ start-ups is not an easy task (Audretsch and Lehmann 2005; Audretsch et al. 2008; Douglas 2012; Heblich and Slavtchev 2013). In our case, we have 207 observations that help us to develop a SEM for a small sample that at least needs 50 observations (Loehlin 1992; Shook et al. 2004).

  7. Kaiser–Meyer–Olkin measures the sampling adequacy that indicates the proportion of variance in the variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful. If the value is less than 0.50, the results of the factor analysis probably will not be very useful (Greene 2003).

  8. This measure assumes that items on a scale are positively correlated with each other because all are tapping into the same construct. Therefore, a high alpha (0.70 and higher) represents that all scale items are measuring the same construct (Greene 2003).

  9. A good measure is more than 0.50 and close to 1 (Greene 2003).

  10. In technological universities focused on applied research that tries to solve practical problems that have immediate commercial objectives versus broad-based universities oriented to basic research that is associated to theoretical works to acquire new knowledge.

  11. Using AMOS, the estimations of mediation were developed using the option of analysis properties (output: the direct, indirect and total effects; and bootstraps: perform bootstraps standard errors and bias-corrected confidential intervals).

  12. Shook et al. (2004) argue that a good fit is showed when: the root mean square error of approximation (RMSEA) is close or less than 0.05; the Comparative Fit Index (CFI) and Goodness of fit index (GFI) are at least 0.80 or higher; and the X 2 normalized is low as 2 indicates a reasonable fit.

  13. Total effect (c) = direct effect (c′) + indirect effect (ab). Regarding university policies, total effect is 1.69 (1.087 + 0.603). A related measure of mediation is the proportion of the effect that is mediated, or the indirect effect divided by the total effect (ab/c) (Sobel 1982).

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Acknowledgments

A previous version of this paper was presented at the 2012 Workshop on Academic Policy and the Knowledge Theory of Entrepreneurship that took place at the University of Augsburg on August 20-21 in Augsburg, Germany (Bavaria). Special thanks go to Erik Lehmann, David Audretsch, and Zoltan Acs for their invaluable suggestions and support. The authors are also grateful to the anonymous reviewers for their helpful and constructive comments. Maribel Guerrero recognizes the support of Mexico’s National Council of Science and Technology (CONACYT). David Urbano acknowledges financial support from Projects ECO2010-16760 (Spanish Ministry of Science and Innovation) and 2005SGR00858 (Catalan Government Department for Universities, Research and Information Society).

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  1. Orkestra-Basque Institute of Competitiveness and Deusto Business School, Mundaiz 50, 20012, Donostia-San Sebastián, Spain

    Maribel Guerrero

  2. Business Economics Department, Autonomous University of Barcelona, Bellaterra, Building B, 08193, Barcelona, Spain

    David Urbano

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  1. Maribel Guerrero
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Appendix

Appendix

See Tables 2, 3 and 4.

Table 2 Estimations of direct and indirect effects on academics’ start-up intentions by type of university (Model Ia)
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Table 3 Estimations of direct and indirect effects on academics’ start-up intentions by groups of control variables (Model Ib)
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Table 4 Estimations of direct and indirect effects on academics’ start-up intentions by each motivational factor (Model II)
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Guerrero, M., Urbano, D. Academics’ start-up intentions and knowledge filters: an individual perspective of the knowledge spillover theory of entrepreneurship. Small Bus Econ 43, 57–74 (2014). https://doi.org/10.1007/s11187-013-9526-4

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Keywords

  • Knowledge spillover theory of entrepreneurship
  • Entrepreneurial universities
  • Academic entrepreneurship
  • Start-up intentions
  • Knowledge filters

JEL classifications

  • M13
  • L26
  • I23
  • I28