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How innovative are spin-offs at later stages of development? Comparing innovativeness of established research spin-offs and otherwise created firms

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Abstract

The literature argues that research spin-offs (RSOs)—enterprises originating from a university or research institute—appear to have higher innovative potential and capabilities than other start-ups, at least in the early stages of their development. Yet, little is known about the innovative performance of these companies at later development phases. Thus, the main goal of this study is to investigate whether there are any differences in research and development (R&D) and innovation behavior between established and/or mature RSOs and otherwise created firms and, if so, to what extent they are driven by networking and cooperation activities as suggested by some scholars. To this end, we employ probit regression analysis and a matching approach using survey data on more than 6,000 East German firms, among which are 179 RSOs. Our first findings suggest that established RSOs engage in R&D and innovation activities more frequently than companies whose genesis was of another type. Nevertheless, the results obtained when accounting for collaboration measures show that the precedence of RSOs in further development stages over otherwise created firms in terms of innovation outputs is related to their higher intensity of cooperation activity and close, face-to-face interactions with universities, and not to type of firm creation. Moreover, our findings reveal that cooperating in various fields may be of different importance for specific inputs and outputs of the innovation activity. Finally, based on our results, we draw some implications for both practicing managers and public policymakers.

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Notes

  1. Since the vast majority of regions are rather averagely developed areas and/or historically less dynamic regions and, thereby, we think that East Germany is comparable to those, we believe that the external validity of our study is higher than in the case of studies focusing on highly developed and unique high-tech environments. In fact, actors operating in more disadvantaged regions—both companies and policymakers—might find the results of this study more useful than those of highly developed regions. Indeed, best-practice sharing among regions with significantly different degrees of economic development appears to be quite limited due to the fact that an innovative milieu and specific locational conditions of highly developed regions are difficult, if not impossible, to reproduce.

  2. The costs of cooperation and alliance include the transaction costs (i.e., the costs involved in searching for a suitable partner, the costs of partner selection, negotiation costs, and appropriability hazards), as well as administrative overhead incurred in coordinating the collaboration (Colombo et al. 2006). Moreover, cooperation carries the risk of knowledge leaking out, which, in turn, raises the costs of intellectual property protection.

  3. The survey was carried out on behalf of the German Ministry of Education and Science.

  4. Thus, in further steps of the analysis, we use the whole sample in order to keep the number of observations (especially that of RSOs) as large as possible. However, the results remain comparable when employing the subsamples of companies older than 5 years and older than 8 years. For details, see Sect. 5.3, which provides the results of the sensitivity analysis.

  5. At this point, it is important to remember that, in general, regression can yield misleading findings, for several reasons. One of these involves possible model misspecification resulting from the need to impose a functional form on the outcome equation and thereby often oversimplifying these important model specification issues. Further, regression analysis ignores the common support problem, which occurs if there are fundamental mismatches between the compared groups (in our case, spin-offs and otherwise created firms), resulting in what is basically a comparison of incomparable units (see, e.g., Caliendo and Hujer 2006; Morgan and Harding 2006). Thus, we additionally employ a nonparametric matching approach that allows for implementing the common support restriction when comparing the R&D and innovation behavior of established spin-offs with that of companies created in other ways.

  6. ATTs for outcome variables are estimated as follows: First, we estimate the propensity score using a probit model. Here, we consider one of the two sets of firm characteristics—either that specified in the regression model of Eq. (1) or that including additionally cooperation variables [see Eq. (2)]. To improve the matching quality, we imposed the common support restriction; that is, we dropped those controls (otherwise created firms) that have a propensity score lower than the minimum and higher than the maximum propensity score of the treated units (spin-offs) and vice versa. Second, on the basis of the propensity score, we match treated and nontreated units using one-to-one nearest-neighbor matching. Third, we calculate the differences in the means of the outcome variables between spin-offs and matched control firms created in other ways (i.e., average treatment effect on the treated ATT). Finally, the reliability of the matching was checked by testing the balance in covariates of treated and matched control firms. Here, we use the t test on mean differences in covariates between the firm groups (Dehejia and Wahba 2002).

  7. Note that, to improve the matching quality, we include industry dummies at the four-digit level (and not that at the two-digit level as in probit models).

  8. Detailed results on the t test on the mean differences in the covariates after matching are available from the author.

  9. In addition, we employ a Wald χ 2 test to test whether the effects of the dummy variable indicating a RSO on R&D and innovation activities calculated when using the whole sample (Table 2) differ from those computed when using the subsamples of firms older than 5 and/or 8 years (cf. Table 6). The results show that there are no significant differences in the coefficients of the dummy for being a RSO between the models estimated on the basis of the whole sample and each of the subsamples at the 5 % significance level.

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Acknowledgments

I gratefully acknowledge useful comments and suggestions by the anonymous referees and the editors of this special issue. The usual disclaimer applies.

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  1. BSP Business School Berlin Potsdam, Calandrellistraße 1-9, 12247, Berlin, Germany

    Anna Lejpras

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Appendix

Appendix

See Tables 5, 6, 7, 8 and 9.

Table 5 Pairwise correlation coefficients
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Table 6 Results from probit model estimations for firms older than 5 and 8 years: average marginal effects and robust standard errors in parentheses of the propensity of conducting R&D and innovation activities in 2003/2004
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Table 7 Results from nearest-neighbor (nn1) matching for firms older than 5 and 8 years: R&D and innovation activities of spin-offs versus otherwise created firms after matching
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Table 8 Rosenbaum bounds for the effects of being a spin-off on R&D and innovation activities
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Table 9 Results from nearest-neighbor (nn5) matching, kernel matching, and Mahalanobis-distance matching
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Lejpras, A. How innovative are spin-offs at later stages of development? Comparing innovativeness of established research spin-offs and otherwise created firms. Small Bus Econ 43, 327–351 (2014). https://doi.org/10.1007/s11187-013-9534-4

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