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How do different motives for R&D cooperation affect firm performance? – An analysis based on Swiss micro data

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Abstract

The starting point of our analysis is the empirical fact that firms pursue different goals when getting engaged in R&D collaborations, often more than one goal at the same time. Given that firms are driven by different motives for R&D cooperation, the aim of this article is to investigate the differences related to different motives with respect to the impact of R&D cooperation on firm innovativeness and firm productivity. Not only R&D cooperation in general but also cooperation driven by each of the seven motives considered in this paper correlate positively with the sales share of innovative products. With respect to innovativeness, the characterization of cooperation by the driving motive did not add much more than could be gained through the overall variable ‘R&D cooperation yes/no’. Technology-motivated collaborative activities show a weaker tendency to positive direct effects on productivity than cost-motivated cooperation. In this case, the distinction of several cooperation motives yields some additional insights as compared to the overall cooperation variable. On the whole, distinguishing various cooperation motives appears to be fruitful because it allows more differentiated insights that would remain hidden behind the overall variable “R&D cooperation yes/no”.

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Notes

  1. See Link and Spiegel (2003; Ch. 11) for a more detailed discussion of other types of much less frequently used IO models that do not come to clear-cut results with respect to the impact of cooperation on firm performance.

  2. Some studies used explicitly motive variables as right-hand variables in cooperation equations in addition to the factors postulated by theory. (See, e.g., Sakakibara 1997; Bayona et al. 2001; Lopez 2008; Arvanitis and Bolli 2012; Woerter 2011). We could find only one study that investigated motives of innovation R&D cooperation in a setting using motive variables as left-hand variables in a cooperation equation (Schmidt 2007).

  3. Versions of the questionnaire in German, French and Italian are available at www.kof.ethz.ch.

  4. Since we did not correct for a possible sample selection bias for firms that did not conduct R&D, the results can be interpreted as applicable only to firms investing in R&D.

  5. Due to strong multicollinearity, it was not possible to have all seven variables for cooperation motives in the same innovation equation (see Table 8 in the Appendix).

  6. As a referee suggested, the construction of binary variables may entail the possibility of information loss or ambiguity of results, depending on the choice of threshold for the construction of the variable, for example 3 or even 2 instead of 4. We estimated the productivity models (a) adding the variable R&D_COOP in order to control for cooperation activities in general and (b) using the five-level ordinal variables instead of the binary variables. In case (a), the variable R&D_COOP was statistically insignificant and the results for the motive variables were quite similar to those in Table 5. In case (b), the ordinal variables showed qualitatively the same effects as in Table 5. We conclude that the use of binary variables did not cause any discernible distortions.

  7. Belderbos et al. (2004b) recommended controlling for external knowledge sources and R&D expenditures in the productivity equation. We refrained here from taking the external source variables into consideration because of strong multicollinearity between some of these variables and the motive variables.

  8. See Belderbos et al. (2004a), Capron and Cincera (2004) and Schmidt (2007) for a similar approach. See also the discussion on this issue in Mohnen and Hoareau (2003) and Schmidt (2007).

  9. We refrain here from estimating first-difference equations for innovation and productivity as well as using lags for right-hand variables because our panel is strongly unbalanced. For the same reason, we do not investigate persistence of cooperation as in Belderbos et al. (2004b).

  10. All motive variables are binary variables, i.e. they are identically scaled, so that, for the discussion of relative magnitude, it does not make a difference if we consider coefficients or marginal effects.

  11. Unfortunately, our data do not provide us with some additional information in this direction.

  12. However, one has to take into account that the variables for the motives correlate strongly with each other reflecting the fact that firms pursue more than one motive at the same time.

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Acknowledgements

The helpful comments and suggestions of three reviewers and the editor-in-chief of this journal are gratefully acknowledged. The study was financially supported by the Swiss National Science Foundation (SNF).

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  1. ETH Zurich, Swiss Economic Institute, Zurich, Switzerland

    Spyros Arvanitis

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Appendix

Appendix

Table 6, 7, 8 and 9.

Table 6 Composition of sample by industry; firm size class; year
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Table 7 Descriptive statistics
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Table 8 Correlations
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Table 9 Results of endogeneity tests (Rivers and Vuong 1988)
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Arvanitis, S. How do different motives for R&D cooperation affect firm performance? – An analysis based on Swiss micro data. J Evol Econ 22, 981–1007 (2012). https://doi.org/10.1007/s00191-012-0273-5

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