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Determinants of R&D cooperation in small and medium-sized enterprises

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Abstract

We investigate the determinants of research and development (R&D) cooperation in small and medium-sized enterprises (SMEs). Using firm-level data from the 2002 Korean Innovation Survey and applying a probit model with sample selection, we find that incoming spillovers of knowledge have a significant and positive impact on SMEs’ decisions to engage in R&D cooperation. In particular, the effect of knowledge spillovers on R&D cooperation is much larger for smaller firms. Despite the importance of external knowledge for SMEs, the estimation results suggest that SMEs may be at a disadvantage in establishing external R&D linkages because of their absolute size limitations.

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Notes

  1. See Acs and Audretsch (1990) for an overview of factors affecting innovation activities in SMEs. Using the 2002 KIS and the Community Innovation Survey 2 (CIS2), Mun and Chun (2006) and Vaona and Pianta (2008) examine the relationship between firm size and innovation activities for Korean and European manufacturing firms, respectively.

  2. For a recent survey on the role of SMEs in technological change, see Ortega-Argilés et al. (2009).

  3. In addition to R&D cooperation as interfirm or interorganization contacts, Simonen and McCann (2008) examine face-to-face knowledge spillovers from labor acquisition.

  4. Human resource and knowledge management can also be crucial factors determining firms’ absorptive capacity (Schmidt 2010).

  5. The OECD Frascati Manual is a standard for surveys of R&D expenditures (input), whereas the Oslo Manual is a guideline for collecting data on innovation activities (output).

  6. Under the Framework Act on SMEs in Korea, a company with fewer than 300 employees in the manufacturing sector is classified as an SME.

  7. We exclude R&D cooperation within a Korean business group (so-called chaebol). Moreover, we use a business group dummy variable to estimate the determinants of R&D cooperation to control for possibly different behavior of firms affiliated with the business group.

  8. In this paper, we define a firm as innovative if the firm is engaged in technological innovations. Thus, a firm is noninnovative if the firm conducts only nontechnological innovations. However, a research department can play a role in nontechnological innovations as well as technological innovations. Table 1 shows that 14.6% of noninnovative firms have research departments. These noninnovative firms with research departments might conduct only nontechnological innovations, which is also confirmed in the sample.

  9. To obtain the four-digit-level three-firm concentration ratio, we aggregate the five-digit-level ratio published by the Korea Development Institute using the weights of the industry sales.

  10. Korean standard industry classification (KSIC) codes include 21 two-digit-level manufacturing industries.

  11. The bivariate probit model corrects for a possible sample selection bias and also provides more accurate estimates through the inclusion of noninnovative firms. In fact, the total sample size (2,190) is about twice as large as the number of innovative firms (1,043).

  12. However, in a study of cooperative R&D behavior in Italian manufacturing firms by Piga and Vivarelli (2003), estimation results of the bivariate model with sample selection show that the correlation coefficient is positive but is not statistically significant.

  13. The negative effect found in the study by López (2008) suggests that stronger legal protection may hamper internalization of knowledge flows and therefore decrease the probability of R&D cooperation.

  14. Instead of the firm size dummy variable, we also tried to include the firm size variable (number of employees) together with the size-squared variable. The size variable is not significant at the 10% level in the simple probit model but is highly significant in the selection model. Firm size is reported to be significantly positive by Cassiman and Veugelers (2002) for Belgium and by Lee and Choe (2006) for Korea, but not by Abramovsky et al. (2005) for France, Germany, and the UK.

  15. We are grateful to two anonymous referees for the suggestion that two different sources of bias should be distinguished: sample selection and simultaneity.

  16. We use a two-step methodology in implementing IVs into the bivariate probit model with sample selection. The same two-step method is also used in Piga and Vivarelli (2004) in analyzing a firm’s decisions regarding internal and external R&D. A full-information maximum-likelihood estimator may be more desirable, but requires some fairly strong assumptions (Wooldridge 2002).

  17. Definitions of basicness of R&D and length of product lives are described in the Appendix.

  18. Since the model includes the three possible endogenous variables, we perform the joint test of the three residuals. Moreover, none of each coefficient of the first-stage residuals is statistically significant at the 10% level.

  19. Consistently, Chun et al. (2007) find that incoming spillovers constitute a significant factor in determining cooperative R&D for firms in high-technology industries, but not in low-technology industries.

  20. We are grateful to an anonymous referee for suggesting a possible multicollinearity problem.

  21. In addition, Oh (2006) emphasizes trust among partners as an important factor determining the performance of R&D collaboration and shows that the free-riding problem is significantly reduced as the amount of firms’ investment in the R&D project increases.

  22. Maximum values of VIF tests for Table 5 are less than 10, which indicates no multicollinearity problem.

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Acknowledgments

We are most grateful to the associate editor Marco Vivarelli and two anonymous referees for valuable comments. We would also like to thank Jungsoo Park and participants in the Comparative Analysis of Enterprise Data (CAED) Conference and the Western Economic Association Conference for their helpful comments, and the Science and Technology Policy Institute (STEPI) for providing the 2002 Korean Innovation Survey dataset. Chun gratefully acknowledges the financial support from the Sogang University Research Fund.

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Authors and Affiliations

  1. Department of Economics, Sogang University, Seoul, 121-742, Korea

    Hyunbae Chun

  2. Korea Information Society Development Institute, Kwachun, Kyunggi-do, 427-710, Korea

    Sung-Bae Mun

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  1. Hyunbae Chun
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  2. Sung-Bae Mun
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Correspondence to Hyunbae Chun.

Appendix

Appendix

See Table 6.

Table 6 Description of variables
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Chun, H., Mun, SB. Determinants of R&D cooperation in small and medium-sized enterprises. Small Bus Econ 39, 419–436 (2012). https://doi.org/10.1007/s11187-010-9312-5

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