Multilevel heterogeneity of R&D cooperation and innovation determinants

Eurasian Business Review

Volumn 7, Issue 1, 2017, Pages 93-120

  Amoroso, Sara ^a  

^a JOINT RESEARCH CENTRE   (Italy)

Author keywords

Bayesian analysis; Innovation; R D collaboration; R D policy

Indexed keywords

EID: 85028078813     PISSN: 13094297     EISSN: None     Source Type: Journal    
DOI: 10.1007/s40821-015-0041-1     Document Type: Article

References (46)

1. Almus, M., & Czarnitzki, D. (2003). The effects of public R&D subsidies on firms’ innovation activities: The case of Eastern Germany. Journal of Business & Economic Statistics, 21(2), 226–236.
2. Arora, A., & Cohen, W. M. (2015). Public support for technical advance: the role of firm size. Industrial and Corporate Change, 24(4), 791–802.
3. Belderbos, R., Carree, M., Diederen, B., Lokshin, B., & Veugelers, R. (2004a). Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization, 22(8–9), 1237–1263.
4. Belderbos, R., Carree, M., & Lokshin, B. (2004b). Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492.
5. Belderbos, R., Carree, M., & Lokshin, B. (2006). Complementarity in R&D cooperation strategies. Review of Industrial Organization, 28(4), 401–426.
6. Busom, I., & Fernández-Ribas, A. (2008). The impact of firm participation in R&D programmes on R&D partnerships. Research Policy, 37(2), 240–257.
7. Cassiman, B., & Veugelers, R. (2002). R&D cooperation and spillovers: Some empirical evidence from Belgium. Open access publications from katholieke universiteit leuven, Katholieke Universiteit Leuven.
8. Cassiman, B., & Veugelers, R. (2006). In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Open access publications from katholieke universiteit leuven, Katholieke Universiteit Leuven: Open access publications from katholieke universiteit leuven.
9. Catozzella, A., & Vivarelli, M. (2014). The possible adverse impact of innovation subsidies: some evidence from Italy. International Entrepreneurship and Management Journal, 1–18.
10. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.
11. Coull, B., & Agresti, A. (2000). Random effects modeling of multiple binomial responses using the multivariate binomial logit-normal distribution. Biometrics, 56(1), 73–80.
12. Crespi, F., Ghisetti, C., & Quatraro, F. (2015). Environmental and innovation policies for the evolution of green technologies: A survey and a test. Eurasian Business Review, 5(2), 343–370.
13. d’Aspremont, C., & Jacquemin, A. (1988). Cooperative and Noncooperative R&D in Duopoly with Spillovers. American Economic Review, 78(5), 1133–1137.
14. Dewar, R. D., & Dutton, J. E. (1986). The adoption of radical and incremental innovations: An empirical analysis. Management science, 32(11), 1422–1433.
15. Dosi, G. (1999). Some notes on national systems of innovation and production, and their implications for economic analysis. In D. Archibugi, J. Howells, & J. Michie (Eds.), Innovation policy in a global economy. Cambridge University Press.
16. Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2003). Bayesian data analysis (2nd ed.). UK: Chapman and Hall.
17. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In J. Berger, J. Bernardo, A. Dawid, & A. Smith (Eds.), Bayesian statistics (pp. 169–194). Oxford: Oxford University Press.
18. Goldstein, H. (1995). Multilevel statistical models (2nd ed.). New York: Halstead Press.
19. Hadfield, J., & Kruuk, L. (2010). MCMC methods for multi-response generalised linear mixed models: The MCMCglmm R package. Journal of Statistical Software, 33(2), 1–22.
20. Hanley, A., Liu, W.-H., & Vaona, A. (2015). Credit depth, government intervention and innovation in China: Evidence from the provincial data. Eurasian Business Review, 5(1), 73–98.
21. Heckman, J. J., Lochner, L., & Taber, C. (1998). General-equilibrium treatment effects: A study of tuition policy. American Economic Review, 88(2), 381–86.
22. Hedeker, D., & Gibbons, R. D. (1996). MIXOR: A computer program for mixed-effects ordinal regression analysis. Computer Methods and Programs in Biomedicine, 49, 157–176.
23. Heidelberger, P., & Welch, P. D. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31(6), 1109–1144.
24. Henderson, R. (1993). Underinvestment and incompetence as responses to radical innovation: Evidence from the photolithographic alignment equipment industry. RAND Journal of Economics, 24(2), 248–270.
25. Hernán, R., Marín, P. L., & Siotis, G. (2003). An empirical evaluation of the determinants of Research Joint Venture Formation. Journal of Industrial Economics, 51(1), 75–89.
26. Kaiser, U. (2002). An empirical test of models explaining research expenditures and research cooperation: Evidence for the german service sector. International Journal of Industrial Organization, 20(6), 747–774.
27. Kamien, M. I., Muller, E., & Zang, I. (1992). Research joint ventures and R&D cartels. American Economic Review, 82(5), 1293–1306.
28. Katz, M. L. (1986). An analysis of cooperative research and development. RAND Journal of Economics, 14(4), 527–543.
29. Kim, J. (2014). Formal and informal governance in biotechnology alliances: Board oversight, contractual control, and repeated deals. Industrial and Corporate Change, 23(4), 903–929.
30. Kirat, T., & Lung, Y. (1999). Innovation and proximity. European Urban and Regional Studies, 6(1), 27–38.
31. Klette, T. J., Moen, J., & Griliches, Z. (2000). Do subsidies to commercial R&D reduce market failures? Microeconometric evaluation studies. Research Policy, 29(4–5), 471–495.
32. Korotayev, A. V., & Tsirel, S. V. (2010). A spectral analysis of world GDP dynamics: Kondratieff waves, Kuznets swings, Juglar and Kitchin cycles in global economic development, and the 2008–2009 economic crisis. Structure and Dynamics, 4(1), 3–57.
33. Kultti, K., Takalo, T., & Tanayama, T. (2015). R&D spillovers and information exchange: a case study. Eurasian Economic Review, 5, 63–76.
34. Leifer, R., Gina Colarelli, O., Rice, M. (2001). Implementing radical innovation in mature firms: The role of hubs. The Academy of Management Executive (1993–2005), 15(3):102–113.
35. Lopez, A. (2008). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms. International Journal of Industrial Organization, 26(1), 113–136.
36. Mohnen, P., & Röller, L.-H. (2005). Complementarities in innovation policy. European Economic Review, 49(6), 1431–1450.
37. OECD, E. (1997). Proposed guidelines for collecting and interpreting technological innovation data: Oslo manual.
38. Piga, C. A., & Vivarelli, M. (2004). Internal and external R&D: A sample selection approach. Oxford Bulletin of Economics and Statistics, 66(4), 457–482.
39. Reinganum, J. (1983). Uncertain innovation and the persistence of monopoly. The American Economic Review, 73(4), 741–748.
40. Rodríguez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. J. Royal Statistical Society, 158(1), 73–90.
41. Schmitz, H. (1999). Collective efficiency and increasing returns. Cambridge Journal of Economics, 23(4), 465–483.
42. Tether, B. (2002). Who co-operates for innovation, and why: An empirical analysis. Research Policy, 31(6), 947–967.
43. Train, K. (2009). Discrete choice methods with simulation (2nd Edn.). Online economics textbooks: Cambridge University Press.
44. Veugelers, R. (1997). Internal R&D expenditures and external technology sourcing. Research policy, 26(3), 303–315.
45. Wang, L., & Zajac, E. (2007). Alliance or acquisition? A dyadic perspective on interfirm resource combinations. Strategic Management Journal, 28(13), 1291–1317.
46. Zeger, S., & Karim, M. (1991). Generalized linear models with random effects; A Gibbs sampling approach. Journal of the American statistical association, 86(413), 79–86.