Artigo completo - Open Access.

Idioma principal | Segundo idioma

The role of academic relations of former graduate students in university-firm collaboration: evidence from Brazil

The role of academic relations of former graduate students in university-firm collaboration: evidence from Brazil

Colombo, Daniel Gama e ; Garcia, Renato de Castro ;

Artigo completo:

Este artigo analisa a contribuição dos laços pessoais de ex-estudantes de mestrado e doutorado para a colaboração universidade-empresa. Com base no arcabouço de proximidade desenvolvido por Boschma (2005) e nas premissas do conceito de proximidade social (confiança, compromisso, linguagem comum e cultura comum), propõe-se que as relações acadêmicas que esses ex-alunos desenvolveram durante a pós-graduação podem reduzir a distância social entre universidades e empresas, favorecendo a pesquisa colaborativa. À luz desse argumento, são apresentadas duas hipóteses para explicar como a contratação de um ex-aluno de pós-graduação está associada à decisão de colaborar de uma organização privada. Essas hipóteses são testadas a partir de uma nova estratégia empírica, utilizando uma nova e abrangente base de dados sobre as parcerias entre universidades e empresas no Brasil, e modelando a decisão da empresa em duas etapas, quais sejam, a escolha do parceiro e a decisão de colaborar. Os resultados indicam que, se um grupo de pesquisa pertencer a uma universidade na qual um ou mais empregados de uma organização privada tenham frequentado a pós-graduação, há maior verossimilhança de que essa organização escolha esse grupo de pesquisa como parceiro (razão de chances cerca de 2,5 vezes maior) e decida colaborar (razão de chances mais de 4 vezes maior). Além disso, a magnitude encontrada dessa associação varia de acordo com a ‘grande área de conhecimento’ em questão, indicando que a área de conhecimento pode constituir um moderador da proximidade social. Esses resultados são as principais contribuições do artigo para a compreensão da colaboração universidade-empresa, e sugerem novas abordagens para políticas públicas para apoiar essas parcerias, que utilizem as relações acadêmicas como alavancas para novos projetos colaborativos.

Artigo completo:

This paper investigates the contribution of the personal ties of former Master and Ph.D. students to university-firm collaboration. Using the proximity framework developed by Boschma (2005) and the underlying assumptions of social proximity (trust, commitment, common language and common culture), we argue that the academic relations these former students developed during graduate education can reduce the social distance between universities and firms, thus favoring collaborative research. Based on this argument, we present two hypotheses to explain how hiring a former graduate student is associated with the collaboration decisions of private organizations. These hypotheses are tested with a new empirical strategy, using a novel and comprehensive dataset on university-industry linkages in Brazil, and modelling the private organization’s decision in two steps, i.e., the choice of a partner and the decision to collaborate. We find that, if a research group is hosted by a university in which one or more employees of a private organization attended graduate education, the employer organization is more likely to choose such group to partner (relative odds around 2.5 times higher) and to engage in collaboration (odds ratio more than 4 times higher). We also find that the magnitude of this association varies substantially per broad field of education, supporting the proposition that scientific disciplines work as ‘moderators’ of the social dimension of proximity. These results are the main contributions of the paper to the understanding of university-firm collaboration, and they suggest new approaches for policy support to these partnerships, using academic relations as a lever to new collaborative projects.

Palavras-chave: colaboração universidade-empresa; logit condicional; pós-graduação; proximidade social.,

Palavras-chave: conditional logit; graduate education; social proximity; university-firm collaborations,

DOI: 10.5151/v-enei-610

Referências bibliográficas
  • [1] Antonelli, C. Collective knowledge communication and innovation: the evidence of technological districts. Regional studies, v. 34, n. 6, p. 535-547, 2000. DOI:https://doi.org/10.1080/00343400050085657.
  • [2] Attia, A. M. National innovation systems in developing countries: barriers to university–industry collaboration in Egypt. International Journal of Technology Management & Sustainable Development, v. 14, n. 2, p. 113-124, 2015. DOI:https://doi.org/10.1386/tmsd.14.113_1.
  • [3] Audretsch, D. B., & Feldman, M. P. R&D spillovers and the geography of innovation and production. The American economic review, v. 86, n. 3, p. 630-640, 1996. DOI:https://www.jstor.org/stable/2118216.
  • [4] Autant‐Bernard, C. et al. Social distance versus spatial distance in R&D cooperation: empirical evidence from European collaboration choices in micro and nanotechnologies. Papers in regional Science, v. 86, n. 3, p. 495-519, 2007. DOI:https://doi.org/10.1111/j.1435-5957.2007.00132.x.
  • [5] Baba, Y., Yarime, M., & Shichijo, N. Sources of success in advanced materials innovation: the role of" core researchers" in university–industry collaboration in Japan. International Journal of Innovation Management, v. 14, n. 02, p. 201-219, 2010. DOI:https://doi.org/10.1142/S1363919610002611.
  • [6] Balconi, M., & Laboranti, A. University–industry interactions in applied research: The case of microelectronics. Research Policy, v. 35, n. 10, p. 1616-1630, 200 DOI:https://doi.org/10.1016/j.respol.20009.018.
  • [7] Balland, P.-A. Proximity and the evolution of collaboration networks: evidence from research and development projects within the global navigation satellite system (GNSS) industry. Regional Studies, v. 46, n. 6, p. 741-756, 2012. DOI:https://doi.org/10.1080/00343404.2010.529121.
  • [8] Balland, P.-A., Boschma, R., & Frenken, K. Proximity and innovation: From statics to dynamics. Regional Studies, v. 49, n. 6, p. 907-920, 2015.
  • [9] Barnes, T., Pashby, I., & Gibbons, A. Effective university–industry interaction:: A multi-case evaluation of collaborative r&d projects. European Management Journal, v. 20, n. 3, p. 272-285, 2002. DOI:https://doi.org/10.1016/S0263-2373(02)00044-0.
  • [10] Boschma, R. Proximity and innovation: a critical assessment. Regional studies, v. 39, n. 1, p. 61-74, 2005. DOI:https://doi.org/1080/0034340052000320887.
  • [11] Boschma, R., & Frenken, K. The spatial evolution of innovation networks: a proximity perspective. In: R. Boschma (Ed.). The Handbook of Evolutionary Economic Geography. Cheltenham: Edward Elgar, 2010. cap. 5, p.120-135. ISBN 9781847204912.
  • [12] Broekel, T. The co-evolution of proximities–a network level study. Regional Studies, v. 49, n. 6, p. 921-935, 2015. DOI:https://doi.org/10.1080/00343404.2014.1001732.
  • [13] Broekel, T., & Hartog, M. Explaining the structure of inter-organizational networks using exponential random graph models. Industry and Innovation, v. 20, n. 3, p. 277-295, 20 DOI:https://doi.org/10.1080/13662716.20791126.
  • [14] Broström, A. Working with distant researchers—Distance and content in university–industry interaction. Research Policy, v. 39, n. 10, p. 1311-1320, 2010.
  • [15] CAPES. Discentes da Pós-Graduação Stricto Sensu do Brasil (base de dados confidencial) [Graduate Students in Brazil - confidential dataset]. C.-C. f. t. I. o. H. E. Personnel. Brasilia: CAPES - Coordination for the Improvement of Higher Education Personnel 2017.
  • [16] Cassi, L., & Plunket, A. Proximity, network formation and inventive performance: in search of the proximity paradox. The Annals of Regional Science, v. 53, n. 2, p. 395-422, 2014. DOI:https://doi.org/10.1007/s00168-014-0612-6.
  • [17] Cheng, S., & Long, J. S. Testing for IIA in the multinomial logit model. Sociological methods & research, v. 35, n. 4, p. 583-600, 2007. DOI:https://doi.org/10.1177/0049124106292361.
  • [18] CNPQ. Censo do Diretório dos Grupos de Pesquisa 2016 [Research Group Census Database]. C.-B. C. o. o. S. a. T. Development: CNPQ - Brazilian Council of of Scientific and Technological Development, Brasilia 2016.
  • [19] Cohen, W. M., & Levinthal, D. A. Absorptive capacity: A new perspective on learning and innovation. Administrative science quarterly, v. 35, n. 1, p. 128-152, 1990. DOI:https://doi.org/10.2307/2393553.
  • [20] Cosh, A., Fu, X., & Hughes, A. Management characteristics, collaboration and innovative efficiency: evidence from UK survey data. Centre for Business Research, University of Cambridge Cambridge, UK, 2005.
  • [21] Cunningham, P., & Gök, A. The Impact and Effectiveness of Policies to Support Collaboration for R&D and Innovation. Compendium of Evidence on the Effectiveness of Innovation Policy. Manchester: Nesta, Manchester Institute of Innovation Research 2012.
  • [22] D'Este, P., Guy, F., & Iammarino, S. Shaping the formation of university–industry research collaborations: what type of proximity does really matter? Journal of economic geography, v. 13, n. 4, p. 537-558, 2013. DOI:https://doi.org/10.1093/jeg/lbs010.
  • [23] De Fuentes, C., & Dutrénit, G. Best channels of academia–industry interaction for long-term benefit. Research Policy, v. 41, n. 9, p. 1666-1682, 2012. DOI:https://doi.org/10.1016/j.respol.2012.03.026.
  • [24] Drejer, I., & Østergaard, C. R. The role of geographical, cognitive and social proximity in university-industry collaboration on innovation. 9th Regional Innovation Policy Conference, 2014.
  • [25] ______. Exploring determinants of firms’ collaboration with specific universities: Employee-driven relations and geographical proximity. Regional Studies, v. 51, n. 8, p. 1192-1205, 2017. DOI:https://doi.org/10.1080/00343404.2017.1281389.
  • [26] Fontana, R., Geuna, A., & Matt, M. Factors affecting university–industry R&D projects: The importance of searching, screening and signalling. Research policy, v. 35, n. 2, p. 309-323, 2006.
  • [27] Freitas, I. M. B., Marques, R. A., & e Silva, E. M. d. P. University–industry collaboration and innovation in emergent and mature industries in new industrialized countries. Research Policy, v. 42, n. 2, p. 443-453, 2013. DOI:https://doi.org/10.1016/j.respol.2012.06.006.
  • [28] Freitas, I. M. B., Rossi, F., & Geuna, A. Collaboration objectives and the location of the university partner: Evidence from the Piedmont region in I taly. Papers in Regional Science, v. 93, p. S203-S226, 2014.
  • [29] Garcia, R. et al. Is cognitive proximity a driver of geographical distance of university–industry collaboration? Area Development and Policy, v. 3, n. 3, p. 349-367, 2018. DOI:https://doi.org/10.1080/23792949.2018.1484669.
  • [30] Garcia, R. et al. Looking at both sides: how specific characteristics of academic research groups and firms affect the geographical distance of university–industry linkages. Regional studies, regional science, v. 2, n. 1, p. 518-534, 2015. DOI:https://doi.org/10.1080/21681376.2015.1099464.
  • [31] Gawel, A. Business collaboration with universities as an example of corporate social responsibility-a review of case study collaboration methods. The Poznan University of Economics Review, v. 14, n. 1, p. 20, 2014.
  • [32] Gertler, M. S. Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there). Journal of economic geography, v. 3, n. 1, p. 75-99, 2003. DOI:https://doi.org/10.1093/jeg/3.1.75.
  • [33] Giuliani, E., & Bell, M. The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research policy, v. 34, n. 1, p. 47-68, 2005. DOI:https://doi.org/10.1016/j.respol.2004.10.008.
  • [34] Granovetter, M. Economic action and social structure: The problem of embeddedness. American journal of sociology, v. 91, n. 3, p. 481-510, 1985. DOI:https://doi.org/10.1086/228311.
  • [35] Greene, W. H. Econometric analysis. 7th. New Jersey: Pearson Education, 2011. ISBN 0273753568.
  • [36] Halse, C., & Mowbray, S. The impact of the doctorate. Studies in Higher Education, v. 36, n. 5, p. 513-525, 2011. DOI:https://doi.org/10.1080/03075079.2011.594590.
  • [37] Hong, W., & Su, Y.-S. The effect of institutional proximity in non-local university–industry collaborations: An analysis based on Chinese patent data. Research Policy, v. 42, n. 2, p. 454-464, 2013. DOI:https://doi.org/10.1016/j.respol.2012.05.012.
  • [38] Huber, F. On the role and interrelationship of spatial, social and cognitive proximity: personal knowledge relationships of R&D workers in the Cambridge information technology cluster. Regional Studies, v. 46, n. 9, p. 1169-1182, 2012. DOI:https://doi.org/10.1080/00343404.2011.569539.
  • [39] Hughes, R. A. et al. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. International journal of epidemiology, v. 48, n. 4, p. 1294-1304, 2019. DOI:https://doi.org/10.1093/ije/dyz032.
  • [40] Lam, A. Work roles and careers of R&D scientists in network organizations. Industrial Relations: A Journal of Economy and Society, v. 44, n. 2, p. 242-275, 2005. DOI:https://doi.org/10.1111/j.0019-8676.2005.00383.x.
  • [41] Lane, P. J., Koka, B. R., & Pathak, S. The reification of absorptive capacity: A critical review and rejuvenation of the construct. Academy of management review, v. 31, n. 4, p. 833-863, 2006.
  • [42] Laursen, K., Reichstein, T., & Salter, A. Exploring the effect of geographical proximity and university quality on university–industry collaboration in the United Kingdom. Regional studies, v. 45, n. 4, p. 507-523, 2011. DOI:https://doi.org/10.1080/00343400903401618.
  • [43] Laursen, K., & Salter, A. Searching high and low: what types of firms use universities as a source of innovation? Research policy, v. 33, n. 8, p. 1201-1215, 2004.
  • [44] Lindley, J., & Machin, S. The rising post-college wage premium in America and Britain. Economica, v. 83, n. 330, p. 281-306, 2016. DOI:https://doi.org/10.1111/ecca.12184.
  • [45] Long, B. T. How have college decisions changed over time? An application of the conditional logistic choice model. Journal of econometrics, v. 121, n. 1, p. 271-296, 2004. DOI:https://doi.org/10.1016/j.jeconom.2003.10.004.
  • [46] Maietta, O. W. Determinants of university–firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, v. 44, n. 7, p. 1341-1359, 2015. DOI:https://doi.org/10.1016/j.respol.2015.03.006.
  • [47] Mazzucato, M., & Penna, C. The Brazilian innovation system: a mission-oriented policy proposal. Brasilia: CGEE, 2016.
  • [48] Mertens, A., & Röbken, H. Does a doctoral degree pay off? An empirical analysis of rates of return of German doctorate holders. Higher education, v. 66, n. 2, p. 217-231, 2013. DOI:https://doi.org/10.1007/s10734-012-9600-x.
  • [49] Ministry of Economics. RAIS - Annual Social Information Report [Relação Anual de Informações Sociais - confidential dataset]. S. S. o. S. S. a. L. M. d. E. Ministry of Economics, Secretaria Especial da Previdência e Trabalho]. Brasilia: Ministry of Economics 2015.
  • [50] Nerad, M., & Evans, B. Globalization and Its Impacts on the Quality of PhD Education: Forces and Forms in Doctoral Education Worldwide. Rotterdam: Sense Publishers, 2014. ISBN 9789462095694.
  • [51] Nooteboom, B. Learning by interaction: absorptive capacity, cognitive distance and governance. Journal of management and governance, v. 4, n. 1-2, p. 69-92, 2000.
  • [52] ______. Trust: Forms, foundations, functions, failures and figures. Cheltenham: Edward Elgar Publishing, 2002. ISBN 1781950881.
  • [53] Nooteboom, B. et al. Optimal cognitive distance and absorptive capacity. Research policy, v. 36, n. 7, p. 1016-1034, 2007. DOI:https://doi.org/10.1016/j.respol.2007.04.003.
  • [54] Østergaard, C. R. Knowledge flows through social networks in a cluster: Comparing university and industry links. Structural Change and Economic Dynamics, v. 20, n. 3, p. 196-210, 2009. DOI:https://doi.org/10.1016/j.strueco.2008.10.003.
  • [55] Petruzzelli, A. M. The impact of technological relatedness, prior ties, and geographical distance on university–industry collaborations: A joint-patent analysis. Technovation, v. 31, n. 7, p. 309-319, 2011. DOI:https://doi.org/10.1016/j.technovation.2011.01.008.
  • [56] Ponds, R., Van Oort, F., & Frenken, K. The geographical and institutional proximity of research collaboration. Papers in regional science, v. 86, n. 3, p. 423-443, 2007. DOI:https://doi.org/10.1111/j.1435-5957.2007.00126.x.
  • [57] Ponomariov, B. Student centrality in university–industry interactions. Industry and Higher Education, v. 23, n. 1, p. 50-62, 2009. DOI:https://doi.org/10.5367/000000009787641369.
  • [58] Raddon, A., & Sung, J. The career choices and impact of PhD graduates in the UK: A synthesis review. Report prepared for the Economic and Social Research Council (ESRC) “Science in Society” Team and the Research Councils UK (RCUK) Research Careers and Diversity Unit. Science in Society Programme: ESRC, Research Councils UK, University of Leicester 2009.
  • [59] Roach, M., & Sauermann, H. A taste for science? PhD scientists’ academic orientation and self-selection into research careers in industry. Research Policy, v. 39, n. 3, p. 422-434, 2010. DOI:https://doi.org/10.1016/j.respol.2010.01.004.
  • [60] Rybnicek, R., & Königsgruber, R. What makes industry–university collaboration succeed? A systematic review of the literature. Journal of business economics, v. 89, n. 2, p. 221-250, 2019. DOI:https://doi.org/10.1007/s11573-018-0916-6.
  • [61] Sauermann, H., & Stephan, P. E. Twins or strangers? Differences and similarities between industrial and academic science. NBER Working Paper Series, n. w16113, 2010. DOI:10.3386/w16113.
  • [62] Seaman, S. R., & White, I. R. Review of inverse probability weighting for dealing with missing data. Statistical methods in medical research, v. 22, n. 3, p. 278-295, 2013. DOI:https://doi.org/10.1177/0962280210395740.
  • [63] Shefer, D., & Frenkel, A. R&D, firm size and innovation: an empirical analysis. Technovation, v. 25, n. 1, p. 25-32, 2005. DOI:https://doi.org/10.1016/S0166-4972(03)00152-4.
  • [64] Skinner, B. T. Choosing College in the 2000s: an updated analysis using the conditional logistic choice model. Research on Higher Education, v. 60, n. 2, p. 153-183, 2019. DOI:https://doi.org/10.1007/s11162-018-9507-1.
  • [65] Sorenson, O., Rivkin, J. W., & Fleming, L. Complexity, networks and knowledge flow. Research policy, v. 35, n. 7, p. 994-1017, 2006. DOI:https://doi.org/10.1016/j.respol.2006.05.002.
  • [66] Suzigan, W. et al. University and industry linkages in Brazil: some preliminary and descriptive results. 2009.
  • [67] Teirlinck, P., & Spithoven, A. Research collaboration and R&D outsourcing: Different R&D personnel requirements in SMEs. Technovation, v. 33, n. 4-5, p. 142-153, 2013. DOI:https://doi.org/10.1016/j.technovation.2012.11.005.
  • [68] Ter Wal, A. L., & Boschma, R. A. Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science, v. 43, n. 3, p. 739-756, 2009. DOI:10.1007/s00168-008-0258-3.
  • [69] Train, K. E. Discrete choice methods with simulation. Cambridge: Cambridge university press, 2003. ISBN 1139480375.
  • [70] UNESCO-UIS. International Standard Classification of Education - Fields of education and training 2013 (ISCED-F 2013) – Detailed field descriptions. Montreal: UNESCO Institute for Statistics, 2015. ISBN 978-92-9189-179-5.
Como citar:

Colombo, Daniel Gama e; Garcia, Renato de Castro; "The role of academic relations of former graduate students in university-firm collaboration: evidence from Brazil", p. 1-20 . In: Anais do V Encontro Nacional de Economia Industrial e Inovação (ENEI): “Inovação, Sustentabilidade e Pandemia”. São Paulo: Blucher, 2021.
ISSN 2357-7592, DOI 10.5151/v-enei-610

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações