Abstract
This paper investigates the individual and collective scientific contributions carried out by the Triple Helix (University, Industry, and Government) in the areas that are considered of significant impact on innovation, such as Science, Technology, Engineering, and Mathematics (STEM) in the leading economies of Latin American, a zone with limited innovation systems and has experienced many changes in its political and economic structure in recent years. Three cooperative game theory metrics (core, shapley value, and nucleolus) were used to model each player’s individual and collective strength to create and maintain synergy. Bibliometric information on STEM areas was collected from the innovation systems of Brazil, Mexico, Chile, and Argentina; all this information was gathered from the Web of Science for ten years (2010–2020). The findings highlight that while universities play a central role in all four countries, government and industry involvement varies, with notable individual government participation in Brazil, Argentina, and Mexico; this scenario reflects that research is often conducted in isolation, marked by agility rather than collaborative efforts, frequently impeded by the extensive time required for organization and navigating bureaucratic processes. In contrast, Chile’s approach to collaboration, integrating government, industry, and universities, stands out for its efficient synergy and communication; it leverages the universities’ deep expertise, ensuring a balanced and effective participation in research across all sectors. This analysis reveals the diverse dynamics and collaborative patterns in these Latin American countries.
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Notes
Comisión Nacional de Investigación Científica y Tecnológica
Institution that supports technological innovation in Chile
Coordenação de aperfeiçoamento de pessoal de nivel superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
References
Archambault, E., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the web of science and scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326. https://doi.org/10.1002/asi.21062
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Cabanac, G. (2014). Extracting and quantifying eponyms in full-text articles. Scientometrics, 98(3), 1631–1645. https://doi.org/10.1007/s11192-013-1091-8
Cai, Y., & Amaral, M. (2021). The triple helix model and the future of innovation: A reflection on the triple helix research agenda. Triple Helix, 8, 217–229. https://doi.org/10.1163/21971927-12340004
Cai, Y., & Etzkowitz, H. (2020). Theorizing the triple helix model: Past, present, and future. Triple Helix, 6, 1–38. https://doi.org/10.1163/21971927-bja10003
Callens, C., & Verhoest, K. (2023). Unlocking the process of collaborative innovation - combining mechanisms of divergence and convergence. Public Management Review. https://doi.org/10.1080/14719037.2023.2171096
Cano-Berlanga, S., Giménez-Gómez, J.-M., & Vilella, C. (2017). Enjoying cooperative games: The r package gametheory. Applied Mathematics and Computation, 305, 381–393. https://doi.org/10.1016/j.amc.2017.02.010
Caulier, J.-F., Skoda, A., & Tanimura, E. (2017). Allocation rules for networks inspired by cooperative game-theory. Revue d’économie politique, 127(4), 517–558.
Chatain, O. (2016). Cooperative and Non-cooperative Game Theory, Palgrave Macmillan UK, London, pp. 1–3. https://doi.org/10.1057/978-1-349-94848-2_468-1.
Cooke, P., & Leydesdorff, L. (2006). Regional development in the knowledge-based economy: The construction of advantage. The Journal of Technology Transfer, 31(1), 5–15. https://doi.org/10.1007/s10961-005-5009-3
de Moya Anegón, F., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Díaz-Pérez, M. (2012). ‘Estudio de la producción científica y tecnológica en colaboración universidad-empresa en iberoamérica’.
Dubina, I. N. (2015). A basic formalization of the interaction of the key stakeholders of an innovation ecosystem. Mathematical Economics, 11(18), 33–42.
Etzkowitz, H., & Leydesdorff, L. (1998). Universities and the global knowledge economy: A triple helix of university- industry relations. Pinter Pub Ltd.
Fidanoski, F., Simeonovski, K., Kaftandzieva, T., Ranga, M., Dana, L.-P., Davidovic, M., Ziolo, M., & Sergi, B. S. (2022). The triple helix in developed countries: when knowledge meets innovation? Heliyon, 8(8), e10168. https://doi.org/10.1016/j.heliyon.2022.e10168
Gilles, R. P. (2010). The Cooperative Game Theory of Networks and Hierarchies, number 978–3-642-05282-8 in ‘Theory and Decision Library C’. Springer. https://doi.org/10.1007/978-3-642-05282-8
Hamilton, N. E., & Ferry, M. (2018). ggtern: Ternary diagrams using ggplot2. Journal of Statistical Software, Code Snippets, 87(3), 1–17. https://doi.org/10.18637/jss.v087.c03
Howarth, R. J. (1996). Sources for a history of the ternary diagram. The British Journal for the History of Science, 29(3), 337–356. https://doi.org/10.1017/S000708740003449X
Ito, S., & Watanabe, T. (2021). Multilevel analysis of research management professionals and external funding at universities: Empirical evidence from japan. Science and Public Policy. https://doi.org/10.1093/scipol/scaa074
Jovanović, M., Savić, G., Cai, Y., & Levi-Jakšić, M. (2022). Towards a triple helix based efficiency index of innovation systems. Scientometrics, 127(5), 2577–2609. https://doi.org/10.1007/s11192-022-04304-x
Katz, J., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. https://doi.org/10.1016/S0048-7333(96)00917-1
Kimms, A., & Çetiner, D. (2012). Approximate nucleolus-based revenue sharing in airline alliances. European Journal of Operational Research, 220(2), 510–521. https://doi.org/10.1016/j.ejor.2012.01.057
Kohlberg, E. (1971). On the nucleolus of a characteristic function game. SIAM Journal on Applied Mathematics, 20(1), 62–66.
Kruss, G., & Visser, M. (2017). Putting university-industry interaction into perspective: a differentiated view from inside south african universities. The Journal of Technology Transfer, 42(4), 884–908. https://doi.org/10.1007/s10961-016-9548-6
Leydesdorff, L. (2013). An evaluation of impacts in “nanoscience & nanotechnology’’: steps towards standards for citation analysis. Scientometrics, 94(1), 35–55. https://doi.org/10.1007/s11192-012-0750-5
Leydesdorff, L., & Meyer, M. (2003). The triple helix of university-industry-government relations. Scientometrics, 58(2), 191–203. https://doi.org/10.1023/A:1026276308287
Leydesdorff, L., & Park, H. W. (2014). Can synergy in triple helix relations be quantified? a review of the development of the triple helix indicator. Triple Helix, 1(1), 4. https://doi.org/10.1186/s40604-014-0004-z
Leydesdorff, L., & Wagner, C. S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–325. https://doi.org/10.1016/j.joi.2008.07.003
Li, K., Rollins, J., & Yan, E. (2018). Web of science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20. https://doi.org/10.1007/s11192-017-2622-5
Maleki, S., Rahwan, T., Ghosh, S., Malibari, A., Alghazzawi, D., Rogers, A., Beigy, H., & Jennings, N. R. (2020). The shapley value for a fair division of group discounts for coordinating cooling loads. PLOS ONE, 15(1), 1–28. https://doi.org/10.1371/journal.pone.0227049
Mêgnigbêto, E. (2018). Measuring synergy within a triple helix innovation system using game theory: cases of some developed and emerging countries. Triple Helix, 5(1), 6. https://doi.org/10.1186/s40604-018-0054-8
Mêgnigbêto, E. (2018). Modelling the triple helix of university-industry-government relationships with game theory: Core, shapley value and nucleolus as indicators of synergy within an innovation system. Journal of Informetrics, 12(4), 1118–1132. https://doi.org/10.1016/j.joi.2018.09.005
Melin, G. (2000). Pragmatism and self-organization: Research collaboration on the individual level. Research Policy, 29(1), 31–40. https://doi.org/10.1016/S0048-7333(99)00031-1
Meyer, M., Grant, K., Morlacchi, P., & Weckowska, D. (2014). Triple helix indicators as an emergent area of enquiry: a bibliometric perspective. Scientometrics, 99(1), 151–174. https://doi.org/10.1007/s11192-013-1103-8
Meyer, M., Siniläinen, T., & Utecht, J. T. (2003). Towards hybrid triple helix indicators: A study of university-related patents and a survey of academic inventors. Scientometrics, 58(2), 321–350. https://doi.org/10.1023/A:1026240727851
Moon, H., Mariadoss, B. J., & Johnson, J. L. (2019). Collaboration with higher education institutions for successful firm innovation. Journal of Business Research, 99, 534–541. https://doi.org/10.1016/j.jbusres.2017.09.033
OECD, Kärkkäinen, K., & Vincent-Lancrin, S. (2013) , ‘Sparking innovation in stem education with technology and collaboration’, Centre for Educational Research and Innovation.
Osborne, M. J., & Rubinstein, A. (1994). A Course in Game Theory, Vol. 1 of MIT Press Books, The MIT Press.
Paswan, J., Singh, V. K., Karmakar, M., & Singh, P. (2022). Does university-industry-government collaboration in research gets higher citation and altmetric impact? a case study from india. Scientometrics, 127(11), 6063–6082. https://doi.org/10.1007/s11192-022-04508-1
Patra, S. K., & Muchie, M. (2018). Research and innovation in south african universities: from the triple helix’s perspective. Scientometrics, 116(1), 51–76. https://doi.org/10.1007/s11192-018-2764-0
Pece, A. M., Simona, O. E. O., & Salisteanu, F. (2015). Innovation and economic growth: An empirical analysis for cee countries. Procedia Economics and Finance, 26, 461–467. https://doi.org/10.1016/S2212-5671(15)00874-6
Perc, M. (2010). Growth and structure of slovenia’s scientific collaboration network. Journal of Informetrics, 4(4), 475–482. https://doi.org/10.1016/j.joi.2010.04.003
Perea, F., & Puerto, J. (2019). A heuristic procedure for computing the nucleolus. Computers Operations Research, 112, 104764. https://doi.org/10.1016/j.cor.2019.104764
Peters, H. (2015). The Nucleolus, in ‘Game Theory’. Springer Texts in Business and Economics, Springer, chapter, 19, 343–359. https://doi.org/10.1007/978-3-662-46950-7
Popadiuk, S., & Choo, C. W. (2006). Innovation and knowledge creation: How are these concepts related? International Journal of Information Management, 26(4), 302–312. https://doi.org/10.1016/j.ijinfomgt.2006.03.011
Saad, W., Han, Z., Debbah, M., Hjorungnes, A., & Basar, T. (2009). Coalitional game theory for communication networks. IEEE Signal Processing Magazine, 26(5), 77–97. https://doi.org/10.1109/MSP.2009.000000
Schmeidler, D. (1969). The nucleolus of a characteristic function game. SIAM Journal on Applied Mathematics, 17(6), 1163–1170.
Shapley, L. S. (1953). 17. A Value for n-Person Games, Princeton University Press, Princeton, pp. 307–318. https://doi.org/10.1515/9781400881970-018.
Shapley, L. S., & Shubik, M. (1969). Pure competition, coalitional power, and fair division. International Economic Review, 10(3), 337–362. https://doi.org/10.2307/2525647
Skoric, M. M. (2014). The implications of big data for developing and transitional economies: Extending the triple helix? Scientometrics, 99(1), 175–186. https://doi.org/10.1007/s11192-013-1106-5
Sunahara, A. S., Perc, M., & Ribeiro, H. V. (2021). Association between productivity and journal impact across disciplines and career age. Physical Review Research, 3, 033158. https://doi.org/10.1103/PhysRevResearch.3.033158
Sutrisna, M., Tjia, D., & Wu, P. (2021). Developing a predictive model of construction industry-university research collaboration. Construction Innovation, 21(4), 761–781. https://doi.org/10.1108/CI-11-2019-0129
Tamada, S., Naito, Y., Kodama, F., Gemba, K., & Suzuki, J. (2006). Significant difference of dependence upon scientific knowledge among different technologies. Scientometrics, 68(2), 289–302. https://doi.org/10.1007/s11192-006-0112-2
Timme, N., Alford, W., Flecker, B., & Beggs, J. M. (2014). Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective. Journal of Computational Neuroscience, 36(2), 119–140. https://doi.org/10.1007/s10827-013-0458-4
Tukoff-Guimarães, Y. B., Kniess, C. T., Renato, R. Silva., & Ruiz, M. (2021). Patents valuation in core innovation: case study of a Brazilian public university. INMR - Innovation amp Management Review, 18(1), 34–50. https://doi.org/10.1108/INMR-03-2019-0027
Vesperi, W. (2017). The conflict in the academic spinoff: the game theory approach. International Journal of Research, Innovation and Commercialisation, 1(2), 191–204. https://doi.org/10.1504/IJRIC.2017.091117
Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.
World Bank Statistics (2021). https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS?locations=AR-BR-US-MX.
World Intellectual Property Indicators (2021).
Xu, H., & Liu, F. (2017). Measuring the efficiency of education and technology via dea approach: Implications on national development. Social Sciences. https://doi.org/10.3390/socsci6040136
Xu, S., Hao, L., An, X., Zhai, D., & Pang, H. (2019). Types of errors of cited references in web of science with a cleaning method. Scientometrics, 120(3), 1427–1437. https://doi.org/10.1007/s11192-019-03162-4
Zhang, Y., Chen, K., & Fu, X. (2019). Scientific effects of triple helix interactions among research institutes, industries and universities. Technovation, 86–87, 33–47. https://doi.org/10.1016/j.technovation.2019.05.003
Zhao, D., & Strotmann, A. (2020). Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis. Scientometrics, 124(1), 255–270. https://doi.org/10.1007/s11192-020-03462-0
Zhou, C. (2014). Four dimensions to observe a triple helix: invention of ‘cored model’ and differentiation of institutional and functional spheres. Triple Helix, 1(1), 11. https://doi.org/10.1186/s40604-014-0011-0
Zhuang, T., Zhao, S., Zheng, M., & Chu, J. (2021). Triple helix relationship research on China’s regional university-industry-government collaborative innovation: Based on provincial patent data. Growth and Change, 52(3), 1361–1386. https://doi.org/10.1111/grow.12490
Oivind, S., & Leydesdorff, L. (2013). Where is synergy indicated in the Norwegian innovation system? triple-helix relations among technology, organization, and geography. Technological Forecasting and Social Change, 80(3), 471–484. https://doi.org/10.1016/j.techfore.2012.08.010
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This study was partially funded by CNPq (315245/2020-4; 315788/2023-2) and CAPES (001), Brazilian research agencies, for which the authors are grateful.
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Acuña, M.A.O., de Almeida Filho, A.T. & Ramos, F.S. Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05020-4
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DOI: https://doi.org/10.1007/s11192-024-05020-4