Skip to main content
SpringerLink
Log in

On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

For modeling the PEI networks, we used the publicly available data from the Mexican government: Gobierno de México, Datos Abiertos. Conacyt, Programa de Estímulos a la Innovación (PEI). URL: https://datos.gob.mx/busca/dataset/programa-de-estimulos-a-la-innovacion-pei. Date of last visit: March 13th, 2023.

Notes

  1. Gobierno de México, Datos Abiertos. Conacyt, Programa de Estímulos a la Innovación (PEI). URL: https://datos.gob.mx/busca/dataset/programa-de-estimulos-a-la-innovacion-pei. Date of last visit: January 11th, 2023.

References

  • Belli, S., & Baltà, J. (2019). Stocktaking scientific publication on bi-regional collaboration between Europe 28 and Latin America and the Caribbean. Scientometrics, 121(3), 1447–1480.

    Article  Google Scholar 

  • Berbegal-Mirabent, J., García, J. L. S., & Ribeiro-Soriano, D. E. (2015). University-industry partnerships for the provision of R &D services. Journal of Business Research, 68(7), 1407–1413.

    Article  Google Scholar 

  • Bramwell, A., Hepburn, N., & Wolfe, D. A. (2012). Growing innovation ecosystems: University-industry knowledge transfer and regional economic development in Canada. Toronto, Canada: Tech rep., University of Toronto. https://hdl.handle.net/1807/80099

  • Butcher, J., & Jeffrey, P. (2005). The use of bibliometric indicators to explore industry-academia collaboration trends over time in the field of membrane use for water treatment. Technovation 25(11), 1273–1280.

    Article  Google Scholar 

  • Chang, S. H. (2017). The technology networks and development trends of university-industry collaborative patents. Technological Forecasting and Social Change 118, 107–113.

    Article  Google Scholar 

  • Chavez, E. (2020). The effects of public R &D subsidies on private R&D activities in Mexico. Tech. rep.. Paris, France: Paris School of Economics. https://shs.hal.science/halshs-02355106v3

  • Cirella, S., & Murphy, S. (2022). Exploring intermediary practices of collaboration in university-industry innovation: A practice theory approach. Creativity and Innovation Management 31(2), 358–375.

    Article  Google Scholar 

  • Cueto, L. J., Frisnedi, A. F. D., Collera, R. B., et al. (2022). Digital Innovations in MSMEs during Economic Disruptions: Experiences and Challenges of Young Entrepreneurs. Administrative Sciences 12(1), 8.

    Article  Google Scholar 

  • D’Agostino, G., & Scala, A. (2014). Networks of networks: the last frontier of complexity. Springer.

    Book  Google Scholar 

  • Dávila-Borbón, C., León-Balderrama, J.I., Preciado-Rodríguez, J.M. (2019). La efectividad del programa de estímulos a la innovación (PEI) en Sonora. ¿Qué factores influyen en el impacto del programa sobre la innovación y la competitividad de las empresas? Estudios Sociales Revista de Alimentación contemporánea y Desarrollo Regional, 29, (53).

  • Fonseca, L. (2019). Designing regional development? Exploring the University of Aveiro’s role in the innovation policy process. Regional Studies, Regional Science 6(1), 186–202.

    Article  Google Scholar 

  • Garousi, V., Eskandar, M. M., & Herkiloğlu, K. (2017). Industry-academia collaborations in software testing: Experience and success stories from Canada and Turkey. Software Quality Journal 25(4), 1091–1143.

  • Gibson, E., Daim, T. U., & Dabic, M. (2019). Evaluating university industry collaborative research centers. Technological Forecasting and Social Change 146, 181–202.

    Article  Google Scholar 

  • González Alamilla, E. (2014). Políticas para impulsar la ciencia, tecnología e innovación en México: el caso del programa de estímulos a la innovación. Master’s thesis, Centro de Investigación y Docencia Económicas (CIDE), Mexico City, Mexico

  • Guerrero, M., & Link, A. N. (2022). Public support of innovative activity in small and large firms in Mexico. Small Business Economics 59, 413–422. https://doi.org/10.1007/s11187-021-00517-1

    Article  Google Scholar 

  • Guerrero, M., Herrera, F., & Urbano, D. (2019). Strategic knowledge management within subsidised entrepreneurial university-industry partnerships. Management Decision, 57(12), 3280–3300. https://doi.org/10.1108/MD-10-2018-1126

    Article  Google Scholar 

  • Guerrero, M., Urbano, D., & Herrera, F. (2019). Innovation practices in emerging economies: Do university partnerships matter? The Journal of Technology Transfer 44(2), 615–646. https://doi.org/10.1007/s10961-017-9578-8

    Article  Google Scholar 

  • Kuys, J., Al Mahmud, A., & Kuys, B. (2021). A case study of university-industry collaboration for sustainable furniture design. Sustainability 13(19), 10915.

    Article  Google Scholar 

  • Lazega, E. (2001). Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership Introduction. Tech. Rep. hal-01800250v1, Oxford University Press, Oxford, UK

  • Lima, J. C. F., Torkomian, A. L. V., Pereira, S. C. F., et al. (2021). Socioeconomic impacts of university-industry collaborations-a systematic review and conceptual model. Journal of Open Innovation: Technology, Market, and Complexity 7(2), 137.

    Article  Google Scholar 

  • López Rivas, V.M. (2020). Vincularse, invertir e innovar: explorando las relaciones industria, academia y gobierno en el caso del PEI-PROINNOVA de Conacyt. PhD thesis, Centro de Investigación y de Estudios Avanzados del IPN, Mexico City, Mexico

  • Lundvall, B. Å., Vang, J., Joseph, K. J., et al. (2009). Bridging Innovation System Research and Development Studies: Challenges and Research Opportunities. 7th International Conference GLOBELICS 2009 (pp. 1–39). Senegal: Dakar.

  • Lundvall, B. Å. (2010). National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning. Anthem Press.

  • Magaldi Hermosillo, R., Dutrénit Bielous, G., & Rivera Huerta, R. (2017). ¿Qué tan eficiente es la política de innovación mexicana para estimular a las empresas a vincularse con la academia? XVII Congreso Latino-Iberoamericano de Gestión Tecnológica (ALTEC) (pp. 1–15). Mexico: Mexico City.

  • Muñoz Martín, A. (2017). El efecto del programa de estímulos a la innovación en la competitividad de las empresas en los estados de Aguascalientes, Jalisco, Guanajuato y Querétaro. Tech. rep., Centro de Investigación y Docencia Económicas (CIDE), Aguascalientes, Mexico, http://hdl.handle.net/11651/2101

  • Min, B., & Zheng, M. (2018). Correlated network of networks enhances robustness against catastrophic failures. PLoS ONE 13(4), 1–13. https://doi.org/10.1371/journal.pone.0195539

    Article  MathSciNet  Google Scholar 

  • Minniti, S., Santoro, V., & Belli, S. (2018). Mapping the development of open access in Latin America and Caribbean countries. An analysis of web of science core collection and SciELO citation index (2005–2017). Scientometrics, 117(3), 1905–1930.

    Article  Google Scholar 

  • Moctezuma, P., López, S., & Mungaray, A. (2017). Innovación y desarrollo: programa de estímulos a la innovación regional en México. Problemas del desarrollo 48(191), 133–159.

    Google Scholar 

  • Montes-Orozco, E., Mora-Gutiérrez, R. A., Obregón-Quintana, B., et al. (2020). Inverse percolation to quantify robustness in multiplex networks. Complexity. https://doi.org/10.1155/2020/8796360

    Article  Google Scholar 

  • Montes-Orozco, E., Mora-Gutiérrez, R,A., Obregón-Quintana, B., et al. (2021). Methodology to quantify robustness in networks: case study-Higher Education System in Mexico. Computing 103, 869–893. https://doi.org/10.1007/s00607-021-00909-x

  • Müller, R., & Wagner, D. (1991). \(\alpha\)-vertex separator is NP-hard even for 3-regular graphs. Computing 46(4), 343–353.

    Article  MathSciNet  Google Scholar 

  • Nagurney, A., & Qiang, Q. (2012). Fragile networks: identifying vulnerabilities and synergies in an uncertain age. International Transactions in Operational Research, 19(1–2), 123–160. https://doi.org/10.1111/j.1475-3995.2010.00785.x

    Article  MathSciNet  Google Scholar 

  • Nam, G. M., Kim, D. G., & Choi, S. O. (2019). How resources of universities influence industry cooperation. Journal of Open Innovation: Technology, Market, and Complexity 5(1), 9.

    Article  Google Scholar 

  • Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), 167–256. https://doi.org/10.1137/S003614450342480

    Article  MathSciNet  Google Scholar 

  • Ortiz Pedroza, A.R. (2020). Análisis del espacio relacional del Programa de Estímulos a la Innovación (2009–2018). Un caso de estudio desde la teoría de las redes sociales. Master’s thesis, Universidad Autónoma Metropolitana, Iztapalapa, Mexico City, Mexico

  • Ouyang, M., Hong, L., Mao, Z. J., et al. (2009). A methodological approach to analyze vulnerability of interdependent infrastructures. Simulation Modelling Practice and Theory, 17(5), 817–828. https://doi.org/10.1016/j.simpat.2009.02.001

    Article  Google Scholar 

  • Pérez Hernández, M. (2013). Governability and vision of innovation inside Mexican public higher education institutions as a challenge to connect Mexican lnnovation government policies and harmonized SME’s needs on triple helix system. In: Triple Helix XI conference (pp. 1–33).

  • Sánchez Flores, V. E., Colin Herrera, M. S., Sánchez Carrillo, S., et al. (2018). El PEI (programa de estímulos a la innovación), para fomentar el trabajo colaborativo y generador de proyectos de investigación, desarrollo tecnológico y social. XX Encuentro Internacional Virtual Educa (pp. 1–13). Argentina: Buenos Aires.

    Google Scholar 

  • Sayama, H. (2015). Introduction to the modeling and analysis of complex systems. Open SUNY Textbooks.

    Google Scholar 

  • Unger, K. (2018). Innovación, competitividad y rentabilidad en los sectores de la economía mexicana. Gestión y política pública 27(1), 3–37.

    Google Scholar 

  • Valenti Nigrini G. (2008). Ciencia, tecnología e innovación: Hacia una agenda de política pública. FLACSO-México

  • Yang, Z., Chen, H., Du, L., et al. (2021). How does alliance-based government-university-industry foster cleantech innovation in a green innovation ecosystem? Journal of Cleaner Production, 283, 124559.

    Article  Google Scholar 

  • Zhou, J., & Wang, M. (2023). The role of government-industry-academia partnership in business incubation: Evidence from new R &D institutions in China. Technology in Society 72, 102194.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Matemáticas Aplicadas y Sistemas, Universidad Autónoma Metropolitana Unidad Cuajimalpa, Av. Vasco de Quiroga 4871, Col. Santa Fe Cuajimalpa, 05348, Ciudad de México, Mexico

    Edwin Montes-Orozco & Abel García-Nájera

  2. Departamento de Sistemas de Información y Comunicaciones, Universidad Autónoma Metropolitana Unidad Lerma, Av. de las Garzas 10, Col. El Panteón, 52005, Lerma de Villada, Estado de México, Mexico

    Karen Miranda

  3. Departamento de Estudios Culturales, Universidad Autónoma Metropolitana Unidad Lerma, Av. de las Garzas 10, Col. El Panteón, 52005, Lerma de Villada, Estado de México, Mexico

    Juan-Carlos López-García

Authors
  1. Edwin Montes-Orozco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karen Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abel García-Nájera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan-Carlos López-García
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karen Miranda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 5 Details of the separator set and of the fragmented network
Full size table

Table 5 provides a comprehensive overview of the separator set and the corresponding details of the fragmented networks. The separator set, composed of specific nodes, plays a crucial role in causing the fragmentation of the largest connected component (GC) into multiple components. For the R &D network, the separator set comprises prestigious institutions like UANL, UNAM, IPN, among others. In the Projects network, the separator set includes distinct project identifiers (PEI) that are instrumental in creating isolated components. Similarly, for the Companies network, the separator set consists of reputable organizations such as ITESM, UAM, UNAM, and more. These separator nodes are pivotal in the network’s structural integrity.

The fragmented networks resulting from these separator nodes exhibit varying characteristics. The R &D network splinters into 466 components, the Projects network fragments into two components, and the Companies network divides into 296 components. The average path length, diameter, and density metrics further highlight the distinctions among these networks. The insights drawn from this table shed light on the essential nodes that influence the fragmentation of the networks and offer a deeper understanding of the intricate relationships within the PEI program.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montes-Orozco, E., Miranda, K., García-Nájera, A. et al. On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program. Scientometrics 129, 1523–1544 (2024). https://doi.org/10.1007/s11192-023-04903-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-023-04903-2

Keywords

Mathematics Subject Classification

JEL Classification

Search

Navigation