Cooperation for Innovation: Main Drivers in Countries with Low Levels of Innovation
DOI:
https://doi.org/10.5281/zenodo.17519170Keywords:
Innovation Results, Cooperation, Machine Learning, Resources.Abstract
This study seeks to identify the factors that drive business innovation by analysing innovation ecosystems from a collaborative perspective in countries with low levels of innovative activity. The study included more than 6,000 companies included in the EDIT 2020 survey conducted by DANE, the agency responsible for official statistics in Colombia. Artificial intelligence techniques such as machine learning were used to process the information. The main findings emphasise the importance of effectively managing internally developed knowledge and diversifying external sources of information.
References
Damanpour, F. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34(3), 555–590.
Diez‐Martinez, I., Peiro‐Signes, A., & Segarra‐Oña, M. (2022). The links between active cooperation and eco‐innovation orientation of firms: A multi‐analysis study. Business Strategy and the Environment.https://doi.org/10.1002/bse.3288
E U R O P E 2 0 2 0. (2010). Europe 2020: A strategy for smart, sustainable and inclusive growth. European Commission.
Freire, J. A. F., & Gonçalves, E. (2021). Cooperation in innovative efforts: A systematic literature review. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-021-00837-3
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis. Cengage Learning.
Hervás-Oliver, J. L., Parrilli, M. D., Rodríguez-Pose, A., & Sempere-Ripoll, F. (2021). The drivers of SME innovation in the regions of the EU. Research Policy, 50(9), 104316. https://doi.org/10.1016/j.respol.2021.104316
Hou, J. (2020). The interdependency of innovation obstacles in Chinese manufacturing firms. In S. Dai & M. Taube (Eds.), China’s quest for innovation: Institutions and ecosystems (pp. 123–141). Routledge.
Kafouros, M., Love, J. H., Ganotakis, P., & Konara, P. (2020). Experience in R&D collaborations, innovative performance and the moderating effect of different dimensions of absorptive capacity. Technological Forecasting and Social Change, 150, 119757. https://doi.org/10.1016/j.techfore.2019.119757
Kafouros, M., Wang, C., Piperopoulos, P., & Zhang, M. (2015). Academic collaborations and firm innovation performance in China: The role of region-specific institutions. Research Policy, 44(3), 803–817. https://doi.org/10.1016/j.respol.2014.11.002
Kleine, M., Heite, J., & Huber, L. R. (2022). Subsidized R&D collaboration: The causal effect of innovation vouchers on innovation outcomes. Research Policy, 51(6), 104515. https://doi.org/10.1016/j.respol.2022.104515
Li-Ying, J., Mothe, C., & Nguyen, T. T. U. (2018). Linking forms of inbound open innovation to a driver-based typology of environmental innovation: Evidence from French manufacturing firms. Technological Forecasting and Social Change, 135, 51–63. https://doi.org/10.1016/j.techfore.2017.05.031
Najafi-Tavani, S., Najafi-Tavani, Z., Naude, P., Oghazi, P., & Zeynaloo, E. (2018). How collaborative innovation networks affect new product performance: Product innovation capability, process innovation capability, and absorptive capacity. Industrial Marketing Management, 73, 193–205. https://doi.org/10.1016/j.indmarman.2018.02.009
Peiró-Signes, Á., Segarra-Oña, M., Trull-Domínguez, Ó., & Sánchez-Planelles, J. (2022). Exposing the ideal combination of endogenous–exogenous drivers for companies’ eco-innovative orientation: Results from machine-learning methods. Socio-Economic Planning Sciences, 79, 101145. https://doi.org/10.1016/j.seps.2021.101145
Xie, X., Wu, Y., & Devece, C. (2022). Is collaborative innovation a double-edged sword for firms? The contingent role of ambidextrous learning and TMT shared vision. Technological Forecasting and Social Change, 175, 121340. https://doi.org/10.1016/j.techfore.2021.121340
Xu, T., Yang, J. J., Zhang, F., & Guo, W. Y. (2021). Interfirm coopetition, interfirm knowledge creation, and collaborative innovation performance: The moderating roles of environmental competitiveness and dysfunctional competition. Industrial Marketing Management, 99, 123–135. https://doi.org/10.1016/j.indmarman.2021.10.003
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Carlos Labarcés-Ballestas, Ángel Peiró-Signes
This work is licensed under a Creative Commons Attribution 4.0 International License.