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Leave me be, make me strong! Growing solo to innovate together. Results from machine learning techniques on the innovation performance in resource-constrained manufacturing industries

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  • Peiró-Signes, Ángel
  • Labarcés-Ballestas, Carlos
  • Segarra-Oña, Marival
  • Trull-Domínguez, Óscar

Abstract

Innovation drives economic growth and competitiveness, particularly for small and medium-sized enterprises; however, firms in countries with low innovation capacity face significant barriers, ranging from limited financial resources to inadequate technological infrastructure. This paper examines the drivers of innovation in a resource-constrained and low-innovation ecosystem by using Colombian manufacturing industry data as a reference case, as it is a context characterized by low levels of innovation activity and underdeveloped innovation ecosystems. Using data from the 2020 Survey on Development and Technological Innovation in the Manufacturing Industry, this study employs machine learning techniques to identify the key internal and external factors influencing innovation outcomes, with results indicating that firms allocating proportionally more labor and investment to scientific, technological, and innovation activities are more likely to achieve innovative outcomes. In contrast to high-innovation regions, collaboration has a less significant effect on innovation, suggesting that the lack of ecosystem support may limit knowledge sharing and resource exchange. These findings highlight the need for targeted policies focused on strengthening financial access, promoting knowledge networks, and building collaborative frameworks. The study concludes by outlining policy recommendations aimed at enhancing innovation in low-innovation contexts and suggests paths for further research on the role of ecosystem support in emerging economies.

Suggested Citation

  • Peiró-Signes, Ángel & Labarcés-Ballestas, Carlos & Segarra-Oña, Marival & Trull-Domínguez, Óscar, 2026. "Leave me be, make me strong! Growing solo to innovate together. Results from machine learning techniques on the innovation performance in resource-constrained manufacturing industries," Socio-Economic Planning Sciences, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:soceps:v:103:y:2026:i:c:s0038012125002149
    DOI: 10.1016/j.seps.2025.102365
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