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Innovation forecasting: mapping pathways with global indicators

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  • Abroon Qazi

    (American University of Sharjah)

Abstract

This study investigates the determinants of innovation performance by applying a Bayesian belief network model to data from 132 countries in the 2023 Global Innovation Index. The model examines dependencies between five input indicators—institutions, human capital and research, infrastructure, market sophistication, and business sophistication—and two output indicators—knowledge and technology outputs, and creative outputs. The analysis identifies business sophistication as the most influential factor, directly driving both types of innovation outputs. Market sophistication and human capital and research also emerge as key contributors, highlighting the importance of competitive markets, skilled talent, and research capacity. By capturing the probabilistic interdependencies among innovation inputs and outputs, this study offers a novel, network-based perspective that advances understanding of innovation dynamics. The findings provide evidence-based insights for policymakers to target resource allocation and policy design, and for businesses to identify strategic priorities that enhance competitiveness and foster sustainable, innovation-led growth.

Suggested Citation

  • Abroon Qazi, 2025. "Innovation forecasting: mapping pathways with global indicators," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:spr:joiaen:v:14:y:2025:i:1:d:10.1186_s13731-025-00595-5
    DOI: 10.1186/s13731-025-00595-5
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    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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