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Modelling innovation performance of European regions using multi-output neural networks

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  • Petr Hajek
  • Roberto Henriques

Abstract

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Suggested Citation

  • Petr Hajek & Roberto Henriques, 2017. "Modelling innovation performance of European regions using multi-output neural networks," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    • Handle: RePEc:plo:pone00:0185755
    DOI: 10.1371/journal.pone.0185755
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    References listed on IDEAS

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    2. Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
    3. Prokop, Viktor & Hajek, Petr & Stejskal, Jan, 2021. "Configuration Paths to Efficient National Innovation Ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
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    5. Wang, Xinyi & Zeng, Deming & Dai, Haiwen & Zhu, You, 2020. "Making the right business decision: Forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods," Technological Forecasting and Social Change, Elsevier, vol. 155(C).

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