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Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective

Author

Listed:
  • Yaliu Yang

    (Business School, Suzhou University, Suzhou 234000, China)

  • Yuan Wang

    (Business School, Suzhou University, Suzhou 234000, China)

  • Cui Wang

    (Business School, Suzhou University, Suzhou 234000, China)

  • Yingyan Zhang

    (Business School, Suzhou University, Suzhou 234000, China)

  • Cuixia Zhang

    (School of Mechanical and Electronic Engineering, Suzhou University, Suzhou 234000, China)

Abstract

This study develops a data-driven, comprehensive evaluation method to improve the science and technology innovative efficiency of industrial enterprises above designated size (hereinafter “industrial enterprises”). Based on an innovation value chain perspective, a two-stage evaluation index system is constructed. Thereafter, the Pearson correlation coefficient method was used to analyze correlations in the constructed index system. A two-stage network data envelopment analysis model with additional intermediate input was constructed to measure and evaluate industrial enterprises’ science and technology innovative efficiency from three aspects—research and development (R&D), commercialization, and comprehensive efficiencies—to reveal the temporal and spatial evolution. The feasibility and effectiveness of the method was verified using the statistical data of industrial enterprises in 16 cities in Anhui Province, China, from 2011 to 2020. The results show that the comprehensive efficiency of the scientific and technological innovation of industrial enterprises in these cities is at a medium level, and the efficiency development of the two stages is uncoordinated; the two-stage efficiency distribution tends to be “high R&D–high commercialization” and “low R&D–low commercialization”, and targeted countermeasures and suggestions are proffered. This study provides a reference for the sustainable development of industrial enterprises in relevant regions.

Suggested Citation

  • Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10721-:d:900250
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    Cited by:

    1. Keyan Zheng & Fagang Hu & Yaliu Yang, 2023. "Data-Driven Evaluation and Recommendations for Regional Synergy Innovation Capability," Sustainability, MDPI, vol. 15(14), pages 1-21, July.

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