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The Driving Impact of Digital Innovation Ecosystems on Enterprise Digital Transformation: Based on an Interpretable Machine Learning Model

Author

Listed:
  • Jiamin Liu

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Yongheng Fang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Yabing Ma

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

Abstract

This paper is based on data from Chinese digital creative enterprises from 2015 to 2023. A regression model is constructed to test the driving mechanism of the digital innovation ecosystem on the digital transformation of enterprises. The Shapley Additive exPlanations (SHAP) machine learning method is also employed to reveal the key factors driving enterprise digital transformation in the digital innovation ecosystem. The results show that (1) the digital innovation ecosystem can significantly drive the digital transformation of enterprises. And this driving effect is influenced by the moderating effect of the dynamic capabilities of enterprises. The moderating effect of innovation capability and absorptive capacity on enterprise digital transformation is the most significant. (2) The heterogeneity test finds that among state-owned enterprises and enterprises with a high degree of industry competition, the digital innovation ecosystem significantly drives enterprises’ digital transformation more strongly. (3) The results of SHAP value identification indicate that the digital intelligence foundation is the most important factor in driving enterprise digital transformation. The degree of participant diversity, the enabling capacity of the system environment, and the comprehensive benefits play different roles in driving enterprise digital transformation under different heterogeneous conditions.

Suggested Citation

  • Jiamin Liu & Yongheng Fang & Yabing Ma, 2025. "The Driving Impact of Digital Innovation Ecosystems on Enterprise Digital Transformation: Based on an Interpretable Machine Learning Model," Sustainability, MDPI, vol. 17(13), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5898-:d:1688325
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