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Optimizing digital transformation paths for industrial clusters: Insights from a simulation

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  • Teng, Yuanyang
  • Zheng, Jianzhuang
  • Li, Yicun
  • Wu, Dong

Abstract

Digital technologies have revolutionised industrial clusters, implementing digital transformation without careful consideration can lead to higher risks and ineffective investments. However, the existing research often focuses on enterprises in a specific position, whereas the entire supply chain or end-to-end research is rarely conducted. To fill this gap, this study proposes a sectoral innovation system. It conducts a simulation model to study the digital transformation process by considering the behaviour, knowledge learning, and innovation of upstream and downstream enterprises in different cluster types. The simulation dynamically presents production and productivity changes during the transformation process of the entire industrial cluster. The results reveal that an orderly transformation path is the most effective for Marshallian clusters, whereas a simultaneous transformation works best for central satellite clusters. In addition, the social network simulation before and after the digital transformation of the two industrial clusters shows that enterprises in central-satellite clusters communicate more frequently during digital transformation, which is ultimately conducive to a better performance of the digital transformation of industrial clusters. These findings emphasise the need for tailored digital transformation strategies based on cluster type to maximise benefits, underscoring the importance of leading firms in industrial clusters. It also guides the government's industrial policy formulation and management enlightenment regarding the digital transformation of enterprises.

Suggested Citation

  • Teng, Yuanyang & Zheng, Jianzhuang & Li, Yicun & Wu, Dong, 2024. "Optimizing digital transformation paths for industrial clusters: Insights from a simulation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008557
    DOI: 10.1016/j.techfore.2023.123170
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