The Driving Forces of Digital Transformation: Navigating Peer Effects in Industrial and Regional Ecosystems

Understanding the systemic synergy of peer effects on digital transformation is essential for overcoming development bottlenecks and stimulating digital vitality across industrial and regional ecosystems. Utilizing data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this study empirically investigates the impact of peer effects on corporate digital transformation and its underlying influencing factors from a systems perspective. The findings reveal significant industry and regional peer effects in corporate digital transformation, indicating that firms’ decision-making is interdependent within broader ecosystems. A greater distance between a focal firm’s prior digital transformation level and that of its peers is associated with a higher level of enthusiasm for such transformation. Similarly, the more a focal firm’s prior performance falls below that of its peers, the stronger its impetus for digital transformation becomes. Furthermore, the influence of the transformation distance on digital transformation enthusiasm exhibits a non-linear threshold effect, which varies with the performance gap. Finally, further analysis indicates that peer effects exert a multiplier effect and that industry-level peer effects in digital transformation significantly enhance firm performance. These conclusions contribute to a deeper understanding of the systemic mechanisms and pathways of corporate digital transformation and offer both theoretical and empirical support for fostering resilient digital economic ecosystems across industries and regions.

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Publication Details

Journal
Systems
Published
2025-10-23
DOI
https://doi.org/10.3390/systems13110940
Primary Topic
University-Industry-Government Innovation Models
Type
article
Field-Weighted Citation Impact
0.00

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article

The Driving Forces of Digital Transformation: Navigating Peer Effects in Industrial and Regional Ecosystems

M.H. Li, Jun Dai
Systems
University-Industry-Government Innovation Models
article

The Driving Forces of Digital Transformation: Navigating Peer Effects in Industrial and Regional Ecosystems

M.H. Li, Jun Dai
article en

Abstract

Understanding the systemic synergy of peer effects on digital transformation is essential for overcoming development bottlenecks and stimulating digital vitality across industrial and regional ecosystems. Utilizing data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this study empirically investigates the impact of peer effects on corporate digital transformation and its underlying influencing factors from a systems perspective. The findings reveal significant industry and regional peer effects in corporate digital transformation, indicating that firms’ decision-making is interdependent within broader ecosystems. A greater distance between a focal firm’s prior digital transformation level and that of its peers is associated with a higher level of enthusiasm for such transformation. Similarly, the more a focal firm’s prior performance falls below that of its peers, the stronger its impetus for digital transformation becomes. Furthermore, the influence of the transformation distance on digital transformation enthusiasm exhibits a non-linear threshold effect, which varies with the performance gap. Finally, further analysis indicates that peer effects exert a multiplier effect and that industry-level peer effects in digital transformation significantly enhance firm performance. These conclusions contribute to a deeper understanding of the systemic mechanisms and pathways of corporate digital transformation and offer both theoretical and empirical support for fostering resilient digital economic ecosystems across industries and regions.

SystemsVol. 13(11)
Wuhan University of Science and Technology (CN)
Wuhan University, Wuhan University of Science and Technology
Openalex Percentile: Top 60%
University-Industry-Government Innovation Models
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