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Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation

Author

Listed:
  • Kun Liu

    (School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China)

  • Xuemin Liu

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Zihao Wu

    (School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China)

Abstract

Corporate digital transformation, as a key and representational component of the larger digital economy, plays a vital role in furthering both green technological innovation and the transition to a more sustainable economic model. This study collects panel data relating to firms listed on China’s A-share exchanges from 2009 to 2020 and employs textual analysis to estimate the extent of digital transformation within these organizations. It methodically examines the influence of this transformation on the volume and quality of green technological innovations. The findings reveal a substantial enhancement in both the volume and quality of green technological innovations as a result of corporate digital transformation, with a more noticeable improvement in innovation quality. This transition, driven by the mediating function of optimizing resource allocation, facilitates green technological innovation by enhancing human capital composition, curtailing information asymmetry, and augmenting investment in research and development (R&D). Heterogeneity research shows that the influence of digital transformation on green technological innovation is more pronounced in state-owned corporations, low-pollution corporations, and corporations situated in low-carbon pilot cities. Furthermore, this study discovers that the promotive influence of corporate digital transformation tends to diminish with the advancement of the corporation’s lifecycle, peaking during the growth phase. Finally, this study still has some limitations, such as its exclusive focus on Chinese corporations, the need for improvements in the measurement of digital transformation, and potential sample selection biases.

Suggested Citation

  • Kun Liu & Xuemin Liu & Zihao Wu, 2024. "Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation," Sustainability, MDPI, vol. 16(3), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1318-:d:1333087
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    Cited by:

    1. Yu Sang & Kannan Loganathan & Priya Sukirthanandan, 2025. "A Study on the Impact of Corporate Digital Transformation on Environmental, Social, and Governance (ESG) Performance: Mechanism Analysis Based on Resource Allocation Efficiency and Technological Gap," Sustainability, MDPI, vol. 17(8), pages 1-31, April.
    2. Kunyan Zhu & Siying Chen & Rufei Ma, 2026. "Speed and sustainability: the impact of digital transformation speed on firms’ low-carbon performance," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(2), pages 5185-5205, February.
    3. Ji, Xinxia & Zhang, Liyan & Cao, Wei, 2025. "Innovative policies, financial mismatch, and corporate innovation performance: A Quasi-natural experiment based on the national innovative city pilot policy," Finance Research Letters, Elsevier, vol. 82(C).
    4. Chao Yang & Qi Liu, 2024. "Driving Green Innovation Through Digital Transformation: Empirical Insights on Regional Variations," Sustainability, MDPI, vol. 16(23), pages 1-24, December.
    5. Chunshan Zhou & Xiaoli Wei & Xiangjun Dai & Guojun Zhang, 2025. "Research on the Spatio-Temporal Evolution and Impact of China’s Digital Economy and Green Innovation," Land, MDPI, vol. 14(3), pages 1-26, March.
    6. Wu, Zihang & Zeng, Ning & Song, Jiansong, 2025. "Tax incentives and corporate technological innovation performance: An analysis from the perspective of R&D investment," International Review of Economics & Finance, Elsevier, vol. 102(C).

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