| Title | Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation |
|---|---|
| ID_Doc | 62516 |
| Authors | Liu, K; Liu, XM; Wu, ZH |
| Title | Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation |
| Year | 2024 |
| Published | Sustainability, 16, 3 |
| 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. |
| https://www.mdpi.com/2071-1050/16/3/1318/pdf?version=1707035137 |
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