The Impact of Artificial Intelligence on ESG Performance of Manufacturing Firms: The Mediating Role of Ambidextrous Green Innovation
Hao Jing, Shiyu Zhang
Shenyang Aerospace University
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摘要与影响
In the context of the worldwide quest for green and sustainable development, there is a growing importance in enhancing the environmental, social, and governance (ESG) performance of manufacturing companies. With the rise of digital transformation and pressing environmental challenges, artificial intelligence (AI) has emerged as a crucial tool for manufacturing organizations to gain a competitive edge in sustainability. While the role of digital technologies in driving ESG improvements has been widely discussed, there is limited scholarly exploration of the specific impact of AI on the ESG performance of manufacturing firms, as well as the underlying mechanisms at play from an AI perspective. Addressing this research gap, this study constructs a theoretical model of AI affecting manufacturing firms’ ESG performance using a sample of Chinese-listed manufacturing firms from 2012–2022. Additionally, this study examines the role of mediating mechanisms of ambidextrous green innovation as well as differences in the intrinsic mechanisms triggered by the equilibrium of ambidextrous green innovation and firm size. The findings indicate that the application of AI technology effectively promotes improvements in the ESG performance of manufacturing firms, with ambidextrous green innovation playing a positive mediating role. Furthermore, manufacturing companies with a strong balance of ambidextrous green innovation and larger scale exhibit enhanced effects of AI on ESG performance. This study serves to supplement existing literature on ESG performance enhancement, elucidate the theoretical rationale behind the non-economic performance of AI-enabled firms, and extend the application of organizational dualism theory to new contexts.
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计算机 / AIImpact of AI and Big Data on Business and Society
Environmental Sustainability in Business · Sustainable Supply Chain Management
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