The results show that AI adoption significantly enhances DGS in manufacturing firms, with stronger impacts in high-tech, non-heavy-polluting, and non-state-owned enterprises.
Abstract
Under the dual pressures of resource constraints and emission regulations facing global manufacturing, digital–green synergy transformation has become crucial to achieving sustainable economic development. However, the existing literature still lacks micro-level evidence on how artificial intelligence (AI) adoption drives digital–green synergy (DGS). This paper takes manufacturing listed companies from 2011 to 2022 as the research sample. Based on the BERT large language model, an index of enterprise AI adoption is constructed, and a two-way fixed effects model is used to empirically test the impact of AI adoption on digital–green synergy in manufacturing firms. The results show that AI adoption significantly enhances DGS in manufacturing firms, with stronger impacts in high-tech, non-heavy-polluting, and non-state-owned enterprises. The mechanism analysis indicates that AI adoption mainly promotes DGS in the manufacturing industry through three paths: expanding knowledge breadth, optimizing resource allocation, and breaking through organizational routines. Further analysis indicates that the positive effect of AI adoption is reinforced by executives’ environmental backgrounds, while climate policy uncertainty exerts a dampening influence. Economic consequence tests confirm that improved DGS simultaneously enhances corporate social responsibility performance, fosters new-quality productivity, and strengthens supply chain resilience.
Micro-level evidence is provided on AI-driven green transformation in manufacturing enterprises from the dynamic capabilities perspective, offering theoretical and practical insights for advancing high-quality transformation of China’s manufacturing sector in the digital-intelligent era.
As global regulatory pressure on corporate sustainability intensifies, manufacturing enterprises face growing challenges in building effective Environmental, Social, and Governance (ESG) internal control systems. Traditional approaches, reliant on manual data collection and fragmented reporting, are increasingly inadequate for the scale and complexity of modern manufacturing operations. This paper investigates how Artificial Intelligence (AI) technologies can systematically optimize ESG internal control in manufacturing enterprises. Employing a combination of longitudinal case study analysis, literature review, and panel regression, this study uses Prince Holdings (Oji Holdings Corporation) as the primary case and draws on a panel dataset of 15,623 firm-year observations from 3,358 A-share manufacturing companies over 2018–2023. The findings demonstrate that AI adoption is significantly and positively associated with ESG internal control quality (β = 1.051, p < 0.001), with data governance capability identified as a partial mediator and organizational readiness as a positive moderator. Based on these findings, a five-layer AI-ESG optimization model aligned with the Committee of Sponsoring Organizations of the Treadway Commission (COSO) framework is proposed as a replicable blueprint for the manufacturing sector.
Shiyang Chen· Journal of Fintech and Busin...· 0 citations
Artificial Intelligence (AI) is increasingly recognized as a foundational technology for enterprise competitiveness in the digital economy. However, limited research explains how AI capabilities create enterprise value in emerging economies with uneven digital readiness. This study synthesizes the mechanisms of AI-driven value creation and examines structural barriers to enterprise AI adoption in Vietnam. Using a qualitative conceptual research design based on secondary data, the study integrates the Resource-Based View (RBV) with a four-layer AI framework covering data, algorithms, infrastructure, and applications. The findings suggest that AI creates enterprise value through cognitive automation, decision intelligence, and business model innovation, but their effectiveness depends on data governance, digital leadership, human capital, financial readiness, and regulatory support. In Vietnam, adoption is constrained by fragmented data systems, shortages of skilled AI professionals, limited SME investment capacity, and evolving governance frameworks. This study contributes a conceptual synthesis linking AI capability layers to enterprise value creation and provides implications for managers and policymakers seeking to accelerate responsible AI adoption.
Diep Van Vu· Tạp chí Khoa học Đại học Côn...· 0 citations
This study uses manufacturing enterprise data spanning 2011–2023 to explore whether and how the synergistic effects of digital and intelligent policies promote ambidextrous innovation in enterprises based on the resource-based view.
This study builds a quasi-natural experiment using the Broadband China Policy (BCP) and the Intelligent Manufacturing Policy (IMP) and drawing on the theory of the resource-based view to discuss the impacts and influencing mechanisms of policy synergy on enterprises' ambidextrous innovation. A difference-in-difference with a double machine learning technique and a four-stage mediation effect model are applied to complete the empirical tests.
(1) The synergy of digital and intelligent policies notably enhances corporate ambidextrous innovation; (2) resource accumulation, utilization and allocation act as mediating mechanisms; (3) this effect is more pronounced among state-owned enterprises, firms in the growth and maturity stages, large enterprises and non-high-tech enterprises and (4) policy synergy yields greater benefits than any single policy alone, with the sequence of BCP followed by IMP being more effective than the reverse order.
This study theoretically explains how the synergy of digital-intelligent policies can promote manufacturing enterprises' ambidextrous innovation by revealing the mediating effects of resource accumulation, utilization and allocation and determines the conditions for amplifying these effects. This research enriches the relevant literature on enterprises' ambidextrous innovation, resource endowments and policy-driven innovation and provides implications for managers and policymakers who seek to use digital-intelligent policy synergy to promote ambidextrous innovation.
Xuena Gao, Wei Gu, Xiaoling Wang et al.· European Journal of Innovati...· 0 citations
This study investigates how AI adoption enhances organizational innovation capability and, in turn, improves economic, environmental, and social dimensions of business performance, and links digital transformation with sustainability outcomes.
S. P, Sriharan M, S. P et al.· International Journal for Re...· 0 citations
Growing pressures for environmental responsibility have intensified the need for manufacturing SMEs to pursue sustainability as a core strategic priority, especially as these firms often operate under severe resource constraints. In this context, firms increasingly adopt advanced digital technologies such as big data analytics–artificial intelligence to strengthen their environmental, social, and economic performance. Guided by the Resource‐Based View (RBV), this study contributes to the ongoing discussion on how digital technologies support sustainable development by proposing the mediating roles of green supply chain management and green innovation in the relationship between BDA–AI capability and sustainable performance. The study further examines whether organizational green culture enhances the ability of BDA–AI to foster green capabilities. Using survey data from 388 Chinese manufacturing SMEs and structural equation modeling through SmartPLS, the findings show that BDA–AI significantly improves GSCM, GI, and sustainable performance. Mediation results confirm that GSCM and GI act as essential mechanisms through which BDA–AI generates environmental and operational benefits. However, the moderating influence of OGC is uneven: it significantly strengthens the pathway from BDA–AI to GI, whereas its effect on the BDA–AI to GSCM relationship remains insignificant. These insights highlight that digital adoption alone is insufficient; sustainability gains emerge when technological investment is accompanied by strong green capabilities and supportive cultural values.
Qing Chong, T. Ramayah· Business Strategy and the En...· 0 citations
