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.
Abstract
As a strategic general-purpose technology reshaping the manufacturing landscape, artificial intelligence (AI) holds significant potential to drive green transformation. However, how AI translates into green transformation through organizational capabilities has received limited attention. This study examines how AI drives green transformation in manufacturing enterprises through dynamic capabilities—specifically, absorptive, innovative, and adaptive capabilities. Drawing on the resource-based view and dynamic capabilities theory, we develop a theoretical framework positioning these capabilities as the core mediating mechanisms. Using panel data from Chinese manufacturing firms (2012–2023) and text mining with fixed-effects models, we find that AI significantly accelerates green transformation. Mechanism analysis confirms that dynamic capabilities mediate this relationship through three pathways: enhancing absorptive capability, stimulating innovation capability, and improving adaptive capability. Heterogeneity analysis reveals that the effect is stronger in non-state-owned, large-scale, and non-high-tech firms, suggesting that institutional and resource contexts shape AI’s impact. This article provides micro-level evidence 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.
Artificial intelligence (AI) is increasingly viewed as a key driver of sustainable development, yet evidence on its role in corporate sustainable green innovation remains limited. Drawing on Resource Orchestration Theory and Signaling Theory, this study examines the impact of AI capability on sustainable green innovation using panel data from Chinese A-share listed companies during 2014–2023. The results show that stronger AI capability significantly promotes sustainable green innovation. AI investment serves as a mediating mechanism. However, the estimated indirect effect is negative, suggesting that the process of AI implementation may involve resource reallocation and organizational adjustment costs before innovation benefits can be fully realized. Environmental investment strengthens the positive impact of AI capability, whereas digital transformation weakens it. Robustness tests confirm the reliability of the findings. Further analyses indicate that the positive effect of AI capability is more pronounced in non-state-owned enterprises, low-technology firms, and firms in the growth stage. By revealing the mechanisms and boundary conditions through which AI capability influences sustainable green innovation, this study enriches the literature on AI-enabled sustainability and offers practical insights for firms pursuing long-term sustainable development.
Shuohuan Yan, Sheng Jin, Ju Wang et al.· Sustainability· 0 citations
Grounded in Resource Orchestration Theory and Upper Echelons Theory, this study aims to explore how artificial intelligence (AI) applications drive green transformation and reveal its underlying mechanisms.
We propose a framework in which AI enhances green transformation performance through improved operational efficiency, contingent on the dual moderating mechanisms of organizational agility and executives’ environmental attention. We employ a comprehensive panel dataset using Chinese manufacturing listed companies from 2012 to 2023 for empirical testing.
Empirical analysis reveals that AI significantly enhances firms’ green transformation, with operational efficiency serving as a mediator in this relationship. Organizational agility amplifies the effect of AI on operational efficiency, and executives’ environmental attention strengthens the effect of operational efficiency on green transformation performance. Both factors positively moderate the mediated pathway. Heterogeneity analysis shows stronger effects among older firms, larger firms, firms located in western regions or regions with lower environmental regulation intensity.
Findings offer practical insights for firms to enhance AI’s benefits by optimizing internal management mechanisms.
Findings provide theoretical foundations for policymakers designing AI-enabled green manufacturing policy tools.
This study applies the resource orchestration perspective to the AI-green transformation context, identifying operational efficiency as a mediating mechanism and organizational agility and executives’ environmental attention as key moderating conditions. It enriches research perspectives on micromanagement mechanisms within the context of green transformation.
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.
Xiao Tang, Guizhu Tan, Da Gao et al.· Sustainability· 0 citations
The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources, thereby extending RBV and DCT within the sustainability and digital transformation domains.
K. S, D. S· World Journal of Advanced Re...· 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 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