Generative artificial intelligence level and firm green knowledge transfer

Purpose Against the backdrop of the diffusion of generative artificial intelligence (GenAI) and the expanding demand for external green technology under the dual-carbon goal, this paper aims to examine how firms’ levels of GenAI adoption affect their acquisition of green technological knowledge from external sources, identify two transmission mechanisms and characterize the firm-level boundary conditions that shape the marginal value of GenAI. Design/methodology/approach Using panel data on listed firms from 2014 to 2023, the authors measure firm-level GenAI by the log-transformed frequency of generative-AI-related keywords in annual reports and measure green knowledge transfer by the log-transformed number of green patents received from external assignors. Findings Firms’ GenAI levels exert a robust positive effect on their external green knowledge transfer, and instrumental-variable estimates survive weak-instrument and over-identification tests with a causal interpretation. GenAI operates through two independent channels: enhancing access to cross-organizational collaboration and raising R&D intensity. Although the positive moderating effect of internalization capability is directionally consistent, it is not statistically significant, while its quadratic term reveals diminishing marginal returns. Heterogeneity analysis reveals four conditional dimensions – substantive adoption, management orientation, supply-chain resilience and green finance pilot status – along which directional between-group differences emerge. Firms that disclose AI keywords without substantive investment display a reverse co-movement with green knowledge transfer. Originality/value This paper links the literature on general-purpose technologies with that on inter-organizational knowledge transfer by proposing and testing a mechanism through which GenAI facilitates green knowledge flows, specifically by reducing identification and evaluation barriers in cross-organizational collaboration.

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Publication Details

Journal
Journal of Knowledge Management
Published
2026-08-21
DOI
https://doi.org/10.1108/jkm-04-2026-0819
Primary Topic
Environmental Sustainability in Business
Type
article
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article

Generative artificial intelligence level and firm green knowledge transfer

Zhiguo Chen, Shuya Zhou, Jian Song
Journal of Knowledge Management
Environmental Sustainability in Business
article

Generative artificial intelligence level and firm green knowledge transfer

Zhiguo Chen, Shuya Zhou, Jian Song
article en

Abstract

Purpose Against the backdrop of the diffusion of generative artificial intelligence (GenAI) and the expanding demand for external green technology under the dual-carbon goal, this paper aims to examine how firms’ levels of GenAI adoption affect their acquisition of green technological knowledge from external sources, identify two transmission mechanisms and characterize the firm-level boundary conditions that shape the marginal value of GenAI. Design/methodology/approach Using panel data on listed firms from 2014 to 2023, the authors measure firm-level GenAI by the log-transformed frequency of generative-AI-related keywords in annual reports and measure green knowledge transfer by the log-transformed number of green patents received from external assignors. Findings Firms’ GenAI levels exert a robust positive effect on their external green knowledge transfer, and instrumental-variable estimates survive weak-instrument and over-identification tests with a causal interpretation. GenAI operates through two independent channels: enhancing access to cross-organizational collaboration and raising R&D intensity. Although the positive moderating effect of internalization capability is directionally consistent, it is not statistically significant, while its quadratic term reveals diminishing marginal returns. Heterogeneity analysis reveals four conditional dimensions – substantive adoption, management orientation, supply-chain resilience and green finance pilot status – along which directional between-group differences emerge. Firms that disclose AI keywords without substantive investment display a reverse co-movement with green knowledge transfer. Originality/value This paper links the literature on general-purpose technologies with that on inter-organizational knowledge transfer by proposing and testing a mechanism through which GenAI facilitates green knowledge flows, specifically by reducing identification and evaluation barriers in cross-organizational collaboration.

Journal of Knowledge Management
Jilin University of Finance and Economics (CN), Jilin University (CN), Baoding University (CN), Hebei University (CN), Changchun University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Environmental Sustainability in Business
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