arbiter
JournalsAccount

Towards a technological future: exploring how human-AI collaboration enhances corporate low-carbon transformation performance

Tao Wang et al.

Industrial Management & Data Systems2026https://doi.org/10.1108/imds-07-2025-0954article
AJG 2ABDC A
Weight
0.50

Abstract

Purpose Digitalization practices have revealed that the application of artificial intelligence (AI) in corporate carbon emissions management may trigger concerns about a potential green paradox effect. To address this tension, this study aims to explore how human-AI collaboration (HAIC) affects corporate low-carbon transformation performance (CLCTP). It further identifies the boundary conditions under which this relationship strengthens or weakens, providing new insights into the deep integration of human and artificial intelligence for sustainability outcomes. Design/methodology/approach Drawing upon socio-technical systems (STS) theory and the awareness-motivation-capability (AMC) framework, this study empirically investigates panel data from Chinese A-share listed companies from 2013 to 2023. A fixed effects model was employed to test the proposed hypotheses. Findings The results indicate that HAIC significantly improves CLCTP. This positive effect is amplified when executives possess environmental backgrounds and firms demonstrate strong absorptive capacity, but it is weakened by high supply chain concentration. Further heterogeneity analysis reveals that the positive effect of HAIC on CLCTP is more pronounced among firms with lower technological uncertainty, larger organizational scales and higher industry concentrations. Originality/value This study extends the theoretical discourse between HAIC and CLCTP in the context of corporate sustainability and low-carbon transformation. It also provides actionable insights for managers and policymakers seeking to leverage HAIC to advance green and digital transitions.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1108/imds-07-2025-0954

Or copy a formatted citation

@article{tao2026,
  title        = {{Towards a technological future: exploring how human-AI collaboration enhances corporate low-carbon transformation performance}},
  author       = {Tao Wang et al.},
  journal      = {Industrial Management & Data Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/imds-07-2025-0954},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Towards a technological future: exploring how human-AI collaboration enhances corporate low-carbon transformation performance

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.