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Leveraging AI-based organizational learning for sustainable performance in manufacturing

Mingxuan Wang et al.

Journal of Manufacturing Technology Management2026https://doi.org/10.1108/jmtm-09-2025-0863article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose The growing integration of artificial intelligence (AI) into manufacturing has intensified interest in its implications for sustainability. This study examines how AI-based organizational learning (AIOL) affects sustainable performance (SP) in manufacturing firms and investigates the mediating roles of three dimensions of green innovation: openness, radicalness, and rhythm. Design/methodology/approach Using panel data from Chinese A-share listed manufacturing firms between 2009 and 2023, this study examines the effect of AIOL on SP using fixed-effects regression. Potential endogeneity issues are addressed using instrumental variable approach and propensity score matching. Findings AIOL significantly enhances SP in manufacturing firms. Furthermore, this relationship is mediated by green innovation openness, radicalness, and rhythm. In addition, the findings indicate heterogeneous effects of AIOL on SP across regions with varying levels of economic development and industries with different levels of competitive intensity. Originality/value This study advances manufacturing literature by integrating AIOL into the explanation of SP pathways in the intelligent manufacturing era. By incorporating green innovation rhythm alongside openness and radicalness, it provides a nuanced understanding of how manufacturing firms leverage AI to enhance SP.

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https://doi.org/https://doi.org/10.1108/jmtm-09-2025-0863

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@article{mingxuan2026,
  title        = {{Leveraging AI-based organizational learning for sustainable performance in manufacturing}},
  author       = {Mingxuan Wang et al.},
  journal      = {Journal of Manufacturing Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jmtm-09-2025-0863},
}

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Leveraging AI-based organizational learning for sustainable performance in manufacturing

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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

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