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Empowering <scp>AI</scp> ‐Driven Proactive and Reactive Green Innovation: Exploring Knowledge Management, Trust and Sustainability

Amir A. Abdulmuhsin et al.

Knowledge and Process Management2026https://doi.org/10.1002/kpm.70026article
AJG 1ABDC B
Weight
0.37

What the paper says

ABSTRACT This study examines the integration of artificial intelligence (AI) and knowledge management (KM) processes and their role in fostering proactive and reactive green innovation (GI) in the Iraqi oil industry. It also explores the moderating effects of trust in technologies and sustainability orientation. Using a cross‐sectional design, data were collected from 612 middle‐level managers in Iraqi oil companies through a structured questionnaire. The data were analysed using SmartPLS v3.9 and SPSS v26 to assess measurement validity, reliability and the hypothesised relationships. The findings indicate that AI has a significant positive effect on both KM processes and GI. KM processes play a crucial mediating role by transforming AI capabilities into proactive and reactive GI outcomes. While trust in technologies and sustainability orientation moderate these relationships, their effects are relatively modest. Theoretically, the study underscores the importance of integrating AI and KM to enhance environmental performance. It contributes original empirical evidence from a challenging and underexplored context, offering insights into the conditions enabling GI in traditional industries.

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https://doi.org/https://doi.org/10.1002/kpm.70026

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@article{amir2026,
  title        = {{Empowering <scp>AI</scp> ‐Driven Proactive and Reactive Green Innovation: Exploring Knowledge Management, Trust and Sustainability}},
  author       = {Amir A. Abdulmuhsin et al.},
  journal      = {Knowledge and Process Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/kpm.70026},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.