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Power of Green Capabilities and Artificial Intelligence (AI): Understanding How and When Green Innovation Promotes Sustainability

Author

Listed:
  • Sikandar Rahman
  • Sheikh Adeel
  • Murad Ali
  • Saleh Bajaba
  • Hengky Latan

Abstract

Green innovation is increasingly recognized as a critical strategy for small‐ and medium‐sized enterprises (SMEs) to enhance sustainable performance. Drawing on the natural resource‐based view (NRBV), this study examines the impact of green innovation on sustainable performance and the mediating roles of green knowledge sharing and green dynamic capabilities. In addition, the moderating effect of artificial intelligence (AI) on these relationships was investigated. Data were collected from 230 SMEs in two phases, and the hypotheses were analyzed. The findings reveal that green innovation is positively related to sustainable performance, with green knowledge sharing and green dynamic capabilities mediating this relationship. Furthermore, AI significantly moderates the relationship between green knowledge sharing, green dynamic capabilities, and sustainable performance. These results suggest that integrating AI into SMEs can enhance the promotion and effectiveness of green innovation strategies. This study contributes to the literature by providing empirical evidence for NRBV and highlighting the importance of green knowledge sharing, green dynamic capabilities, and AI in the context of green innovation and sustainable performance. These findings offer valuable insights for SME managers and policymakers seeking to foster sustainable development through green innovation and AI adoption.

Suggested Citation

  • Sikandar Rahman & Sheikh Adeel & Murad Ali & Saleh Bajaba & Hengky Latan, 2026. "Power of Green Capabilities and Artificial Intelligence (AI): Understanding How and When Green Innovation Promotes Sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 35(2), pages 2502-2525, February.
    • Handle: RePEc:bla:bstrat:v:35:y:2026:i:2:p:2502-2525
    DOI: 10.1002/bse.70252
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