Adopting green AI for SME sustainability: mediating role of green investment and moderation by green servant leadership
Green Artificial Intelligence (Green AI) presents a strategic pathway for small and medium-sized enterprises (SMEs) in emerging economies to enhance sustainability by optimizing energy use, minimizing waste, and fostering eco-innovation. Despite its potential, adoption remains limited due to financi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Sustainable Futures |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825005660 |
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| Summary: | Green Artificial Intelligence (Green AI) presents a strategic pathway for small and medium-sized enterprises (SMEs) in emerging economies to enhance sustainability by optimizing energy use, minimizing waste, and fostering eco-innovation. Despite its potential, adoption remains limited due to financial constraints, technological readiness gaps, and weak institutional support. This study investigates the drivers of Green AI adoption and its impact on sustainable performance within the integrated framework of Technology-Organization-Environment (TOE) and the Technology Acceptance Model (TAM), incorporating Green Investment (GI) as a mediator and Green Servant Leadership (GSL) as a moderator. Survey data from 399 manufacturing SMEs in Pakistan were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that perceived ease of use, perceived usefulness, and organizational readiness significantly influence Green AI adoption, which in turn enhances both environmental and operational performance. GI strengthens this relationship by addressing resource barriers, while GSL moderates the adoption process by fostering leadership commitment to sustainability. By extending the TOE-TAM model, this study contributes new theoretical insights into sustainable technology adoption and offers practical recommendations for policymakers and SME leaders to support Green AI implementation in resource-constrained contexts. |
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| ISSN: | 2666-1888 |