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<p>In an era increasingly shaped by artificial intelligence (AI), understanding what motivates professionals to act sustainably is critical. This study investigates the determinants of AI professionals’ Green AI pro-environmental behavior. Drawing on expert insights, the study identifies key drivers and ranks their importance using an integrated Best–Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). To further explore the interrelationships among these drivers, the Total Interpretive Structural Modeling (TISM) approach is employed. Results indicate that intrinsic motivations play a central role in driving Green AI behavior. Specifically, green attitude, leadership commitment to sustainability, and energy consumption awareness emerged as the top-ranked determinants, with leadership commitment identified as the most foundational driver in the system. In contrast, extrinsic factors such as awards and regulatory compliance were ranked lowest, underscoring the predominance of values and beliefs in shaping sustainable AI practices. The study concludes with practical implications for fostering environmentally responsible behavior among AI professionals.</p>

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Identifying key drivers of professionals’ pro-environmental behavior in green AI adoption: an integrated BWM-TOPSIS-TISM approach

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Mohammad Dalvi-Esfahani,
  • Hajar Barati-Ahmadabadi

摘要

In an era increasingly shaped by artificial intelligence (AI), understanding what motivates professionals to act sustainably is critical. This study investigates the determinants of AI professionals’ Green AI pro-environmental behavior. Drawing on expert insights, the study identifies key drivers and ranks their importance using an integrated Best–Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). To further explore the interrelationships among these drivers, the Total Interpretive Structural Modeling (TISM) approach is employed. Results indicate that intrinsic motivations play a central role in driving Green AI behavior. Specifically, green attitude, leadership commitment to sustainability, and energy consumption awareness emerged as the top-ranked determinants, with leadership commitment identified as the most foundational driver in the system. In contrast, extrinsic factors such as awards and regulatory compliance were ranked lowest, underscoring the predominance of values and beliefs in shaping sustainable AI practices. The study concludes with practical implications for fostering environmentally responsible behavior among AI professionals.