The Sustainability-Performance Trade-off in AI: The Role of Sustainability Information and Unmet Performance Goals in Sustainable AI Decisions

Dirk Leffrang () and Oliver Müller ()
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Dirk Leffrang: Paderborn University
Oliver Müller: Paderborn University

No 135, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: Despite the impressive capabilities of Artificial Intelligence (AI), concerns about its environmental impact continue to grow. However, organizations rarely implement sustainable AI practices in real-world settings. Prior research has predominantly focused on promoting sustainability-related information for non-AI products or exploring technical approaches for AI applications. This paper identifies performance uncertainty as a key factor distinguishing AI from prior technologies. Drawing on goal-setting theory, we investigate the impact of sustainability information and an unmet performance goal on AI retraining decisions. We conducted three incentivized online between-subjects experiments with 343 individuals with data science experience. Our results indicate that visualizing sustainability information increases the likelihood of sustainable AI choices. However, presenting an unmet performance goal decreases the likelihood and offsets the beneficial impact of sustainability information. These findings support sustainability initiatives, AI evaluations, and future research by emphasizing that while sustainability matters, achieving it requires appropriate performance goals for AI.

Keywords: sustainability; artificial intelligence; goal-setting; laboratory experiment (search for similar items in EconPapers)
JEL-codes: C91 D91 Q01 Q55 (search for similar items in EconPapers)
Pages: 31
Date: 2025-04
New Economics Papers: this item is included in nep-ain, nep-env and nep-exp
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