Abstract
Recommender systems play a crucial role in everyday consumption, offering a promising channel for encouraging sustainable choices. Prior research indicates that explanations influence users' perceptions of recommendations and can support more informed decisions. We argue that explanations can also serve as behavioral nudges by foregrounding sustainability information at the moment of choice.
This study investigates how different behavioral framings of sustainability information in recommendation explanations affect user choices and perceptions. Using generative AI, we generate sustainability-aware explanations by drawing on nudge theory and validate them through human evaluation and LLM audits. Building on this foundation, we conduct two randomized studies ($N = 529$) in a low involvement domain (instant coffee) and a high involvement domain (hotel bookings), where participants choose among preference matched recommendations accompanied by these explanations.
Our results show that across both domains, merely disclosing sustainability information in explanations does not change choices, whereas framing that information or invoking a descriptive social norm significantly increases sustainable selections and eases decision-making. Notably, perception and behavior diverge, as plain disclosure improves explanation evaluations without translating into more sustainable selection behavior. Our work demonstrates how LLMs can generate theory-grounded explanations at scale, pointing toward practical explanation-based interventions for social good. We conclude by discussing implications for adaptive explanation design with generative AI.
Blogger's Review: This paper offers a fresh perspective on sustainable recommendation systems, emphasizing the significance and effectiveness of explanations. By integrating generative AI with behavioral economics, the research lays a solid foundation for designing more impactful recommendation systems, and future studies could further explore the application potential across different domains.