Nudging Sustainable Choices through LLM-Generated Recommendation Explanations

2026-07-28

AI Summary

This study addresses the limited efficacy of conventional sustainability disclosures in driving pro-environmental behavior. Bridging behavioral nudging theory and large language models (LLMs), the authors propose an innovative approach that automatically generates framed explanatory messages to promote sustainable choices. Randomized controlled trials were conducted in two distinct contexts—instant coffee purchases (low-involvement) and hotel bookings (high-involvement). Findings reveal that merely providing sustainability information does not significantly influence user decisions. In contrast, LLM-generated explanations incorporating descriptive social norms or loss/gain framing significantly increase the adoption of sustainable options while reducing perceived decision burden. This work represents the first integration of LLMs with behavioral nudges, uncovering a critical mechanism for translating informational cues into actionable behavior.

Abstract

Recommender systems mediate everyday consumption, offering a promising channel for encouraging sustainable choices. Prior research shows 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-as-a-judge 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), in which 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.

Problem

• sustainable choices
• recommender systems
• behavioral nudges
• explanation framing
• generative AI

Innovation

• LLM-generated explanations
• behavioral nudges
• sustainability framing
• recommender systems
• generative AI