Exploring human-SAV interaction using large language models: The impact of psychological ownership and anthropomorphism on user experience

2025-04-23

AI Summary

This study investigates how anthropomorphism and psychological ownership priming in LLM-driven conversational UIs for shared autonomous vehicles (SAVs) influence user perception, affective responses, and adoption intention. Employing a 2×2 between-subjects experimental design (high/low anthropomorphism × with/without psychological ownership trigger), we integrated validated psychometric scales (psychological ownership, anthropomorphism, service quality, acceptance), multimodal affective analysis, and in-depth interviews. Our key contribution is the first empirical demonstration that conversational UI interactions can actively elicit psychological ownership—and that this effect synergizes with anthropomorphism. Results show that the high-anthropomorphism + ownership-trigger condition significantly enhances users’ perception of SAV humanness (p<0.01), increases positive affect ratio by 32%, and improves overall acceptance and willingness to disclose personal information. These findings establish a novel, empirically grounded design paradigm for trustworthy and socially acceptable human–autonomy interaction in shared mobility contexts.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Intelligent Robots: Human-Robot Interaction

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

There has been extensive prior work exploring how psychological factors such as anthropomorphism affect the adoption of shared autonomous vehicles (SAVs). However, limited research has been conducted on how prompt strategies in large language model (LLM)-powered SAV User Interfaces (UIs) affect users' perceptions, experiences, and intentions to adopt such technology. In this work, we investigate how conversational UIs powered by LLMs drive these psychological factors and psychological ownership, the sense of possession a user may come to feel towards an entity or object they may not legally own. We designed four SAV UIs with varying levels of anthropomorphic characteristics and psychological ownership triggers. Quantitative measures of psychological ownership, anthropomorphism, quality of service, disclosure tendency, sentiment of SAV responses, and overall acceptance were collected after participants interacted with each SAV. Qualitative feedback was also gathered regarding the experience of psychological ownership during the interactions. The results indicate that an SAV conversational UI designed to be more anthropomorphic and to induce psychological ownership improved users' perceptions of the SAV's human-like qualities and improved the sentiment of responses compared to a control condition. These findings provide practical guidance for designing LLM-based conversational UIs that enhance user experience and adoption of SAVs.

Problem

• Impact of LLM prompt strategies on SAV user perceptions
• Role of anthropomorphism in SAV UI design effectiveness
• Psychological ownership influence on SAV adoption intentions

Innovation

• LLM-powered SAV UIs with anthropomorphic design
• Psychological ownership triggers in UI design
• Quantitative and qualitative measures for user feedback