The influence of cuteness types and sound characteristics of in-vehicle robots on user trust and experience in automatic driving error reporting scenarios

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Abstract
Purpose: This study explores how the cuteness type and sound characteristics of in-vehicle robots influence driver trust and user experience during error reporting in autonomous driving. As intelligent vehicle technologies advance, human-machine interaction quality has become crucial for user acceptance and satisfaction. By applying cuteness theory, the study examines how cute design fosters positive emotions, strengthens user-vehicle connection, and facilitates trust. Design/methodology/approach: The study investigates two cuteness types (kindchenschema and whimsical) and three sound characteristics (earcon, apology, and explanation prompts). An online survey with convenience sampling was conducted, where participants viewed six robot videos featuring different combinations of cuteness types and sound features and provided feedback. Findings: Kindchenschema cuteness significantly enhances user experience compared to whimsical cuteness, promoting feelings of closeness and protection. Explanatory voice prompts more effectively increase trust than earcon sounds by helping users understand system feedback. No significant interaction between cuteness type and sound characteristics was found, indicating their independent effects on trust and user experience. Originality/value: This study provides insights for designing in-vehicle robots in error-reporting scenarios, guiding the creation of user-friendly intelligent systems. It emphasizes the role of cute design in improving emotional experiences and trust, supporting emotional design principles that prioritize user needs. The findings offer a framework for exploring robot appearance and sound in various driving contexts, advancing human-machine interaction toward greater intelligence. From a managerial perspective, the study provides guidance for enhancing consumer trust and brand loyalty.

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