Nice to Need You! Psychological need fulfillment, well-being, and technology acceptance in human-robot interaction across contexts

Despite growing interest in social robots for well-being support, there is still little systematic understanding of how psychologically grounded design principles translate into measurable effects across different interaction domains. Our work addresses this gap by combining self-Determination Theory as a design rationale with the PERMA framework as an evaluative model, examining how fulfilling psychological needs impacts situational well-being and technology acceptance in human-robot interaction. To this end, we designed and implemented three applications for a social robot that address autonomy, competence, and relatedness in diverse contexts: physical activity, language learning, and storytelling. Across three empirical studies involving 180 participants, we then evaluated the need-supportive robot behaviors and their effects on both momentary well-being and technology acceptance. Results suggest that the effects of need-supportive design differed across application domains. In the storytelling application, participants in the experimental condition reported significantly higher levels of autonomy, competence, and relatedness to the robot, along with improved situational well-being. The language learning study showed a stronger increase in positive affect for the need-supportive condition compared to the control condition, whereas no significant differences in need satisfaction or well-being were observed in the physical activity application. Exploratory descriptive analyses across the three studies further suggested that different application domains emphasize different dimensions of situational well-being, pointing toward the importance of context: learning elicited the highest ratings for accomplishment, whereas storytelling elicited the highest ratings for positive emotions. On the one hand, these results highlight the context-dependency of well-being effects during the interaction; on the other hand, exploratory correlational analyses showed that psychological need satisfaction, especially in regard to competence and relatedness, was positively associated with technology acceptance across all domains, indicating strong correlations with perceived sociability, enjoyment, and intention to use. These findings highlight the potential of theory-driven, need-supportive design to foster both well-being and user engagement in human-robot interaction, while emphasizing that these effects seem to depend on the interaction context.