Abstract

As artificial intelligence (AI) systems increasingly operate in socially meaningful settings, it becomes important to understand how design choices such as physical embodiment shape user judgements. This mixed-methods study examines how people evaluate an embodied robot (Pepper) and a disembodied voice assistant (Alexa) in a competitive rock-paper-scissors task (N = 71). Rather than treating trust as a single unitary construct, we operationalise several task-relevant trust-related judgements (e.g., perceived fair play, comfort competing, reliance on strategic choices) alongside engagement and opponent-realism indicators. Across within-participant comparisons, Pepper was associated with higher enjoyment and stronger opponent realism, and was judged as less likely to be ‘cheating’. Alexa, however, was rated as more predictable and was preferred by some participants for reliance in a more complex game scenario. Exploratory analyses further suggest that embodiment-related cues (e.g., perceived influence of Pepper’s physical actions) are associated with individual differences in these judgements. Qualitative themes contextualise these findings, indicating that visible actions can be interpreted as transparency and ‘fairness’, while familiarity with voice assistants may support competence-oriented trust. Overall, the results underline that embodiment can shift which dimensions of trust become salient in competitive interaction, with implications for designing AI that is both engaging and appropriately trusted. Exploratory within-subject mediation analysis (difference-score formulation with bootstrap confidence intervals) further indicates that perceived realism/‘real-opponent’ judgements partially mediate Pepper’s advantage on trust and enjoyment, while a sensitivity analysis suggests that implausibly large order-based novelty effects would be required to overturn the main Pepper-Alexa differences.