Animating arousal and engagement: empirical insights into AI-enhanced robotic performances and consumer reactions

Purpose This study aims to apply the predictive processing theory to examine the influence of artificial intelligence (AI)-driven robotic performers on audience emotions and the audience’s resulting electronic word-of-mouth (eWOM) behaviors during tourism service encounters. Design/methodology/approach Using a quantitative research methodology, survey responses from 339 regular customers of performing arts in tourism destinations were analyzed. The respondents were recruited through Prolific, a professional data collection platform. SPSS 23.0 was used for the preliminary analysis, from which a research model to achieve the aim was proposed. SmartPLS 3 was used for partial least squares structural equation modeling to test the model. Findings Interactive and novel robotic performances significantly encouraged the consumers to share their experiences online, thereby enhancing eWOM. However, melodic resonance had no significant impact on eWOM intentions. The consumers’ emotional responses fully mediated the relationship of the novelty and interactivity of the performances to the consumers’ eWOM intentions but did not mediate the relationship of the musical elements to their eWOM intentions. Originality/value This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors. It extends the application of the predictive processing theory to the domain of consumer behavior, offering valuable insights for enhancing audience engagement in performances through technological innovation.