The Media Richness of AI Voice Assistants in Service Encounters: Scale Development and Validation

As AI voice assistants (VAs) become integral to service interactions, a robust framework for evaluating their communicative effectiveness is needed. Media richness theory offers a pertinent foundation, yet extant research has largely focused on human-to-human communication via traditional media, leaving VAs' intelligent and interactive attributes under-theorized. To address this gap, this study developed and validated a scale for measuring VA media richness within technology-mediated service contexts. Following established scale-development procedures, we derived three dimensions from literature review and user interviews. Exploratory factor analysis (n = 210) and confirmatory factor analysis (n = 313) refined a 13-item, three-dimensional scale encompassing interactive sensitivity, linguistic flexibility, and personalization. Nomological validity testing (n = 295) confirmed that VA media richness significantly predicts reuse intention and electronic word-of-mouth. This research advances media richness theory in human-AI interaction and provides a validated instrument for VA design and implementation.