User Evaluation of Conversational AI Systems: The Roles of Digital Literacy, Trust, and Perceived Privacy Risk
DOI:
https://doi.org/10.32890/jict2026.25.4.3Keywords:
Digital literacy, trust, perceived privacy risk, conversational AI, user evaluationAbstract
Conversational AI is spreading rapidly, and understanding how users evaluate these systems has become an important issue in information systems research. Drawing on an information systems perspective, this study develops a structural model integrating digital literacy, trust, and perceived privacy risk to examine how digital literacy influences users’ evaluations of conversational AI systems through key psychological mechanisms. Using survey data collected from 580 Chinese Generation Z users, the study employs PLS-SEM to test the proposed model. The results indicate that digital literacy significantly enhances trust, which in turn positively influences system evaluation. Digital literacy is also positively associated with perceived privacy risk, suggesting that more digitally literate users are more sensitive to data practices and system uncertainty. However, perceived privacy risk does not significantly affect system evaluation, indicating that users tend to balance potential risks against perceived benefits during interaction. The study extends the application of Privacy Calculus Theory by identifying digital literacy as an important antecedent of user evaluation and highlighting the central role of trust in conversational AI contexts. Practically, the findings suggest that developers should enhance transparency, trust-building mechanisms, and privacy communication to improve user evaluations. Future research may further examine additional system-related factors and diverse user groups to validate and extend the proposed framework.
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