Perceived Search Overload, Generative AI Credibility, and Comparative Usefulness: A Channel Complementarity Approach to Health Information Seeking

Grounded in Channel Complementarity Theory (CCT), this study assumes that users perceive Generative AI (GenAI) tools not as replacements but as complementary sources for seeking health information. Applying the push-pull-mooring (PPM) framework from information systems research, it examines how perceived information overload with traditional search engines (push factor), the perceived credibility of GenAI-generated health information (pull factor), and perceived hallucination risk (mooring factor) are associated with current users’ intentions to continue using GenAI tools, with comparative usefulness serving as a mediating factor. Using survey data from a nationally representative sample of 1,200 adults in South Korea, the proposed model was tested using PLS-SEM. The results supported all hypotheses: perceived overload and credibility were positively associated with perceived comparative usefulness, while hallucination risk was negatively associated with it. Both overload and credibility were directly and indirectly related to continuance intention, whereas hallucination risk was associated with continuance intention only indirectly through perceived usefulness.