Study of adoption of AI-generated health information in video platforms using SEM and fsQCA

Abstract

Purpose This study is designed to investigate the adoption of AI-generated health information in video platforms, utilizing an integrated theoretical framework that incorporates the SOR, PET and SCM theories. Design/methodology/approach The study conducted a mixed method combining SEM and fsQCA to analyze data gathered from 468 participants. Findings The findings revealed that tailored response, human-like cues and digital culture exposure influenced perceived warmth and competence of AIGC, while bottom-line mentality did not exhibit a significant effect. These two factors further drove the adoption of AI-generated health information. Familiarity with health information technology exerted a negative moderating effect. Additionally, the fsQCA results identified six paths of adoption, highlighting human-like cues and perceived warmth of AIGC as the most critical factors among all determinants. Research limitations/implications This study contributes theoretically to the study of AIGC user behavior. Additionally, it offers practical insights into the deployment of AIGC by platforms and highlights potential directions for the future development of AIGC. Originality/value While existing studies have explored factors influencing standalone AIGC (e.g. ChatGPT), limited research has examined adoption drivers for platform-based AIGC implementations, particularly regarding AI-generated health information. To fill this gap, this study employs an integrated framework that combines the SOR, PET and SCM theories to elucidate the mechanisms underlying the adoption of AI-generated health information in video platforms.

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