Examining AIGC health information adoption: integrating IAM and AI ethics

Purpose Integrating both the information adoption model (IAM) and artificial intelligence (AI) ethics, this research examined the mechanism shaping artificial intelligence-generated content (AIGC) health information adoption. Design/methodology/approach We collected 455 valid samples and adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) to analyze data. Findings The results show that in addition to the IAM factors (health information quality and source credibility), AI ethics (transparency, fairness and privacy risk) also significantly influence information usefulness and trust, both of which further affect adoption intention. Research limitations/implications The findings suggest that AIGC platforms should enhance health information quality and lower AI ethics risks to promote user adoption of health information. Originality/value Prior research has focused on the effects of information characteristics such as information quality, on information adoption. However, AI ethics such as transparency and fairness, may also affect user assessment of health information value and the adoption decision. By integrating the IAM and AI ethics, this study provides a comprehensive understanding of AIGC health information adoption.