New frontiers in digital content adoption: a study based on the cognition-motivation-emotion framework

Purpose This study aims to understand how users decide to adopt artificial intelligence-generated content (AIGC), considering the dynamic nature of the content generation and adoption process, which is understudied and significantly different from that of ready-made digital content (i.e. professionally generated content [PGC] and user-generated content [UGC]). This study proposes an AIGC adoption model that incorporates both content and interactive cues, grounded in the cognition-motivation-emotion framework. Design/methodology/approach Data from 384 valid respondents was analyzed using the partial least squares-structural equation modeling method. Findings The result suggests that the three content cues (i.e. perceived intelligence, perceived quality and perceived anthropomorphism) significantly influence the two interactive cues (i.e. performance and effort expectations), which further positively affect users’ emotions and satisfaction, subsequently leading to favorable impacts on AIGC adoption. In addition, this study finds that performance expectancy positively influences AIGC adoption, whereas effort expectancy has a nonsignificant impact. Originality/value This study offers a new examination of the adoption of AIGC, a subject that has received less attention in comparison to PGC and UGC. This approach presents a novel viewpoint by emphasizing the dynamic and interactive characteristics of AIGC, rooted in the cognition-motivation-emotion framework. This study identifies perceived intelligence, perceived quality and anthropomorphism as significant factors influencing user perceptions and expectations, emphasizing their effect on performance and effort expectancy. The research expands the theoretical framework surrounding AIGC adoption while offering actionable insights for developers aiming to improve user engagement via optimized content and interactive cues.