Antecedents of Generative Artificial Intelligence Technology Adoption: Extended Innovation of Diffusion Model with Cultural Dimensions and Risks Perceptions

Abstract

As Artificial Intelligence (AI) technologies are taking the lead among the technological advancements around the world, societies are increasingly becoming interwoven with Generative AI (GAI) technologies in all aspects, including higher education (HE). This study’s main aim is to examine how individual-level cultural dimensions influence students’ adoption of GAI in learning, drawing on an extended Innovation of Diffusion Theory (IDT) model. It explores the impact of individual-level cultural dimensions (individualism/collectivism and uncertainty avoidance), IDT innovation factors (relative advantage, complexity, compatibility, observability, trialability), and individual factors (self-efficacy, perceived risk) on Saudi students’ perceptions of GAI adoption across several universities. Quantitative data were collected from 306 online survey and analyzed using CB-SEM. Results highlight the instrumental role of cultural dimensions, with individualism/collectivism and uncertainty avoidance negatively affecting GAI adoption. While complexity showed no significant impact, all other IDT variables positively influenced adoption. Furthermore, self-efficacy and perceived risk were found to be significant indicators of GAI use. The study emphasizes the cultural differences that shape technology adoption in collectivist societies that are moving toward individualism such as Saudi. It identifies limitations, provides useful insights, and suggests recommendations for future research on GAI uptake in culturally diverse HE contexts.