The Role of Experience in AI Adoption: The Moderating Effect on Predictors of ChatGPT Usage Intention Among Generation Z - Biloš, Antun; Turkalj, Davorin; Kelić, Ivan (9th FEB International Scientific Conference: Sustainable Management in the Age of ESG and AI: Navigating Challenges and Opportunities, 2025)
The objective of this study was to investigate the role of experience in the adoption of artificial intelligence (AI) tools, with a specific focus on its moderating effect on the predictors of ChatGPT usage intention among Generation Z in Croatia. The research employed the extended UTAUT2 model, examining crucial predictors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HT), and Personal Innovativeness (PI). The moderation analysis indicated that experience significantly moderates the effect of Habit (HT) on usage intention, suggesting that users with less experience tend to rely more on habitual usage. Furthermore, Social Influence (SI) displayed a marginally significant moderation effect, which suggests that less experienced users are somewhat more affected by social norms. Comparative analysis among the three experience level groups revealed significant differences in key predictors, thereby reinforcing the role of experience in shaping AI adoption behaviours. These findings underscore the significance of habit formation and social influence in the context of AI adoption, and they emphasize that strategies aimed at enhancing the adoption of AI tools should account for user experience levels.