This study based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, examines factors influencing graduate students' behavioural intention towards adoption and actual use of generative artificial intelligence (GenAI). l. Using a cross-sectional data from 415 graduate students in Ghana, this study employed the partial least squares structural equation modeling (PLS-SEM), importance-performance map analysis (IPMA), and fuzzy-set qualitative comparative analysis (fsQCA) for the analytical approach. The results from the PLS-SEM indicate that performance expectancy, hedonic motivation, facilitating conditions, personal innovativeness, perceived learning opportunities and satisfaction significantly drive graduate students' behavioural intention towards the adoption of GenAI, whereas effort expectancy, social influence, habit, perceived enjoyment, and perceived risk showed no significant effects. Personal innovativeness, satisfaction, and behavioural intention were found to be the significant predictors of actual use behaviour of GenAI, while perceived risk did not. The IPMA highlighted satisfaction as the highest ranked importance factor and personal innovativene ranked as the highest performance factor, while perceived risk was ranked lowest for both importance and performance. The fsQCA further reveals nuanced results indicating that graduate students' low perceived risk combined with high perception of satisfaction, personal innovativeness, learning opportunities predict their intention and actual usage of GenAI. The combined PLS-SEM, IPMA, and fsQCA approach provides robust insights into GenAI adoption among students in higher education, highlighting the interplay of psychosocial perspectives and the need to enhance the functional benefits, user engagement, and institutional support for effective GenAI integration. Thus, this study has practical implications for improving GenAI infrastructure, enhancing user-centered design, fostering curiosity, and strengthening institutional support.

ChatGPT, a generative artificial intelligence (GAI) tool, has gained recognition as a valuable educational technology (EdTech) resource, yet its impact on students' academic outcomes in Ghana remains unclear. This study examined the impact of use of ChatGPT on academic achievement among higher education (HE) students using the “interactive-constructive-active–passive (ICAP)” framework and “self-determination theory (SDT)”. A purposive sample of 457 HE economics students from a public university in Ghana was selected. The study used adapted scales of ICAP (i.e. measuring the use of ChatGPT), “desire to learn (DFL), self-efficacy (SE), beliefs about the future (BAF), epistemic curiosity (EP) and perceived academic performance (AP)”. Data were analysed using “partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA)”. PLS-SEM results indicated that the use of ChatGPT (ICAP) positively influenced DFL, SE, BAF and AP. DFL and BAF were positively associated with AP, whereas SE showed a negative association with AP. Furthermore, DFL, SE and BAF mediated the relationship between the use of ChatGPT and AP. EP directly influenced DFL, SE and BAF and moderated the positive influence of ChatGPT usage on DFL and BAF. FsQCA results revealed four configurations for achieving high AP. This study highlights the positive role of ChatGPT in the academic performance of economics students and underlines the importance of considering multiple factors in policy and intervention strategies to optimise the use of AI in higher education.