A cross-country analysis of self-determination and continuance use intention of AI tools in business education: Does instructor support matter?

This study builds on the recent interest in AI adoption research in academic settings by highlighting the need for culturally sensitive AI educational tools. The study achieved this by demonstrating how cultural differences shape students' motivation and AI use. This study adopts a cross-country comparative analytical approach to explore postgraduate students’ motivation to continue using AI tools in the context of higher education. The study developed a theoretical model based on Self-Determination Theory (SDT) and Expectation Disconfirmation Theory (EDT) to explore how perceived competence, perceived relatedness and perceived autonomy influence the continuance use intention of AI tools in two culturally unique higher education contexts – United Kingdom and Nigeria. The study also investigates how instructor support, AI anxiety and Trust in AI moderate the relationship between self-determination and AI continuance use intention of students. The data for this study was collected using Qualtrics online survey to generate responses from postgraduate students in the UK and Nigerian HEIs contexts. The questionnaire was designed using validated existing scales. Overall, 245 and 214 valid responses were received from Nigeria and UK postgraduate students respectively. The data was analysed using Structural Equation Modelling. The findings show that perceived relatedness and perceived autonomy are important predictors of AI tools continuance use intention in both countries. The findings reveal the role of cultural differences in AI use and the relative importance of relatedness and autonomy. The results also demonstrate that instructor support plays a fundamental role in AI use. The perceived impact of AI anxiety and trust in AI on competence, relatedness and autonomy vary between the different contexts. The findings emphasise the need for culturally adaptable AI systems capable of prioritizing either collaborative or individual characteristics based on the cultural setting. The findings provide useful insights for institutions and technology firms who are interested in developing globally acceptable AI tools for educational use