Explaining ChatGPT use among accounting students in higher education: an integrated task–technology fit and DeLone–McLean perspective
TL;DR
The findings suggest that when AI technologies are perceived as reliable, accurate and supportive of academic tasks, students are more likely to integrate them effectively into their learning activities, and frequent and functional use of AI technologies significantly enhances students’ academic performance.
Abstract
This research examines the determinants of AI utilization among accounting students using an integrated task-technology fit (TTF) and DeLone and McLean (D&M) information systems success framework and assesses their impact on academic performance. This study employs a quantitative survey method using purposive sampling, distributing structured questionnaires to 313 accounting students in higher education institutions in Indonesia. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the relationships among variables and evaluate the proposed conceptual model. The results reveal that task characteristics, technological characteristics, system quality, information quality and service quality all exert a positive influence on the intensity of AI use in academic contexts. These findings suggest that when AI technologies are perceived as reliable, accurate and supportive of academic tasks, students are more likely to integrate them effectively into their learning activities. Furthermore, the results indicate that frequent and functional use of AI technologies significantly enhances students’ academic performance − reflected in improved comprehension of course material, greater efficiency in task completion, and more effective achievement of academic objectives. The findings offer valuable insights for higher education institutions to strategically integrate artificial intelligence technologies in enhancing teaching and learning processes. Technology developers are encouraged to improve the academic rigor, transparency and reliability of AI-based systems. Moreover, institutional policies and ethical guidelines must be established to guide the responsible and constructive use of AI within academic environments. This study introduces a novel approach by integrating two theoretical frameworks − TTF and the DeLone and McLean information systems success Model − that have rarely been combined in prior research on AI adoption in education. Unlike prior studies that primarily focus on the technical or acceptance aspects of technology, this research emphasizes how generative AI aligns with students’ academic tasks and learning needs. Its primary contribution lies in empirically examining this fit within the Indonesian context, specifically among accounting students, a demographic that has remained largely underrepresented in discussions on AI-based educational technologies.