Drivers and Barriers of ChatGPT Adoption in Higher Education: Insights from Multiple Theories

Abstract

This study aims to discuss the driving forces and obstacles that influence the usage of ChatGPT among higher education postgraduate research scholars. The data analysis was conducted using the partial least squares structural equation modeling (PLS-SEM) with the consideration of 436 valid responses which were collected from post graduate research scholars in Pakistan. The study relies on the behavioral reasoning theory (BRT), the technology acceptance model (TAM-2), and the theory of innovation resistance (IRT) to explain determinants that facilitate and hinder the use of technology. The findings confirmed that perceived ease of use, perceived usefulness, and social influence (reason for) are positively related. Tradition, risk, and usage barriers (reasons against), on the other hand, have a negative relationship with the intention of the users to use ChatGPT. These results indicate that adoption drivers and resistance factors influence simultaneously the usage of ChatGPT in academic context. In addition to integrating three prominent theoretical perspectives on technology adoption and resistance, this study contributes to the literature on AI-based technology adoption and advances understanding of the behavioral mechanisms underlying ChatGPT usage. This study also has practical implications to the institutions of higher learning and policymakers on how to effectively and responsibly integrate the use of generative AI tools in higher learning institutions.

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