The Role of ARCS Motivation in Predicting ChatGPT Adoption Based on UTAUT3 Framework

Abstract

As technology rapidly evolves, generative AI tools are increasingly integrated across various fields, including education. ChatGPT, a well-known language model developed by OpenAI, has gained significant importance in educational settings. This study employed a quantitative, cross-sectional survey design and employed the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) and the ARCS motivation model to examine the behavioral intention and ChatGPT usage among university students. Data were collected through a structured questionnaire from 455 randomly selected students from Pakistani universities, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study identified key drivers of ChatGPT acceptance framework, including effort expectancy, performance expectancy, hedonic motivation, social influence, habit, and ARCS motivation, all of which significantly influenced students’ behavioral intention to adopt the technology. However, personal innovativeness did not show a significant impact. Behavioral intention and ARCS motivation significantly and jointly predicted actual ChatGPT usage, with motivation acting as a moderator. ARCS motivation also moderated the relationships between facilitating conditions, behavioral intention, and ChatGPT usage, enhancing understanding of the interaction between motivation and technology acceptance. This integration of UTAUT3 and ARCS motivation provides a novel perspective on the motivational mechanisms underlying ChatGPT adoption among university students.

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