This study used extended Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine the effect of personal expectancy (PE), efforts expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV) and habit (H) on behavioural intention (BI) to use Chat Generative Pre-Trained Transformer (ChatGPT) in higher education for an Indian context. The study also examined moderating effects of students’ self-innovativeness (SIN) and integrity (INT) on the relationship between BI and use behaviour (UB) for ChatGPT.
A sample of 311 students has been selected from Northern states of India by applying stratified proportionate random sampling method and four disciplines – engineering, business administration, science and fine arts were used as different strata in the sample selection process. Partial least squares structural equation modelling (PLS-SEM) approach has been used in data analysis to assess proposed theoretical model.
The study found PE, FC, PV and H significantly account for students’ BI and actual use of ChatGPT, whereas EE, SI and HM showed a negligible impact on BI. The BI was also found non-significant in predicting the usage behaviour for ChatGPT. The moderation effects of SIN and INT of students were also found non-significant on the relationship between BI and UB.
A limited sample size of study and its focus on Northern states of India constrain the generalizability of findings to the other parts of world.
The study helps to understand the nuances associated with advanced artificial intelligence (AI)-driven tool such as ChatGPT in higher education for the application of best-practices. Therefore, this study aims to bridge this gap by examining determinants of ChatGPT usage with extended version of UTAUT model in Indian context by using additional constructs such as SIN and INT.
