The Influence of Performance Expectancy, Effort Expectancy, and Social Influence on Artificial Intelligence Adoption Behaviour: A Case Study from a Malaysian University

Abstract

This paper explores the variables that affect the use of Artificial Intelligence among students at the ABC university at Malaysia, based on major constructs of Unified Theory of Acceptance and Use of Technology (UTAUT). The study concentrates on three independent variables, which include the performance expectancy (PE), effort expectancy (EE), and social influence (SE), on Artificial Intelligence (AI) adoption behavior. Data was collected from a sample of 211 students at ABC University via an online questionnaire. The descriptive statistics, reliability test, Spearman correlation were used to analyze the data. Results indicate high levels of internal consistency of all the constructs and high positive associations between each variable and AI adoption behavior. The strongest predictor was effort expectancy, which demonstrates the significance of AI systems that are intuitive and easy to use. The social influence and performance expectancy were also found to play significant roles. Meaning that students are both social-validation- and perceived-academic-benefit-motivated.