Consumer Purchase Intention in AI-Enabled Digital Commerce: An Extended UTAUT2 Perspective

Abstract

Artificial intelligence has been a leading dimension in the sense of e-commerce, thus the need to describe how consumers create purchase intentions in conditions of AI. The objective of the current study is to identify the impact of primary technology acceptance beliefs on the behavioral intentions to purchase mediated by customer satisfaction and trust in AI, while disaggregating the moderating role of task complexity. The research is based on UTAUT2, which includes performance expectancy, effort expectancy, hedonic motivation and habit as the key antecedent variables. The data were collected through a structured questionnaire, which was applied to 377 British consumers and then analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical evidence shows that performance expectancy, effort expectancy, hedonic motivation and habit have important positive impacts on customer satisfaction and trust in AI. In line with this, customer satisfaction and trust in AI have a significant positive effect on behavioral intention to purchase and thus serve as important mediating variables in the relationship between the beliefs about acceptance and purchase intention. The findings also indicate that customer satisfaction and behavioral intention to purchase, or trust in AI and behavioral intention to purchase are positively moderated by task complexity, indicating that these relationships are stronger when task complexity is high. Overall, this study augments the UTAUT2 framework by the incorporation of satisfaction, trust in AI, and task complexity and thereby offers a more complete explanation of consumer purchase intention in AI-mediated digital commerce and provides valuable information on scholarly investigation and practical implementation.