Purchase Behavior in AI-Enabled Livestream Commerce: Evidence from an Emerging Economy - Thanh D. Nguyen; Hoan D. D. Ly (Emerging Science Journal, 2026)
This study aims to examine consumer purchase behavior in AI-enabled livestream commerce as an emerging form of AI-driven digital commerce. Specifically, the research investigates how technological and social–psychological factors influence perceived ease of use, perceived usefulness, intention to use, and purchase behavior. Data were collected through an online survey using convenience and snowball sampling methods, resulting in 248 valid responses. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the proposed relationships. The findings indicate that compatibility and self-satisfaction positively influence both perceived ease of use and perceived usefulness, while perceived risk negatively affects these perceptions. Social influence significantly enhances perceived usefulness but does not have a significant effect on perceived ease of use. In addition, perceived ease of use and perceived usefulness significantly strengthen intention to use, which subsequently drives purchase behavior. This study contributes to the literature by extending the TAM–UTAUT framework within the context of AI-enabled livestream commerce and by offering new insights into how AI streamers reshape consumer–platform interactions. These findings provide both theoretical contributions and practical implications for AI-driven commerce in emerging markets.