This study investigates how algorithmic technology influences consumers’ shopping intentions in the context of livestreaming bandwagon consumption. Grounded in the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), it develops an integrated framework that combines psychological and technological dimensions. Based on data collected through an online questionnaire and analyzed via structural equation modeling, the study finds that algorithmic technology enhances consumers’ perceived ease of use by improving access to live-streaming interfaces and increases perceived enjoyment by strengthening anchor effectiveness. These factors jointly shape consumers’ attitudes toward live shopping. Additionally, algorithmic mechanisms such as data targeting, user labeling, scoring systems, and groupbased recommendations significantly affect behavioral intentions through both direct and mediated pathways, including attitudes, perceived behavioral control, and promotional incentives. This research offers a novel empirical contribution by modeling how algorithmic personalization impacts live shopping behaviors, providing theoretical and practical insights for digital commerce applications.