Time-Series Recommendation Quality, Algorithm Aversion, and Data-Driven Decisions: A Temporal Human–AI Interaction Perspective

Shan Jiang, Tianyu Chen (), Yufei Tan, Shiqi Gao and Lanhao Li
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Shan Jiang: School of Economics, Wuhan University of Technology, Wuhan 430070, China
Tianyu Chen: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Yufei Tan: School of Economics, Wuhan University of Technology, Wuhan 430070, China
Shiqi Gao: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Lanhao Li: School of Computer Science and Engineering, Beihang University, Beijing 100191, China

Mathematics, 2025, vol. 13, issue 21, 1-19

Abstract: New AI technologies have empowered e-commerce personalized recommendation systems, many of which now leverage time-series forecasting to capture dynamic user preferences. However, buyers’ algorithm aversion hinders these systems from realizing their full potential in enabling data-driven decisions. Current research focuses heavily on artifact design and algorithm optimization to reduce aversion, with insufficient attention to the temporal dimensions of human–AI interaction (HAII). To address this gap, this study explores how recommendation accuracy, novelty, and diversity—key attributes in time-series recommendation contexts—influence buyers’ algorithm aversion from a temporal HAII perspective. Data from 205 online survey responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results reveal that accuracy (encompassing sequential prediction consistency), novelty (balanced with temporal relevance), and diversity (covering long-term preferences) negatively impact algorithm aversion, with perceived usefulness as a mediator. Reduced aversion further facilitates data-driven purchasing decisions. This study enriches the algorithm aversion literature by emphasizing temporal HAII in time-series recommendation scenarios, bridging human factors research with data-driven decision-making in e-commerce.

Keywords: time-series recommendation; temporal human–AI interaction; algorithm aversion; data-driven decision-making; perceived usefulness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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