Abstract
The value of experiential products, such as movies, is difficult to directly perceive, making online reviews a key factor in decision-making. The overwhelming volume of reviews exacerbates information overload, increasing the cognitive burden on consumers during decision-making. Therefore, the effective processing of information is crucial to the decision-making experience, and human-AI collaboration offers a new pathway in this regard. In this context, this study, based on cognitive load theory and cognitive fit theory, explores the impact mechanisms of AI-driven review presentation formats on consumer movie decisions, and the differences across various movie types. We classify movies into high cognitive demand (complex narrative, information-dense) and low cognitive demand (simple narrative, straightforward information), and design a 2 (review presentation format: bullet-point vs. paragraph) × 2 (movie type: low vs. high cognitive demand) experiment. The results reveal that, in high cognitive demand contexts, bullet-point reviews significantly reduce cognitive load by 11.8% (see the Results section for details), while in low cognitive demand contexts, no significant differences are found. Additionally, cognitive load plays a key mediating role in these effects, and the strength of this mediation is moderated by the cognitive demands of the task. This study uncovers the interaction between review presentation formats and task complexity, and how this interaction influences decision-making through cognitive load. Based on these findings, we propose contextualized and personalized information presentation design principles, offering new theoretical insights and practical frameworks for AI-driven information presentation research and platform review system optimization.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This study was funded by the Humanities and Social Science Research Youth Fund Project of the Ministry of Education of the People’s Republic of China " Research on the Reliability Evaluation and Optimization Mechanism of Online Film Reviews Based on Artificial Intelligence " (No. 21YJC760081).
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Q.W: Conceptualization, Methodology, Data curation, Supervision, Funding acquisition; Y.W: Methodology, Data curation, Investigation, Formal analysis, Writing original draft. T.W: Methodology, Data curation, Investigation. S.F: Methodology, Data curation, Investigation, Writing-Reviewing and Editing, Supervision, Funding acquisition; All authors have read and agreed to the published version of the manuscript.
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The present study was conducted in accordance with the ethical principles outlined in the 2013 Declaration of Helsinki and its subsequent amendments or equivalent ethical standards. This research obtained the ethical approval of the Research Ethics and Science and Technology Safety Committee of Hubei University of Technology (No. HBUT20240009).
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Wang, Q., Wang, Y., Wei, T. et al. Effects of AI-assisted review presentation formats on consumer decision-making efficiency from a cognitive load perspective. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45101-3
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DOI: https://doi.org/10.1038/s41598-026-45101-3
