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AI-powered personalization in online fashion stores: Exploring the impact on shoppers’ perceived risk and intention to buy and recommend

K. Mohamed Jasim et al.

Information & Management2026https://doi.org/10.1016/j.im.2026.104347article
AJG 3ABDC A*
Weight
0.50

Abstract

With AI becoming ubiquitous in digital commerce, online fashion retailers increasingly use AI-powered personalization (AIP) to enhance consumer engagement. However, consumer responses and expectations regarding these technologies remain underspecified for fashion AIP, where shoppers evaluate output credibility, fit realism, and identity alignment in addition to utility/price. This study examines how exploratory value drivers (EVD) and personal value drivers (PVD) as benefits, and perceived complexity and trust deficit as sacrifices—synthesized into the overall shopping experience (SHX)—influence purchase intention and intention to recommend. A theoretical framework integrates the value-based adoption model (VAM) and trust theory to assess how consumers balance AIP’s benefits—such as convenience and engagement—against sacrifices, including privacy concerns and loss of autonomy. The study is operationalized through a structured survey, collecting 375 valid responses from shoppers who experienced AIP in online fashion stores, and analyzed using SEM. Results indicate that EVD and PVD (positive) and perceived complexity and trust deficit (negative) affect intentions with SHX mediating the effects of benefits and sacrifices. Perceived risk also mediates these relationships, with trust concerns negatively impacting purchase intent. Additionally, perceived complexity—interpreted as an interaction/interpretability burden—diminishes willingness to buy, emphasizing the need to refine personalization strategies. This study extends VAM and trust theory by demonstrating the psychological dimensions of AIP adoption. Findings provide insights for online fashion retailers to enhance AI-driven experiences by providing concise on-path explanations, clear data-use notices, and controls that let users steer curation—balancing predictive accuracy with consumer agency to foster trust, drive conversions, and strengthen loyalty.

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https://doi.org/https://doi.org/10.1016/j.im.2026.104347

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@article{k.2026,
  title        = {{AI-powered personalization in online fashion stores: Exploring the impact on shoppers’ perceived risk and intention to buy and recommend}},
  author       = {K. Mohamed Jasim et al.},
  journal      = {Information & Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.im.2026.104347},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.