The impact of user experience on trust in AI systems in online shopping

The gap

Evidence profile

Sourced from the stated research gap and inline gaps of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• The role of AI competencies: How do AI knowledge, skills, and attitudes shape users’ understanding and evaluation of explainable news recommendations? (2026) · New Media & Society · doi

  There is a lack of understanding of how explanations in AI-driven news recommender systems influence user understanding, trust, and attitudes. There is a need to examine whether these effects depend on individual differences in AI competence. The study aims to address this gap by investigating the role of explanations in AI-driven news recommender systems.

  generalstated research gap
  Keywords: there lack understanding explanations ai-driven news recommender systems
• How User Experience Affects Engagement with AI Chatbots and Recommendation Systems in Shopping Websites (2026) · International Journal of Multicultural and Multireligious Understanding · doi

  The study identifies a gap in the understanding of the impact of user experience on trust in AI systems in online shopping. The study aims to address this gap by investigating the influence of feedback quality, bias level, trust history, and cognitive load on user trust in AI chatbots and recommendation systems.

  generalstated research gapevidence 5/5
  Keywords: study identifies gap understanding impact user experience trust
• The role of explainable AI social assistants in digital socialization among older adults in China: impacts on social satisfaction, human–AI trust, and digital anxiety (2026) · Universal Access in the Information Society · doi

  Fur- thermore, this study primarily focused on transparency in AI recommendation logic, but future research could explore more complex explainability designs in different contexts. One limitation of the present study is that it did not for- mally model more complex relational mechanisms among social satisfaction, human-AI trust, and digital anxiety beyond the primary between-group comparisons. When AI responses appear opaque, users may remain uncertain about the legitimacy of the recommendation and therefore hesitate to rely on it.

  generalinline gapsevidence 5/5
  Keywords: recommendation complex thermore primarily focused transparency logic future explore explainability designs different contexts limitation present

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