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Purpose <p>This study investigates the effects of trust, satisfaction, and loyalty on AI-driven e-commerce, with a particular focus on how personalized recommendations moderate these relationships. It aims to explore how personalized AI features reshape consumer perceptions and decision-making.</p> Design/methodology/approach <p>A quantitative research approach was used to collect data from a diverse group of e-commerce users who had interacted with AI-based recommendation systems. An online survey employing standardized scales for trust, satisfaction, loyalty, and personalization was administered, and data were analyzed using structural equation modeling (SEM) to test the hypotheses.</p> Findings <p>The study reveals that trust has a significant positive influence on both satisfaction and loyalty. Personalization further strengthens these relationships by moderating the trust–satisfaction–loyalty dynamic. Satisfaction partially mediates the relationship between trust and loyalty, with the model’s explanatory power improving by 5% when personalization is included as a moderator. These results highlight the pivotal role of personalized recommendations in shaping consumer trust and satisfaction in AI-driven e-commerce.</p> Practical implications <p>Businesses can use personalized recommendation systems to enhance trust and satisfaction, thereby fostering loyalty. For example, platforms like Amazon and Netflix have successfully employed personalized AI algorithms to boost customer retention and engagement. Transparency features, such as explaining why certain products are recommended, and cultural sensitivity in algorithm design can further enhance customer trust and acceptance. e-commerce organizations should also invest in data privacy measures and clear algorithms to maintain consumer confidence while leveraging AI to improve customer experience and achieve sustainable competitive advantages.</p> Originality/value <p>This study contributes to the growing body of knowledge on AI-driven e-commerce by demonstrating how personalized recommendations influence trust, satisfaction, and loyalty. It provides actionable insights for leveraging AI tools to build stronger consumer relationships in dynamic digital marketplaces.</p>

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The moderating role of personalized recommendations in the trust–satisfaction–loyalty relationship: an empirical study of AI-driven e-commerce

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

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  • Noha Hassan,
  • Mohamed Abdelraouf,
  • Dina El-Shihy

摘要

Purpose

This study investigates the effects of trust, satisfaction, and loyalty on AI-driven e-commerce, with a particular focus on how personalized recommendations moderate these relationships. It aims to explore how personalized AI features reshape consumer perceptions and decision-making.

Design/methodology/approach

A quantitative research approach was used to collect data from a diverse group of e-commerce users who had interacted with AI-based recommendation systems. An online survey employing standardized scales for trust, satisfaction, loyalty, and personalization was administered, and data were analyzed using structural equation modeling (SEM) to test the hypotheses.

Findings

The study reveals that trust has a significant positive influence on both satisfaction and loyalty. Personalization further strengthens these relationships by moderating the trust–satisfaction–loyalty dynamic. Satisfaction partially mediates the relationship between trust and loyalty, with the model’s explanatory power improving by 5% when personalization is included as a moderator. These results highlight the pivotal role of personalized recommendations in shaping consumer trust and satisfaction in AI-driven e-commerce.

Practical implications

Businesses can use personalized recommendation systems to enhance trust and satisfaction, thereby fostering loyalty. For example, platforms like Amazon and Netflix have successfully employed personalized AI algorithms to boost customer retention and engagement. Transparency features, such as explaining why certain products are recommended, and cultural sensitivity in algorithm design can further enhance customer trust and acceptance. e-commerce organizations should also invest in data privacy measures and clear algorithms to maintain consumer confidence while leveraging AI to improve customer experience and achieve sustainable competitive advantages.

Originality/value

This study contributes to the growing body of knowledge on AI-driven e-commerce by demonstrating how personalized recommendations influence trust, satisfaction, and loyalty. It provides actionable insights for leveraging AI tools to build stronger consumer relationships in dynamic digital marketplaces.