article · Future Business Journal
Online shoppers increasingly encounter artificial intelligence systems that tailor product suggestions to individual preferences. Examining customer interactions with AI-driven recommendation tools shows that consumer trust directly enhances both user satisfaction and brand loyalty. Furthermore, satisfaction partially explains the link between trust and loyalty. Introducing personalised recommendations moderates these dynamics, increasing the explanatory power of the trust, satisfaction, and loyalty relationship by five percent. These findings indicate that customisation significantly reinforces consumer relationships in digital marketplaces. For businesses operating online retail platforms, deploying tailored recommendation algorithms can build deeper engagement and customer retention. Maintaining consumer confidence in such systems requires clear algorithm explanations, cultural sensitivity, and strong data privacy safeguards, which together help sustain competitive advantage in digital commerce.
As artificial intelligence becomes central to online shopping, businesses need to know how automated systems affect customer perceptions. Demonstrating that personalised recommendations strengthen the pathway from trust to loyalty offers clear guidance for digital retailers seeking to maintain user confidence, improve retention, and deploy algorithmic tools responsibly.
This empirical study applies directly to e-commerce platforms and digital retail businesses aiming to deploy AI recommendation tools. The findings suggest near-market implementation, guiding the design of customer-facing recommendation engines. To maximise commercial return, system developers should incorporate explainability features, culturally sensitive algorithms, and robust data privacy controls that safeguard consumer trust and improve long-term retention.
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Abstract 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.
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DOI: 10.1186/s43093-025-00476-z
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