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<p>Business intelligence (BI) and AI-driven personalization are increasingly used in e-commerce to enable fine-grained customer segmentation, individualized recommendations, and dynamic marketing interventions. At the same time, these data-intensive practices raise concerns about privacy, transparency, fairness, and potential manipulation of consumer behaviour. This article presents a systematic review of peer-reviewed journal and conference/proceedings studies on AI-based personalization and customer segmentation in e-commerce published between 2018 and 2025. Using structured searches in Scopus and the Web of Science Core Collection, together with predefined inclusion and exclusion criteria and a documented screening protocol, we identify a corpus of 41 studies and analyse them through descriptive bibliometric mapping and qualitative content analysis. The review synthesizes how BI-enabled personalization relates to consumer trust, perceived value, and shopper experience, and examines the extent to which the literature engages with ethical and governance issues such as data protection, transparency, algorithmic opacity, bias, perceived control, and consumer autonomy. Across the corpus, personalization is generally associated with more favourable perceived relevance, satisfaction, and loyalty; however, ethical dimensions remain under-theorised and are rarely operationalised in empirical models, in part because much of the evidence base relies on cross-sectional survey designs (often analysed via SEM/PLS-SEM) that are poorly suited to assessing systemic constructs such as discrimination or manipulation. Privacy concerns, perceived control, and transparency emerge as important conditions for positive trust-related outcomes, while constructs such as fairness, discrimination, and manipulation are only sporadically examined. We propose an integrative framework linking BI infrastructure, segmentation mechanisms, ethical safeguards, and consumer outcomes, and use it to articulate a focused research agenda for responsible, transparent, and human-centred BI systems for e-commerce personalization.</p>

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Business intelligence, AI-driven personalization, and ethical customer segmentation in e-commerce: A systematic review of consumer trust and digital marketing

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Ansuman Das,
  • Sasmita Mishra,
  • Zefree Lazarus Mayaluri

摘要

Business intelligence (BI) and AI-driven personalization are increasingly used in e-commerce to enable fine-grained customer segmentation, individualized recommendations, and dynamic marketing interventions. At the same time, these data-intensive practices raise concerns about privacy, transparency, fairness, and potential manipulation of consumer behaviour. This article presents a systematic review of peer-reviewed journal and conference/proceedings studies on AI-based personalization and customer segmentation in e-commerce published between 2018 and 2025. Using structured searches in Scopus and the Web of Science Core Collection, together with predefined inclusion and exclusion criteria and a documented screening protocol, we identify a corpus of 41 studies and analyse them through descriptive bibliometric mapping and qualitative content analysis. The review synthesizes how BI-enabled personalization relates to consumer trust, perceived value, and shopper experience, and examines the extent to which the literature engages with ethical and governance issues such as data protection, transparency, algorithmic opacity, bias, perceived control, and consumer autonomy. Across the corpus, personalization is generally associated with more favourable perceived relevance, satisfaction, and loyalty; however, ethical dimensions remain under-theorised and are rarely operationalised in empirical models, in part because much of the evidence base relies on cross-sectional survey designs (often analysed via SEM/PLS-SEM) that are poorly suited to assessing systemic constructs such as discrimination or manipulation. Privacy concerns, perceived control, and transparency emerge as important conditions for positive trust-related outcomes, while constructs such as fairness, discrimination, and manipulation are only sporadically examined. We propose an integrative framework linking BI infrastructure, segmentation mechanisms, ethical safeguards, and consumer outcomes, and use it to articulate a focused research agenda for responsible, transparent, and human-centred BI systems for e-commerce personalization.