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Generative AI in E-commerce: a literature review of current applications, gaps and future directions

Maria Ijaz Baig & Elaheh Yadegaridehkordi

Asia-Pacific Journal of Business Administration2026https://doi.org/10.1108/apjba-08-2025-0575article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose This study systematically investigates the integration of Generative Artificial Intelligence (GenAI) in the e-commerce sector to enhance customer interactions, optimize business operations and support decision-making processes across online platforms. Design/methodology/approach A comprehensive search of the Web of Science, Scopus and Google Scholar identified 52 empirical research articles published between 2022 and 2025 that met the predefined inclusion criteria. Findings The review categorizes the selected studies according to their research methodologies and data collection techniques. Major areas of GenAI application include: (1) enhancing customer purchase intention and experience; (2) implementing GenAI-driven optimization strategies; (3) supporting business decision-making; (4) improving information retrieval; (5) enabling GenAI-driven advertising; and (6) developing GenAI implementation strategies. Findings also highlight theoretical models and their predictive factors, which play a significant role in explaining the effectiveness of GenAI applications in e-commerce. In addition, challenges and future research directions related to GenAI integration are identified. Practical implications The study offers actionable insights for e-commerce practitioners by guiding the effective integration of GenAI into business strategies. By understanding the current trends, theoretical models and key predictive factors, organizations can enhance operational efficiency and decision-making. Originality/value This study offers a novel synthesis of GenAI applications in e-commerce, focusing on empirical research published between 2022 and 2025. By systematically categorizing prior studies, it offers a structured overview of GenAI's impact across various e-commerce functions. The inclusion of theoretical models and their predictive factors further enriches the understanding of GenAI's effectiveness in real-world e-commerce contexts. This study enriches the literature and delivers actionable guidance for practitioners to apply GenAI in boosting customer engagement and operational efficiency.

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https://doi.org/https://doi.org/10.1108/apjba-08-2025-0575

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@article{maria2026,
  title        = {{Generative AI in E-commerce: a literature review of current applications, gaps and future directions}},
  author       = {Maria Ijaz Baig & Elaheh Yadegaridehkordi},
  journal      = {Asia-Pacific Journal of Business Administration},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/apjba-08-2025-0575},
}

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Generative AI in E-commerce: a literature review of current applications, gaps and future directions

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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.