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Towards the development of an explainable e-commerce fake review index: An attribute analytics approach

Author

Listed:
  • Ronnie Das

    (Audencia Business School)

  • Wasim Ahmed
  • Kshitij Sharma

    (NTNU - Norwegian University of Science and Technology [Trondheim] - NTNU - Norwegian University of Science and Technology = Norges Teknisk-Naturvitenskapelige Universitet = Norjan teknis-luonnontieteellinen yliopisto)

  • Mariann Hardey

    (Durham University)

  • Yogesh Dwivedi

    (Swansea University)

  • Ziqi Zhang

    (JLU - Jilin University)

  • Chrysostomos Apostolidis
  • Raffaele Filieri

    (Audencia Business School)

Abstract

Instruments of corporate risk and reputation assessment tools are quintessentially developed on structured quantitative data linked to financial ratios and macroeconomics. An emerging stream of studies has challenged this norm by demonstrating improved risk assessment and model prediction capabilities through unstructured textual corporate data. Fake online consumer reviews pose serious threats to a business' competitiveness and sales performance, directly impacting revenue, market share, brand reputation and even survivability. Research has shown that as little as three negative reviews can lead to a potential loss of 59.2 % of customers. Amazon, as the largest e-commerce retail platform, hosts over 85,000 small-to-medium-size (SME) retailers (UK), selling over fifty percent of Amazon products worldwide. Despite Amazon's best efforts, fake reviews are a growing problem causing financial and reputational damage at a scale never seen before. While large corporations are better equipped to handle these problems more efficiently, SMEs become the biggest victims of these scam tactics. Following the principles of attribute (AA) and responsible (RA) analytics, we present a novel hybrid method for indexing enterprise risk that we call the Fake Review Index ( ). The proposed modular approach benefits from a combination of structured review metadata and semantic topic index derived from unstructured product reviews. We further apply LIME to develop a Confidence Score, demonstrating the importance of explainability and openness in contemporary analytics within the OR domain. Transparency, explainability and simplicity of our roadmap to a hybrid modular approach offers an attractive entry platform for practitioners and managers from the industry.

Suggested Citation

  • Ronnie Das & Wasim Ahmed & Kshitij Sharma & Mariann Hardey & Yogesh Dwivedi & Ziqi Zhang & Chrysostomos Apostolidis & Raffaele Filieri, 2024. "Towards the development of an explainable e-commerce fake review index: An attribute analytics approach," Post-Print hal-04715613, HAL.
    • Handle: RePEc:hal:journl:hal-04715613
    DOI: 10.1016/j.ejor.2024.03.008
    Note: View the original document on HAL open archive server: https://hal.science/hal-04715613v1
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    References listed on IDEAS

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    Cited by:

    1. Roy, Sanjit K. & Tehrani, Ali N. & Pandit, Ameet & Apostolidis, Chrysostomos & Ray, Subhasis, 2025. "AI-capable relationship marketing: Shaping the future of customer relationships," Journal of Business Research, Elsevier, vol. 192(C).
    2. Rao, Susie Xi & Han, Zhichao & Yin, Hang & Jiang, Jiawei & Zhang, Zitao & Zhao, Yang & Shan, Yinan, 2025. "Fraud detection at eBay," Emerging Markets Review, Elsevier, vol. 66(C).
    3. Vecchietti, Giuseppe & Liyanaarachchi, Gajendra & Viglia, Giampaolo, 2025. "Managing deepfakes with artificial intelligence: Introducing the business privacy calculus," Journal of Business Research, Elsevier, vol. 186(C).

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