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

Ronnie Das, Wasim Ahmed Orcid Logo, Kshitij Sharma Orcid Logo, Mariann Hardey Orcid Logo, Yogesh Dwivedi Orcid Logo, Ziqi Zhang Orcid Logo, Chrysostomos Apostolidis Orcid Logo, Raffaele Filieri Orcid Logo

European Journal of Operational Research, Volume: 317, Issue: 2, Pages: 382 - 400

Swansea University Author: Yogesh Dwivedi Orcid Logo

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DOI (Published version): 10.1016/j.ejor.2024.03.008

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 capabiliti...

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Published in: European Journal of Operational Research
ISSN: 0377-2217
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65780
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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. 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spelling v2 65780 2024-03-06 Towards the development of an explainable e-commerce fake review index: An attribute analytics approach d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2024-03-06 CBAE 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. Journal Article European Journal of Operational Research 317 2 382 400 Elsevier BV 0377-2217 Fake reviews; Amazon; Risk analysis; AI explainability; BERT; Topic model indexing; LIME confidence score 1 9 2024 2024-09-01 10.1016/j.ejor.2024.03.008 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee 2024-06-06T16:48:38.8859787 2024-03-06T14:21:41.2310121 Faculty of Humanities and Social Sciences School of Management - Business Management Ronnie Das 1 Wasim Ahmed 0000-0001-8923-1865 2 Kshitij Sharma 0000-0003-3364-637x 3 Mariann Hardey 0000-0002-1027-0165 4 Yogesh Dwivedi 0000-0002-5547-9990 5 Ziqi Zhang 0000-0002-8587-8618 6 Chrysostomos Apostolidis 0000-0002-9613-880x 7 Raffaele Filieri 0000-0002-3534-8547 8 65780__30559__78eeea1114994f5b9b95699424a39cb0.pdf 65780.VoR.pdf 2024-06-06T16:46:59.9129432 Output 9497900 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
spellingShingle Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
Yogesh Dwivedi
title_short Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
title_full Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
title_fullStr Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
title_full_unstemmed Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
title_sort Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Ronnie Das
Wasim Ahmed
Kshitij Sharma
Mariann Hardey
Yogesh Dwivedi
Ziqi Zhang
Chrysostomos Apostolidis
Raffaele Filieri
format Journal article
container_title European Journal of Operational Research
container_volume 317
container_issue 2
container_start_page 382
publishDate 2024
institution Swansea University
issn 0377-2217
doi_str_mv 10.1016/j.ejor.2024.03.008
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_title Faculty of Humanities and Social Sciences
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department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
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description 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.
published_date 2024-09-01T16:48:38Z
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