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dc.contributor.authorGhosh, A.
dc.contributor.authorPathak, D.
dc.contributor.authorBhola, P.
dc.contributor.authorBhattacharjee, D.
dc.contributor.authorSivarajah, Uthayasankar
dc.date.accessioned2024-02-27T11:27:30Z
dc.date.accessioned2024-03-27T15:48:56Z
dc.date.available2024-02-27T11:27:30Z
dc.date.available2024-03-27T15:48:56Z
dc.date.issued2023
dc.identifier.citationGhosh A, Pathak D, Bhola P et al (2023) Analysing product attributes of refurbished laptops based on customer reviews and ratings: machine learning approach to circular consumption. Annals of Operations Research. Accepted for Publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19856
dc.descriptionYesen_US
dc.description.abstractReviews and ratings of consumers towards a product impact consumer decision-making and their perceptions. Such information is key in measuring consumer satisfaction and net promoter scores. However, when the reviewed products are refurbished, consumer reviews become more important because information influences consumer behaviour and attitude toward looped products. This research explores the decision-influencing attributes of consumers while purchasing refurbished goods using quantitative and qualitative methods. Online after-sales 1986 laptop customers’ review and rating data in the public domain were analysed to reveal the decision-influencing attributes and their impact on potential consumers. The study envisions assisting the operations of sellers in the refurbished market by strengthening their businesses' value proposition and stimulating reverse logistics entrepreneurs to use the opportunity. Review data containing lifecycle valuation of old laptops induced feature extraction by machine learning applications. It is beneficial to sellers in the refurbished product segment. It provides information to strengthen their value proposition and is informative to entrepreneurs wanting to enter the segment. Based on the text analysis of consumer reviews, the study's results show that price, brand, design, performance, services, and utility influence consumers. The frequency analysis technique was used to extract attributes, followed by content analysis and feature selection using SHapley Additive exPlanations (SHAP) for exploring correlations between features and star ratings. Lastly, multinomial logistic regression was used to validate the generated model. The results show that brand, design, price, and utility are the most prominent attributes influencing consumers' decision-making with positive sentiments. In contrast, performance and services often generate neutral and negative sentiments.en_US
dc.language.isoenen_US
dc.rights© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10479-023-05758-9.en_US
dc.subjectCircular economyen_US
dc.subjectRefurbishen_US
dc.subjectReview and ratingen_US
dc.subjectConsumer behaviouren_US
dc.subjectMachine learningen_US
dc.subjectSHAPen_US
dc.titleAnalysing product attributes of refurbished laptops based on customer reviews and ratings: machine learning approach to circular consumptionen_US
dc.status.refereedYesen_US
dc.date.Accepted2023-11-27
dc.date.application2023-12-27
dc.typeArticleen_US
dc.date.EndofEmbargo2024-12-27
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo on 27 Dec 2024.en_US
dc.identifier.doihttps://doi.org/10.1007/s10479-023-05758-9en_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2024-02-27T11:27:32Z
refterms.dateFOA2024-03-27T15:49:36Z
dc.openaccess.statusembargoedAccess


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