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dc.contributor.authorChen, S.
dc.contributor.authorKe, S.
dc.contributor.authorHan, S.
dc.contributor.authorGupta, S.
dc.contributor.authorSivarajah, Uthayasankar
dc.date.accessioned2023-09-03T14:52:26Z
dc.date.accessioned2023-09-14T14:47:17Z
dc.date.available2023-09-03T14:52:26Z
dc.date.available2023-09-14T14:47:17Z
dc.date.issued2024-01
dc.identifier.citationChen S, Ke S, Han S et al (2024) Which product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regression. Decision Support Systems. 176: 114065.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19575
dc.descriptionYes
dc.description.abstractThe rapid rise of many e-commerce platforms for individual consumers has generated a large amount of text-based data, and thus researchers have begun to experiment with text mining techniques to extract information from the large amount of textual data to assist in sales forecasting. The existing literature focuses textual data on product reviews; however, consumer reviews are not something that companies can directly control, here we argue that textual product descriptions are also important determinants of consumer choice. We construct an artificial intelligence (AI) framework that combines text mining, WaveNet neural networks, multiple regression, and SHAP model to explain the impact of product descriptions on sales forecasting. Using data from nearly 200,000 sales records obtained from a cross-border e-commerce firm, an empirical study showed that the product description presented to customers can influence sales forecasting, and about 44% of the key phrases greatly affect sales forecasting results, the sales forecasting models that added key product description phrases had improved forecasting accuracy. This paper provides explainable results of sales forecasting, which can provide guidance for firms to design product descriptions with reference to the market demand reflected by these phrases, and adding these phrases to product descriptions can help win more customers.en_US
dc.language.isoenen_US
dc.rights© 2023 Published by Elsevier B.V. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectText mining
dc.subjectSales forecasting
dc.subjectWaveNet neural network
dc.subjectExplainable AI
dc.subjectCross-border e-commerce
dc.titleWhich product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regressionen_US
dc.status.refereedYes
dc.date.Accepted20/08/2023
dc.date.application24/08/2023
dc.typeArticle
dc.date.EndofEmbargo24/02/2025
dc.type.versionAccepted manuscript
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo on 24 Feb 2025.en_US
dc.identifier.doihttps://doi.org/10.1016/j.dss.2023.114065
dc.rights.licenseCC-BY-NC-NDen_US
dc.date.updated2023-09-03T14:52:28Z
refterms.dateFOA2023-09-14T14:48:00Z
dc.openaccess.statusembargoedAccess


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