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Article: Consumer preference analysis: A data-driven multiple criteria approach integrating online information

TitleConsumer preference analysis: A data-driven multiple criteria approach integrating online information
Authors
KeywordsE-commerce
Multiple criteria decision making
Online reviews
Preference analysis
Preference modeling
Issue Date2020
Citation
Omega (United Kingdom), 2020, v. 96, article no. 102074 How to Cite?
AbstractMultiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer's purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making.
Persistent Identifierhttp://hdl.handle.net/10722/330604
ISSN
2023 Impact Factor: 6.7
2023 SCImago Journal Rankings: 2.647
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGuo, Mengzhuo-
dc.contributor.authorLiao, Xiuwu-
dc.contributor.authorLiu, Jiapeng-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:12:11Z-
dc.date.available2023-09-05T12:12:11Z-
dc.date.issued2020-
dc.identifier.citationOmega (United Kingdom), 2020, v. 96, article no. 102074-
dc.identifier.issn0305-0483-
dc.identifier.urihttp://hdl.handle.net/10722/330604-
dc.description.abstractMultiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer's purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making.-
dc.languageeng-
dc.relation.ispartofOmega (United Kingdom)-
dc.subjectE-commerce-
dc.subjectMultiple criteria decision making-
dc.subjectOnline reviews-
dc.subjectPreference analysis-
dc.subjectPreference modeling-
dc.titleConsumer preference analysis: A data-driven multiple criteria approach integrating online information-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.omega.2019.05.010-
dc.identifier.scopuseid_2-s2.0-85066935511-
dc.identifier.volume96-
dc.identifier.spagearticle no. 102074-
dc.identifier.epagearticle no. 102074-
dc.identifier.isiWOS:000541944700006-

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