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Author:

Yang, Z. (Yang, Z..) | Li, Q. (Li, Q..) | Charles, V. (Charles, V..) | Xu, B. (Xu, B..) | Gupta, S. (Gupta, S..)

Indexed by:

EI Scopus SCIE

Abstract:

The maturity of Industry 4.0 technologies such as the Internet of Things and cloud computing has accelerated the development of various platforms. In new energy vehicle (NEV) recommendation platforms, customer reviews have been well recognized for their ability to provide value-added information to customers interested in purchasing NEVs. However, the countless NEV reviews on recommendation platforms make it difficult for consumers to select their preferred NEV. The existing NEV recommendation platforms also do not automatically perform fine-grained sentiment analysis of the product attributes contained in reviews. Consequently, they cannot provide personalized purchase recommendations for consumers. To this end, this study aims to propose a product purchase decision support method based on sentiment analysis and multi-attribute decision-making to improve the accuracy of personalized NEV recommendation platforms. Sentiment analysis was conducted on the attribute reviews of NEVs on a product recommendation platform. Subsequently, the positive, negative, and neutral sentiment ratios obtained based on sentiment analysis were regarded as q-rung orthopair fuzzy numbers. The ratios were then recognized as cumulative prospect theory (CPT) inputs. The prospect values of each NEV under each attribute were calculated and further aggregated into a Muirhead mean operator to finally obtain the product rankings. This method was used to portray the consumers' decision-making process considering various situations and irrational psychological factors (e.g., risk-preference attitude). The results show that our proposal can recommend NEVs that are more consistent with consumers' personalized requirements. To conclude, our study can enhance the decision-making support capacity of product recommendation platforms by providing sentiment analysis and capturing customers’ preferences for product attributes. Additionally, it can recommend more suitable NEVs to meet personalized customer requirements. © 2023

Keyword:

New energy vehicles Personalized purchase Sentiment analysis of customer reviews Product recommendation platform

Author Community:

  • [ 1 ] [Yang Z.]College of Economics and Management, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Li Q.]College of Economics and Management, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Charles V.]Queen's Business School, Queen's University Belfast, Belfast, BT9 5EE, United Kingdom
  • [ 4 ] [Xu B.]Edinburgh Business School, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
  • [ 5 ] [Gupta S.]Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, Reims, France

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Source :

International Journal of Production Economics

ISSN: 0925-5273

Year: 2023

Volume: 265

1 2 . 0 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:19

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 18

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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