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Supporting personalized new energy vehicle purchase decision-making: Customer reviews and product recommendation platform

Author

Listed:
  • Yang, Zaoli
  • Li, Qin
  • Charles, Vincent
  • Xu, Bing
  • Gupta, Shivam

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.

Suggested Citation

  • Yang, Zaoli & Li, Qin & Charles, Vincent & Xu, Bing & Gupta, Shivam, 2023. "Supporting personalized new energy vehicle purchase decision-making: Customer reviews and product recommendation platform," International Journal of Production Economics, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:proeco:v:265:y:2023:i:c:s0925527323002359
    DOI: 10.1016/j.ijpe.2023.109003
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    as
    1. Zhang, Junhui & Balaji, M.S. & Luo, Jun & Jha, Subhash, 2022. "Effectiveness of product recommendation framing on online retail platforms," Journal of Business Research, Elsevier, vol. 153(C), pages 185-197.
    2. Hajek, Petr & Sahut, Jean-Michel, 2022. "Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    3. Yu, Dian & Gao, Jianjun & Wang, Tongyao, 2022. "Betting market equilibrium with heterogeneous beliefs: A prospect theory-based model," European Journal of Operational Research, Elsevier, vol. 298(1), pages 137-151.
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Juheng Zhang & Selwyn Piramuthu, 2018. "Product recommendation with latent review topics," Information Systems Frontiers, Springer, vol. 20(3), pages 617-625, June.
    6. Cui, Lixin & Wang, Yonggui & Chen, Weiming & Wen, Wen & Han, Myat Su, 2021. "Predicting determinants of consumers' purchase motivation for electric vehicles: An application of Maslow's hierarchy of needs model," Energy Policy, Elsevier, vol. 151(C).
    7. Debnath, Ramit & Bardhan, Ronita & Reiner, David M. & Miller, J.R., 2021. "Political, economic, social, technological, legal and environmental dimensions of electric vehicle adoption in the United States: A social-media interaction analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    8. Ji, Dandan & Gan, Hongcheng, 2022. "Effects of providing total cost of ownership information on below-40 young consumers’ intent to purchase an electric vehicle: A case study in China," Energy Policy, Elsevier, vol. 165(C).
    9. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    10. Sun, Miao & Chen, Jing & Tian, Ye & Yan, Yufei, 2021. "The impact of online reviews in the presence of customer returns," International Journal of Production Economics, Elsevier, vol. 232(C).
    11. Zhang, Min & Sun, Lin & Wang, G. Alan & Li, Yuzhuo & He, Shuguang, 2022. "Using neutral sentiment reviews to improve customer requirement identification and product design strategies," International Journal of Production Economics, Elsevier, vol. 254(C).
    12. Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
    13. Lin, Boqiang & Shi, Lei, 2022. "Do environmental quality and policy changes affect the evolution of consumers’ intentions to buy new energy vehicles," Applied Energy, Elsevier, vol. 310(C).
    14. Leoneti, Alexandre Bevilacqua & Gomes, Luiz Flavio Autran Monteiro, 2021. "A novel version of the TODIM method based on the exponential model of prospect theory: The ExpTODIM method," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1042-1055.
    15. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    16. Wu, Zhonghuan & Duan, Chunlin & Cui, Yuting & Qin, Rong, 2023. "Consumers' attitudes toward low-carbon consumption based on a computational model: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    17. Tseng, Ming-Lang & Bui, Tat-Dat & Lan, Shulin & Lim, Ming K. & Mashud, Abu Hashan Md, 2021. "Smart product service system hierarchical model in banking industry under uncertainties," International Journal of Production Economics, Elsevier, vol. 240(C).
    18. Gao, Yongling & Leng, Mingming & Zhang, Yaping & Liang, Liping, 2022. "Incentivizing the adoption of electric vehicles in city logistics: Pricing, driving range, and usage decisions under time window policies," International Journal of Production Economics, Elsevier, vol. 245(C).
    19. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    20. Su, Dan & Zhang, Lijun & Peng, Hua & Saeidi, Parvaneh & Tirkolaee, Erfan Babaee, 2023. "Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    21. Upadhyay, Nitin & Kamble, Aakash, 2023. "Examining Indian consumer pro-environment purchase intention of electric vehicles: Perspective of stimulus-organism-response," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    22. Xiaosong Ren & Sha Sun & Rong Yuan, 2021. "A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-23, August.
    23. Zhang, Linda L., 2015. "A literature review on multitype platforming and framework for future research," International Journal of Production Economics, Elsevier, vol. 168(C), pages 1-12.
    24. Youngsoo Kim & Ramayya Krishnan, 2015. "On Product-Level Uncertainty and Online Purchase Behavior: An Empirical Analysis," Management Science, INFORMS, vol. 61(10), pages 2449-2467, October.
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