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A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

Author

Listed:
  • Sumin Yu

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China
    College of Management, Shenzhen University, Shenzhen 518060, China)

  • Xiaoting Zhang

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Zhijiao Du

    (Business School, Sun Yat-sen University, Shenzhen 518107, China)

  • Yanyan Chen

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China
    College of Management, Shenzhen University, Shenzhen 518060, China)

Abstract

Under the global consensus of carbon peaking and carbon neutrality, new energy vehicles have gradually become mainstream, driven by the dual crises regarding the atmospheric environment and energy security. When choosing new energy vehicles, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms. However, it is easy for consumers to become lost in the high-star text reviews and mismatched reviews. To solve the above two issues, this study selected nine new energy vehicles and used a multi-attribute decision making method to rank the vehicles. We first designed adjustment rules based on star ratings and text reviews to cope with the issue of high star ratings but negative text reviews. Secondly, we classified consumers and recommended the optimal alternative for each type of consumer to deal with the issue of mismatched demands between review writers and viewers. Finally, this study compared the ranking results with the sales charts of the past year to verify the feasibility of the proposed method initially. The feasibility and stability of the proposed method were further verified through comparative and sensitivity analyses.

Suggested Citation

  • Sumin Yu & Xiaoting Zhang & Zhijiao Du & Yanyan Chen, 2023. "A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection," Mathematics, MDPI, vol. 11(9), pages 1-32, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2037-:d:1132497
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    References listed on IDEAS

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    1. Zhaohua Wang & Xiaoyang Dong, 2016. "Determinants and policy implications of residents’ new energy vehicle purchases: the evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(1), pages 155-173, May.
    2. Sumin Yu & Zhijiao Du & Xuanhua Xu, 2021. "Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic Large-Group Decision Making with Application to Global Supplier Selection," Group Decision and Negotiation, Springer, vol. 30(6), pages 1343-1372, December.
    3. Song, Yongming & Li, Guangxu & Li, Tie & Li, Yanhong, 2021. "A purchase decision support model considering consumer personalization about aspirations and risk attitudes," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    4. Meng, Weidong & Ma, Miaomiao & Li, Yuyu & Huang, Bo, 2022. "New energy vehicle R&D strategy with supplier capital constraints under China's dual credit policy," Energy Policy, Elsevier, vol. 168(C).
    5. 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).
    6. Heidary Dahooie, Jalil & Raafat, Romina & Qorbani, Ali Reza & Daim, Tugrul, 2021. "An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Himanshu Sharma & Abhishek Tandon & P. K. Kapur & Anu G. Aggarwal, 2019. "Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 973-983, October.
    8. Dong Zhang & Yongli Li & Chong Wu, 2020. "An extended TODIM method to rank products with online reviews under intuitionistic fuzzy environment," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(2), pages 322-334, February.
    9. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao & Hui Li, 2022. "Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations," Information Technology & Tourism, Springer, vol. 24(1), pages 127-155, March.
    10. Yetano Roche, María & Mourato, Susana & Fischedick, Manfred & Pietzner, Katja & Viebahn, Peter, 2010. "Public attitudes towards and demand for hydrogen and fuel cell vehicles: A review of the evidence and methodological implications," Energy Policy, Elsevier, vol. 38(10), pages 5301-5310, October.
    11. Jiayan Huang & Nanyue Jiang & Ji Chen & Tomas Balezentis & Dalia Streimikiene, 2022. "Multi-criteria group decision-making method for green supplier selection based on distributed interval variables," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 746-761, December.
    12. Ma, Shao-Chao & Fan, Ying & Feng, Lianyong, 2017. "An evaluation of government incentives for new energy vehicles in China focusing on vehicle purchasing restrictions," Energy Policy, Elsevier, vol. 110(C), pages 609-618.
    13. Bowen Cai & Naeem Jan, 2021. "Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry," Journal of Mathematics, Hindawi, vol. 2021, pages 1-10, December.
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    1. Gang Ma & Zhengming Zhou & Shilei Wang & Ke Zhou & Junjun Zheng & Chujian Wang, 2023. "Combinatorial Auction of Used Cars Considering Pro-Environment Attribute: A Social Welfare Perspective," Sustainability, MDPI, vol. 15(16), pages 1-16, August.

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