IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2037-d1132497.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    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. 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).
    11. 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.
    12. 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).
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Yongming & Li, Yanhong & Zhu, Hongli & Li, Guangxu, 2023. "A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    2. Ma, Miaomiao & Meng, Weidong & Li, Yuyu & Huang, Bo, 2023. "Impact of dual credit policy on new energy vehicles technology innovation with information asymmetry," Applied Energy, Elsevier, vol. 332(C).
    3. Hsiao, Cody Yu-Ling & Yang, Rui & Zheng, Xin & Chiu, Yi-Bin, 2023. "Evaluations of policy contagion for new energy vehicle industry in China," Energy Policy, Elsevier, vol. 173(C).
    4. Zhao, Jinjing & Su, Yiming & Fang, Mingjie & Su, Miao, 2024. "Embracing new energy vehicles: An empirical examination of female consumer perspectives," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    5. Feng, Xiao & Li, Yuyu & Huang, Bo, 2023. "Research on manufacturer's investment strategy and green credit policy for new energy vehicles based on consumers' preferences and technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Wang, Shanyong & Wang, Jing & Li, Jun & Wang, Jinpeng & Liang, Liang, 2018. "Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 58-69.
    7. Wang, Zhongcheng & Li, Xinyue & Xue, Xinhong & Liu, Yahuan, 2022. "More government subsidies, more green innovation? The evidence from Chinese new energy vehicle enterprises," Renewable Energy, Elsevier, vol. 197(C), pages 11-21.
    8. 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).
    9. Lijie Feng & Kehui Liu & Jinfeng Wang & Kuo-Yi Lin & Ke Zhang & Luyao Zhang, 2022. "Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes," Energies, MDPI, vol. 15(20), pages 1-22, October.
    10. Dong, Feng & Liu, Yajie, 2020. "Policy evolution and effect evaluation of new-energy vehicle industry in China," Resources Policy, Elsevier, vol. 67(C).
    11. Xuemeng Zhao & Weilun Huang, 2024. "Global Geopolitical Changes and New/Renewable Energy Game," Energies, MDPI, vol. 17(16), pages 1-27, August.
    12. Kang, Min Jung & Park, Heejun, 2011. "Impact of experience on government policy toward acceptance of hydrogen fuel cell vehicles in Korea," Energy Policy, Elsevier, vol. 39(6), pages 3465-3475, June.
    13. Lee, Juyong & Cho, Youngsang, 2020. "Estimation of the usage fee for peer-to-peer electricity trading platform: The case of South Korea," Energy Policy, Elsevier, vol. 136(C).
    14. Zhao, Meng & Xu, Chang & Zhao, Wenxian & Lin, Mingwei, 2023. "New energy vehicle online selection method considering attribute compensation relationship and aspiration strength," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    15. Zhang, Chonghui & Zhang, Na & Su, Weihua & Balezentis, Tomas, 2024. "Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM," Omega, Elsevier, vol. 129(C).
    16. Suyang Zhou & Jinyi Chen & Zhi Wu & Yue Qiu, 2021. "Electrification of Online Ride-Hailing Vehicles in China: Intention Modelling and Market Prediction," Energies, MDPI, vol. 14(21), pages 1-21, November.
    17. Gordon, Joel A. & Balta-Ozkan, Nazmiye & Nabavi, Seyed Ali, 2022. "Homes of the future: Unpacking public perceptions to power the domestic hydrogen transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    18. Di Wang & Yuman Li, 2022. "Measuring the Policy Effectiveness of China’s New-Energy Vehicle Industry and Its Differential Impact on Supply and Demand Markets," Sustainability, MDPI, vol. 14(13), pages 1-16, July.
    19. Zhou, Wei & Zhang, Keang & Zhang, Ying & Duan, Yunlong, 2021. "Operation strategies with respect to insurance subsidy optimization for online retailers dealing with large items," International Journal of Production Economics, Elsevier, vol. 232(C).
    20. Chorus, Caspar G. & Koetse, Mark J. & Hoen, Anco, 2013. "Consumer preferences for alternative fuel vehicles: Comparing a utility maximization and a regret minimization model," Energy Policy, Elsevier, vol. 61(C), pages 901-908.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2037-:d:1132497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.