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Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China

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  • Tao Shu

    (Department of Information Management and Information Systems, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Zhiyi Wang

    (Department of Information Management and Information Systems, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Ling Lin

    (Department of Information Management and Information Systems, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Huading Jia

    (Department of Information Management and Information Systems, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Jixian Zhou

    (Department of Information Management and Information Systems, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China)

Abstract

In recent years, as people’s awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers’ perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of “Technology Maturity” (A13) which reached 25,329, and which belongs to the “Performance Risk” (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the “Social Risk” (PR5) dimension, the abstract “Social Needs” (A51) received only 3224 comments and “Preference and Trust Rank” (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension’s emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the “Time Risk” (PR4), accounting for 54%. On a finer scale, the top three negative emotions are “Charging Time” (A42), “EV Charging Facilities” (A41), and “Maintenance of Value” (A33). Another interesting result is that “Social Needs” (A51)’s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers’ perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs’ consumption market in China.

Suggested Citation

  • Tao Shu & Zhiyi Wang & Ling Lin & Huading Jia & Jixian Zhou, 2022. "Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China," Energies, MDPI, vol. 15(5), pages 1-23, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1637-:d:756026
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    References listed on IDEAS

    as
    1. Egbue, Ona & Long, Suzanna, 2012. "Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions," Energy Policy, Elsevier, vol. 48(C), pages 717-729.
    2. Ye, Fei & Kang, Wanlin & Li, Lixu & Wang, Zhiqiang, 2021. "Why do consumers choose to buy electric vehicles? A paired data analysis of purchase intention configurations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 14-27.
    3. Wang, Yacan & Hazen, Benjamin T., 2016. "Consumer product knowledge and intention to purchase remanufactured products," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 460-469.
    4. Dowling, Grahame R & Staelin, Richard, 1994. "A Model of Perceived Risk and Intended Risk-Handling Activity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 119-134, June.
    5. Wenbo Li & Ruyin Long & Hong Chen & Jichao Geng, 2017. "Household factors and adopting intention of battery electric vehicles: a multi-group structural equation model analysis among consumers in Jiangsu Province, 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. 87(2), pages 945-960, June.
    6. Park, Eunil & Ohm, Jay Y., 2014. "Factors influencing the public intention to use renewable energy technologies in South Korea: Effects of the Fukushima nuclear accident," Energy Policy, Elsevier, vol. 65(C), pages 198-211.
    7. Zhang, Xian & Wang, Ke & Hao, Yu & Fan, Jing-Li & Wei, Yi-Ming, 2013. "The impact of government policy on preference for NEVs: The evidence from China," Energy Policy, Elsevier, vol. 61(C), pages 382-393.
    8. White, Lee V. & Sintov, Nicole D., 2017. "You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 94-113.
    9. Prithwiraj Choudhury & Dan Wang & Natalie A. Carlson & Tarun Khanna, 2019. "Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1705-1732, November.
    10. Chanwit Kongklaew & Khamphe Phoungthong & Chanwit Prabpayak & Md. Shahariar Chowdhury & Imran Khan & Nuttaya Yuangyai & Chumpol Yuangyai & Kuaanan Techato, 2021. "Barriers to Electric Vehicle Adoption in Thailand," Sustainability, MDPI, vol. 13(22), pages 1-13, November.
    11. Wang, Shanyong & Li, Jun & Zhao, Dingtao, 2017. "The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 14-26.
    12. 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.
    13. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    14. Dumortier, Jerome & Siddiki, Saba & Carley, Sanya & Cisney, Joshua & Krause, Rachel M. & Lane, Bradley W. & Rupp, John A. & Graham, John D., 2015. "Effects of providing total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehicle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 72(C), pages 71-86.
    15. Schuitema, Geertje & Anable, Jillian & Skippon, Stephen & Kinnear, Neale, 2013. "The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 39-49.
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