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

Tao Shu, Zhiyi Wang, Ling Lin, Huading Jia and Jixian Zhou
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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

Energies, 2022, vol. 15, issue 5, 1-23

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.

Keywords: electric vehicles; EVs; consumption market; perceived risk; emotion analysis; social media comment; natural language processing; NLP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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