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

Omnichannel and consumer and retailer perceived risks and benefits: a review

“…Delays, changes of mind or cancelations by consumers are related to the influence of perceived risks (Dou et al. , 2021; Shu et al. , 2022).…”

“…Perceived risk is consumers' expectation of negative utility when purchasing a particular product/service. Delays, changes of mind or cancelations by consumers are related to the influence of perceived risks (Dou et al, 2021;Shu et al, 2022). Perceived risk negatively affects consumers' attitudes and intentions to buy innovative products/services via omnichannel (Huang et al, 2014;Shu et al, 2022).…”

Analysis of China’s New Energy Vehicle Marketing Strategy

“…Third, it is suggested to provide convenient services for customers. Despite the rapid acceptance in China's NEVs market, Chinese consumers still have various perceived risks with negative emotions, particularly time risks [19]. Time problems are the most likely to give users a bad experience.…”

“…The fourth is to communicate with customers. Among the consumers' considerations when buying NEVs, social connection is one of the most important factors [19]. This requires brands to strengthen interaction with consumers in order to understand their needs.…”

The Bias Analysis of Oil and Gas Companies’ Credit Ratings Based on Textual Risk Disclosures

“…Bennett et al [19] measured the legal risk by counting the number of litigious words in Form 10-Ks and found that the legal risk has negative effects on S&P credit ratings. Shu et al [20] deconstructed the customer-perceived risk from comment data in social media and provided a new tool for the multi-dimensional capture of risk. However, other than the risk information, more information included in Form 10-Ks, such as the business, history, and staff comment section of companies, would disturb the risk measurement.…”