Is negative e-WOM more powerful? Multimodal data analysis on air passengers’ perception of COVID-19 safety measures

Abstract

During the normalization stage of the COVID-19 epidemic prevention and control, the safety threats caused by improper epidemic prevention measures of airlines have become the primary concern for air passengers. Negative e-WOM related to safety perception obtained based on online multimodal reviews of travel websites has become an important decision-making basis for potential air passengers when making airline choices. This study aims to examine the relationship between potential air passengers’ negative safety perception and the usefulness of online reviews, as well as to test the moderating effect of review modality and airline type. It also further explores the effectiveness and feasibility of applying big data sentiment analysis to e-WOM management. To this end, the theoretical model of negative safety perception, review modality, and airline type affecting review usefulness was constructed. Then we select 10 low-cost airlines and 10 full-service airlines, respectively, according to the number of reviews sorted by the TripAdvisor website, and use crawling techniques to obtain 10,485 reviews related to COVID-19 safety of the above companies from December 2019 to date, and conduct safety perception sentiment analysis based on Python’s Textblob library. Finally, to avoid data overdispersion, the model is empirically analyzed by negative binomial regression using R software. The results indicate that (1) Negative safety perception significantly and negatively affects review usefulness, that is, extreme negative safety perception can provide higher review usefulness for potential air passengers. (2) Review modality and airline type have a significant moderating effect on the relationship between negative safety perception and review usefulness, in which multimodal reviews and full-service airlines both weakened the negative impact of negative safety perception on review usefulness. The theoretical model in this paper is both an extension of the application of big data sentiment analysis techniques and a beneficial supplement to current research findings of e-WOM, providing an important reference for potential air passengers to identify useful reviews accurately and thus reduce safety risks in online decision-making.

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

“…In the future, multimodal research that incorporates other types of data such as image and video can be conducted to better reflect the dynamic nature of customers. (Bai et al, 2022; Xiao et al, 2022).…”

“…For example, Park (2019) conducted sentiment analysis on customer reviews to demonstrate that customers' emotional perception (hedonic value and user burden) has a positive impact on customer revisitations (Park, 2019). Existing research using online review data to employ consumers' satisfaction mainly focuses on hospitalities (Ahani et al, 2019), restaurants (Diab et al, 2016), airlines (Bai et al, 2022), and other fields, few studies have investigated the influencing factors of theme park visitor satisfaction, especially for China.…”