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

Abstract

This study aims to explore thematic influences on theme park visitors' satisfaction through user‐generated data. To this end, we first used an unsupervised machine learning method, structural topic modeling, and analyzed 112,000 reviews post by visitors to Shanghai Disney Resort from June 16, 2016 to March 4, 2022. Our findings are of great significance for reflecting consumer behavior through user‐generated data. Specifically, we find that visitors' satisfaction is highly related to service in the theme park and their playing feeling, and early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall playing experience. In addition, an empirical study is conducted by treating the ratings associated with each review as dependent variable and each topic represented by comments as independent variables, which shows that the relationship between the customer reviews and ratings by tourists becomes less pronounced over time. In other words, as time goes, customers review can reflect their subjective feelings or experience, but the rating is not. We discover the “dynamics” of user‐generated data over time and gain a better understanding of the aspects and concerns of visitors' satisfaction over time. The findings of the study contribute to the literature on tourism, service, and consumer behavior while also providing valuable practical implications.

Exploring the Over-Time Variation in Customer Concerns on Sharing Economy Services

“…For example, (Gao et al, 2022) set accommodation type, property rating, price, and comment sentiment as covariates, and used STM to analyze Airbnb consumer reviews to understand users' preferences. The effectiveness of adding the time covariate to STM to observe the change of topic prevalence over time has been confirmed in previous studies (Ding et al, 2020;Bai, He, Han, Yang, Yu, Bi, Gupta, Fan & Panigrahi, 2023). However, very few studies use STM and add the time covariate to analyze the change of the proportion of topics formed by Airbnb users' reviews.…”

What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China

“…Despite the several valuable insights obtained by this research, there are limitations that could be explored in future research. Firstly, this study examined only one theme park (Bai et al, 2023); therefore, the conclusions may be limited to a certain context. Future research can be extended to multiple theme parks.…”

“…Theme parks in particular attract over 500 million visitors globally each year (Association/AECOM, 2019, 2020), which has made pivotal contributions to the development of the tourism industry. With the advent of Web media, visitors are able to share their travel experiences through online platforms such as TripAdvisor (Heo and Lee, 2009;Luo et al, 2020;Albayrak et al, 2021;Yang et al, 2021;Bai et al, 2023). The utilization and analysis of the large amount of online review data has been found to have great importance for improving business performance as the analysis results provide significant insights into customers' expectations and their experiences (Kim et al, 2015;Gao et al, 2018).…”

Enablers or Inhibitors? Unpacking the Emotional Power Behind In-Vehicle AI Anthropomorphic Interaction: A Dual-Factor Approach by Text Mining

“…For example, Yang and Han [83] used STM to conduct a real-time survey of the UGC on Twitter to reveal the impact of COVID-19 on the hospitality industry, the challenges it faces, and the industry's response. Bai [84] explored the impact of visitor experience on visitor satisfaction in theme parks over time using dynamic visitor-generated review data.…”