What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China
The aim of this study was to explore the causes of tourists’ lesser enjoyment of theme parks through an unsupervised machine learning approach—structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1120483/full |
| _version_ | 1797791574411706368 |
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| author | Shizhen Bai Hao He Chunjia Han Mu Yang Xinrui Bi Weijia Fan |
| author_facet | Shizhen Bai Hao He Chunjia Han Mu Yang Xinrui Bi Weijia Fan |
| author_sort | Shizhen Bai |
| collection | DOAJ |
| description | The aim of this study was to explore the causes of tourists’ lesser enjoyment of theme parks through an unsupervised machine learning approach—structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through the analysis of 112,000 customer reviews released by visitors to the Shanghai Disney Resort from 16 June 2016 to 4 March 2022. Then, we used sentiment analysis to distinguish positive and negative topics and to explore the relationship between tourists who buy different ticket types and sentiments in negative topics. The results show that problems such as “parking,” “service attitude,” “recommendation feeling,” “experience comparison,” and “entrance” may be the main reasons for an unpleasant experience. In addition, we also found that when tourists travel in groups (e.g., via family tickets), customers feel unhappy because of parking and fast track problems. Moreover, when tourists travel alone or with small groups, staff service attitudes, experience comparisons, and entrance processes are the sources of greater concern. Our findings can help theme park managers to better understand the expectations of tourists and take effective measures to tackle issues causing customer dissatisfaction, and they can also contribute to theme park studies in tourism management. |
| first_indexed | 2024-03-13T02:20:48Z |
| format | Article |
| id | doaj.art-345b1096f5c54c999b62ca189a8136ba |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-03-13T02:20:48Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-345b1096f5c54c999b62ca189a8136ba2023-06-30T09:48:24ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-06-011410.3389/fpsyg.2023.11204831120483What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland ChinaShizhen Bai0Hao He1Chunjia Han2Mu Yang3Xinrui Bi4Weijia Fan5School of Management, Harbin University of Commerce, Harbin, ChinaSchool of Management, Harbin University of Commerce, Harbin, ChinaDepartment of Management, Birkbeck, University of London, London, United KingdomDepartment of Management, Birkbeck, University of London, London, United KingdomSchool of Management, Harbin University of Commerce, Harbin, ChinaSchool of Management, Harbin University of Commerce, Harbin, ChinaThe aim of this study was to explore the causes of tourists’ lesser enjoyment of theme parks through an unsupervised machine learning approach—structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through the analysis of 112,000 customer reviews released by visitors to the Shanghai Disney Resort from 16 June 2016 to 4 March 2022. Then, we used sentiment analysis to distinguish positive and negative topics and to explore the relationship between tourists who buy different ticket types and sentiments in negative topics. The results show that problems such as “parking,” “service attitude,” “recommendation feeling,” “experience comparison,” and “entrance” may be the main reasons for an unpleasant experience. In addition, we also found that when tourists travel in groups (e.g., via family tickets), customers feel unhappy because of parking and fast track problems. Moreover, when tourists travel alone or with small groups, staff service attitudes, experience comparisons, and entrance processes are the sources of greater concern. Our findings can help theme park managers to better understand the expectations of tourists and take effective measures to tackle issues causing customer dissatisfaction, and they can also contribute to theme park studies in tourism management.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1120483/fulltourists’ dissatisfactiononline reviewsShanghai Disney resorttopic modellingsentiment (SEN) analysis |
| spellingShingle | Shizhen Bai Hao He Chunjia Han Mu Yang Xinrui Bi Weijia Fan What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China Frontiers in Psychology tourists’ dissatisfaction online reviews Shanghai Disney resort topic modelling sentiment (SEN) analysis |
| title | What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China |
| title_full | What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China |
| title_fullStr | What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China |
| title_full_unstemmed | What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China |
| title_short | What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China |
| title_sort | what makes a theme park experience less enjoyable evidence from online customer reviews of disneyland china |
| topic | tourists’ dissatisfaction online reviews Shanghai Disney resort topic modelling sentiment (SEN) analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1120483/full |
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