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Exploring success factors of tourism performing arts by analyses of online reviews

Exploring success factors of tourism performing arts by analyses of online reviews This study aims to explore the success factors of tourism performing arts (TPA) programs by analyzing a large data set of online reviews.Design/methodology/approachA total of 195,230 reviews from Ctrip.com were collected and preprocessed. A deep learning method was leveraged to estimate the similarity between words. Then, regression analysis was conducted to determine success factors.FindingsThis study extracted four positive and two negative factors affecting tourist satisfaction with tourism performance arts. The results demonstrate that the tourists paid the most attention to the traditional Chinese cultural aspects, audiovisual effects and the actors’ performing enthusiasm.Research limitations/implicationsDespite this study’s large data set, the focused was only on Chinese reviews. It would be useful and interesting to compare the success factors of tourism performance arts programs offered in different countries.Practical implicationsThe study findings can contribute to the development of TPA programs to attract tourists to travel destinations.Originality/valueThis study demonstrates that analyzing online reviews of TPA through text mining technology is an effective method of understanding tourist satisfaction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hospitality and Tourism Technology Emerald Publishing

Exploring success factors of tourism performing arts by analyses of online reviews

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1757-9880
eISSN
1757-9880
DOI
10.1108/jhtt-05-2021-0140
Publisher site
See Article on Publisher Site

Abstract

This study aims to explore the success factors of tourism performing arts (TPA) programs by analyzing a large data set of online reviews.Design/methodology/approachA total of 195,230 reviews from Ctrip.com were collected and preprocessed. A deep learning method was leveraged to estimate the similarity between words. Then, regression analysis was conducted to determine success factors.FindingsThis study extracted four positive and two negative factors affecting tourist satisfaction with tourism performance arts. The results demonstrate that the tourists paid the most attention to the traditional Chinese cultural aspects, audiovisual effects and the actors’ performing enthusiasm.Research limitations/implicationsDespite this study’s large data set, the focused was only on Chinese reviews. It would be useful and interesting to compare the success factors of tourism performance arts programs offered in different countries.Practical implicationsThe study findings can contribute to the development of TPA programs to attract tourists to travel destinations.Originality/valueThis study demonstrates that analyzing online reviews of TPA through text mining technology is an effective method of understanding tourist satisfaction.

Journal

Journal of Hospitality and Tourism Technology – Emerald Publishing

Published: Jan 11, 2023

Keywords: Tourism performing arts; Success factor; Tourist satisfaction; Text mining; Tourism online review; Clustering algorithm; 关键词 旅游演艺; 成功因素; 游客满意度; 文本挖掘; 旅游在线评论; 聚类算法

References

  • Ctrip.com competitive analysis, marketing mix and traffic
  • Reflections on tourist satisfaction research: past, present and future
    Bowen, D.; Clarke, J.
  • Indices of tourism industry
  • Tourism performance arts review dataset
    Cui, Y.; Kim, S.W.; Feng, S.
  • The box office ranking list of Chinese tourism performing arts
  • A review and extension of the flow experience concept. Insights and directions for tourism research
    Dematos, N.; S´a, E.; Duarte, P.
  • Behind the numbers: questioning questionnaires
    Einola, K.; Alvesson, M.
  • Clustering by passing messages between data points
    Frey, B.J.; Dueck, D.
  • Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness
    Gonz´alez-Rodr´ıguez, M.R.; Mart´ınez-Torres, R.; Toral, S.
  • Satisfaction in performing arts: the role of value?
    Hume, M.M.; Mort, G.S.
  • Understanding the role of involvement in customer repurchase of the performing arts
    Hume, M.M.; Mort, G.S.
  • Episodic, extended and continuous service encounters: a theoretical framework
    Hume, M.M.; Mccoll-Kennedy, J.R.
  • Motivation and satisfaction of Chinese and US tourists in restaurants: a cross-cultural text mining of online reviews
    Jia, S.S.(.
  • Intra-Asian performing arts tourism – the motivations, intentions, and performance preferences of Japanese visitors
    Kim, S.; Chung, J.Y.; King, B.
  • What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management
    Kim, K.; Park, O.J.; Yun, S.; Yun, H.
  • Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews
    Kirilenko, A.P.; Stepchenkova, S.O.; Hernandez, J.M.
  • Thematic framework of online review research: a systematic analysis of contemporary literature on seven major hospitality and tourism journals
    Kwok, L.; Xie, K.L.; Richards, T.
  • Predicting hotel choice: the factors underlying perception
    Lewis, R.C.
  • Big data in tourism research: a literature review
    Li, J.; Xu, L.; Tang, L.; Wang, S.; Li, L.
  • Word embedding for understanding natural language: a survey
    Srinivasan, S.
  • Personalizing yelp star ratings: a semantic topic modeling approach
    Linshi, J.
  • Big data for big insights: investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews
    Liu, Y.; Teichert, T.; Rossi, M.; Li, H.; Hu, F.
  • A retrospective commentary: on the article ‘new tools for achieving service quality’
    Lovelock, C.H.
  • Destination image perception of Shenzhen: an analysis of discourse based on Chinese and Western visitors’ online reviews
    Luo, J.; Okumus, F.; Taheri, B.
  • Big data in action: an overview of big data studies in tourism and hospitality literature
    Lyu, J.; Khan, A.; Bibi, S.; Chan, J.H.; Qi, X.
  • Efficient estimation of word representations in vector space
    Bengio, Y.; LeCun, Y.
  • A comparisonal study on satisfaction of Chinese and foreign visitors about performance arts of intangible cultural heritage – taking impression of Liu Sanjie as example
    Mo, J.; Fu, Y.
  • Service quality, customer satisfaction, and customer value: a holistic perspective
    Oh, H.
  • Zipf’s word frequency law in natural language: a critical review and future directions
    Piantadosi, S.T.
  • Big data technologies: an empirical investigation on their adoption, benefits and risks for companies
    Raguseo, F.L.
  • GB/T 32941.1-2016: Service specification for real-life scenery performance-part 1: guidelines
  • GB/T 32941.2-2016: Service specification for real-life scenery performance-part 2: performance management
  • GB/T 32941.3-2016: service specification for real-life scenery performance-part 3: service quality
  • Arts consumption, customer satisfaction and personal well-being: a study of performing arts in Hong Kong
    Siu, N.Y.M.; Kwan, H.Y.; Zhang, T.J.F.; Ho, C.K.Y.
  • Factors affecting tourist satisfaction with theatrical performances: a case study of the romance of the song dynasty in Hangzhou, China
    Song, H.; Cheung, C.
  • An analysis of Airbnb online reviews: user experience in 16 US cities
    Thomsen, C.; Jeong, M.
  • What can big data and text analytics tell us about hotel guest experience and satisfaction?
    Xiang, Z.; Schwartz, Z.; Gerdes, J.H.; Uysal, M.
  • Forecasting Chinese tourist volume with search engine data
    Yang, X.; Pan, B.; Evans, J.A.; Lv, B.
  • Assessing technology adoption practices in Chinese theme parks: text mining and sentiment analysis
    Zhang, T.; Li, B.; Milman, A.; Hua, N.
  • Emotional responses toward tourism performing arts development: a comparison of urban and rural residents in China
    Zheng, D.; Ritchie, B.W.; Benckendorff, P.J.; Bao, J.
  • Performing heritage: rethinking authenticity in tourism
    Zhu, Y.