IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i2p664-d720027.html
   My bibliography  Save this article

Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation

Author

Listed:
  • Jianhong Luo

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Shifen Qiu

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Xuwei Pan

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Ke Yang

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Yuanqingqing Tian

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

With the improvements in per capita disposable income, and an increase in work-related pressure, demand for leisure consumption such as foot bath spas is constantly increasing. Analysis of leisure consumption sentiment is of great importance for the leisure service industry—to meet customer needs, improve service quality and improve customer relationship management. However, traditional sentiment analysis approaches only aimed to ascertain the overall sentiment of the customer, which is less effective for analyzing customer satisfaction on account of customer size, different customer locations, and different leisure holidays. Sentiment analysis via online reviews can assist different businesses, including foot bath spa services, to better inform the development of customer segmentation strategies and ensure optimal customer relationship management. Hence, the objective of this paper is to explore foot bath spa leisure consumption sentiment towards different holidays and different cities by applying data mining via online reviews, so as to help optimize customer segmentation. A novel general framework and related sentiment analysis methods were proposed and then conducted through a collection of datasets from customers’ textual reviews of foot bath spa merchants in three cities in China on the Meituan social media platform. Findings confirm that the proposed general framework and methods can be used to gain insights into the swing characteristics of sentiment towards different holidays and different cities, to better develop customer segmentation according to the city-holiday emoticon face patterns obtained through sentiment tendency analysis from online social media review data. The study results can help to develop better customer and marketing strategies, thereby creating sustainable competitive advantages, and can be extended to other fields to support sustainable development.

Suggested Citation

  • Jianhong Luo & Shifen Qiu & Xuwei Pan & Ke Yang & Yuanqingqing Tian, 2022. "Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation," Sustainability, MDPI, vol. 14(2), pages 1-16, January.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:664-:d:720027
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/2/664/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/2/664/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Erick Kauffmann & Jesús Peral & David Gil & Antonio Ferrández & Ricardo Sellers & Higinio Mora, 2019. "Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    3. Wen-Jie Ye & Anthony J. T. Lee, 2021. "Mining sentiment tendencies and summaries from consumer reviews," Information Systems and e-Business Management, Springer, vol. 19(1), pages 107-135, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song Liu & Lin-Lin Xue, 2022. "How to Promote Balanced and Healthy Development of Residents’ Leisure: Based on the Analysis on the Spatiotemporal Evolution of the Scale Structure of Leisure Consumption of Urban Residents in China," Sustainability, MDPI, vol. 14(22), pages 1-15, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    2. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    3. von Hippel, Eric & Kaulartz, Sandro, 2021. "Next-generation consumer innovation search: Identifying early-stage need-solution pairs on the web," Research Policy, Elsevier, vol. 50(8).
    4. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    5. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    6. Fei Li & Yang Zhao & Jaime Ortiz & Yan Chen, 2023. "How Does Digital Technology Innovation Affect the Internationalization Performance of Chinese Enterprises? The Moderating Effect of Sustainability Readiness," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    7. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    8. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    9. Boegershausen, Johannes & Datta, Hannes & Borah, Abhishek & Stephen, Andrew, 2022. "Fields of Gold: Web Scraping and APIs for Impactful Marketing Insights," Other publications TiSEM 5f1ed70a-48c3-422c-bc10-0, Tilburg University, School of Economics and Management.
    10. Jonah Berger & Grant Packard & Reihane Boghrati & Ming Hsu & Ashlee Humphreys & Andrea Luangrath & Sarah Moore & Gideon Nave & Christopher Olivola & Matthew Rocklage, 2022. "Marketing insights from text analysis," Marketing Letters, Springer, vol. 33(3), pages 365-377, September.
    11. Shimi Naurin Ahmad & Michel Laroche, 2023. "Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 662-676, December.
    12. Constantin Bratianu & Shahrazad Hadad & Ruxandra Bejinaru, 2020. "Paradigm Shift in Business Education: A Competence-Based Approach," Sustainability, MDPI, vol. 12(4), pages 1-17, February.
    13. Cheng Chai & Yao Song & Zhenzhen Qin, 2021. "A Thousand Words Express a Common Idea? Understanding International Tourists’ Reviews of Mt. Huangshan, China, through a Deep Learning Approach," Land, MDPI, vol. 10(6), pages 1-15, May.
    14. Fangfang Li & Jorma Larimo & Leonidas C. Leonidou, 2021. "Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 51-70, January.
    15. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    16. Shaojie Liu & Jing Teng & Yue Gong, 2020. "Extraction Method and Integration Framework for Perception Features of Public Opinion in Transportation," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    17. Ho-Dac, Nga N., 2020. "The value of online user generated content in product development," Journal of Business Research, Elsevier, vol. 112(C), pages 136-146.
    18. Garner, Benjamin & Thornton, Corliss & Luo Pawluk, Anita & Mora Cortez, Roberto & Johnston, Wesley & Ayala, Cesar, 2022. "Utilizing text-mining to explore consumer happiness within tourism destinations," Journal of Business Research, Elsevier, vol. 139(C), pages 1366-1377.
    19. Enrique Bigne & Carla Ruiz & Carmen Perez-Cabañero & Antonio Cuenca, 2023. "Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 281-314, March.
    20. Gliceria Gómez-Ceballos & Sandys Menoya-Zayas & Juan Pablo Vázquez-Loaiza, 2023. "ICT as a Support for Value Chain Management in Tourism Destinations: The Case of the City of Cuenca, Ecuador," Sustainability, MDPI, vol. 15(13), pages 1-30, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:664-:d:720027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.