IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6702-d1124278.html
   My bibliography  Save this article

An Investigation of the Key Attributes of Korean Wellness Tourism Customers Based on Online Reviews

Author

Listed:
  • Aura Lydia Riswanto

    (Department of Global Business, Kyungsung University, Busan 48434, Republic of Korea)

  • Hak-Seon Kim

    (School of Hospitality & Tourism Management, Kyungsung University, Busan 48434, Republic of Korea
    Wellness & Tourism Big Data Research Institute, Kyungsung University, Busan 48434, Republic of Korea)

Abstract

With its fast-growing trend, wellness tourism is transforming the client base and service and product offerings, and it is attracting new suppliers. The purpose of understanding the customer experience as portrayed in online reviews is to sustainably maintain customer loyalty and satisfaction. The objective of this research is to identify the critical attributes and their structural relationships to Korean wellness tourism. The study analyzed 24,060 Google-based customer reviews on 11 wellness tourism destinations in South Korea. Following the calculation of word frequencies in a matrix, UCINET 6.0 was utilized to analyze the centrality of the network and perform a CONCOR analysis. Based on the findings of the CONCOR analysis, the review data were sorted into four distinct categories. Following the quantitative analysis led to the identification of six variables that were grouped together through exploratory factor analysis.: wellness, tangible, value, F&B, purpose, and service. Whereas value, F&B, and service negatively affected the satisfaction of guests, the study also revealed that wellness, tangible, and purpose all had positive impacts and contributed to increased trust among wellness tourism customers. In terms of managerial implication, the results will enable wellness tourism destination managers to focus more on improving the factors of value, food, and service.

Suggested Citation

  • Aura Lydia Riswanto & Hak-Seon Kim, 2023. "An Investigation of the Key Attributes of Korean Wellness Tourism Customers Based on Online Reviews," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6702-:d:1124278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6702/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/8/6702/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadine Angelita Valentine, 2016. "Wellness Tourism: Using Tourists’ Preferences to Evaluate the Wellness Tourism Market in Jamaica," Review of Social Sciences, LAR Center Press, vol. 1(3), pages 25-44, March.
    2. Park, Cheol & Lee, Thae Min, 2009. "Information direction, website reputation and eWOM effect: A moderating role of product type," Journal of Business Research, Elsevier, vol. 62(1), pages 61-67, January.
    3. Aralbayeva Shadiyar & Hyun-Jeong Ban & Hak-Seon Kim, 2020. "Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    4. Gabriela Cecilia Stanciulescu & Gabriela Nicoleta Diaconescu & Dan Mihnea Diaconescu, 2015. "Health, Spa, Wellness Tourism. What is the Difference?," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 7(3), pages 158-161, September.
    5. Klein, Lisa R., 1998. "Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods," Journal of Business Research, Elsevier, vol. 41(3), pages 195-203, March.
    6. Xiaobin Zhang & Hak-Seon Kim, 2021. "Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    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. Tibert Verhagen & Daniel Bloemers, 2018. "Exploring the cognitive and affective bases of online purchase intentions: a hierarchical test across product types," Electronic Commerce Research, Springer, vol. 18(3), pages 537-561, September.
    2. Cho, Yun Kyung, 2015. "Creating customer repurchase intention in Internet retailing: The effects of multiple service events and product type," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 213-222.
    3. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    4. Yang, Jun & Mai, Enping (Shirley), 2010. "Experiential goods with network externalities effects: An empirical study of online rating system," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1050-1057, September.
    5. Qingfeng Zeng & Qian Guo & Wei Zhuang & Yu Zhang & Weiguo Fan, 2023. "Do Real-Time Reviews Matter? Examining how Bullet Screen Influences Consumers’ Purchase Intention in Live Streaming Commerce," Information Systems Frontiers, Springer, vol. 25(5), pages 2051-2067, October.
    6. Eunae Jung & Hyungun Sung, 2017. "The Influence of the Middle East Respiratory Syndrome Outbreak on Online and Offline Markets for Retail Sales," Sustainability, MDPI, vol. 9(3), pages 1-23, March.
    7. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    8. Heyes, Anthony & Kapur, Sandeep, 2012. "Angry customers, e-word-of-mouth and incentives for quality provision," Journal of Economic Behavior & Organization, Elsevier, vol. 84(3), pages 813-828.
    9. Hongjuan Song & Yushi Jiang, 2019. "Dynamic pricing decisions by potential tourists under uncertainty: The effects of tourism advertising," Tourism Economics, , vol. 25(2), pages 213-234, March.
    10. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2023. "Fake or credible? Antecedents and consequences of perceived credibility in exaggerated online reviews," Journal of Business Research, Elsevier, vol. 156(C).
    11. Weitzl, Wolfgang & Hutzinger, Clemens, 2017. "The effects of marketer- and advocate-initiated online service recovery responses on silent bystanders," Journal of Business Research, Elsevier, vol. 80(C), pages 164-175.
    12. Hsu, Sheila Hsuan-Yu & Tsou, Hung-Tai & Chen, Ja-Shen, 2021. "“Yes, we do. Why not use augmented reality?†customer responses to experiential presentations of AR-based applications," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    13. Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.
    14. Amin Ansary & Nik M. Hazrul Nik Hashim, 2018. "Brand image and equity: the mediating role of brand equity drivers and moderating effects of product type and word of mouth," Review of Managerial Science, Springer, vol. 12(4), pages 969-1002, October.
    15. Merle, Aurélie & St-Onge, Anik & Sénécal, Sylvain, 2022. "Does it pay to be honest? The effect of retailer-provided negative feedback on consumers’ product choice and shopping experience," Journal of Business Research, Elsevier, vol. 147(C), pages 532-543.
    16. Liu, Xiaotian & Popkowski Leszczyc, Peter T.L., 2023. "The reference price effect of historical price lists in online auctions," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    17. Sae Kim & Chong Choi, 2007. "Habits, Self-Control and Social Conventions: The Role of Global Media and Corporations," Journal of Business Ethics, Springer, vol. 76(2), pages 147-154, December.
    18. Yu-Hsien Lin, 2022. "Determinants of Green Purchase Intention: The Roles of Green Enjoyment, Green Intrinsic Motivation, and Green Brand Love," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    19. Ana Alina Tudoran, 2022. "A machine learning approach to identifying decision-making styles for managing customer relationships," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 351-374, March.
    20. Zhani, Najlae & Mouri, Nacef & Ahmed, Tariq, 2022. "The role of mobile value and trust as drivers of purchase intentions in m-servicescape," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).

    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:15:y:2023:i:8:p:6702-:d:1124278. 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.