Analysis of the Reasons for Travelers' Intentions and Reluctance to Revisit Kish Island

Document Type : Research Article

Author

Department of Management and Economics, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran

10.22059/jut.2025.385669.1244

Abstract

A B S T R A C T
The intention to revisit is one of the key research topics in tourism destinations and has been considered a prominent behavioral intention. The present study aims to analyze the reasons for the enthusiasm and return of travelers to Kish Island, so the opinions of tourists on the "Hellokish.com" website were extracted, and the necessary analyses were performed using a mixed method. In the qualitative step, all the opinions were reviewed using content analysis, and categories and subcategories were extracted to separate enthusiastic and turned-away tourists. In the quantitative step, tourists' opinions were divided into three groups as positive, negative, and neutral. The data were filtered and cleaned, and super words were drawn for enthusiastic and turned-away tourists returning to travel. Then, to achieve the best model with the highest accuracy to predict the behavior of tourists, three methods of artificial neural networks, decision trees, and naive Bayes were used to model the data. The results showed that the model built through simple Bayes achieved the highest accuracy, i.e., 31.79, the best predictor of tourist behavior. In the next step, dependency rules were used to extract if-then rules that differentiate enthusiastic and reluctant tourists. The results showed that the most important reasons for not returning to Kish Island are the poor quality of hotels, high costs and the lack of proportion between prices and services and quality, poor health and safety conditions, and dissatisfaction with the quality of entertainment and recreation. On the other hand, for travelers willing to revisit Kish Island, their positive experience is mainly due to the quality and access to amenities and recreational facilities, the professional and friendly attitude of hotel staff, and the pleasant and peaceful environment of the island.
Extended Abstract
Introduction
For many countries, tourism is an important source of business activity, income, employment, and international exchange. Many developed countries receive a large annual income from this tourism industry, so other countries are interested in expanding this industry so that the government can make a profit. Still, a country needs to develop the right infrastructure to meet the needs of tourists and provide them with the right facilities. This can be relatively profitable for the industry. In the current era, due to the spread of social networks, tourists can easily transfer their positive and negative experiences to other tourists through electronic word of mouth. Customers tend to search for more information before making any decision. The best way to do this is to search for reviews and opinions published by other consumers online. This process is known as electronic word of mouth.
 
Methodology
The present study is applied in terms of purpose and descriptive-analytical, and in terms of research strategy, it is a mixed sequential and exploratory type and in the form of a cross-sectional study. The research data is related to the opinions expressed online by travelers to Kish Island on the hellokish.com site in the period from the first of March 2023 to the end of October 2024, which was extracted and collected through the Instant Data Scraper plugin. In order to achieve the study's objectives and address its questions, two sequential qualitative and quantitative steps were employed. In the qualitative step, the philosophy of interpretivism and the inductive approach were used in terms of applied research strategy, qualitative strategy, content analysis method, and cross-sectional studies. The research data were collected from the aforementioned site, implemented in MAXQDA20 software, analyzed, and categorized. The Crisp data mining process and Rapid miner data mining software were used in the quantitative step. The research population includes travelers who expressed their opinions about their travel experience to this island on the hellokish.com website. Due to the use of all data extracted from user comments during the aforementioned period, sampling was unnecessary, and all relevant data was included in the analyses.
 
Results and discussion
According to the data extracted from the opinions of travelers on the hellokish.com website and the analyses conducted in the qualitative and quantitative sections, the results indicate significant differences in the travelers' experience and the factors that cause their desire or reluctance to return to Kish Island. What distinguishes the present study and its results from previous studies is that the categories and subcategories extracted from the opinions of travelers who have turned away and are eager to return to Kish Island, as well as the dependency rules (if-then) extracted from a multitude of travelers' opinions on social networks, were selected and, due to the anonymity of the individuals in presenting their opinions and their presentation without bias and bias and the absence of an interviewer or other external factor, their authenticity can be largely assured. On the other hand, the present study focused on processing and discovering rules and patterns from seemingly abundant and less-considered data in previous studies that, despite being important, had not been considered. The results showed that the most important reasons for not returning to Kish Island are the poor quality of hotels, high costs and the lack of proportion between prices and services and quality, poor health and safety conditions, and dissatisfaction with the quality of entertainment and recreation. On the other hand, travelers willing to revisit Kish Island attribute their positive experience mainly to the quality and appropriate access to amenities and recreational facilities, the professional and friendly attitude of hotel staff, and the pleasant and peaceful environment of the island.
 
Conclusion
The analysis of the research results indicates that Kish Island is capable of creating a peaceful and enjoyable experience for tourists, especially if high-quality and reasonably priced services are maintained at a high level. Travelers who are eager to return have mentioned the high quality of hotels, cleanliness of rooms, varied breakfast, and easy access to shopping centers and recreational activities. Also, the island's peaceful atmosphere and beautiful coastal nature are other attractions of this destination that encourage many travelers to return. Improving the quality of hotels and amenities is of great importance. Renovating hotels, especially worn-out hotels with inadequate facilities, and improving the level of cleanliness and service standards can have a positive impact on the traveler experience. Furthermore, special attention should be paid to improving the health and safety conditions of hotels and other recreational places so travelers can use these facilities more confidently. In addition, controlling prices and their proportion to the quality of services should be prioritized. Proper policies regarding the pricing of services, especially food, transportation, and entertainment, can increase traveler satisfaction and reduce dissatisfaction. It is suggested that prices be adjusted to match the quality of services and that discounts and special packages be considered to attract more tourists.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords


  1. Abubakar, A. M., Ilkan, M., Al-Tal, R. M., & Eluwole, K. K. (2017). eWOM, revisit intention, destination trust and gender. Journal of Hospitality and Tourism Management, 31, 220-227. https://doi.org/10.1016/j.jhtm.2016.12.005
  2. Adam, M., Ibrahim, M., Putra, T. R. I., & Yunus, M. (2022). The effect of e-WOM model mediation of marketing mix and destination image on tourist revisit intention. International Journal of Data and Network Science, 7(1), 265-274. DOI: 10.5267/j.ijdns.2022.10.007
  3. Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
  4. Agarwal, V., Taware, S., Yadav, S. A., Gangodkar, D., Rao, A., Srivastav, V. (2022). Customer-churn prediction using machine learning, in: 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), IEEE, 893–899. DOI: 10.1109/ICTACS56270.2022.9988187
  5. Armis, R., & Kanegae, H. (2020). The attractiveness of a post-mining city as a tourist destination from the perspective of visitors: a study of Sawahlunto old coal mining town in Indonesia. Asia-Pacific Journal of Regional Science, 4(2), 443–461. DOI: 10.1007/s41685-019-00137-4
  6. Broxterman, D., Letdin, M., Coulson, E., & Zabel, J. (2000). Endogenous amenities and cities. Journal of Regional Science, 59(3), 369–408. doi/10.1111/jors.12449
  7. Che, T., Peng, Z., Lim, K., H., & Hua, Z. (2015). Antecedents of consumers’ intention to revisit an online group-buying website: A transaction cost perspective, Information and Management, 52(5), 588-598. https://doi.org/10.1016/j.im.2015.04.004
  8. Cheng, T., & Lu, C. (2013). Destination Image, Novelty, Hedonics, Perceived Value, and Revisiting Behavioral Intention for Island Tourism. Asia Pacific Journal of Tourism Research, 18(7), 766–783. https://doi.org/10.1080/10941665.2012.697906
  9. Choi, C.S., Cho, Y.-N., Ko, E., Kim, S.J., Kim, K.H., Sarkees, M.E.: (2019). Corporate sustainability efforts and e-WOM intentions in social platforms. International Journal of Advertising, 38(8), 1224–1239. https://doi.org/10.1080/02650487.2019.1613858
  10. Harsani, P. (2020). Empowerment of Gunung Sari Village Community Groups, to optimize the potential of the village towards the Tourism Independent Village. International Journal of Quantitative Research and Modeling, 1(2), 93-99. https://doi.org/10.46336/ijqrm.v1i2.38
  11. Hung, N. P., & Khoa, B. T. (2022). Examining the structural relationships of electronic word of mouth, attitude toward destination, travel intention, tourist satisfaction and loyalty: a meta-analysis. Geo Journal of Tourism and Geosites, 45, 1650-1660. http://dx.doi.org/10.30892/gtg.454spl15-986
  12. Jani, D., & Han, H. (2011). Investigating the key factors affecting behavioral intentions: evidence from a full-service restaurant setting. International Journal of Contemporary Hospitality Management, 23(7), 1000–1018. https://doi.org/10.1108/09596111111167579
  13. Khadivar, A., Mohseni, S., Abbasi, F. (2022). Sentiment Analysis of Trip Advisor Comments for Iranian Restaurants With a Deep Learning Approach, Journal of Business Intelligence Management Studies, 10(40), 17-41. DOI: 10.22054/IMS.2022.63437.2051 [In Persian].
  14. Khosravi manesh, L., Ershadi, R., & Rasekh, N. (2022). Identifying Factors Affecting The Development of Sports Tourism in Kish Island With an Emphasis on Women's Recreational Sports. Tourism and Leisure Time, 7(14), 101-117. doi: 10.22133/tlj.2023.382875.1074. [In Persian]
  15. Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848. https://doi.org/10.3390/su14020848
  16. Lam-González, Y. E., Clouet, R., Cruz Sosa, N., & de León, J. (2021). Dissatisfaction responses of tourists in the Havana World Heritage Site. Sustainability, 13(19), 11015. https://doi.org/10.3390/su131911015
  17. Loi, L. T. I., So, A. S. I., Lo & Fong, L. H. N. (2017). Does the quality of tourist shuttles influence revisit intention through destination image and satisfaction? The case of Macao. Journal of Hospitality and Tourism Management, (32), 115-123. https://doi.org/10.1016/j.jhtm.2017.06.002
  18. Lyu, S. O. (2016). Travel selfies on social media as objectified self-presentation. Tourism Management, 54, 185-195. https://doi.org/10.1016/j.tourman.2015.11.001
  19. Lapré, M. A. (2011). Reducing customer dissatisfaction: How important is learning to reduce service failure?. Production and Operations Management, 20(4), 491-507. https://doi.org/10.1111/j.1937-5956.2010.01149.x
  20. Mokhtari, M., AZAD, N., & Rousta, A. (2022). Investigating the factors affecting the return of tourists to Kish Island with a Persuasive Performance Theory Approach: Mixed method Research. Journal of Tourism and Development, 11(1), 53-68. doi: 10.22034/jtd.2021.296396.2403 [In Persian].
  21. Oh, S., Ji, H., Kim, J., Park, E., & del Pobil, A. P. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), 109–126.
  22. Pourahmad, A., Farhoudi, R. and Baradaran Nia, A. (2018). Assessing the Role of Information Technology in Promoting Tourism Industry in Kish Island. Journal of urban tourism, 4(4), 13-34. doi: 10.22059/jut.2018.224979.278 [In Persian].
  23. Prabadevi, B., Shalini, R., Kavitha, B. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4, 145-154. https://doi.org/10.1016/j.ijin.2023.05.005
  24. Raguseo, E., Neirotti, P., & Paolucci., E. (2017). How small hotels can drive value their way in infomediation. The case of ‘Italian hotels vs. OTAs and TripAdvisor. Information and Management, 54(6), 745-756. https://doi.org/10.1016/j.im.2016.12.002
  25. Safazade aval, Z., Rousta, A., & Jamshidi, D. (2021). Effective Components of Marketing on the Image of Tourism Destination Case Study: Kish Island. Journal of urban tourism, 8(2), 35-50. doi: 10.22059/jut.2021.318164.877 [In Persian].
  26. Siahsarani Kojuri, M. A. (2024). Designing and Explaining the Purchase Journey Map of Moviegoers Based on Contact Points. New Marketing Research Journal, 14(1), 79-102. doi: 10.22108/nmrj.2024.140171.3007 [In Persian].
  27. Sparks, B. A., So, K. K. F. & Bradley, G. L. (2016). Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, 53, 74-85. https://doi.org/10.1016/j.tourman.2015.09.011
  28. Vallejo, J., Redondo, Y., Acerete, A. (2015). Las características del boca-oído electrónico y su influ-encia en la intención de recompra online. Revista Europea de Direccion y Economia de La Empresa, 24(2), 61–75. https://doi.org/10.1016/j.redee.2015.03.002
  29. Viñán-ludeña, M. S., & De Campos, L. (2024). Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data. Advances in Hospitality and Tourism Research, 12(3), 254-286. https://doi.org/10.30519/ahtr.1436175
  30. Viñán-Ludeña, M. S., & de Campos, L. M. (2022a). Analyzing tourist data on twitter: A case study in the province of Granada at Spain. Journal of Hospitality and Tourism Insights, 5(2), 435–464. https://doi.org/10.1108/JHTI-11-2020-0209
  31. Viñán-Ludeña, M. S., Mora-J´acome, V., Viñán-Merecí, C. S., & Sánchez-Cevallos, E. (2022). Exploratory data analysis of the tourist profile: Case study in Loja-Ecuador. In A. Abreu, D. Liberato, & J. C. Garcia Ojeda (Eds.), Advances in tourism, technology and systems, 351–360. Singapore: Springer Nature Singapore.
  32. Viñan-Ludeña, M. S. (2019). A systematic literature review on social media analytics and smart tourism. In Smart Tourism as a Driver for Culture and Sustainability: Fifth International Conference IACuDiT, Athens, 357-374. Springer International Publishing.
  33. Wu, X. & Meng, S. (2016). E-commerce customer churn prediction based on improved SMOTE and AdaBoost, in: 2016 13th International Conference on Service Systems and Service Management (ICSSSM), IEEE, June, 1–5. https://doi.org/10.1109/ICSSSM.2016.7538581
  34. Wu, X., Li, P., Zhao, M., Liu, Y., Crespo, R. G., & Herrera-Viedma, E. (2022). Customer churn prediction for web browsers. Expert Systems with Applications, 209, 118177. https://doi.org/10.1016/j.eswa.2022.118177
  35. Yanfang, Q. & Chen, L. (2017). Research on E-commerce user churn prediction based on logistic regression, in: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, 87–91. https://doi.org/10.1109/ITNEC.2017.8284914