Application of data mining techniques to present model of tourist’s health behavior analysis

Document Type : Original Article

Authors

1 Student of information technology management, department of management, faculty of management and economics, Tehran Science and research branch, Islamic Azad University, Tehran, Iran branch, Islamic Azad University, Tehran, Iran

2 Assistant professor of computer engineering, department of computer, faculty of electrical and computer engineering, Mahshahrbranch, Islamic Azad University, Mahshahr, Iran University, Mahshahr, Iran

3 Assistant professor of information technology management, department of management, faculty of management and economics, Tehran Science and research branch, Islamic Azad University, Tehran, Iran

Abstract

Background and Objectives: Today, increasing consumer desire for health tourism has led to a greater understanding of the behavioral patterns of tourists. It becomes clear that intervention in that process is necessary to achieve the desired results The development of tourism services at a specific time is essential for the target market and meeting the needs of tourists to succeed in the tourism market.


Methodology: In this article, a Quantitative and qualitative method has been used. In the qualitative method, 50 people were interviewed and in the quantitative part, 156 questionnaires were distributed, and finally, its validity and reliability were examined. SmartPLS2 software has been used for modeling and data analysis.


Results: After analyzing the data, the effective factors in tourists' decision to choose Iran as a health tourism destination were examined. By focusing on the obtained factors, the needs of health tourists can be met and more motivation can be created.


Conclusion: In this article, the behavior of health tourists is analyzed and finally a model based on tourist behavior is designed to better manage capacity and meet the challenges and needs of tourists So that agencies can predict their future behavior based on the past behavior of tourists.

Keywords

Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

References

1.Yang J.T, Knowledge sharing: Investigating appropriate leadership roles and collaborative culture. Tourism management 2007; 28(2): pages 530-543
https://doi.org/10.1016/j.tourman.2006.08.006
 
2. Raffaele. Appling tourist consumption behavior from destination card data. International journal of tourismresearch2017; 48(2): pages 216-229.
 
3. Banki,Ismail,Dalil,Kawu.ModeratingRoleofAffective destination image on the Relationship between tourists satisfaction and behavioral Intention. Evidence from Obudu Mountain Resort. Journal of Environment and Earth science 2014; 30(3): pages 47-60.
 
4. Beerli A and Marting. Factors influence destination image, Annals of tourism research 2004; 31(3): pages 657-681
https://doi.org/10.1016/j.annals.2004.01.010
 
5. Breslin J. G, Passant & Decker S. Introduction to the social web (web 2.0, social media, social software). In the social semantic web. Springer 2009; pages 21-44.
https://doi.org/10.1007/978-3-642-01172-6_3
 
6. Khaniki, Movahedifar.Analysis of the model of tourist destinations in travel with the help of data mining studied in Mashhad. The second national conference on applied research in computer science and information technology. Tehran 2019.
 
7.Jalali, Abdolvand, Harandi.Analysis of customer behavior using data mining, new marketing research. Summer 2018; pages 93-110.
 
8. Abdi, 0. The Study of Tourists' Propensity to Revisit Iran. International Journal of Social Sciences2017; 6(1), pages 19-31.
https://doi.org/10.21298/IJTHR.2017.10.31.10.19
 
9. Rani Praveen, Vohra Rajan, Gulia Anju. Association Rule Mining in Discovering Travel Pattern in Passport data Analysis. International Journal of Computer Science and Information Technologies 2014; pages 5015-5019.
 
10. Jeuring, Discursive contradictions in regional tourism marketing strategies: The case of Fryslân the Netherlands1. 2017; Perspectives on proximity tourism in Fryslân.
 
11. Shaolonge Sun. Forecasting tourist arrivals with machine learning and internet search index. (2019); pages 24-38
 
12. Taghizadeh, Shami Zanjani, Haghighi, Hashemabadi. Components and content of medical tourism facilitation websites. Tourism management studies quarterly, 2016; pages 1- 18.
 
13. Dharshini Azmi. Analysis of accuracy K-Means and Apriori algorithms for patient data clusters. Journal of Physics.2019; pages 1230 -12020.
https://doi.org/10.1088/1742-6596/1230/1/012020
 
14. Harandi, Mirzaeian Khamseh. Explaining the model of attracting healthy tourists: using the data theory strategy of the classical foundation. Urban tourism, spring 2017; pages 87-98
 
15. Minaei, Namamian, Maroofi, Moradi. Model of the effect of E-Tourism on the purchase of tourism services and its compliance with the behavior ofIranian tourists. Bi-Quarterly Journal of Social Tourism Studies.2020; pages 412-453
 
16.Taghi Pourian, Yazdani, Aghaeifar. The role of destination brand image on the Behavioral tendencies of Tourists Case study: Tourists in the west of Mazandaran province, Urban Tourism Quarterly. Winter 2019; pages 37-50.
 
17. Tahmasebi, Roshanian. Characteristics and shopping behavior of tourists in Baneh. Quarterly Journal of Tourism Management Studies. Year 11, Issue 36, winter 2016, pages 31-59
 
18. Slabbert Elmarie.Vuuren Van travel motivations and behavior of tourist to a South African resort. Book of Proceedings Vol. l-International conference on tourism & management studies Algarve 2011.
 
19.Bagheri, Shad, Ghaemi, Honarparvar, Ajagh. A review of mobile advisory systems in the field of tourism, journal of surveying engineering and spatial information 2018.
 
20. Saeed Hashemzadeh, Niloufar A'alami, health tourism facilitator companies in Iran: A missing trade.
Razavi International Journal of Medicine
Volume 9, Issue 1
January 2021
Pages 27-31

Files

Share

How to cite

Statistics