Causal Relationship Model of Marketing Mix Affecting Travel of Chinese Tourists in Chiang Mai Province
Article Sidebar
Main Article Content
Abstract
The aim of this research is to develop a causal relationship model and examine the coherence of the model with the empirical data on the marketing mix affecting the travel of Chinese tourists in Chiang Mai. The sample consisted of 240 Chinese tourists traveling to Chiang Mai using the purposive sampling method. Structural equation modeling was used to analyze the data.
The results of the research showed that: 1) the causal relationship model was harmonious with the empirical data; For the root index of the squared mean of the estimate, the relative chi-square (CMIN/DF) is 1.528, which is less than 2, the GFI is 0.911, which is greater than 0.90, and the CFI (comparative fit index) is 0.957, which is greater than 0.90. Assessment (RMSEA) was 0.052, which is less than 0.08. 2) The models were consistent with the empirical data on educational issues in the physical environment, price, distribution channel promotion, service provider, and service process, and the product had a statistically significant direct influence at the level of 0.001. The results of this research are very useful to academics in the field of expanding and building on the knowledge of the relationship of marketing ingredients in order to be able to develop tourism to accommodate Chinese tourists, especially with the emphasis on product development that promotes the process of marketing ingredients consistently as a model so that the results of the study can be formulated as a plan to accommodate tourists.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Abubakar, A. M., Elrehail, H., Alatailat, M. A. & Elçi, A. (2019). Knowledge Management, Decision-Making Style and Organizational Performance. Journal of Innovation & Knowledge, 4, 104–114.
Aggarwal, S. & Gour, A. (2020). Peeking Inside the Minds of Tourists Using a Novel Web Analytics Approach. Journal of Hospitality and Tourism Management, 45, 580–591.
Aydin, G. (2020). Social Media Engagement and Organic Post Effectiveness: A Roadmap for Increasing the Effectiveness of Social Media Use in Hospitality Industry. Journal of Hospitality Marketing & Management, 29, 1–21.
Chu, S. C., Deng, T. & Cheng, H. (2020). The Role of Social Media Advertising in Hospitality, Tourism and Travel: A Literature Review and Research Agenda. International Journal of Contemporary Hospitality Management, 32, 34193438.
Kitsios, F., Kamariotou, M., Karanikolus, P. & Grigoroudis, E. (2021). Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining. Applied Sciences, 11, 8032.
Obembe, D., Kolade, O., Obembe, F., Owoseni, A. & Mafimisebi, O. (2021). Covid–19 and the Tourism Industry: An Early Stage Sentiment Analysis of the Impact of Social Media and Stakeholder Communication. International Journal of Information Management Data Insights, 1, 100040.
Onder, I., Gunter, U. & Gindl, S. (2020). Utilising Facebook Statistics in Tourism Demand Modelling and Destination Marketing. Journal of Travel Research, 59, 195–208.
Poltanee, I. & Thavornsil, T. (2022). Destination Image of Street Food on China Town an Impact on Tourists’ Dining Experiences. Central Asia and the Causasus, 23, 191–200.
Saheb, T., Amini, B. & Alamdari, F. K. (2021). Quantitative Analysis of the Development of Digital Marketing Field: Bibliometric Analysis and Network Mapping. International Journal of Information Management Data Insights, 1, 100018.
Taecharungroj, V. & Mathayomchan, B. (2019). Analysing TripAdvisor Reviews of Tourist Attractions in Phuket, Thailand. Tourism Management, 75, 550–568.