Document Type : Original Article
Highlights
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Subjects
Tourism is one of the largest service industries and a crucial component of the economy in many countries across different stages of development. Even with the deregulation of airlines, technological advancements, the rise of e-commerce, and demographic shifts, the travel and tourism industry continues to generate both direct and indirect jobs, which in turn boosts the Gross Domestic Product (GDP) in the global economy. Improvements in the tourism industry not only benefit the sector itself but also create diverse opportunities for related industries such as retail, construction, and transportation. Additionally, the growing public interest in travel, coupled with increasing disposable income among tourists, has heightened the demand for higher-quality services. Therefore, the global expansion of tourism as a strategic sector for socio-economic and regional development has underscored the need for more sustainable tourism planning. By 2021, tourism had become a key driver for several local and national economies. The tourism industry is expected to grow at a rate of 3.3% annually, reaching 1.8 billion tourists by 2030. This growth in the number of tourists visiting various attractions worldwide has led to an increase in tourism marketing among these attractions, indicating that a tourist attraction's ability to maintain a marketing advantage is critical.
This study is quantitative, with an applied objective, and employs both descriptive and analytical methods. The research utilized two approaches for data collection: documentary (gathering information from books, journals, statistical yearbooks, maps, and websites) and field (using questionnaires). Accordingly, various studies on tourism marketing strategies and tourist satisfaction, as outlined in Table (1), were employed. The validity of the research tool was confirmed through academic experts and after necessary revisions in several stages. The reliability of the overall questionnaire was determined using Cronbach's alpha, which yielded a coefficient of 0.977, indicating high reliability. Each index was calculated separately. The research indicators were measured using a Likert scale ranging from 1 (very low) to 5 (very high). Statistical methods in this study included descriptive statistics (mean and standard deviation) and inferential statistics (using correlation analysis and stepwise regression) conducted with SPSS software. Additionally, Structural Equation Modelling (SEM) was performed using SMART PLS4 software.
Due to the non-normality of the indices, the non-parametric Kendall’s Tau test was used. The results of the statistical test indicated a positive and significant relationship between tourism marketing strategies and tourist satisfaction variables. Additionally, the results from Kendall’s Tau test show a positive and significant relationship between tourist satisfaction and tourism marketing strategy variables at a significance level of (p = 0.000). There is a significant and positive relationship between these variables at a 99% confidence level. Overall, all examined relationships are positive and significant, meaning that improving any of these indices can lead to increased tourist satisfaction with facilities and marketing strategies.
To further investigate the effects of tourism marketing strategy indices on tourist satisfaction, stepwise multiple linear regression was employed. The stepwise regression model illustrates how independent variables, representing tourism marketing strategies, affect tourist satisfaction. In Model 1, after including the variable of tourist attractions, this model was able to predict 73.2% of the variance in tourist satisfaction. Subsequently, Models 2 and 3 show that, after adding the variables of management and planning and goods and services, the percentage of variance explained increased to approximately 79.4% and 79.9%, respectively. In Model 4, after adding the variable of advertising and information dissemination, the explained variance increased to approximately 80.3%. The final model's effect coefficients for the independent variables are displayed, with tourist attractions having the highest impact (0.470), indicating that diverse and attractive tourist attractions are a major factor in increasing tourist satisfaction.
Further, based on the path coefficients (beta), T-statistics, and significance, the results show that tourism marketing strategies (independent variable) have a significant positive effect on tourist satisfaction (dependent variable), with a T-value of 36.279 and a path coefficient of 0.771. This demonstrates a positive and significant relationship between tourism marketing strategies and tourist satisfaction, thus confirming the main hypothesis of the study. The T-statistic exceeds 1.96, and the significance level is 0.000, indicating that effective marketing strategies significantly enhance tourist satisfaction, which can contribute to increased positive feedback and tourist loyalty.
To assess the effects of tourism marketing indices on tourist satisfaction, Kendall’s Tau correlation test, stepwise regression analysis, and Structural Equation Modeling (SEM) using SMART PLS software were employed. The results from Kendall’s Tau correlation test indicate that tourism marketing strategies have a significant positive relationship with tourist satisfaction, with a correlation coefficient of 0.538. This finding is consistent with studies by Sanaei Moghadam et al. (2020), Andervazh and Jannatkhaah (2023), and Kusumah (2024).
Further analysis using stepwise multiple linear regression revealed that the independent variables of tourism marketing strategies significantly explain tourist satisfaction. In Model 1, after including the variable of tourist attractions, the model was able to predict 73.2% of the variance in tourist satisfaction. Subsequently, Models 2 and 3 showed that the addition of management and planning and goods and services variables increased the explained variance to approximately 79.4% and 79.9%, respectively. In Model 4, after including the variable of advertising and information dissemination, the explained variance increased to approximately 80.3%.
The final model's effect coefficients reveal that among the variables, "tourist attractions" has the highest impact (0.470), aligning with studies by Sanaei Moghadam et al. (2020), Andervazh and Jannatkhaah (2023), and Matiza & Slabbert (2024). This indicates that diverse and attractive tourist attractions are the primary factors in increasing tourist satisfaction and can serve as a key strength in tourism marketing. Conversely, "advertising and information dissemination" had the lowest impact (0.085), consistent with studies by Braimah et al. (2024), Ragavan et al. (2014), and Valduga et al. (2020).
The manuscript did not receive a grant from any organization.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
