Insights into tourism demand and tourism behavior / four papers using multiple perspectives and structural equation modeling
Rapid industry expansion has elevated research on tourism demand to an important area of inquiry. This work examines the topic through four interconnected studies. It starts by reviewing statistical methods previously used in tourism demand forecasting and how structural equation modeling (SEM) has...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | masterThesis |
| منشور في: |
2010
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/6354 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php https://search.proquest.com/docview/910563782/abstract/A4DBB06A63414D86PQ/1?accountid=27870 |
| الوسوم: |
إضافة وسم
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| الملخص: | Rapid industry expansion has elevated research on tourism demand to an important area of inquiry. This work examines the topic through four interconnected studies. It starts by reviewing statistical methods previously used in tourism demand forecasting and how structural equation modeling (SEM) has typically been applied. Twenty-one papers published in tourism and service industry journals are reviewed, culminating with guidelines for how SEM’s use in tourism research could be improved. Best practice SEM techniques are then applied to data on 162 countries to validate the relationships between supply-side factors and tourist inflows. Supply-side variables are incorporated into demand equations to understand the factors that develop a country’s tourism. Next, multigroup analysis in SEM was used to test whether a country’s level of economic development moderates the previously validated relationships. Findings show that relationships between tourism and supply-side variables hold true regardless of the level of economic development. A final study examines tourists’ behavior across time. It uses the latent growth curve model of SEM to estimate the impact of different predictors on how individual return behavior develops. Together, these papers’ findings help enhance readers’ comprehension of tourism demand forecasting and provide insights to practitioners on factors they can use to drive tourisms to their destinations. |
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