Examining educational tourist motivation facets, emotion, cognitive image and destination selection behaviour in an extended stimulus-organism-response model / Alfian Thomas
The significant growth of new educational tourism destinations, as well as the development of higher education, intensifies competition in this industry. Recommendation has been identified as a significant factor in the tourism industry's survival and growth. Consequently, this research exami...
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Online Access: | https://ir.uitm.edu.my/id/eprint/89359/1/89359.pdf |
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| Summary: | The significant growth of new educational tourism destinations, as well as the
development of higher education, intensifies competition in this industry. Recommendation has been identified as a significant factor in the tourism industry's
survival and growth. Consequently, this research examined the relationships between educational tourists' motivational facets, emotions, cognitive images, and destination selection behaviour. Previous studies have produced a range of results regarding the effect of the mediator destination image operated by emotion and cognitive image. In various environments and study settings, destination image is said to be a predictor of destination selection behaviour. The role of destination images as mediators within the Stimulus-Organism-Response (S-O-R) theory is, however, limited in an educational tourism context. Since environmental stimuli and international educational tourists' perceptions of destination image and behaviour are complex processes, this study aims to validate and extend research areas in an educational tourism setting by integrating theories, models, and constructs within the behavioural context of educational tourism. The S-O-R theory was chosen as the underpinning theory, and the conceptual framework for this study was a modified Mehrabian-Russell model. A survey of 208 international educational tourists residing in Malaysia was used to evaluate the theoretical framework empirically. Partial Least Squares methods (PLS-SEM) was used to analyse the resulting data. In this evaluation, the measurement models were tested for convergent and discriminant validity and then the structural model. |
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