The influence of online user-generated content (UGC) information qualities on perceived destination image, perceived travel risk and tourist's behavioural intentions / Muhammad Aliff Asyraff Kamal Nurzaman
Although online User-generated Content (UGC) platforms have been used widely, people are exposed to abundant information generated by common online users via social media and travel review websites. Misleading and defamatory published information could jeopardise tourism destination image and lead t...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/75481/1/75481.pdf |
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| Summary: | Although online User-generated Content (UGC) platforms have been used widely, people are exposed to abundant information generated by common online users via social media and travel review websites. Misleading and defamatory published information could jeopardise tourism destination image and lead to negative tourist behavioural intentions. Since the unprecedented COVID-19 pandemic hit the travel industry badly, tourists projected high concern on the safety and security aspects during visitation. This study adopts the extended Mehrabian and Russel's Stimulus-Organism-Response (SOR) model, with the following objectives: (i) to examine the effect of perceived information qualities of online UGC as the stimulus factor on destination image, (ii) to evaluate the influence of destination image components towards behavioural intentions, considered as the final response, (iii) to assess the role of perceived destination images as the mediator, and (iv) to determine the perceived travel risk of COVID-19 as moderating variable between destination image components and behavioural intentions. A total of 255 eligible data were analysed to yield descriptive statistics, and Partial-least Square - Structural Equation Modelling (PLS-SEM) was used to test the hypotheses of the study and evaluate the model. The result indicated that intrinsic travel information quality most significantly influenced the perceived cognitive image. Meanwhile, contextual travel information quality was the most significant predictor of the perceived affective image. This study has also confirmed that cognitive image positively influenced affective image. Both destination image components, cognitive and affective, significantly affected behavioural intentions. Based on the SmartPLS 3.1.1 bootstrapping analysis, cognitive image indirectly affected the relationship between intrinsic information quality and behavioural intentions. On the other hand, affective image had a significant indirect effect on the relationship between contextual information quality and behavioural intentions. Interestingly, affective image mediated oppositely on the relationship between social information quality and behavioural intentions. Simple Moderation analysis revealed that the perceived travel risk of COVID-19 did not influence the relationship between both destination image components towards tourists' behavioural intentions. The result from the present study verified that the extended SOR model could be integrated into the research of online travel UGC, destination image, and behavioural intentions of tourists, particularly in Malaysia. The inclusion of travel risk also enhances the use of SOR in a pandemic situation. Given the positive outlook from the respondents, understanding online travel UGC could assist organisations to improve business performance by leveraging consumer content, enhancing destination or company's image influences, and setting a benchmark for competitors in market share. |
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