Factors That Trigger The Use Of Social Media Tools, Applications And Networks Of Tourist

Kaldeen, Mubarak

doi:10.1109/iccct2.2019.8824837

Cited by 2 publications

(3 citation statements)

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“…In the context of travel planning, PEOU and PU significantly positively affect tourists' intentions to use social media to plan travel [33]. PEOU refers to "the degree to which tourists perceive that using social media for planning travel to Saudi Arabia is simple, easy, and effortless" [33]. PU refers to "the degree to which tourists perceive that using social media for planning travel to Saudi Arabia might improve their travel plans and provide them with more best travel options" [33].…”

Section: Tourist-based Theoretical Frameworkmentioning

confidence: 99%

“…PEOU refers to "the degree to which tourists perceive that using social media for planning travel to Saudi Arabia is simple, easy, and effortless" [33]. PU refers to "the degree to which tourists perceive that using social media for planning travel to Saudi Arabia might improve their travel plans and provide them with more best travel options" [33]. SAT is tourists' satisfaction with the tourism content about Saudi Arabia on social media platforms.…”

Section: Tourist-based Theoretical Frameworkmentioning

confidence: 99%

“…PEOU positively affects tourists' intentions to use social media for planning travel to Saudi Arabia. In the context of travel planning, PEOU and PU significantly positively affect tourists' intentions to use social media to plan travel [33]. PEOU refers to "the degree to which tourists perceive that using social media for planning travel to Saudi Arabia is simple, easy, and effortless" [33].…”

Section: Hypothesis 2 (H2)mentioning

confidence: 99%

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A Tourist-Based Framework for Developing Digital Marketing for Small and Medium-Sized Enterprises in the Tourism Sector in Saudi Arabia

Alnajim,

Fakieh

2023

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Social media has become an essential tool for travel planning, with tourists increasingly using it to research destinations, book accommodation, and make travel arrangements. However, little is known about how tourists use social media for travel planning and what factors influence their intentions to use social media for this purpose. This thesis aims to understand tourists’ intentions to use social media for travel planning. Specifically, it investigates the factors influencing tourists’ intentions to use social media for planning travel to Saudi Arabia. It develops a machine learning (ML) classification model to assist Saudi tourism SMEs in creating effective digital marketing strategies for social media platforms. A survey was conducted with 573 tourists interested in visiting Saudi Arabia, using the Design Science Research (DSR) approach. The findings support the tourist-based theoretical framework, showing that perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SAT), marketing-generated content (MGC), and user-generated content (UGC) significantly impact tourists’ intentions to use social media for travel planning. Tourists’ characteristics and visit characteristics influenced their intentions to use MGC but not UGC. The tourist-based ML classification model, developed using the LinearSVC algorithm, achieved an accuracy of 99% when evaluated using the K-Fold Cross-Validation (KF-CV) technique. The findings of this study have several implications for Saudi tourism SMEs. First, the results suggest that SMEs should focus on developing social media content that is perceived as useful, easy to use, and satisfying. Second, the findings suggest that SMEs should focus on using MGC in their social media marketing campaigns. Third, the results suggest that SMEs should tailor their social media marketing campaigns to the characteristics of their target tourists. This study contributes to the literature on tourism marketing and social media by providing a better understanding of how tourists use social media for travel planning. Saudi tourism SMEs can use the findings of this study to develop more effective digital marketing strategies for social media platforms.

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Section: Tourist-based Theoretical Frameworkmentioning

confidence: 99%

Section: Tourist-based Theoretical Frameworkmentioning

confidence: 99%