{"title":"基于游客的沙特阿拉伯旅游业中小型企业数字营销发展框架","authors":"Rishaa Alnajim, Bahjat Fakieh","doi":"10.3390/data8120179","DOIUrl":null,"url":null,"abstract":"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. 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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. 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引用次数: 0
摘要
社交媒体已成为旅行规划的重要工具,游客越来越多地使用社交媒体来研究目的地、预订住宿和安排旅行。然而,人们对游客如何使用社交媒体进行旅行规划以及哪些因素会影响他们使用社交媒体进行旅行规划的意图知之甚少。本论文旨在了解游客使用社交媒体进行旅行规划的意图。具体而言,论文将研究影响游客使用社交媒体规划前往沙特阿拉伯旅游的意向的因素。论文开发了一个机器学习(ML)分类模型,以帮助沙特旅游业中小型企业为社交媒体平台制定有效的数字营销战略。采用设计科学研究(DSR)方法对 573 名有意前往沙特阿拉伯旅游的游客进行了调查。研究结果支持基于游客的理论框架,表明感知有用性(PU)、感知易用性(PEOU)、满意度(SAT)、营销生成内容(MGC)和用户生成内容(UGC)显著影响游客使用社交媒体进行旅游规划的意愿。游客的特征和访问特征会影响他们使用 MGC 的意愿,但不会影响 UGC 的意愿。使用 LinearSVC 算法开发的基于游客的 ML 分类模型,在使用 K-Fold Cross-Validation (KF-CV) 技术进行评估时,准确率达到了 99%。这项研究的结果对沙特旅游业中小型企业有几方面的启示。首先,研究结果表明,中小型企业应注重开发有用、易用和令人满意的社交媒体内容。其次,研究结果表明,中小企业应注重在社交媒体营销活动中使用 MGC。第三,研究结果表明,中小企业应根据其目标游客的特点调整社交媒体营销活动。通过更好地了解游客如何使用社交媒体进行旅游规划,本研究为旅游营销和社交媒体方面的文献做出了贡献。沙特旅游中小型企业可以利用本研究的结论为社交媒体平台制定更有效的数字营销战略。
A Tourist-Based Framework for Developing Digital Marketing for Small and Medium-Sized Enterprises in the Tourism Sector in Saudi Arabia
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.