Online travel agencies (OTAs) have been offering tourists trip planning services (TPS) for more than a decade. However, they are less popular than other online travel…
Online travel agencies (OTAs) have been offering tourists trip planning services (TPS) for more than a decade. However, they are less popular than other online travel services such as metasearch with price comparison. This study aims to investigate why TPS on the internet, although important to tourists, are not well accepted by young mainland Chinese tourists.
A trip planning service acceptance model (TPSAM) was constructed and tested by inviting participants to take part in a trial using the TPS of a China OTA and then participants were asked to complete a questionnaire based on their user experience. Partial least square technique was used to perform a path analysis on the model.
Social influence and effort expectancy have significant direct influence on reuse intention. Social influence increases the trust level of the tourists on the TPS and effort expectancy’s strong influence on joy suggest that a joyful and effortless experience is critical for tourists to consider reusing the TPS.
The findings could provide some insight to the OTAs on improving their TPS. For instance, OTAs should let tourists feel that the TPS requires little effort and is fun to use and more promotion is needed through social media.
Although trip planning is essential for tourists in achieving a delightful travel experience, few studies have examined the adoption of Web-based TPS. This study contributes to the literature by establishing a TPSAM and extends previous work by showing that a causal relationship exists between social influence and trust in the service acceptance context.
线上旅游代理（OTA）已经十多年为游客提供旅游计划服务（TPS）。然而, OTA比其他在线旅游服务相较则受欢迎程度下降, 比如价格比对的元搜索服务。本论文旨在研究网络TPS, 即便对游客重要, 但是为什么不受中国大陆年轻游客的欢迎。
本论文通过邀请受访者参与中国OTA提供的旅游计划服务试点样品, 并完成针对他们的用户体验的问卷, 来开发和测验这个旅游计划服务接受模型（TPSAM）。本论文采用PLS分析法来测验模型。
社会影响和努力预期对再使用意图起到直接影响。社会影响增强了游客对TPS的信任度, 努力预期对愉悦感有强烈影响, 这预示着对于游客而言, 一个愉悦的且不太费劲的体验对于再次使用TPS起到关键作用。
本论文研究结果对于OTA增强其TPS起到启示作用。比如, OTA应该让游客感受TPS不需要费很多力气来学习使用并且使用过程很有趣, 此外, 通过社交媒体来增强更多宣传是有必要的。
尽管旅游计划对游客而言获得愉快旅游体验是必要的, 然而, 很少文章研究线上TPS使用现象。本论文建立了TPSAM, 对理论做出贡献, 并且本论文对之前的文献做出扩展, 验证了服务接受背景下社会影响和信任之间的直接联系。
This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to…
This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences.
To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP).
The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level.
The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency.
This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy.
This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.
为了达到研究目的, 本研究建立了一种个性化推荐旅游胜地的信息系统。其系统通过分析每个游客的旅游偏好来建设一种定性旅游胜地排名模型。这种新型模型主要通过结合以下两种分析算法：（1）K平均聚类算法（K-means clustering）（2）层次分析法（AHP）。
本研究建立的推荐信息系统经过了准确率和拓展性的测评。实验结果表明这种排名模型的准确率并不受旅游胜地多少和游客样本大小的影响。此外, 这种排名模型也具有拓展性, 因为算法时间并不受游客样本大小的影响。最后, 研究实验表明此排名模型客户体验性达到合格满意要求。
本研究的主要理论意义在于其结合了K平均聚类算法和层次分析法, 并建立了一种新型定性排名模型, 这种排名模型个性化地推荐更高质量的旅游胜地给游客。然而, 这种推荐信息系统有一些局限性。第一, 新旅游胜地的信息需要手动输入。第二, 整个系统的处理时间决定于网络带宽和延迟状况。