The Impact of Restaurant Recommendation Information and Recommendation Agent in the Tourism Website on the Satisfaction, Continuous Usage, and Destination Visit Intention

Abstract

Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com ) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.

Exploration of Moderated, Mediated, and Configurational Outcomes of Tourism-Related Content (TRC) on TikTok in Predicting Enjoyment and Behavioral Intentions

“…Understandability relates to how easily consumers comprehend information from travel experience content [36]. These three dimensions of TRC on TikTok encourage consumers to become more engaged, motivated, and persuaded to intend to visit tourist destinations [119].…”

Destination Restaurants’ Practices and the Production of Locality: The Case of Michelin Restaurants in China

“…Restaurant-based e-services can be classified into informational, transactional, and interactive services [ 73 , 74 ], which induce customer’s perceptions of e-servicescape and influence customer experiences. These restaurant servicescape elements can reinforce place information, offering an opportunity for customers to learn about local culture and foods and the host destination [ 50 , 71 , 75 ].…”

Establishing an intelligent and smart tourism model using data mining in the context of big data

“…Arif et al [32] stated that smart travel is a combination of cloud computing, the Internet of Things (IoT), and other technologies, using portable smart terminals to access the mobile internet and the Internet in order to use and intelligently perceive a variety of travel information [33]. Muniz et al [34] implemented intelligent management of the tourist experience by integrating destination management organizations and user knowledge management and comprehensively analyzing CKM models and frameworks to improve and innovate the tourist experience and tourist products.…”