Smart Tourism is regarded as a new paradigm in the tourism industry. The purpose of this article is to present the first bibliometric study of Smart Tourism to help understanding the means of smart tourism by applying a co-word analysis. Co-word analysis is a content analysis technique which uses a pattern of co-occurrence of pair of items such as words, nouns or phrases in a corpus of texts to identify the relationships between ideas and the main themes within the subject areas. Smart tourism has been an issue in tourism industry these days. Some scholars are trying to define the ‘smart tourism’ as a different meaning from the ‘mobile tourism’ that is still commonly used. Practically, the term ‘smart’ is getting popular in tourism industry, since the advent of smartphones. Though the term ‘smart’ has not been defined clearly, it is still used even in academia. To examine the intellectual structureof Smart Tourism, this study used keywords in articles from the academic database in KCI (Korea Citation Index) from 2010 to February, 2014. This study used both quantitative and qualitative measures. Quantitative data are used to put very related concepts together, while qualitative indicators are used to measure the impact of the detected themes. In addition, the study presented bibliometric maps to show the networks between the main concepts treated by the Smart Tourism domains. The maps provide insights into the structure of the Smart Tourism with visualizing the division of the field into several subfields. For a method of the study, the main 10 keywords-‘smartphone’, ‘SNS’, ‘ubiquitous’, ‘service quality’, ‘LBS’, ‘AR’,…and so on.- related to Smart Tourism extracted from policy and trend reports of governmental offices(i.e. Mistry of Culture, Sport and Tourism), company of government(i.e. Korea Tourism Organization), and research institutes(Samsung Economic Research Institute, Korea Culture & Tourism Institute). Using 10 main keywords, we collected 185 articles related to the smart tourism filed.Then within 185 articles, there are 1,347author keywords extracted.After thenormalization of 1,347 keywords, 366 author keywords were selected for data analysis.A co-occurrence matrix and an affinity matrix were generated based on Pearson’s correlation coefficients to create a clustering of the words using hierarchical clustering analysis. To visualize these intellectual structures, this study carried out a network analysis with NodeXL to which a data algorithm in R was applied. These findings appear to indicate the potential usefulness of bibliometric studies in uncovering the different research fields’ intellectual structure and evolution. This evolution providesan opportunity to anticipate interesting developments in Smart Tourism field with respect to key topics of the field as well as predicting which topics are less likely to assume a central role in Tourism IT inthe near future.The study benefits researchers and practitionersby offering guidelines to the process of selecting research topics and of formulating policy regarding smart tourism.