@DMOs Promote Hotel Occupancy in Tourist Destinations: An Abstract

Abstract

Destination marketing organizations (DMOs) are aware of Twitter’s relevance, and they have now integrated Twitter into their own websites (Luna-Nevarez and Hyman 2012). Because of their growing practical importance, social media have become strong allies for tourism destinations. Social media are used by both tourists and providers (Leung et al. 2013), so research is needed to show social media’s direct contribution to the tourism sector. To improve our knowledge of the effect of social media in tourism, this study examines how DMOs’ use of Twitter affects hotel occupancy in tourist destinations. Our conceptual framework presents a model of the links between predictors of tourist occupancy (i.e., activity, content, followers, and actions of Twitter users with respect to accounts managed by DMOs) and the outcome (i.e., occupancy rates in tourist destinations). Five Spanish DMOs with high levels of Twitter activity before and during the 2015 Holy Week were selected for study. Using Twitter application programming interfaces, the numbers of followers, tweets, retweets, and replies tweeted by DMOs and users were obtained. Text mining was used to analyze the tweets by DMOs, differentiating between tweets related to events, tourism resources/attractions, socialization, and commercial. Data were analyzed using artificial neural networks (ANNs). The best fit was achieved through a multilayer perceptron where the content of DMO tweets, level of DMO activity, followers of DMOs, and user actions acted as predictors of occupancy in different destinations. Results show that the number of retweets and replies achieved by DMOs had a greater effect than any other predictor of the hotel occupancy rate. Furthermore, the type of content had a different influence on hotel occupancy in tourism destinations. Tweets about events strongly influenced occupancy (importance of 74 %). These tweets were concise and informative—two characteristics highly valued by users (Andre et al. 2012)—and hence conducive to generating interest in destinations. Although tweets about tourist resources/attractions and socialization had some influence on occupancy, these tweets were not fundamental. Finally, marketing tweets scarcely influenced occupancy in the individual models and the clustered model. Results thereby provide evidence of the impact of social media, an area that requires greater attention from scholars (Anderson 2012; Leung et al. 2013; Zeng and Gerritsen 2014). Managerial implications regarding optimizing DMOs’ Twitter strategies are discussed, and avenues for further research are highlighted.