Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.

This study contributes to the development of knowledge on transferring the concept of customer-based brand equity to a tourism destination context. Keller’s brand equity pyramid is utilized as the comparison framework to reveal similarities, but also overlaps, differences and gaps on both the conceptual and measurement level of existing brand equity models for destinations. Particularly, the inner core of the model depicts the complex mechanisms of how destination resources transform into benefits for tourists overlooked by prior research. This study proposes a customer-based brand equity model for destinations, which consists of five dependent constructs, including awareness, loyalty, and three destination brand promise constructs constituting the inner core of the model, namely, destination resources, value in use, and value for money. The model was repeatedly tested for the leading Swedish mountain destination Åre, by using a linear structural equation modeling approach. Findings confirm the path structure of the proposed model.