Purpose-This study examines the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach-The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; data reporting and visualization. Findings-Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of Business Intelligence and Big Data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big datadriven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications-This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value-This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on Business Intelligence and Big Data. To the best of our knowledge, it is the first systematic literature review within hospitality and tourism research dealing with Business Intelligence and Big Data.

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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.

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The majority of today's information and communication technology (ICT) impact studies disregard infrastructural, organizational, and environmental factors typically responsible for successful e-business adoption and use. This article proposes an empirical approach that shows how the mentioned factors determine both e-business adoption and the impact of information and communication technologies. The research framework is based on E. Rogers' Innovation Diffusion Theory and is tested with survey data gathered in the Austrian destination management organization sector. By referring to K. Zhu and K. L. Kraemer's ( 2005) e-business impact model, the proposed approach explicates how the use of e-business applications may positively affect the performance of tourism organizations. Online survey data are analyzed through a linear structural equation modeling approach.