Data Science has brought marvelous opportunities to many industries, and tourism is no exception. Although tourism is known as an interdisciplinary field that crosses sociology, economics, geography, psychology, and communication sciences, tourism researchers have long been constrained by the classical repertoire of research methodologies. Besides the widely applied quantitative and qualitative approaches, we could see advancements especially in quantitative methods over time. In an era of digitization, data comes in new unstructured forms along with traditionally structured datasets, which result in the rise of Big Data. Meanwhile, advancements in computing and the rapid development of algorithms lead to the emergence of advanced analytics that goes beyond conventional business intelligence to gain deeper insights and make predictions. Data Science is more than a set of methods and tools in elevating the typical ways of doing empirical research, allowing researchers to even find answers for previously unknown questions. However, Data Science is yet to be embraced by tourism scholars potentially because of the bigness, messiness, and unstructured nature of data that fuel confusion and uncertainty. At the same time, because Data Science has altered the epistemological foundations, the interplay between Data Science and theory deserves much attention.

By learning how to develop research questions that can be supported by theories, Data Science helps researchers better understand the data, uncover unknown relationships and patterns, and improve data visualization. In tourism, examples of Data Science applications include route optimization, real-time analysis, predictive analysis, personalization, customer sentiment analysis, alerting and monitoring systems, and much more. Nevertheless, adopting Data Science in tourism is not an easy task as it requires an interdisciplinary understanding between computer sciences as its original discipline. Tourism researchers are often not aware of these upcoming techniques and not familiar with their usage, contributions, advantages, pitfalls, and limitations.

This book is intended to serve as a starting point that connects Data Science to the tourism industry, being helpful for both, researchers and practitioners alike. It aims to present an overview of Data Science techniques relevant for tourism by offering a theoretical foundation for these concepts and a how-to-approach which facilitates readers in developing their research projects. Of course, this book cannot claim to cover the individual chapters and topics in their completeness. Rather, the aim is to provide the reader with the necessary knowledge to facilitate the decision regarding the choice of method.

Roman Egger Salzburg University of Applied Sciences, Innovation and Management in Tourism

Roman Egger (Salzburg University of Applied Sciences, Innovation and Management in Tourism), Mike O´Connor (booking.com), Liliya Lavitas (Tripavisor), Holger Sicking (Austrian National Tourist Office), Jeroen Mulder (Air France-KLM)

Luisa Mich University of Trento

Roman Egger & Chung-En Yu Salzburg University of Applied Sciences, Innovation and Management in Tourism

Roman Egger & Chung-En Yu Salzburg University of Applied Sciences, Innovation and Management in Tourism

Roman Egger, Larissa Neuburger & Michelle Mattutzzi Salzburg University of Applied Sciences, Innovation and Management in Tourism University of Florida University of Groningen

Roman Egger, Markus Kroner, Andreas Stöckl Salzburg University of Applied Sciences, Legalcounsel.at School of Informatics, Communications and Media, University of Applied Sciences Hagenberg

Roman Egger Salzburg University of Applied Sciences, Innovation and Management in Tourism

Pablo Duboue Textualization Software Ltd.

Matthias Fuchs & Wolfram Höpken Department of Economics, Geography, Law and Tourism, Mid Sweden University University of Applied Science Ravensburg-Weingarten

Nikolay Oskolkov Lund University and National Bioinformatics Infrastructure Sweden (NBIS)

Ulrich Bodenhofer & Andreas Stöckl School of Informatics, Communications and Media, University of Applied Sciences Upper Austria

Andreas Stöckl & Ulrich Bodenhofer School of Informatics, Communications and Media, University of Applied Sciences Upper Austria

Pier Paolo Ippolito SAS Institute

Ajda Pretnar Faculty of Computer and Information Science, University of Ljubljana

Urszula Czerwinska no academic affiliation atm

Roman Egger & Enes Gokce Salzburg University of Applied Sciences, Innovation and Management in Tourism Pennsylvania State University

Roman Egger Salzburg University of Applied Sciences, Innovation and Management in Tourism

Andrei P. Kirilenko, Svetlana Stepchenkova & Luyu Wang University of Florida

Roman Egger Salzburg University of Applied Sciences, Innovation and Management in Tourism

Ivan Bilan TrustYou

Mayank Kejriwal University of Southern California

Rodolfo Baggio Bocconi University

Irem Önder Univesity of Massachusetts Amherst

JillianStudent Wageningen University

Andrei P. Kirilenko University of Florida

Johanna Schmidt VRVis

Roman Egger Salzburg University of Applied Sciences, Innovation and Management in Tourism

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