ABSTRACT
The field of tourism and hospitality has witnessed remarkable academic achievements in the last four decades. The degree of complexity in knowledge generation and fast data accumulation are posing new challenges (Mayer-Schonberger & Cukier, 2013, Rivera & Pizam, 2015). Today, decision makers and researchers must function more efficiently in real time and need to be more creative and innovative in solving problems and developing unique approaches to solutions in this highly dynamic and ever-increasingly competitive business environment. At the same time, the pace of data generation and research not only creates new opportunities for researchers, but also influences the manners in which researchers conduct empirical studies. For example, with a conventional theory-driven study, the researcher develops and conceptualizes his/her hypotheses based on relevant literature, supported by theory and reasonable argument, and then tests and verifies the hypotheses by the use of samples and appropriate tools and techniques. On the other hand, the richness of big data in today’s world allow the researcher to proceed without a priori set of conditions on the content of data and reveal patterns and structures that may be reflective of the industry and market structure. Furthermore, the convergence of quantitative and qualitative approaches, supported by solid and verifiable research findings, linear and nonlinear data analyses, and utilization of mixed methods in research and development, have enabled researchers to offer science-based solutions to today’s complex problems. Baggio (2008) argues that a shift in management attitude is needed, and that dynamic and adaptive methods may be better suited and sought to deal with today’s complex tourism and hospitality systems.
