Zheng Xiang, Daniel R. Fesenmaier's Analytics in Smart Tourism Design: Concepts and Methods PDF

By Zheng Xiang, Daniel R. Fesenmaier

ISBN-10: 3319442627

ISBN-13: 9783319442624

ISBN-10: 3319442635

ISBN-13: 9783319442631

This publication offers innovative study at the improvement of analytics in trip and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to functions and case experiences for vacation spot advertising and administration. it really is divided into 5 elements: half one on shuttle call for analytics specializes in conceptualizing and enforcing go back and forth call for modeling utilizing giant info. It illustrates new how you can determine, generate and make the most of huge amounts of information in tourism call for forecasting and modeling. half makes a speciality of analytics in shuttle and lifestyle, providing contemporary advancements in wearable pcs and physiological size units, and the consequences for our figuring out of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based info with tourism facts. half 4 discusses web-based and social media analytics and provides the newest advancements in using user-generated content material on the web to appreciate a couple of managerial difficulties. the ultimate half is a set of case reviews utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line reports within the lodge undefined, and comparing vacation spot communications and marketplace intelligence with on-line inn studies. The chapters during this part jointly describe a variety of diverse techniques to knowing marketplace dynamics in tourism and hospitality.

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This flexibility can even offset the specificity of the distributional assumptions. 2 Choice Set One major issue in demand analysis is the definition of the choice set, that is, the alternatives from which the tourist selects the preferred option. The analyst is always uncertain about the set of alternative that the individual considered when making the decision. Obviously, the more data exists, the more alternatives, and the more the potential error of omitting alternatives considered by the tourist but not regarded by the analyst will increase.

5) Value Value is frequently seen as another important characteristic of big data. The value of tourism big data can be described by its novel application in the tourism industry. First, there is the personalized application of tourism big data. Personalized marketing and targeted product design are extremely powerful opportunities that can be obtained from big data (Jani, Jang, & Hwang, 2014). Using a series of interviews conducted within the travel industry, Radovich (2015) showed how big data can be used to increase impact and reduce friction across disciplines, both within a company and within the industry.

Stock, J. , & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179. Stock, J. , & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405–430. Stock, J. , & Watson, M. W. (2006). Forecasting with many predictors. In C. W. J. G. G. Elliott & A. ), Handbook of economic forecasting (pp. 515–554). North Holland: Elsevier.

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Analytics in Smart Tourism Design: Concepts and Methods by Zheng Xiang, Daniel R. Fesenmaier

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