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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 e-book provides leading edge study at the improvement of analytics in go back and forth and tourism. It introduces new conceptual frameworks and size instruments, in addition to functions and case reports for vacation spot advertising and administration. it's divided into 5 components: half one on trip call for analytics specializes in conceptualizing and enforcing shuttle call for modeling utilizing colossal 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 commute and way of life, proposing fresh advancements in wearable desktops and physiological dimension units, and the consequences for our realizing of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based facts with tourism records. half 4 discusses web-based and social media analytics and provides the most recent advancements in using user-generated content material on the web to appreciate a couple of managerial difficulties. the ultimate half is a suite of case stories utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line studies within the lodge undefined, and comparing vacation spot communications and industry intelligence with on-line inn studies. The chapters during this part jointly describe more than a few varied techniques to knowing industry dynamics in tourism and hospitality.

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Song and H. Liu preferences; (3) optimize the forecast results and decrease the forecast failure risk by model selection and combination forecasting. 9 Conclusions Big data is a social, cultural, technological, and ethical phenomenon that is not all good, all bad, or consistently neutral. With the proliferation and explosive increase in the application of big data, it has become a common tool in corporate decisions and a number of new social perils have arisen. At the same time, as data technologies become more pervasive, there are also privacy concerns and the potential for the abuse and misuse of big data (Bollier & Firestone, 2010).

2011). FaMIDAS: A mixed frequency factor model with MIDAS structure. 788. Frederiksen, L. (2012). Big data. Public Services Quarterly, 8(4), 345–349. , & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations—A case from Sweden. Journal of Destination Marketing and Management, 3(4), 198–209. , & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509–548. , & Lisˇka, R. (2011). Dynamic factors in the presence of blocks.

Fesenmaier, D. R. (2014). Tourism information technology. Wallingford: Cab International. Bessec, M. (2013). Short-term forecasts of French GDP: A dynamic factor model with targeted predictors. Journal of Forecasting, 32(6), 500–511. Beyer, M. , & Laney, D. (2012). The importance of ‘big data’: A definition. Stamford, CT: Gartner. , & Firestone, C. M. (2010). The promise and peril of big data. Washington, DC: Aspen Institute. , & Raimbault, T. (2015). Big data: A new challenge for tourism. Paper presented at the 2nd IEEE International conference on big data.

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


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