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

By Zheng Xiang, Daniel R. Fesenmaier

This e-book provides innovative examine at the improvement of analytics in trip and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to purposes and case reports for vacation spot advertising and administration. it truly is divided into 5 elements: half one on commute call for analytics specializes in conceptualizing and enforcing go back and forth call for modeling utilizing gigantic facts. It illustrates new how you can establish, generate and make the most of huge amounts of information in tourism call for forecasting and modeling. half specializes in analytics in commute and lifestyle, proposing fresh advancements in wearable pcs and physiological size units, and the results for our knowing of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based info with tourism information. 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 suite of case reports utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line reports within the resort undefined, and comparing vacation spot communications and marketplace intelligence with on-line inn experiences. The chapters during this part jointly describe quite a number various ways to knowing industry dynamics in tourism and hospitality.

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Html Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–27. , & Black, R. (2014). Tour guiding research: Insights, issues and implications. Bristol: Channel View Publications. Wong, K. K. , Witt, S. , & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068–1078. , Evans, J. , & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. , & Song, H.

Bates, J. , & Granger, C. W. (1969). The combination of forecasts. Or, 20(4), 451–468. Benckendorff, P. , Sheldon, P. , & 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.

2015) available data stored in the cloud, analytics are needed in order to make sense of the information within the data. If you are a potential customer planning a trip, you probably get more than a little help from the Internet when you are searching for inspiration, buying tickets, reserving accommodation, or researching attractions. Participants in the tourism industry are increasingly turning to big data to discover new ways to improve decision-making, opportunities, and overall performance (Irudeen & Samaraweera, 2013): for example, big data can be used to interconnect the dispersed information from different systems and then improve decisionmaking capability.

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