Analytics in Smart Tourism Design: Concepts and Methods - download pdf or read online

By Zheng Xiang, Daniel R. Fesenmaier

ISBN-10: 3319442627

ISBN-13: 9783319442624

ISBN-10: 3319442635

ISBN-13: 9783319442631

This e-book offers leading edge study at the improvement of analytics in shuttle and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to functions and case reviews for vacation spot advertising and administration. it really is divided into 5 elements: half one on trip call for analytics specializes in conceptualizing and enforcing trip call for modeling utilizing massive information. It illustrates new how you can establish, generate and make the most of huge amounts of knowledge in tourism call for forecasting and modeling. half makes a speciality of analytics in trip and way of life, providing contemporary advancements in wearable pcs and physiological size units, and the results 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 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 resort undefined, and comparing vacation spot communications and industry intelligence with on-line lodge experiences. The chapters during this part jointly describe a number of assorted techniques to realizing industry dynamics in tourism and hospitality.

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Tourism Management, 46, 386–397. , & Song, H. (2014). Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research, 53(4), 433–447. Travel Demand Modeling with Behavioral Data Juan L. Nicolau 1 Introduction Today’s travelers demand personalized and comprehensive experiences, and guided by their personal motivations, they try to back their decisions on recommendations expressed on the Internet. Besides, they write on official and unofficial websites their personal preferences, and tell other travelers about their intentions on their next destinations, plan the itinerary of the visit, compare, make reservations and pay with a few clicks from home just seating at their computer.

Lisˇka, R. (2011). Dynamic factors in the presence of blocks. Journal of Econometrics, 163(1), 29–41. Hashem, I. A. , Anuar, N. , & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115. , & Silva, E. (2015). Forecasting with big data: A review. Annals of Data Science, 2(1), 5–19. , & Perdana, D. H. F. (2014). Trip guidance: A linked data based mobile tourists guide. Advanced Science Letters, 20(1), 75–79. , & Samaraweera, S.

Perdana, D. H. F. (2014). Trip guidance: A linked data based mobile tourists guide. Advanced Science Letters, 20(1), 75–79. , & Samaraweera, S. (2013). Big data solution for Sri Lankan development: A case study from travel and tourism. Paper presented at the 2013 International Conference on Advances in ICT for Emerging Regions, ICTer 2, Colombo. , Jang, J. , & Hwang, Y. H. (2014). Big five factors of personality and tourists’ internet search behavior. Asia Pacific Journal of Tourism Research, 19(5), 600–615.

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


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