Asian Journal of Computer Science and Technology (AJCST)
Big Data Analytics Implications for Smart Tourism Destinations Towards the Enrichment of Content Tourism
Author : N. Padmaja and T. SudhaVolume 8 No.3 Special Issue:June 2019 pp 7-11
Abstract
Smart tourism has huge amount of Social Big data available from tourists can cherish the value conception process for a Smart Tourism Destination. Applying a multiple-case study analysis, a set of regional tourist experiences related and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Near conclusions and data in terms of improving decision making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models, exploitation of Big Data in the context of information-intensive industries and mainly in Tourism. Smart Tourism Destination today is the front line of study in the tourism field and is a promising area from various research perspectives in terms of models, tools and strategies to keep up the process of intelligent configuration of destinations.
Keywords
Smart Tourism, Stakeholders, Business Models, Patterns
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References
[1] R. Akerkar, Big data computing, 1st Edition Boca Raton, FL: CRC Press, 2013.
[2] C. Artola, F. Pinto and P.D. Pedraza, ―Can internet searches forecast tourism inflows?‖, International Journal of Manpower, Vol. 36, No.1, pp.103–116, 2015.
[3] J. Bai, and N.G. Serena, ―Confidence intervals for diffusion index forecasts and inference for factor‐Augmented regressions‖, Journal of Econometrica, Vol.74, No.4, pp.1133–1150, 2006.
[4] J. Bai and N.G. Serena, ―Forecasting economic time series using targeted predictors‖, Journal of Econometrics, Vol. 146, No. 2, pp.304–317, 2008.
[5] P. Bajari, D. Nekipelov, S. P. Ryan and M. Yang, ―Machine learning methods for demand estimation‖, Journal of American Economic Review, Vol. 105,No. 5, pp. 481–485,2015.
[6] M. Banbura and G. Reunstler, ―A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP‖, International Journal of Forecasting, Vol.27, No. 2, pp.333–346, 2011.
[7] P. F. Bangwayo-Skeete and R.W Skeete, ―Can Google data improve the forecasting performance of tourist arrivals Mixed-data sampling approach‖, Tourism Management Journal, Vol. 46, pp. 454-464,2015.
[8] P. J Benckendorff, P. J Sheldon and D. R Fesenmaier, Tourism information technology, 2ndedition, Wallingford: Cab-International, 2014.
[9] M. Bessec, ―Short-term forecasts of French GDP: A dynamic factor model with targeted predictors‖, Journal of Forecasting, Vol. 32,No. 6, pp. 500–511, 2013.
[10] J. T Coshall and R.Charles worth, ―A management orientated approach to combination forecasting of tourism demand‖, Tourism Management Journal, Vol.32, No. 4, pp. 759–769,2011.
[11] A. De Mauro, M. Greco and Grimaldi, ―What is big data? A consensual definition and a review of key research topics‖, Inproc. AIP, 2011, paper 1644, pp. 97–104.
[12] M. DeutschGranger, and T. Tera¨svirta, ―The combination of forecasts using changing weights‖, International Journal of Forecasting, Vol. 10, No. 1, pp.47–57, 1994.
[13] M. Fuchs, W. Heopken and, M. Lexhagen, ―Big data analytics for knowledge generation in tourism destinations—A case from Sweden‖, Journal of Destination Marketing and Management, Vol.3, No. 4, pp.198–209, 2014
[14] M. Hallin and R. Liska, ―Dynamic factors in the presence of blocks‖, Journal of Econometrics, Vol. 163, No. 1, pp. 29–41, 2011.
[15] W.Q. Meeker and Y. Hong, Reliability meets big data: Opportunities and challenges. 2nd Edition, Quality Engineering, 2014.
[16] A. McAfee, Brynjolfsson, E. Davenport, T. H. Patil and D. Barton, ―Big data: The management revolution‖, Harvard Business Review, Vol.90, No. 10, pp. 61–67, 2012.