A Big Data Analytics Method for Tourist Behaviour Analysis

Abstract Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed.

[1]  Pasquale Lops,et al.  CrowdPulse: A framework for real-time semantic analysis of social streams , 2015, Inf. Syst..

[2]  Xiang Li,et al.  China's “smart tourism destination” initiative: A taste of the service-dominant logic , 2013 .

[3]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[4]  Ickjai Lee,et al.  Points-of-Interest Mining from People's Photo-Taking Behavior , 2013, 2013 46th Hawaii International Conference on System Sciences.

[5]  Alexandra Zbuchea.  Cultural Interests While on Holidays. An Exploratory Investigation , 2012 .

[6]  Detlef Schoder,et al.  Social Media and Collective Intelligence—Ongoing and Future Research Streams , 2012, KI - Künstliche Intelligenz.

[7]  Pietro Perona,et al.  Learning Object Categories From Internet Image Searches , 2010, Proceedings of the IEEE.

[8]  Diego Klabjan,et al.  Innovation Patterns and Big Data , 2013 .

[9]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[10]  Salvador Ruiz de Maya,et al.  The effect of user-generated content on tourist behavior: the mediating role of destination image , 2014 .

[11]  Estela Marine-Roig,et al.  Tourism analytics with massive user-generated content: a case study of Barcelona. , 2015 .

[12]  Chen Xu,et al.  Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform , 2015, Comput. Environ. Urban Syst..

[13]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[14]  Mohamed F. Mokbel,et al.  Recommendations in location-based social networks: a survey , 2015, GeoInformatica.

[15]  Detlef Schoder,et al.  Assessing The Potential Of Social Media To Reflect Global Tourism , 2013, ECIS.

[16]  M. Jalilvand,et al.  Examining the structural relationships of electronic word of mouth, destination image, tourist attitude toward destination and travel intention: an integrated approach. , 2012 .

[17]  Astrid Dickinger,et al.  Analyzing destination branding and image from online sources: A web content mining approach , 2015 .

[18]  Athanasios V. Vasilakos,et al.  Big data analytics: a survey , 2015, Journal of Big Data.

[19]  Xin Yang,et al.  Forecasting Chinese tourist volume with search engine data , 2015 .

[20]  Hamish Cunningham,et al.  GATE-a General Architecture for Text Engineering , 1996, COLING.

[21]  S. Stepchenkova,et al.  User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software , 2015 .

[22]  Qihui Wu,et al.  A survey of machine learning for big data processing , 2016, EURASIP Journal on Advances in Signal Processing.

[23]  Golshah Naghdy,et al.  Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images , 2014, 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[24]  Matteo Golfarelli,et al.  Advanced topic modeling for social business intelligence , 2015, Inf. Syst..

[25]  N. Couldry.  Media, Society, World: Social Theory and Digital Media Practice , 2012 .

[26]  H. Charles Chancellor,et al.  Applying Travel Pattern Data to Destination Development and Marketing Decisions , 2012 .

[27]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[28]  Salvatore T. March,et al.  Design and natural science research on information technology , 1995, Decis. Support Syst..

[29]  T.M.J.A. Cooray.  Applied Time Series: Analysis and Forecasting , 2008 .

[30]  Arturo Molina,et al.  Key quality attributes according to the tourist product , 2012, European Journal of Tourism Research.

[31]  Marius-Răzvan Surugiu,et al.  Heritage Tourism Entrepreneurship and Social Media: Opportunities and Challenges , 2015 .

[32]  Binoy Pinto,et al.  Speeded Up Robust Features , 2011 .

[33]  Y. C. Wang,et al.  Destination marketing and management: scope, definition and structures. , 2011 .

[34]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[35]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[36]  Alan Fyall,et al.  Destination collaboration: A critical review of theoretical approaches to a multi-dimensional phenomenon , 2012 .

[37]  M. Fuchs,et al.  Big data analytics for knowledge generation in tourism destinations – A case from Sweden , 2014 .

[38]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[39]  Yani Nurhadryani,et al.  Tourism recommendation based on vector space model using composite social media extraction , 2014, 2014 International Conference on Advanced Computer Science and Information System.

[40]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[41]  Bernt Schiele,et al.  A Semantic Typicality Measure for Natural Scene Categorization , 2004, DAGM-Symposium.

[42]  Vincenzo Morabito.  Big Data and Analytics: Strategic and Organizational Impacts , 2015 .

[43]  Martha Larson,et al.  Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites , 2011, ICWSM.

[44]  Melda Akın,et al.  A novel approach to model selection in tourism demand modeling , 2015 .

[45]  Andrea De Mauro,et al.  A formal definition of Big Data based on its essential features , 2016 .

[46]  Teresa Correa,et al.  Who interacts on the Web?: The intersection of users' personality and social media use , 2010, Comput. Hum. Behav..

[47]  Alan R. Hevner,et al.  POSITIONING AND PRESENTING DESIGN SCIENCE RESEARCH FOR MAXIMUM IMPACT 1 , 2013 .

[48]  Daniel J. Power,et al.  'Big Data' Decision Making Use Cases , 2015, ICDSST.

[49]  Su Chen,et al.  Anticipating Chinese Tourists Arrivals in Australia: A Time Series Analysis , 2016 .

[50]  E. Marcheggiani,et al.  Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy , 2016 .

[51]  Yu Ye,et al.  Understanding tourist space at a historic site through space syntax analysis: The case of Gulangyu, China , 2016 .

[52]  Slava Kisilevich,et al.  A GIS-based decision support system for hotel room rate estimation and temporal price prediction: The hotel brokers' context , 2013, Decis. Support Syst..

[53]  Kyoji Kawagoe,et al.  Tweet-mapping Method for Tourist Spots Based on Now-Tweets and Spot-photos , 2015, KES.

[54]  Mingming Cheng,et al.  Social media in tourism: a visual analytic approach , 2015 .

[55]  Jay F. Nunamaker,et al.  Analyzing firm-specific social media and market: A stakeholder-based event analysis framework , 2014, Decis. Support Syst..

[56]  Jason J. Jung,et al.  Social big data: Recent achievements and new challenges , 2015, Information Fusion.

[57]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[58]  Mehmet Mehmetoglu,et al.  Nature-based Tourists: The Relationship Between their Trip Expenditures and Activities , 2007 .

[59]  D. Fesenmaier,et al.  CONCEPTUALIZATION OF MULTI- DESTINATION PLEASURE TRIPS , 1993 .

[60]  Wu He,et al.  A novel social media competitive analytics framework with sentiment benchmarks , 2015, Inf. Manag..

[61]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[62]  D. W. Scott,et al.  Variable Kernel Density Estimation , 1992 .

[63]  M. Wachowicz,et al.  Exploring visitor movement patterns in natural recreational areas. , 2012 .

[64]  DelWayne R. Bohnenstiehl,et al.  Geospatial analytics for federally managed tourism destinations and their demand markets , 2015 .

[65]  Muhammad Shiraz,et al.  Big Data: Survey, Technologies, Opportunities, and Challenges , 2014, TheScientificWorldJournal.

[66]  Gloria E. Phillips-Wren,et al.  An analytical journey towards big data , 2015, J. Decis. Syst..

[67]  Malin Zillinger.  Germans' tourist behaviour in Sweden , 2008 .

[68]  Ling Chen,et al.  A context-aware personalized travel recommendation system based on geotagged social media data mining , 2013, Int. J. Geogr. Inf. Sci..

[69]  Tomoharu Iwata,et al.  Travel route recommendation using geotagged photos , 2012, Knowledge and Information Systems.

[70]  Billy Bai,et al.  Overseas Tourist Movement Patterns in Beijing: The Impact of the Olympic Games , 2012 .

[71]  Slava Kisilevich,et al.  P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos , 2010, COM.Geo '10.