Advertisement

Information Technology & Tourism

, Volume 15, Issue 2, pp 101–130 | Cite as

Business intelligence for cross-process knowledge extraction at tourism destinations

  • Wolfram HöpkenEmail author
  • Matthias Fuchs
  • Dimitri Keil
  • Maria Lexhagen
Original Research

Abstract

Decision-relevant data stemming from various business processes within tourism destinations (e.g. booking or customer feedback) are usually extensively available in electronic form. However, these data are not typically utilized for product optimization and decision support by tourism managers. Although methods of business intelligence and knowledge extraction are employed in many travel and tourism domains, current applications usually deal with different business processes separately, which lacks a cross-process analysis approach. This study proposes a novel approach for business intelligence-based cross-process knowledge extraction and decision support for tourism destinations. The approach consists of (a) a homogeneous and comprehensive data model that serves as the basis of a central data warehouse, (b) mechanisms for extracting data from heterogeneous sources and integrating these data into the homogeneous data structures of the data warehouse, and (c) analysis methods for identifying important relationships and patterns across different business processes, thereby bringing to light new knowledge. A prototype of the proposed concepts was implemented for the leading Swedish mountain destination Åre, which demonstrates the effectiveness of the proposed business intelligence architecture and the gained business benefits for a tourism destination.

Keywords

Business intelligence Knowledge extraction Data mining Data warehousing Management information systems Tourism destinations 

Notes

Acknowledgements

This research was financed by the KK-Foundation project ‘Engineering the Knowledge Destination’ (no. 20100260; Stockholm, Sweden). The authors would like to thank the managers Lars-Börje Eriksson (Åre Destination AB), Niclas Sjögren-Berg and Anna Wersén (Ski Star Åre), Peter Nilsson and Hans Ericsson (Tott Hotel Åre), and Pernilla Gravenfors (Copperhill Mountain Lodge Åre) for their excellent cooperation.

References

  1. Back A, Enkel E, Krogh G (2007) Knowledge networks for business. Springer, New YorkGoogle Scholar
  2. Bloom J (2004) Tourist market segmentation with linear and non-linear techniques. Tour Manag 25(6):723–733CrossRefGoogle Scholar
  3. Bornhorst T, Ritchie J, Sheehan L (2010) Determinants for DMO and destination success: an empirical examination. Tour Manag 31(5):572–589CrossRefGoogle Scholar
  4. Brandt R (1988) How service marketers can identify value enhancing service elements. J Serv Mark 2(3):35–41CrossRefGoogle Scholar
  5. Bronner F, Hoog R (2011) Vacationers and eWOM: who posts, and why, where, and what? J Travel Res 50(1):15–26CrossRefGoogle Scholar
  6. Buhalis D (2006) The impact of ICT on tourism competition. In: Paptheodorou A (ed) Corporate rivalry and market power. IB Tauris, London, pp 143–171Google Scholar
  7. Chaudhuri S, Dayal U (1996) An overview of data warehousing and OLAP technologyGoogle Scholar
  8. Chekalina T, Fuchs M, Lexhagen M (2014) A-value creation perspective on the customer-based brand equity model for tourism destinations. Finn J Tour Res 10(1):7–23Google Scholar
  9. Cho V, Leung P (2002) Knowledge discovery techniques in database marketing for the tourism industry. Qual Assur Hosp Tour 3(3):109–131CrossRefGoogle Scholar
  10. Chu F (2004) Forecasting tourism demand: a cubic polynomial approach. Tour Manag 25(2):209–218CrossRefGoogle Scholar
  11. Dell’Erba M, Fodor O, Höpken W, Werthner H (2005) Exploiting semantic web technologies for harmonizing e-markets. Inf Technol Tour 7(3/4):201–220CrossRefGoogle Scholar
  12. Fuchs M (2004a) Pilot Project Destinometer™: the Tyrolean Benchmarking System (in German: “Pilotprojekt DESTINOMETER®—Benchmarkingsystem des Tiroler Tourismus”). Tour J 7(1):65–76Google Scholar
  13. Fuchs M (2004b) Strategy development in tourism destinations: a data envelopment analysis approach. Poznan Econ Rev 4(1):52–73Google Scholar
  14. Fuchs M, Höpken W (2005) Towards @Destination: a data envelopment analysis based decision support framework. In: Frew A (ed) Information and communication technologies in tourism. Springer, New York, pp 57–66Google Scholar
  15. Fuchs M, Höpken W (2009) Data mining im tourismus. Praxis der Wirtschaftsinformatik 270(12):73–81CrossRefGoogle Scholar
  16. Fuchs M, Weiermair K (2004) Destination benchmarking—an indicator-system’s potential for exploring guest satisfaction. J Travel Res 42(3):212–225CrossRefGoogle Scholar
  17. Fuchs M, Abadzhiev A, Svensson B, Höpken W, Lexhagen M (2013) Knowledge Destination framework for tourism sustainability—a business intelligence application from Sweden. Tourism 61(2):121–148Google Scholar
  18. Fuchs M, Höpken W, Lexhagen M (2014) Big data analytics for knowledge generation in tourism destinations—a case from Sweden. J Destin Mark Manag 3(4):198–209Google Scholar
  19. Garrow L, Koppelman F (2004) Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information. J Air Transp Manag 10(6):401–411CrossRefGoogle Scholar
  20. Gräbner D, Zanker M, Fliedl G, Fuchs M (2012) Classification of customer reviews based on sentiment analysis. In: Fuchs M, Ricci F, Cantoni L (eds) Information and communication technologies in tourism. Springer, Wien, pp 460–470Google Scholar
  21. Gretzel U, Fesenmaier D (2004) Implementing a knowledge-based tourism marketing information system: the Illinois tourism network. Inf Technol Tour 6:245–255CrossRefGoogle Scholar
  22. Höpken W (2004) Reference model of an electronic tourism market—version 1.3. http://www.rmsig.de/documents/referencemodel.doc
  23. Höpken W, Scheuringer M, Linke D, Fuchs M (2008) Context-based adaptation of ubiquitous web applications in tourism. In: O’Connor P, Höpken W, Gretzel U (eds) Information and communication technologies in tourism. Springer, New York, pp 533–544Google Scholar
  24. Höpken W, Fuchs M, Keil D, Lexhagen M (2011) The knowledge destination—a customer information-based destination management information system. In: Law R, Fuchs M, Ricci F (eds) Information and communication technologies in tourism. Springer, New York, pp 417–429Google Scholar
  25. Höpken W, Deubele P, Höll G, Kuppe J, Schorpp D, Licones R, Fuchs M (2012) Digitalizing loyalty cards in tourism. In: Fuchs M, Ricci F, Cantoni L (eds) Information and communication technologies in tourism. Springer, New York, pp 272–283Google Scholar
  26. Höpken W, Fuchs M, Lexhagen M (2014) The knowledge destination—applying methods of business intelligence to tourism applications. In: Wang J (ed) Encyclopedia of business analytics and optimization. IGI Global, Hershey, pp 2542–2556CrossRefGoogle Scholar
  27. Inmon W (2002) Building the Data Warehouse, 2nd edn. Wiley, New YorkGoogle Scholar
  28. Jiang N, Gruenwald L (2006) Research issues in data stream association rule mining. SIGMOD 35(1):14–19CrossRefGoogle Scholar
  29. Kano N (1984) Attractive quality and must-be quality. J Jpn Soc Qual Control 14(2):39–48Google Scholar
  30. Kasavana M, Knutson B (1999) A primer on siftware: warehousing, marting and mining hospitality data for more effective marketing decisions. J Hosp Leisure Mark 6(1):83–96CrossRefGoogle Scholar
  31. Kasper W, Vela M (2011) Sentiment analysis for hotel reviews. In: Computational linguistics-applications conference. Katowice, pp 45–52Google Scholar
  32. Kepplinger D (2006) Tourismus WEBMART—Interaktive Datenerfassung und Ergebnisdarstellung durch Online-Datenbanken. In: Bachleitner R, Egger R, Herdin T (eds) Innovationen in der Tourismusforschung: Methoden und Anwendungen. Lit Verlag, Wien, pp 63–76Google Scholar
  33. Kimball R (1997) A dimensional modeling manifesto. DBMS 10(9):58–70Google Scholar
  34. Kimball R, Reeves L, Ross M, Thornwaite W (1998) The data warehouse lifecycle toolkit. Wiley, New YorkGoogle Scholar
  35. Kimball R, Ross M, Thronthwaite W, Mundy J (2008) The data warehouse lifecycle toolkit, 2nd edn. Wiley, IndianapolisGoogle Scholar
  36. Kuttainen C, Lexhagen M, Fuchs M, Höpken W (2012) Social media monitoring and analysis in tourism. In: Christou E, Chionis D, Gursory D, Sigala M (eds) Advances in hospitality and tourism marketing and managementGoogle Scholar
  37. Laine M, Frühwirth C (2010) Monitoring social media: tools. Characteristics and implications. Business information processing 51(2):193–198Google Scholar
  38. Law R (1998) Room occupancy rate forecasting—a neural network approach. Int J Contemp Hosp Manag 10(6):234–239CrossRefGoogle Scholar
  39. Lexhagen M, Kuttainen C, Fuchs M, Höpken W (2012) Destination talk in social media: a content analysis for innovation. In: Christou E, Chionis D, Gursory D, Sigala M (eds) Advances in hospitality and tourism marketing and management. CorfuGoogle Scholar
  40. Liu B (2008) Web data mining (2nd Ausg.). Springer, New YorkGoogle Scholar
  41. Manning C, Schütz H (2001) Foundations of statistical natural language processing. MIT, CambridgeGoogle Scholar
  42. Meyer V, Höpken W, Fuchs M, Lexhagen M (2015) Integration of data mining results into multi-dimensional data models. Information and communication technologies in tourism. Springer, Heidelberg, pp 155–168Google Scholar
  43. Min H, Emam A (2002) A DM approach to develop the profile of hotel customers. Contemp Hosp Manag 14(6):274–285CrossRefGoogle Scholar
  44. Morales D, Wang J (2008) Passenger name record data mining based cancellation forecasting for revenue management. Innov Appl OR 202(2):554–562Google Scholar
  45. Olmeda I, Sheldon P (2002) Data mining techniques and applications for tourism internet marketing. Travel Tour Mark 11(2/3):1–20CrossRefGoogle Scholar
  46. Pitman A, Zanker M, Fuchs M, Lexhagen M (2010) Web usage mining in tourism—a query term analysis and clustering approach. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. Springer, New York, pp 393–403Google Scholar
  47. Pyo S (2005) Knowledge-map for tourist destinations. Tour Manag 26(4):583–594CrossRefGoogle Scholar
  48. Pyo S, Uysal M, Chang H (2002) Knowledge discovery in databases for tourist destinations. J Travel Res 40(4):396–403CrossRefGoogle Scholar
  49. Ritchie R, Ritchie J (2002) A framework for an industry supported destination marketing information system. Tour Manag 23:439–454CrossRefGoogle Scholar
  50. Sambamurthy V, Subramani M (2005) Information technologies and knowledge management. Manag Inf Syst Q 29(1):1–7Google Scholar
  51. Schmunk S, Höpken W, Fuchs M, Lexhagen M (2014) Sentiment analysis—extracting decision-relevant knowledge from UGC. In: Xiang Z, Tussyadiah I (eds) Information and communication technologies in tourism. Springer, Heidelberg, pp 253–265Google Scholar
  52. Smith B, Leimkuhler J, Darrow R (1992) Yield management at American Airlines. Interfaces 22(1):8–31CrossRefGoogle Scholar
  53. Subramanian J, Stidham S, Lautenbacher C (1999) Airline yield management with overbooking, cancellations, and no-shows. Transp Sci 33(2):147–167CrossRefGoogle Scholar
  54. Vela B, Blanco C, Fernández-Medina E, Marcos E (2012) A practical application of our MDD approach for modeling secure XML data warehouses. Decis Support Syst 52:899–925CrossRefGoogle Scholar
  55. Vlahogianni EI, Karlaftis MG (2010) Advanced computational approaches for predicting tourist arrivals. In: Evans T (ed) Nonlinear dynamics. InTech, Vienna, pp 309–324Google Scholar
  56. Walchhofer N, Hronsky M, Pöttler M, Baumgartner R, Fröschl K (2010) Semantic online tourism market monitoring. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. Springer, Wien, pp 629–641Google Scholar
  57. Wallace M, Maglogiannis I, Karpouzis K, Kormentzas G, Kollias S (2004) Intelligent one-stop-shop travel recommendations using an adaptive neural network. Inf Technol Tour 6(3):181–193CrossRefGoogle Scholar
  58. Wang Y, Russo S (2007) Conceptualizing and evaluating the functions of destination marketing systems. J Vacat Mark 13(3):187–203CrossRefGoogle Scholar
  59. Wang D, Park S, Fesenmaier D (2012) The role of smartphones in mediating the touristic experience. J Travel Res 51(4):371–387CrossRefGoogle Scholar
  60. Weiermair K, Fuchs M (2007) Productivity differentials across tourist destinations—a theoretical/empirical analysis. In: Keller P, Bieger T (eds) Productivity in tourism—fundamentals and concepts for achieving growth and competitiveness. Erich Schmidt Verlag, Berlin, pp 41–54Google Scholar
  61. Wöber K (1998) Global statistical sources- TourMIS: an adaptive distributed marketing information system for strategic decision support in national, regional or city tourist offices. Pac Tour Rev 2(3):273–286Google Scholar
  62. Wong J-Y, Chen H-J, Chung P-H, Kao N-C (2006) Identifying valuable travellers by the application of data mining. Asia Pac J Tour Res 11(4):355–373CrossRefGoogle Scholar
  63. Zanker M, Jessenitschnig M, Fuchs M (2010) Automated semantic annotation of tourism resources based on geo-spatial data. Inf Technol Tour 11(4):341–354CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Wolfram Höpken
    • 1
    Email author
  • Matthias Fuchs
    • 2
  • Dimitri Keil
    • 2
  • Maria Lexhagen
    • 2
  1. 1.Business Informatics GroupUniversity of Applied Sciences Ravensburg-WeingartenWeingartenGermany
  2. 2.European Tourism Research Institute (ETOUR)Mid-Sweden UniversityÖstersundSweden

Personalised recommendations