CoChat
Sign Up
Cited by 78Big Data and Business IntelligenceDiverse Aspects of Tourism ResearchKnowledge Management and Technology

Business intelligence for cross-process knowledge extraction at tourism destinations

Open access
Höpken, Wolfram, Fuchs, Matthias, Keil, Dimitri, Lexhagen, Maria·2015·Information Technology & Tourism
DOI 10.1007/s40558-015-0023-2
Open this paper in CoChat — free accountOpen PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s40558-015-0023-2

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Integration of multiple Big Data sources may reveal new insights that were not able to be detected with traditional approaches.

    “Integration of multiple Big Data sources, e.g., heterogeneous data sources, in the form of structured and unstructured data, such as customer feedback, reservation and booking data, and web search / navigation data, in customer and supplier sides (Höpken et al., 2013, Höpken et al., 2015), may reveal new insights that were not able to be detected with traditional approaches.”

    From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Höpken et al., 2015

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 47

  1. A, Enkel E, Krogh G (2007) Knowledge networks for business. Springer, New York
  2. Bloom J (2004) Tourist market segmentation with linear and non-linear techniques. Tour Manag 25(6):723-733
  3. Bornhorst T, Ritchie J, Sheehan L (2010) Determinants for DMO and destination success: an empirical examination. Tour Manag 31(5):572-589
  4. Brandt R (1988) How service marketers can identify value enhancing service elements. J Serv Mark 2(3):35-41
  5. Bronner F, Hoog R (2011) Vacationers and eWOM: who posts, and why, where, and what? J Travel Res 50(1):15-26
  6. Chaudhuri S, Dayal U (1996) An overview of data warehousing and OLAP technology 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):7-23
  7. Cho V, Leung P (2002) Knowledge discovery techniques in database marketing for the tourism industry. Qual Assur Hosp Tour 3(3):109-131
  8. Chu F (2004) Forecasting tourism demand: a cubic polynomial approach. Tour Manag 25(2):209-218
  9. Fuchs M (2004a) Pilot Project Destinometer TM : the Tyrolean Benchmarking System (in German: ''Pilotprojekt DESTINOMETER Ò -Benchmarkingsystem des Tiroler Tourismus''). Tour J 7(1):65-76
  10. Fuchs M (2004b) Strategy development in tourism destinations: a data envelopment analysis approach. Poznan Econ Rev 4(1):52-73
  11. Fuchs M, Ho ¨pken 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 57-66
  12. Fuchs M, Ho ¨pken W (2009) Data mining im tourismus. Praxis der Wirtschaftsinformatik 270(12):73-81
  13. Fuchs M, Weiermair K (2004) Destination benchmarking-an indicator-system's potential for exploring guest satisfaction. J Travel Res 42(3):212-225
  14. Fuchs M, Abadzhiev A, Svensson B, Ho ¨pken W, Lexhagen M (2013) Knowledge Destination framework for tourism sustainability-a business intelligence application from Sweden. Tourism 61(2):121-148
  15. 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):401-411
  16. Gra ¨bner 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 460-470
  17. Ho ¨pken W, Scheuringer M, Linke D, Fuchs M (2008) Context-based adaptation of ubiquitous web applications in tourism. In: O'Connor P, Ho ¨pken W, Gretzel U (eds) Information and communication technologies in tourism. Springer, New York, pp 533-544
  18. Ho ¨pken 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 417-429
  19. Ho ¨pken W, Deubele P, Ho ¨ll 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 272-283
  20. Inmon W (2002) Building the Data Warehouse, 2nd edn. Wiley, New York
  21. Jiang N, Gruenwald L (2006) Research issues in data stream association rule mining. SIGMOD 35(1):14-19
  22. Kano N (1984) Attractive quality and must-be quality. J Jpn Soc Qual Control 14(2):39-48
  23. 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):83-96
  24. Kasper W, Vela M (2011) Sentiment analysis for hotel reviews. In: Computational linguistics- applications conference. Katowice, pp 45-52
  25. Kimball R (1997) A dimensional modeling manifesto. DBMS 10(9):58-70
  26. Kimball R, Reeves L, Ross M, Thornwaite W (1998) The data warehouse lifecycle toolkit. Wiley, New York
  27. Kimball R, Ross M, Thronthwaite W, Mundy J (2008) The data warehouse lifecycle toolkit, 2nd edn. Wiley, Indianapolis
  28. Kuttainen C, Lexhagen M, Fuchs M, Ho ¨pken 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 management Laine M, Fru ¨hwirth C (2010) Monitoring social media: tools. Characteristics and implications. Business information processing 51(2):193-198
  29. Law R (1998) Room occupancy rate forecasting-a neural network approach. Int J Contemp Hosp Manag 10(6):234-239
  30. Lexhagen M, Kuttainen C, Fuchs M, Ho ¨pken 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. Corfu Liu B (2008) Web data mining (2nd Ausg.). Springer, New York
  31. Manning C, Schu ¨tz H (2001) Foundations of statistical natural language processing. MIT, Cambridge
  32. Meyer V, Ho ¨pken 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 155-168
  33. Min H, Emam A (2002) A DM approach to develop the profile of hotel customers. Contemp Hosp Manag 14(6):274-285
  34. Morales D, Wang J (2008) Passenger name record data mining based cancellation forecasting for revenue management. Innov Appl OR 202(2):554-562
  35. 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 393-403
  36. Pyo S (2005) Knowledge-map for tourist destinations. Tour Manag 26(4):583-594
  37. Pyo S, Uysal M, Chang H (2002) Knowledge discovery in databases for tourist destinations. J Travel Res 40(4):396-403
  38. Sambamurthy V, Subramani M (2005) Information technologies and knowledge management. Manag Inf Syst Q 29(1):1-7
  39. Schmunk S, Ho ¨pken 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 253-265
  40. Subramanian J, Stidham S, Lautenbacher C (1999) Airline yield management with overbooking, cancellations, and no-shows. Transp Sci 33(2):147-167
  41. Walchhofer N, Hronsky M, Po ¨ttler M, Baumgartner R, Fro ¨schl K (2010) Semantic online tourism market monitoring. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. Springer, Wien, pp 629-641
  42. 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):181-193
  43. Wang Y, Russo S (2007) Conceptualizing and evaluating the functions of destination marketing systems. J Vacat Mark 13(3):187-203
  44. Wang D, Park S, Fesenmaier D (2012) The role of smartphones in mediating the touristic experience. J Travel Res 51(4):371-387
  45. Wo ¨ber 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):273-286
  46. 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):355-373
  47. Zanker M, Jessenitschnig M, Fuchs M (2010) Automated semantic annotation of tourism resources based on geo-spatial data. Inf Technol Tour 11(4):341-354

Cited by

Sorted by citations

Authors
Höpken, W., Fuchs, M., Keil, D., & Lexhagen, M.
Affiliations
  • University of Applied Sciences Ravensburg-Weingarten (DE)
  • Mid Sweden University (SE)
Corresponding author
Höpken, Wolfram
ORCID
Venue
Information Technology & Tourism
Publisher
Springer Science and Business Media LLC
Published
2015-05-06
Volume
15
Issue
2
Pages
101-130
ISSN
  • 1098-3058 (ISSN-L)
  • 1943-4294
DOI
10.1007/s40558-015-0023-2
OpenAlex
W1881208908
Cited by
78
References
2
Citations by year
2026: 3 · 2025: 5 · 2024: 6 · 2023: 5 · 2022: 18 · 2021: 6 · 2020: 14 · 2019: 7 · 2018: 5 · 2017: 3 · 2016: 4 · 2015: 2
FWCI
9.48
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Information Technology & Tourism (publishedVersion)
  • Diva portal (Dalarna University Library) (submittedVersion, other-oa)
  • Open Research (University of Surrey) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Sociology and Political Science › Diverse Aspects of Tourism Research
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Knowledge Management and Technology
Keywords
business intelligence, tourism destinations, decision support, data warehouse, data integration, heterogeneous data sources, data model, booking data, customer feedback, product optimization, Ares

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review