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Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context?

  • Nikolaos StylosEmail author
  • Jeremy Zwiegelaar
Chapter

Abstract

With the advent of web analytics, data mining and predictive modeling, businesses have nowadays a better knowledge in creating more efficient and effective processes for meeting customers’ needs, driven by a wealth of available information. The value of big data in influencing business intelligence in the tourism and hospitality industry has also been widely acknowledged, as the synergetic utilization of big data can enhance organizations’ decision support systems to reach process optimization. Notwithstanding empirical research on exploring the implications of utilizing big data in the tourism sector has been published in the last few years, there is still need of a framework that would serve as the bedrock of taking the relevant conceptualization one step forward. Therefore, this chapter demonstrates the crucial role of big data in matching organizational objectives with tourist needs through delineating and detailing the analytical frameworks to support an advanced B2C interface, based on various internal databases and external data sources. The role of stakeholders and necessary resources are explained, and the full potential of big data in tourism and hospitality is revealed.

Keywords

Big data Analytical framework Business intelligence Marketing Stakeholders Tourism Hospitality 

References

  1. Amado A, Cortez P, Rita P, Moro S (2018) Research trends on big data in marketing: a text mining and topic modeling based literature analysis. Euro Res Manage Business Economics 24(1):1–7Google Scholar
  2. Banerjee S, Chua AY (2016) In search of patterns among travellers’ hotel ratings in TripAdvisor. Tourism Manage 53:125–131Google Scholar
  3. Barile S, Pels J, Polese F, Saviano M (2012) An introduction to the viable systems approach and its contribution to marketing. J Business Market Manage 5(2):54–78Google Scholar
  4. Bean R, Kiron D (2013) Organizational alignment is key to big data success. MIT Sloan Manage Rev 54(3):1–6Google Scholar
  5. Beer D (2018) Envisioning the power of data analytics. Information Commun Soc 21(3):465–479Google Scholar
  6. Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895Google Scholar
  7. Brown B, Chui M, Manyika J (2011) Are you ready for the era of ‘big data’. McKinsey Q 4(1):24–35Google Scholar
  8. Brynjolfsson E, Hitt LM, Kim HH (2011) Strength in numbers: how does data-driven decision making affect firm performance? Social Science Research Network (SSRN) [Online]. Available at https://ssrn.com/abstract=1819486 or http://dx.doi.org/10.2139/ssrn.1819486.
  9. Buhalis D, Amaranggana A (2013) Smart tourism destinations. Information and communication technologies in tourism 2014. Springer, Cham, pp 553–564Google Scholar
  10. Buhalis D, Foerste M (2015) SoCoMo marketing for travel and tourism: Empowering co-creation of value. J Destination Marketing Manage 4(3):151–161Google Scholar
  11. Daas PJ, Puts M (2014) Big Data as a source of statistical information. Survey Statistician 69(1):22–31Google Scholar
  12. Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harvard Business PressGoogle Scholar
  13. Dedić N, Stanier C (2016) Measuring the success of changes to existing business intelligence solutions to improve business intelligence reporting. In: Tjoa AM, Xu LD, Raffai M, Novak NM (Eds), Research and practical issues of enterprise information systems. 10th IFIP WG 8.9 Working Conference, CONFENIS 2016, Vienna, Austria, December 13–14, 2016, Proceedings. Springer, Cham: pp 225–236Google Scholar
  14. Erevelles S, Fukawa N, Swayne L (2016) Big Data consumer analytics and the transformation of marketing. J Business Res 69(2):897–904Google Scholar
  15. Ferretti V, Comino E (2015) An integrated framework to assess complex cultural and natural heritage systems with Multi-attribute value theory. J Cultural Heritage 16(5):688–697Google Scholar
  16. Fuchs M, Höpken W, Lexhagen M (2014) Big data analytics for knowledge generation in tourism destinations–a case from Sweden. J Destination Marketing Manage 3(4):198–209Google Scholar
  17. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Lett 12(3):211–223Google Scholar
  18. Golinelli GM, Barile S, Saviano M, Polese F (2012) Perspective shifts in marketing: toward a paradigm change? Ser Sci 4(2):121–134Google Scholar
  19. Gretzel U, Sigala M, Xiang Z, Koo C (2015a) Smart tourism: Foundations and developments. Electronic Markets 25(3):179–188Google Scholar
  20. Gretzel U, Werthner H, Koo C, Lamsfus C (2015b) Conceptual foundations for understanding smart tourism ecosystems. Comput Human Beha 50:558–563Google Scholar
  21. Grigsby M (2015) Marketing analytics: A practical guide to real marketing science. Kogan Page Publishers, LondonGoogle Scholar
  22. Grossman RL, Siegel KP (2014) Organizational models for Big Data and analytics. J Organisational Design 3(1):20–25Google Scholar
  23. Guo Y, Liu H, Chai Y (2014) The embedding convergence of smart cities and tourism internet of things in China: an advance perspective. Adv Hospitality Tourism Res 2(1):54–69Google Scholar
  24. Hazen BT, Boone CA, Ezell JD, Jones-Farmer LA (2014) Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications. Int J Prod Economics 154:72–80Google Scholar
  25. Kunz W, Aksoy L, Bart Y, Heinonen K, Kabadayi S, Ordenes FV, Sigala M, Diaz D, Theodoulidis B (2017) Customer engagement in a big data world. J Serv Marketing 31(2):161–171Google Scholar
  26. LaValle S, Hopkins MS, Lesser E, Shockley R, Kruschwitz N (2010) Analytics: the new path to value. MIT Sloan Manage Rev 52(1):1–25Google Scholar
  27. Liu Y, Teichert T, Rossi M, Li H, Hu F (2017) Big data for big insights: Investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews. Tourism Manage 59:554–563Google Scholar
  28. Lu Q, Ye Q, Law R (2014) Moderating effects of product heterogeneity between online word-of-mouth and hotel sales. J Electron Commer Re 15(1):1–12Google Scholar
  29. Maglio PP, Spohrer J (2008) Fundamentals of service science. J Academy Marketing Sci 36(1):18–20Google Scholar
  30. Malthouse EC, Haenlein M, Skiera B, Wege E, Zhang M (2013) Managing customer relationships in the social media era: introducing the social CRM house. J Interactive Marketing 27(4):270–280Google Scholar
  31. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C et al (2011) Big Data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute.Google Scholar
  32. Marine-Roig E, Clavé SA (2015) Tourism analytics with massive user-generated content: A case study of Barcelona. J Destination Marketing Manage 4(3):162–172Google Scholar
  33. McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data: the management revolution. Harvard Bus Rev 90(10):60–68Google Scholar
  34. Miah SJ, Vu HQ, Gammack J, McGrath M (2017) A big data analytics method for tourist behaviour analysis. Information Manage 54(6):771–785Google Scholar
  35. Morabito V (2015) Big data and analytics. Strategic and organisational impacts. Springer, ChamGoogle Scholar
  36. Moro S, Rita P, Coelho J (2017) Stripping customers’ feedback on hotels through data mining: the case of Las Vegas Strip. Tourism Manage Perspect 23:41–52Google Scholar
  37. Pantano E, Priporas CV, Stylos N (2017) ‘You will like it!’ Using open data to predict tourists’ response to a tourist attraction. Tourism Manage 60:430–438Google Scholar
  38. Phillips-Wren G, Hoskisson A (2015) An analytical journey towards big data. J Decisi Syst 24(1):87–102Google Scholar
  39. Priporas CV, Stylos N, Fotiadis AK (2017) Generation Z consumers’ expectations of interactions in smart retailing: A future agenda. Comput Human Beha 77:374–381Google Scholar
  40. Russom P (2013) Managing big data. TDWI Best Practices Report, TDWI Res 1–40Google Scholar
  41. Tam SM, Clarke F (2015) Big data, official statistics and some initiatives by the Australian Bureau of Statistics. Int Stat Rev 83(3):436–448Google Scholar
  42. Vargo SL, Lusch RF (2004) Evolving to a new dominant logic for marketing. J Marketing 68(1):1–17Google Scholar
  43. Xiang Z, Schwartz Z, Gerdes JH Jr, Uysal M (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction? Int J Hospitality Manage 44:120–130Google Scholar
  44. Ye Q, Law R, Gu B, Chen W (2011) The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Comput Hum Behav 27(2):634–639Google Scholar
  45. Zhang L (2012) Smart tourism: the coming age of customization and intelligent public services. J Tourism Tribune 27(2):3–5Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of BristolBristolEngland, UK

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