Advertisement

Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data

  • Marianna SigalaEmail author
  • Andrew Beer
  • Laura Hodgson
  • Allan O’Connor
Chapter

Abstract

Big data revolutionalise the way organisations measure their performance and subsequently how they work. Technological advances allow organisations to access more data than they know how to handle and translate into value. However, although the literature has started investigating the use of big data for generating economic value, there has been a lack of research into the use of big data for delivering social value. To address these gaps, this chapter reviewed the related literature, in order to assist economic development agencies on integrating and using big data into their decision-making process and work related to the management of tourism economic development programs. To that end, the chapter develops and discusses a process framework for implementing big data initiatives and a decision framework for selecting and evaluating big data sources. The framework identifies four criteria for evaluating and selecting big data sources namely: need, value, time and utility. The implications of this framework for future research are discussed.

Keywords

Big data Decision-making Performance measurement Economic development programs Process framework Evaluation framework 

Notes

Acknowledgements

Support for this project was provided by Economic Development Australia with funding assistance from the Local Government Association of South Australia Research and Development Fund and in conjunction with the City of Adelaide, City of Salisbury, and the Eastern Region Alliance of Councils.

References

  1. Batini C, Cappiello C, Francalanci C, Maurino A (2009) Methodologies for data quality assessment and improvement. ACM Comput Surveys (CSUR) 41(3):16CrossRefGoogle Scholar
  2. Becken S, Stantic B, Chen J, Alaei AR, Connolly R (2017) Monitoring the environment and human sentiment on the Great Barrier Reef: assessing the potential of collective sensing. J Environ Manage 203:87–97CrossRefGoogle Scholar
  3. Beer A, Haughton G, Maude A (2003) Developing locally. Polity Press, BristolCrossRefGoogle Scholar
  4. Beer A, Hodgson L, O’Connor A, Sigala M (2018) Development and evaluation of economic development measures. Report prepared for Economic Development Australia (EDA)Google Scholar
  5. Braganza A, Brooks L, Nepelski D, Ali M, Moro R (2017) Resource management in big data initiatives: processes and dynamic capabilities. J Bus Res 70:328–337CrossRefGoogle Scholar
  6. Dorofeyuk AA, Pokrovskaya IV, Chernyavkii AL (2004) Expert methods to analyze and perfect management systems. Autom Remote Control 65(10):1675–1688CrossRefGoogle Scholar
  7. Economic Development Australia, Victoria Committee & Urban Enterprise (2015) Local government industry performance monitoring and benchmarking surveyGoogle Scholar
  8. Economic Development Australia (EDA) & Urban Enterprises Victorian State Practitioners Network (2016) Annual performance measures of local economic development in Victoria. EDA, Melbourne, VictoriaGoogle Scholar
  9. Economic Development Australia, & Urban Enterprises—Victorian State Practitioners Network (2018) Best practice in economic development strategy: National survey results and discussionGoogle Scholar
  10. Eppler MJ (2006) Managing information quality: increasing the value of information in knowledge-intensive products and processes. Springer Science & Business MediaGoogle Scholar
  11. European Parliament (2009) Regulation (EC) No 223/2009 of the European Parliament and the Council of 11 March 2009 on European statistics and repealing Regulation (EC, Euratom). Official J Eur Union 52Google Scholar
  12. European Statistical System (2014) ESS handbook for quality reports. EurostatGoogle Scholar
  13. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144CrossRefGoogle Scholar
  14. Günther WA, Mehrizi MHR, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26(3):191–209CrossRefGoogle Scholar
  15. Heinrich B, Klier M (2015) Metric-based data quality assessment—developing and evaluating a probability-based currency metric. Decis Support Syst 72:82–96CrossRefGoogle Scholar
  16. International Economic Development Council (2014) Making it count: metrics for high performing EDOs. IECD, WashintonGoogle Scholar
  17. International Economic Development Council, IEDC (2016) A new standard: achieving data excellence in economic development. IEDC, WashingtonGoogle Scholar
  18. Kim GH, Trimi S, Chung JH (2014) Big-data applications in the government sector. Commun ACM 57(3):78–85CrossRefGoogle Scholar
  19. Lavertu S (2016) We all need help: big data and the mismeasure of public administration. Public Adm Rev 76(6):864–872CrossRefGoogle Scholar
  20. Lehrer C, Wieneke A, Brocke L, Jung R, Seidel S (2018) How big data analytics enables service innovation: materiality, affordance, and the individualization of service. J Manage Inf Syst 35(2):424–460CrossRefGoogle Scholar
  21. Neumaier S, Umbrich J, Polleres A (2016) Automated quality assessment of metadata across open data portals. J Data Inf Qual (JDIQ) 8(1):2Google Scholar
  22. Raguseo E (2018) Big data technologies: an empirical investigation on their adoption, benefits and risks for companies. Int J Inf Manage 38(1):187–195CrossRefGoogle Scholar
  23. Rula A, Zaveri A (2014) Methodology for assessment of linked data quality. In: 1st workshop on linked data quality, LDQ 2014, vol 1215. CEUR-WSGoogle Scholar
  24. Sigala M, Marinidis D (2012) e-Democracy and web 2.0: a framework enabling DMOs to engage stakeholders in collaborative destination management. Tourism Anal 17(2):105–120CrossRefGoogle Scholar
  25. Sigala M (2012) Social media and crisis management in tourism: applications and implications for research. Inf Technol Tourism 13(4):269–283CrossRefGoogle Scholar
  26. Sigala M (2014) Evaluating the performance of destination marketing systems (DMS): stakeholder perspective. Mark Intell Plan 32(2):208–231CrossRefGoogle Scholar
  27. Stróżyna M, Eiden G, Abramowicz W, Filipiak D, Małyszko J, Węcel K (2018) A framework for the quality-based selection and retrieval of open data-a use case from the maritime domain. Electron Markets 28(2):219–233CrossRefGoogle Scholar
  28. Turok I (1989) Evaluation and understanding in local economic policy. Urban Stud 26(6):587–606CrossRefGoogle Scholar
  29. Zaveri A, Rula A, Maurino A, Pietrobon R, Lehmann J, Auer S (2016) Quality assessment for linked data: a survey. Semantic Web 7(1):63–93CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Marianna Sigala
    • 1
    Email author
  • Andrew Beer
    • 1
  • Laura Hodgson
    • 1
  • Allan O’Connor
    • 1
  1. 1.University of South AustraliaAdelaideAustralia

Personalised recommendations