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Big Data for Decision Making: Are Museums Ready?

  • Deborah AgostinoEmail author
  • Michela Arnaboldi
  • Eleonora Carloni
Chapter
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Abstract

This study investigates the extent to which big data support decision making in museums by highlighting the main opportunities, threats and novel requirements connected with the usage of big data for decision making in museums.

This study is based on an action research project carried out in three Italian state museums that were provided with an online platform that generated real time (big) data about online users. This platform offered the opportunity to investigate “what” type of big data are used, “who” are the big data users and “how” big data were used by museums decision makers.

Results show a contradictory picture about the usage of big data for decision making in museums. Big data are not used alone, but need to be combined with traditional data that support big data interpretation. A central element for big data usage is represented by human resources: even though data are already collected, analysed and integrated by predefined algorithms, the key challenge is about human resources and their required mix of analytical, IT and communication skills. Also the external environment influences the extent of big data usage.

Keywords

Big data Social media data Museums Decision making Performance measurement 

References

  1. Abdullah, A., Khadaroo, I., & Napier, C. J. (2018). Managing the performance of arts organization: Pursuing heterogeneity objectives in an era of austerity. The British Accounting Review, 50(2), 174–184.CrossRefGoogle Scholar
  2. Agostino, D., & Arnaboldi, M. (2017). Social media data used in the measurement of public services effectiveness: Empirical evidence from Twitter in higher education institutions. Public Policy and Administration, 32(4), 296–322.CrossRefGoogle Scholar
  3. Agostino, D., & Arnaboldi, M. (2018). Performance measurement systems in public service networks. The what, who and how of control. Financial Accountability and Management, 34(2), 103–116.CrossRefGoogle Scholar
  4. Agostino, D., & Sidorova, Y. (2017). How social media reshapes action on distant customers: Some empirical evidence. Accounting Auditing and Accountability Journal, 17(4), 777–794.CrossRefGoogle Scholar
  5. Ahrens, T., & Chapman, C. S. (2006). Doing qualitative field research in management accounting: Positioning data to contribute to theory. Accounting Organizations and Society, 31(8), 819–841.CrossRefGoogle Scholar
  6. Al-Htaybat, K., & von Alberti-Alhtaybat, L. (2017). Big data and corporate reporting: Impacts and paradoxes. Accounting, Auditing and Accountability Journal, 30(4), 850–873.CrossRefGoogle Scholar
  7. Arnaboldi, M., Busco, C., & Cuganesan, S. (2017). Accounting, accountability, social media and big data: Revolution or hype? Accounting Auditing and Accountability Journal, 30(4), 762–766.CrossRefGoogle Scholar
  8. Avison, D., Baskerville, R., & Myers, M. (2001). Controlling action research projects. Information Technology and People, 14(1), 28–45.CrossRefGoogle Scholar
  9. Bhimani, A., & Willcocks, L. (2014). Digitisation, ‘big data’ and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.CrossRefGoogle Scholar
  10. Bolognini, L., & Bistolfi, C. (2017). Pseudonymization and impacts of Big (personal/anonymous) Data processing in the transition from the Directive 95/46/EC to the new EU General Data Protection Regulation. Computer Law and Security Review, 33(2), 171–181.CrossRefGoogle Scholar
  11. Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2), 423–429.CrossRefGoogle Scholar
  12. Chen, H., Chiang, R. H. L., & Storey, V. D. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.CrossRefGoogle Scholar
  13. Chianese, A., & Piccialli, F. (2016). A perspective on applications of in-memory and associative approaches supporting cultural big data analytics. International Journal of Computational Science and Engineering, 16(3), 219–233.CrossRefGoogle Scholar
  14. De Santis, F., & Presti, C. (2018). The relationship between intellectual capital and big data: A review. Meditari Accountancy Research, 26(3), 361–380.CrossRefGoogle Scholar
  15. Eden, C., & Huxham, C. (1996). Action research for the study of organizations. Handbook of Organizational Studies, 52, 526–542.Google Scholar
  16. Federal Trade Commission. (2014). Data brokers: A call for transparency and accountability. Data Brokers and the Need for Transparency and Accountability. Accessed August, 2019, from https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf
  17. Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods and analytics. International Journal of Information Management, 35(2), 137–144.CrossRefGoogle Scholar
  18. Hausmann, A. (2012). The importance of word of mouth for museums: An analytical framework. International Journal of Arts Management, 14(3), 32–43.Google Scholar
  19. Kuempel, A. (2016). The invisible middlemen: A critique and call for reform of the data broker industry. Northwestern Journal of International Law and Business, 36(1), 207–234.Google Scholar
  20. LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The new path to value. MIT Sloan Management Review, 52(1), 1–25.Google Scholar
  21. Lindquist, E. A. (2018). Visualization practice and government: Strategic investments for more democratic governance. Public Administration and Information Technology, 25, 225–246.CrossRefGoogle Scholar
  22. Malina, M. A., & Selto, F. H. (2001). Communicating and controlling strategy: An empirical study of the effectiveness of the balanced scorecard. Journal of Management Accounting Research, 13(1), 47–90.CrossRefGoogle Scholar
  23. Martin, K. (2018). Ethical implications and accountability of algorithms. Journal of Business Ethics, 1–16.Google Scholar
  24. Musei Italiani. (2014–2017). L’esperienza dei primi direttori dei musei autonomi. Report presented on 13th November 2017 in Roma, Terme di Diocleziano. Accessed July, 2019, from http://www.camera.it/temiap/allegati/2018/02/16/OCD177-3381.pdf
  25. Pesce, D., Neirotti, P., & Paolucci, E. (2019). When culture meets digital platforms: Value creation and stakeholders’ alignment in big data use. Current Issues in Tourism, 22(15), 1883–1903.CrossRefGoogle Scholar
  26. Priestley, J., & McGrath, R. J. (2019). The evolution of data science: A new mode of knowledge production. International Journal of Knowledge Management, 15(2), 97–109.CrossRefGoogle Scholar
  27. Quattrone, P. (2016). Management accounting goes digital: Will the move make it wiser? Management Accounting Research, 31, 118–122.CrossRefGoogle Scholar
  28. Rogge, N., Agasisti, T., & De Witte, K. (2017). Big data and the measurement of public organizations’ performance and efficiency: The state-of-the-art. Public Policy and Administration, 32(4), 263–281.CrossRefGoogle Scholar
  29. Romanelli, M. (2018). Museums creating value and developing intellectual capital by technology. Meditari Accountancy Research, 26(3), 483–498.CrossRefGoogle Scholar
  30. Seddon, J. J. J. M., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300–307.CrossRefGoogle Scholar
  31. Sivarajah, U., Kamal, M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.CrossRefGoogle Scholar
  32. Stevenson, D., Balling, G., & Kann-Rasmussen, N. (2017). Cultural participation in Europe: Shared problem or shared problematisation? International Journal of Cultural Policy, 23(1), 89–106.CrossRefGoogle Scholar
  33. Teoh, S. H. (2018). The promise and challenge of new datasets for accounting research. Accounting Organizations and Society, 68–69, 109–117.CrossRefGoogle Scholar
  34. Uluwiyah, A. (2017). Trusted big data for official statistics: Study case: Statistics Indonesia (BPS). 2016 International Conference on Information Technology Systems and Innovation, ICITSI 2016 – Proceedings, art. no. 7858196.Google Scholar
  35. Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397–407.CrossRefGoogle Scholar
  36. Zhang, G., Yang, Y., Zhai, X., Huang, W., & Wang, J., (2016). Public cultural big data analysis platform, 2016 IEEE second international conference on multimedia big data (BigMM) (pp. 398–403), Taipei.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Deborah Agostino
    • 1
    Email author
  • Michela Arnaboldi
    • 1
  • Eleonora Carloni
    • 1
  1. 1.Department of Management Economics and Industrial EngineeringPolitecnico di MilanoMilanoItaly

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