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Big Data and Behavior in Operational Research: Towards a “Smart OR”

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Behavioral Operational Research

Abstract

Organisations are today characterised by increased dynamism and complexity. The challenges that have appeared as a result of these conditions have made organisations and the behaviour within them difficult to examine. We define SMART OR as the creative use of Big Data and hard and soft OR to enhance behaviour and positive results for decision-makers. In this chapter we describe the background to SMART OR, which is essentially action oriented and located within a particular problem context and stakeholder grouping and can take into account a number of biases—should an external observer observe them.

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Notes

  1. 1.

    http://www.tylervigen.com/spurious-correlations.

  2. 2.

    https://changingbehaviours.wordpress.com/2015/02/18/behavioural-science-meets-data-science/.

References

  • Babai, M.Z., M.M. Ali, and K. Nikolopoulos. 2012. Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis. Omega 40: 713–721.

    Article  Google Scholar 

  • Baron, R.A., and M.D. Ensley. 2006. Opportunity recognition as the detection of meaningful patterns: Evidence from comparisons of novice and experienced entrepreneurs. Management Science 52: 1331–1344.

    Article  Google Scholar 

  • Bentley, R.A., M.J. O’Brien, and W.A. Brock. 2014. Mapping collective behaviour in the big-data era. Behavioural and Brain Sciences 37: 63–76.

    Article  Google Scholar 

  • Brabham, D.C. 2009. Crowdsourcing the public participation process for planning projects. Planning Theory 8: 242–262.

    Article  Google Scholar 

  • Brown, P., and H. Lauder. 2012. The great transformation in the global labour market. Soundings 51: 41–53.

    Article  Google Scholar 

  • Buchanan, J., & Kock, N. (2001). Information overload: A decision making perspective. In Multiple Criteria Decision Making in the New Millennium 51: (pp. 49-58). Springer Berlin Heidelberg.

    Google Scholar 

  • Chen, C.P., and C.Y. Zhang. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences 275: 314–347.

    Article  Google Scholar 

  • Chen, H., R. Chiang, and V. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36: 1165–88.

    Google Scholar 

  • Davenport, T., and J. Harris. 2007. Competing on analytics: The new science of winning. Boston: Harvard Business School Press.

    Google Scholar 

  • Davenport, T., J. Harris, and R. Morison. 2010. Analytics at work: Smarter decisions better results. Boston: Harvard Business School Press.

    Google Scholar 

  • Eppler, M.J., and J. Mengis. 2004. The concept of information overload: A review of literature from organization science, accounting, marketing, MIS and related disciplines. The Information Society 20: 325–344.

    Article  Google Scholar 

  • Frisch, D., and J. Baron. 1988. Ambiguity and rationality. Journal of Behavioral Decision Making 1: 149–157.

    Article  Google Scholar 

  • Giguère, G., and B.C. Love. 2013. Limits in decision making arise from limits in memory retrieval. Proceedings of the National Academy of Sciences USA 110: 7613–7618.

    Article  Google Scholar 

  • Gilovich, T., D.W. Griffin, and D. Kahneman. 2002. Heuristics and biases: The psychology of intuitive judgement. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Goel, S., J.M. Hofman, S. Lahaie, D.M. Pennock, and D.J. Watts. 2010. Predicting consumer behaviour with web search. Proceedings of the National Academy of Sciences USA 107: 17486–17490.

    Article  Google Scholar 

  • Gurstein, M.B. 2011. Open data: Empowering the empowered or effective data use for everyone? First Monday 16: 2–7.

    Article  Google Scholar 

  • Hilbert, M. 2012. Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making. Psychological Bulletin 138: 211–237.

    Article  Google Scholar 

  • Jacoby, J. 1977. Information load and decision quality: Some contested issues. Journal of Marketing Research 14: 569–573.

    Article  Google Scholar 

  • Kahneman, D., and A. Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society 47(2): 263291.

    Article  Google Scholar 

  • Kahneman, D., P. Slovic, and A. Tversky. 1982. Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • King, G. 2011. Ensuring the data-rich future of the social sciences. Science 331: 719.

    Article  Google Scholar 

  • Liberatore, M., and W. Luo. 2010. The analytics movement: Implications for operations research. Interfaces 40: 313–336.

    Article  Google Scholar 

  • McAfee, A., and E. Brynjolfsson. 2012. Big data: The management revolution. Harvard Business Review 90: 60–68.

    Google Scholar 

  • Mingers, J., and J. Rosenhead. 2004. Problem structuring methods in action. European Journal of Operational Research 152: 530–554.

    Article  Google Scholar 

  • Moat, H.S., C. Curme, H.E. Stanley, and T. Preis. 2014. Anticipating stock market movements with Google and Wikipedia. In Nonlinear phenomena in complex systems: From nano to macro scale, ed. D. Matrasulov and H.E. Stanley, 47–59. Netherlands: Springer.

    Chapter  Google Scholar 

  • Ormerod, R.J., and W. Ulrich. 2013. Operational research and ethics: A literature review. European Journal of Operational Research 228: 291–307.

    Article  Google Scholar 

  • Pfister, H.R., and G. Böhm. 2008. The multiplicity of emotions: A framework of emotional functions in decision making. Judgment and Decision Making 3: 5–17.

    Google Scholar 

  • Ranyard, J.C., R. Fildes, and T.I. Hu. 2015. Reassessing the scope of OR practice: The influences of problem structuring methods and the analytics movement. European Journal of Operational Research 245: 1–13.

    Article  Google Scholar 

  • Schintler, L., and K. Kulkani. 2014. Big data for policy analysis: The good, the bad and the ugly. Review of Policy Research 31: 343–348.

    Article  Google Scholar 

  • Schroder, H.M., M.J. Driver, and S. Streufert. 1967. Human information processing. New York: Holt, Rinehart & Winston.

    Google Scholar 

  • Sen, A. 2013. A comparison of mixed and dynamic pricing policies in revenue management. Omega 41: 586–597.

    Article  Google Scholar 

  • Simchi-Levi, D. 2014. OM forum-OM research: From problem-driven to data-driven research. Manufacturing & Service Operations Management 16: 210.

    Article  Google Scholar 

  • Simon, H.A. 1955. A behavioural model of rational choice. Quarterly Journal of Economics 69: 99–118.

    Article  Google Scholar 

  • Singhal, K., and J. Singhal. 2012. Imperatives of the science of operations and supply-chain management. Journal of Operations Management 30: 237–244.

    Article  Google Scholar 

  • Surowiecki, J. 2004. The wisdom of crowds: Why the many are smarter than the few. London: Little, Brown.

    Google Scholar 

  • Van de Ven, A.H., and M.S. Poole. 2005. Alternative approaches for studying organizational change. Organization Studies 26: 1377–1404.

    Article  Google Scholar 

  • White, L. 2016. Behavioural operational research: Towards a framework for understanding behaviour in or interventions. European Journal of Operational Research 249: 827–841.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK

    Leroy White

  2. Portsmouth Business School, University of Portsmouth, Portland Street, Portsmouth, PO1 3DE, UK

    Katharina Burger

  3. Faculty of Engineering, University of Bristol, Queen’s Building, University Walk, Clifton, BS8 1TR, UK

    Mike Yearworth

Authors
  1. Leroy White
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  2. Katharina Burger
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  3. Mike Yearworth
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Editor information

Editors and Affiliations

  1. Warwick Business School, University of Warwick, Coventry, UK

    Martin Kunc

  2. BT Technology Service & Operations, Martlesham Heath, UK

    Jonathan Malpass

  3. Warwick Business School, University of Warwick, Coventry, UK

    Leroy White

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© 2016 The Author(s)

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White, L., Burger, K., Yearworth, M. (2016). Big Data and Behavior in Operational Research: Towards a “Smart OR”. In: Kunc, M., Malpass, J., White, L. (eds) Behavioral Operational Research. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-53551-1_9

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