Advertisement

The Importance of Human Behavior in Practice: Insights from the Modeling Cycle

  • Sean ManziEmail author
Chapter

Abstract

As an applied field of study, Operational Research (OR) projects start by defining a problem to be solved. The modeling cycle then proceeds through a conceptual modeling stage to a formal model and then a solution. This chapter uses the modeling cycle to present a discussion and reflections from the author on what he has found to be important considerations when human behavior is encountered in modeling. Interleaved within the chapter are considerations for both understanding human behavior throughout a modeling project and integrating human behavior in models. The discussions and reflections are all based on the authors own reflections on conducting OR projects in the healthcare sector. The chapter begins with a brief introduction of its structure and intention. Within each of the following sections, behavioral considerations are presented and their importance for facilitating an OR project that leads to successful change is discussed.

References

  1. Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nature Reviews Neuroscience, 4, 165–178.CrossRefGoogle Scholar
  2. Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621.CrossRefGoogle Scholar
  3. De Gooyert, V., Rouwette, E., Van Kranenburg, H., & Freeman, E. (2017). Reviewing the role of stakeholders in operational research: A stakeholder theory perspective. European Journal of Operational Research, 262, 402–410.CrossRefGoogle Scholar
  4. De Souza, D. E. (2013). Elaborating the Context-Mechanism-Outcome configuration (CMOc) in realist evaluation: A critical realist perspective. Evaluation, 19, 141–154.CrossRefGoogle Scholar
  5. Eden, C. (1994). Cognitive mapping and problem structuring for system dynamics model building. System Dynamics Review, 10, 257–276.CrossRefGoogle Scholar
  6. Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44, 350–383.CrossRefGoogle Scholar
  7. Elf, M., Eldh, A. C., Malmqvist, I., Öhrn, K., and Von Koch, L. (2016). Using of group-modeling in predesign phase of new healthcare environments: Stakeholders experiences. HERD: Health Environments Research and Design Journal, 9, 69–81.Google Scholar
  8. Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641–666.CrossRefGoogle Scholar
  9. Franco, L. A., & Hämäläinen, R. P. (2016). Behavioural operational research: Returning to the roots of the OR profession. European Journal of Operational Research, 3, 791–795.CrossRefGoogle Scholar
  10. Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205, 489–500.CrossRefGoogle Scholar
  11. Greasley, A., & Owen, C. (2016). Behavior in models: A framework for representing human behavior. In M. Kunc, J. Malpass, & L. White (Eds.), Behavioral Operational Research: Theory, Methodology and Practice. London: Palgrave Macmillan.Google Scholar
  12. Heaton, J., Day, J., & Britten, N. (2015). Collaborative research and the co-production of knowledge for practice: An illustrative case study. Implementation Science, 11, 20.CrossRefGoogle Scholar
  13. Kahn, A. B. (1994). The operations research development cycle. Socio-Economic Planning Sciences, 28, 47–66.CrossRefGoogle Scholar
  14. Kunc, M., Malpass, J., & White, L. (2016). Behavioral Operational Research: Theory, Methodology and Practice. London: Palgrave MacMillan.CrossRefGoogle Scholar
  15. Landry, M., Malouin, J.-L., & Oral, M. (1983). Model validation in operations research. European Journal of Operational Research, 14, 207–220.CrossRefGoogle Scholar
  16. Manzi, S., Chalk, D., Pearson, M., Day, J., Stein, K., and Lang, I. (2016). Opening the black box: Combining agent based simulation and realism in intervention development. In Anagnostou, A., Hoad, K., & Kunc, M. (Ed.), Operational Research Society Simulation Workshop 2016 (SW16). Stratford, UK.Google Scholar
  17. Manzi, S., Reuter-Oppermann, M., Rachuba, S., and Morana, S. (2018). Assessing information requirements in for complex decision making in healthcare. 26th European Conference on Information Systems (ECIS). Portsmouth, UK.Google Scholar
  18. Mingers, J., & Rosenhead, J. (2004). Problem structuring methods in action. European Journal of Operational Research, 152, 530–554.CrossRefGoogle Scholar
  19. Monks, T. (2015). Operational research as implementation science: Definitions, challenges and research priorities. Implementation Science, 11, 81.CrossRefGoogle Scholar
  20. Reed, M. S., Graves, A., Dandy, N., Posthumus, H., Hubacek, K., Morris, J., et al. (2009). Who’s in and why? A typology of stakeholder analysis methods for natural resource management. Journal of Environmental Management, 90, 1933–1949.CrossRefGoogle Scholar
  21. Robinson, S., Worthington, C., Burgess, N., & Radnor, Z. J. (2014). Facilitated modelling with discrete-event simulation: Reality or myth? European Journal of Operational Research, 234, 231–240.CrossRefGoogle Scholar
  22. Rooney, J. J., & Heuvel, L. N. V. (2004). Root cause analysis for beginners. Quality Progress, 37, 45–56.Google Scholar
  23. Siebers, P.-O., Macal, C. M., Garnett, J., Buxton, D., & Pidd, M. (2010). Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation, 4, 204–210.CrossRefGoogle Scholar
  24. Simmons, J., & Lovegrove, I. (2005). Bridging the conceptual divide: Lessons from stakeholder analysis. Journal of Organizational Change Management, 18, 495–513.CrossRefGoogle Scholar
  25. Small, A., & Wainwright, D. (2018). Privacy and security of electronic patient records—Tailoring multimethodology to explore the socio-political problems associated with Role Based Access Control systems. European Journal of Operational Research, 265, 344–360.CrossRefGoogle Scholar
  26. Tako, A. A., & Kotiadis, K. (2015). PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare. European Journal of Operational Research, 244, 555–564.CrossRefGoogle Scholar
  27. Vandenbosch, B., & Higgins, C. (1996). Information acquisition and mental models: An investigation into the relationship between behaviour and learning. Information Systems Research, 7, 198–214.CrossRefGoogle Scholar
  28. Van Nistelrooij, L. P., Rouwette, E. A., Verstijnen, I. M., & Vennix, J. A. (2015). The eye of the beholder: A case example of changing clients’ perspectives through involvement in the model validation process. Systems Research and Behavioral Science, 32, 437–449.CrossRefGoogle Scholar
  29. Walker, B., & Haslett, T. (2001). System dynamics and action research in aged care. Australian Health Review, 24, 183–191.CrossRefGoogle Scholar
  30. Zimmerman, L., Lounsbury, D. W., Rosen, C. S., Kimerling, R., Trafton, J. A., & Lindley, S. E. (2016). Participatory system dynamics modeling: Increasing stakeholder engagement and precision to improve implementation planning in systems. Administration and Policy in Mental Health and Mental Health Services Research, 43, 834–849.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.College of Medicine and HealthUniversity of ExeterExeterUK

Personalised recommendations