Toward Wearable Devices for Multiteam Systems Learning

  • Brenda BannanEmail author
  • Samantha Dubrow
  • Christian Dobbins
  • Stephen Zaccaro
  • Hemant Purohit
  • Mohammed Rana


This chapter provides an overview of an exploratory case study involving a multiteam system in the fire and rescue emergency context incorporating human sensor analytics (e.g., proximity sensors) and other data sources to reveal important insights on within- and between-team learning and training. Incorporating a design research approach, the case study consisting of two live simulation scenarios that informed the design and development of a wearable technology-based system targeted to capture team-based behavior in the live simulation and visualize it during the debriefing session immediately following to potentially inform within- and cross-team behavior from a multiteam systems perspective informed by theory and practice.



This material is based upon work supported by the National Science Foundation under Grant No. 1637263. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


  1. Abrahamson, E., & Fairchild, G. (1999). Management fashion: Lifecycles, triggers, and collective learning processes. Administrative Science Quarterly, 44(4), 708–740.CrossRefGoogle Scholar
  2. Asencio, R., & DeChurch, L. A. (2017). Assessing collaboration within and between teams: A multiteam systems perspective. In A. von Davier, M. Zhu, & P. Kyllonen (Eds.), Innovative assessment of collaboration. Methodology of educational measurement and assessment. Cham: Springer.Google Scholar
  3. Bannan, B., Gallagher, P., & Lewis, B. (2017). A case study for next generation learning – Smart medical team training. In H. Geng (Ed.), Internet of things/cyber-physical systems handbook. Hoboken: Wiley.Google Scholar
  4. Bannan-Ritland, B. (2009). The integrative learning design framework: An illustrated example from the domain of instructional technology. In T. Plomp & N. Nieveen (Eds.), An introduction to educational design research. Enschede: SLO Netherlands Institute for Curriculum Development.Google Scholar
  5. Buck, D. A., Trainor, J. E., & Aguirre, B. E. (2006). A critical evaluation of the incident command system and NIMS. Journal of Homeland Security and Emergency Management, 3(3), 1–27.CrossRefGoogle Scholar
  6. Davison, R. B., Hollenbeck, J. R., Barnes, C. M., Sleesman, D. J., & Ilgen, D. R. (2012). Coordinated action in multiteam systems. Journal of Applied Psychology, 97(4), 808–824.CrossRefGoogle Scholar
  7. DeChurch, L. A., & Marks, M. A. (2006). Leadership in multiteam systems. Journal of Applied Psychology, 91(2), 311.CrossRefGoogle Scholar
  8. DeChurch, L. A., & Mathieu, J. E. (2009). Thinking in terms of multiteam systems. In Team effectiveness in complex organizations: Cross-disciplinary perspectives and approaches (pp. 267–292). New York: Routledge/Taylor & Francis.Google Scholar
  9. DeChurch, L. A., Burke, C. S., Shuffler, M. L., Lyons, R., Doty, D., & Salas, E. (2011). A historiometric analysis of leadership in mission critical multiteam environments. The Leadership Quarterly, 22(1), 152–169.CrossRefGoogle Scholar
  10. Dubrow, S., Dobbins, C., Bannan, B., Zaccaro, S., Peixoto, N., Purohit, H., Rana, M., & Au, M. (2017). Using IoT sensors to enhance simulation and training in multiteam systems. The Interservice/Industry Training, Simulation and Education Conference (I/ITSEC) published proceedings (pp. 1–10).Google Scholar
  11. Edmondson, A. C., Bohmer, R. M., & Pisano, G. P. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46(4), 685–716.CrossRefGoogle Scholar
  12. Feese, S. F. (2014). Observing Teams with Wearable Sensors (Doctoral dissertation, ETH Zurich).Google Scholar
  13. Feese, S., Burscher, M. J., Jonas, K., & Tröster, G. (2014). Sensing spatial and temporal coordination in teams using the smartphone. Human-centric Computing and Information Sciences, 4(1), 15.CrossRefGoogle Scholar
  14. Firth, B. M., Hollenbeck, J. R., Miles, J. E., Ilgen, D. R., & Barnes, C. M. (2015). Same page, different books: Extending representational gaps theory to enhance performance in multiteam systems. Academy of Management Journal, 58(3), 813–835.Google Scholar
  15. Isella, L., Romano, M., Barrat, A., Cattuto, C., & Colizza, V. (2011). Close encounters in a pediatric ward: Measuring face-to-face proximity and mixing. PLoS One, 6(2), 1–10.CrossRefGoogle Scholar
  16. Kayhan, V. O., Zheng, C., French, K. A., Allen, T. D., Salomon, K., & Watkins, A. (2018). How honest are the signals? A protocol for validating wearable sensors. Behavior Research Methods, 50(1), 57–83.CrossRefGoogle Scholar
  17. Kelly, A. E. (2006). Quality criteria for design research: Evidence and commitments. In J. Van den akker, K. Gravemeijer, S. McKenney, & N. Nieveen (Eds.), Educational design research. London: Routledge.Google Scholar
  18. Kozlowski, S. W., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and teams. Psychological Science in the Public Interest, 7(3), 77–124.CrossRefGoogle Scholar
  19. Lacerenza, C. N., Rico, R., Salas, E., & Shuffler, M. L. (2014). The future of multiteam systems: Implications for research and practice. In Pushing the boundaries: Multiteam systems in research and practice (pp. 223–240). Bingley: Emerald Group Publishing Limited.CrossRefGoogle Scholar
  20. Lanaj, K., Hollenbeck, J. R., Ilgen, D. R., Barnes, C. M., & Harmon, S. J. (2013). The double-edged sword of decentralized planning in multiteam systems. Academy of Management Journal, 56(3), 735–757.CrossRefGoogle Scholar
  21. Lazzara, E. H., Keebler, J. R., Shuffler, M. L., Patzer, B., Smith, D. C., & Misasi, P. (2015). Considerations for multiteam systems in emergency medical services. Journal of Patient Safety, 1–4. Retrieved from
  22. Luciano, M. M., DeChurch, L. A., & Mathieu, J. E. (2018). Multiteam systems: A structural framework and meso-theory of system functioning. Journal of Management, 44(3), 1065–1096.Google Scholar
  23. Marks, M. A., Sabella, M. J., Burke, C. S., & Zaccaro, S. J. (2002). The impact of cross-training on team effectiveness. Journal of Applied Psychology, 87(1), 3.CrossRefGoogle Scholar
  24. Mathieu, J. E., Marks, M. A., & Zaccaro, S. J. (2001). Multi-team systems. International Handbook of Work and Organizational Psychology, 2(2), 289–313.Google Scholar
  25. Olguin, D., Gloor, P. A., & Pentland, A. (2009). Capturing individual and group behavior with wearable sensors. Paper presented at the AAAI spring symposium on human behavior modeling, Stanford, CA.Google Scholar
  26. Parlak, S., Sarcevic, A., Marsic, I., & Burd, R. S. (2012). Introducing RFID technology in dynamic and time-critical medical settings: Requirements and challenges. Journal of Biomedical Informatics, 45(5), 958–974.CrossRefGoogle Scholar
  27. Rico, R., Hinsz, V. B., Burke, S., & Salas, E. (2017). A multilevel model of multiteam motivation and performance. Organizational Psychology Review, 7(3), 197–226.CrossRefGoogle Scholar
  28. Rosen, M. A., Weaver, S. J., Lazzara, E. H., Salas, E., Wu, T., Silvestri, S., Schiebel, N., Almeida, S., & King, H. B. (2010). Tools for evaluating team performance in simulation-based training. Journal of Emergencies, Trauma and Shock, 3(4), 353.CrossRefGoogle Scholar
  29. Rosen, M. A., Dietz, A. S., Yang, T., Priebe, C. E., & Pronovost, P. J. (2014). An integrative framework for sensor-based measurement of teamwork in healthcare. Journal of the American Medical Informatics Association, 22(1), 11–18.Google Scholar
  30. Salas, E., & Cannon-Bowers, J. A. (2001). The science of training: A decade of progress. Annual Review of Psychology, 52(1), 471–499.CrossRefGoogle Scholar
  31. Salas, E., Burke, C. S., Bowers, C. A., & Wilson, K. A. (2001). Team training in the skies: Does crew resource management (CRM) training work? Human Factors, 43(4), 641–674.CrossRefGoogle Scholar
  32. Salas, E., Nichols, D. R., & Driskell, J. E. (2007). Testing three team training strategies in intact teams: A meta-analysis. Small Group Research, 38(4), 471–488.CrossRefGoogle Scholar
  33. Schon, D. (1983). The reflective practitioner: How professionals think in action (1st ed.). London: Temple Smith.Google Scholar
  34. Vankipuram, M., Kahol, K., Cohen, T., & Patel, V. L. (2011). Toward automated workflow analysis and visualization in clinical environments. Journal of Biomedical Informatics, 44(3), 432–440.CrossRefGoogle Scholar
  35. Zaccaro, S. J., Marks, M. A., & DeChurch, L. A. (2012). Multiteam systems: An introduction. In S. J. Zaccaro, M. A. Marks, & L. DeChurch (Eds.), Multiteam Systems: An Organization Form for Dynamic and Complex Environments (pp. 3–31). New York, NY: Routledge.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Brenda Bannan
    • 1
    Email author
  • Samantha Dubrow
    • 2
  • Christian Dobbins
    • 2
  • Stephen Zaccaro
    • 2
  • Hemant Purohit
    • 3
  • Mohammed Rana
    • 3
  1. 1.Learning Technologies, George Mason UniversityFairfaxUSA
  2. 2.Organizational Psychology, George Mason UniversityFairfaxUSA
  3. 3.Information Science and Technology, George Mason UniversityFairfaxUSA

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