Quadratic Model of Reciprocal Causation for Monitoring, Improving, and Reflecting on Design Team Performance

Part of the Understanding Innovation book series (UNDINNO)


Design team performance is a complex phenomenon that involves person, behavior and environment parameters interacting with and influencing each other over time. In this chapter, we propose a quadratic model for team performance that allows for monitoring, improving, and reflecting on design teams at the individual, interactional and environmental levels. This model is an extension of Bandura’s theory of reciprocal causation and a synthesis of concepts from psychology, semiotics, improvisational theater, evolutionary biology, design thinking and innovation practice. We describe the development of the model based on cases of student behavior from a graduate level design course, and discuss its implications for design practice and design research.


  1. Ahmed, S., Wallace, K. M., & Blessing, L. T. (2003). Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design, 14(1), 1–11.CrossRefGoogle Scholar
  2. Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.CrossRefGoogle Scholar
  3. Baird, F., Moore, C. J., & Jagodzinski, A. P. (2000). An ethnographic study of engineering design teams at Rolls-Royce Aerospace. Design Studies, 21(4), 333–355.CrossRefGoogle Scholar
  4. Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology, 2(1), 21–41.CrossRefGoogle Scholar
  5. Bratman, M. (1987). Intention, plans, and practical reason. Cambridge, MA: Harvard University Press.Google Scholar
  6. Brereton, M., & McGarry, B. (2000, April). An observational study of how objects support engineering design thinking and communication: Implications for the design of tangible media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 217–224). New York: ACM.Google Scholar
  7. Cash, P. J., Hicks, B. J., & Culley, S. J. (2013). A comparison of designer activity using core design situations in the laboratory and practice. Design Studies, 34(5), 575–611.CrossRefGoogle Scholar
  8. Chaiklin, S. (2003). The zone of proximal development in Vygotsky’s analysis of learning and instruction. In Vygotsky’s educational theory in cultural context (Vol. 1, pp. 39–64). Cambridge: Cambridge University Press.Google Scholar
  9. Chakrabarti, A., & Blessing, L. T. (2014). An anthology of theories and models of design. London: Springer.CrossRefGoogle Scholar
  10. Doorley, S., & Witthoft, S. (2011). Make space: How to set the stage for creative collaboration. Hoboken: Wiley.Google Scholar
  11. Edelman, J., & Currano, R. (2011). Re-representation: Affordances of shared models in team-based design. In Design thinking (pp. 61–79). Berlin: Springer.Google Scholar
  12. Edelman, J. A., Leifer, L., Banerjee, B., Sonalkar, N., Jung, M., & Lande, M. (2009). Hidden in plain sight: Affordances of shared models in team based design. DS 58-2: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Palo Alto, CA, USA. Google Scholar
  13. Goodwin, C. (1994). Professional vision. American Anthropologist, 96(3), 606–633.CrossRefGoogle Scholar
  14. Hatchuel, A., & Weil, B. (2003). A new approach of innovative design: An introduction to CK theory. DS 31: Proceedings of ICED 03, the 14th International Conference on Engineering Design, Stockholm. Google Scholar
  15. Jablokow, K. W., & Booth, D. E. (2006). The impact and management of cognitive gap in high performance product development organizations. Journal of Engineering and Technology Management, 23(4), 313–336.CrossRefGoogle Scholar
  16. Jung, M., Chong, J., & Leifer, L. (2012, May). Group hedonic balance and pair programming performance: Affective interaction dynamics as indicators of performance. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 829–838). New York: ACM.Google Scholar
  17. Jung, M. F. (2011). Engineering team performance and emotion: Affective interaction dynamics as indicators of design team performance. PhD dissertation, Stanford University.Google Scholar
  18. Jung, M. F. (2016). Coupling interactions and performance: Predicting team performance from thin slices of conflict. ACM Transactions on Computer-Human Interaction (TOCHI), 23(3), 18.CrossRefGoogle Scholar
  19. Katzenbach, J. R., & Smith, D. K. (1993). The wisdom of teams: Creating the high-performance organization. Harvard Business Press.Google Scholar
  20. Kichuk, S. L., & Wiesner, W. H. (1997). The big five personality factors and team performance: Implications for selecting successful product design teams. Journal of Engineering and Technology Management, 14(3), 195–221.CrossRefGoogle Scholar
  21. Kress, G., & Schar, M. (2011). Initial conditions: The structure and composition of effective design teams. DS 68-7: Proceedings of the 18th International Conference on Engineering Design (ICED 11), Lyngby/Copenhagen, Denmark.Google Scholar
  22. Leifer, L. (1998). Design-team performance: Metrics and the impact of technology. In Evaluating Corporate Training: Models and issues (pp. 297–319). New York: Springer.CrossRefGoogle Scholar
  23. Nicolai, C., Klooker, M., Panayotova, D., Hüsam, D., & Weinberg, U. (2016). Innovation in creative environments: Understanding and measuring the influence of spatial effects on design thinking-teams. In Design Thinking Research (pp. 125–139). Cham: Springer International Publishing.Google Scholar
  24. Pressfield, S. (2002). The war of art: Break through the blocks and win your inner creative battles. New York: Black Irish Entertainment.Google Scholar
  25. Rittel, H. W., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.CrossRefGoogle Scholar
  26. Schar, M. F. (2011). Pivot thinking and the differential sharing of information within new product development teams. PhD Dissertation. Stanford University.Google Scholar
  27. Sonalkar, N., Jung, M., & Mabogunje, A. (2011). Emotion in engineering design teams. In Emotional Engineering (pp. 311–326). London: Springer.CrossRefGoogle Scholar
  28. Sonalkar, N., Mabogunje, A., Hoster, H., & Roth, B. (2016). Developing Instrumentation for design thinking team performance. In Design Thinking Research (pp. 275–289). Cham: Springer International Publishing.Google Scholar
  29. Valkenburg, R., & Dorst, K. (1998). The reflective practice of design teams. Design Studies, 19(3), 249–271.CrossRefGoogle Scholar
  30. Weinberg, U., Nicolai, C., Hüsam, D., Panayotova, D., & Klooker, M. (2014). The impact of space on innovation teams. 19th DMI: Academic design management conference design management in an era of disruption, London.Google Scholar
  31. Wilde, D. J. (2008). Teamology: The construction and organization of effective teams. London: Springer.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Center for Design ResearchStanford UniversityStanfordUSA
  2. 2.Department Mechanical EngineeringStanford UniversityStanfordUSA

Personalised recommendations