Hostname: page-component-848d4c4894-p2v8j Total loading time: 0 Render date: 2024-06-12T20:16:51.190Z Has data issue: false hasContentIssue false
  • English
  • Français

A Computational Framework for Exploring the Socio-Cognitive Features of Teams and their Influence on Design Outcomes

Part of: Collaborative Design

Published online by Cambridge University Press:  26 July 2019

Harshika Singh ,
Gaetano Cascini ,
Hernan Casakin  and
Vishal Singh
Harshika Singh*
Affiliation:
Politecnico di Milano;
Gaetano Cascini
Affiliation:
Politecnico di Milano;
Hernan Casakin
Affiliation:
Ariel University;
Vishal Singh
Affiliation:
Aalto University
*
Contact: Singh, Harshika, Politecnico di Milano, Department of Mechanical Engineering Italy, harshika.singh@polimi.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The dynamics of design teams play a critical role in product development, mainly in the early phases of the process. This paper presents a conceptual framework of a computational model about how cognitive and social features of a design team affect the quality of the produced design outcomes. The framework is based on various cognitive and social theories grounded in literature. Agent-Based Modelling (ABM) is used as a tool to evaluate the impact of design process organization and team dynamics on the design outcome. The model describes key research parameters, including dependent, independent, and intermediates. The independent parameters include: duration of a session, number of times a session is repeated, design task and team characteristics such as size, structure, old and new members. Intermediates include: features of team members (experience, learning abilities, and importance in the team) and social influence. The dependent parameter is the task outcome, represented by creativity and accuracy. The paper aims at laying the computational foundations for validating the proposed model in the future.

Keywords

Collaborative designProcess modellingDesign management
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1 - 10
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Abar, S., Theodoropoulos, G. K., Lemarinier, P. and O'Hare, G. M. (2017), “Agent Based Modelling and Simulation tools: A review of the state-of-art software”, Computer Science Review, Vol. 24, pp. 13–33. https://dx.doi.org/10.1016/j.cosrev.2017.03.001Google Scholar
Badke-Schaub, P. and Frankenberger, E. (1999), “Analysis of design projects”, Design Studies, Vol. 20 No. 5, pp. 465–480. https://dx.doi.org/10.1016/S0142-694X(99)00017-4Google Scholar
Carberry, A. R., Lee, H.-S. and Ohland, M. W. (2010), “Measuring Engineering Design Self-Efficacy”, Journal of Engineering Education, Vol. 99 No. 1, pp. 71–79.Google Scholar
Carley, K. M. and Gasser, L. (1999), “Computational organization theory”. In: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, s.l.: MIT Press, pp. 1–32.Google Scholar
Dionne, S. D. and Dionne, P. J. (2008), “Levels-based leadership and hierarchical group decision optimization: A simulation”, The Leadership Quarterly, Vol. 19 No. 2, pp. 212–234. https://dx.doi.org/10.1016/j.leaqua.2008.01.004Google Scholar
Dorst, K. and Cross, N. (2001), “Creativity in the design process: co-evolution of problem–solution”, Design Studies, Vol. 22 No. 5, pp. 425–437. https://dx.doi.org/10.1016/S0142-694X(01)00009-6Google Scholar
Gero, J. S. and Kannengiesser, U. (2004), “Modelling Expertise of Temporary Design Teams”, Journal of Design Research, Vol. 4, pp. 1–13.Google Scholar
Herrmann, J. W. (2015), “Predicting the Performance of a Design Team Using a Markov Chain Model”, IEEE Transactions on Engineering Management, Vol. 62 No. 4, pp. 507–516. https://dx.doi.org/10.1109/TEM.2015.2456833Google Scholar
Jamshidnezhad, B. and Carley, K. M. (2015), “Agent-based modelling of quality management effects on organizational productivity”, Journal of Simulation, Vol. 9 No. 1, pp. 73–82. https://dx.doi.org/10.1057/jos.2014.26Google Scholar
Jin, Y. and Levitt, R. E. (1996), “The Virtual Design Team: A Computational Model of Project Organizations”, Computational & Mathematical Organization Theory, Vol. 2 No. 3, pp. 171–196.Google Scholar
Kelman, H. C. (1958), “Compliance, identification, and internalization three processes of attitude change”, Journal of Conflict Resolution, Vol. 2 No. 1, pp. 51–60.Google Scholar
Leibowitz, N., Baum, B., Enden, G. and Karniel, A. (2010), “The exponential learning equation as a function of successful trials results in sigmoid performance”, Journal of Mathematical Psychology, Vol. 54 No. 3, pp. 338–340. https://dx.doi.org/10.1016/j.jmp.2010.01.006Google Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2015), “Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model”, Design Studies, Vol. 40, pp. 119–142. https://dx.doi.org/10.1016/j.destud.2015.06.005Google Scholar
Perišić, M. M., Štorga, M. and Gero, J. S. (2019), “Exploring the Effect of Experience on Team Behavior: A Computational Approach”, International Conference on - Design Computing and Cognition, Lecco, Italy, https://dx.doi.org/10.1007/978-3-030-05363-5_32Google Scholar
Prussia, G. E., Anderson, J. S. and Manz, C. C. (1998), “Self-leadership and performance outcomes: the mediating influence of self-efficacy”, Journal of Organizational Behavior, Vol. 19, pp. 523–538. https://dx.doi.org/10.1002/(SICI)1099-1379(199809)19:5<523::AID-JOB860>3.0.CO;2-I3.0.CO;2-I>Google Scholar
Salas, E. et al. (2005), “Modeling Team Performance: The Basic Ingredients and Research Needs”, In: Rouse, W. B. and Boff, K. R. (Eds.), Organizational simulation. Wiley, Hoboken, NJ, pp. 185−228. https://dx.doi.org/10.1002/0471739448.ch7Google Scholar
Sayama, H., Farrell, D. L. and Dionne, S. D. (2010), “The Effects of Mental Model Formation on Group Decision Making: An Agent-Based Simulation”, Complexity, Vol. 16 No. 3, pp. 49–57. https://dx.doi.org/10.1002/cplx.20329Google Scholar
Singh, V. (2009), Computational Studies on the Role of Social Learning in the Formation of Team Mental Models, Design Lab Faculty of Architecture, Design and Planning, The University of Sydney, Sydney.Google Scholar
Singh, V. and Casakin, H. (2015), “Developing a computational framework to study the effects of use of analogy in design on team cohesion and team collaboration”, International Conference on Engineering Design, ICED15, Milan, ItalyGoogle Scholar
Singh, V., Dong, A. and Gero, J. S. (2011), “How important is team structure to team performance?”, International Conference on Engineering Design, ICED11, Technical University of Denmark.Google Scholar
Sosa, R. and Gero, J. S. (2013), “The creative value of bad ideas: A computational model of creative ideation”, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong, and Center for Advanced Studies in Architecture (CASA), Department of Architecture-NUS, Singapore, pp. 853–862Google Scholar
Sparks, J. (2018), Types of Conformity. [Online]. Available at: https://www.tutor2u.net/psychology/reference/types-of-conformityGoogle Scholar
Stempfle, J. and Badke-Schaub, P. (2002), “Thinking in design teams - an analysis of team communication”, Design Studies, Vol. 23 No. 5, pp. 473–496. https://dx.doi.org/10.1016/S0142-694X(02)00004-2Google Scholar