Abstract

High-performing design teams are characterized by their ability to maintain performance across a variety of problem types. This is often referred to as robustness, and is usually achieved through careful management of team processes. However, there exists an opportunity to design teams that are likely to be inherently robust by addressing and embracing the individual variability of team members. Cognitive style provides an avenue by which we can compose robust teams based on the problem-solving approach of the individual. In this work, we used the KAI agent-based organizational optimization model (KABOOM) to evaluate the effects of team composition and team structure on the robustness of overall team performance. Teams of homogeneous and heterogeneous KAI styles were tasked to solve a variety of different abstract design problems and evaluated based on their performance with and without sub-teams. Results indicate that there is a significant difference in the distribution of aggregate scores for homogeneous and heterogeneous teams without sub-teams, and heterogeneous teams may be more robust. Sub-teams were found to significantly increase the overall median score and robustness for some teams.

References

1.
Monalisa
,
M.
,
Daim
,
T.
,
Mirani
,
F.
,
Dash
,
P.
,
Khamis
,
R.
, and
Bhusari
,
V.
,
2015
, “
Managing Global Design Teams
,”
Res. Technol. Manag.
,
51
(
4
), pp.
48
59
.
2.
Paretti
,
M. C.
,
Layton
,
R. A.
,
Laguette
,
S.
, and
Speegle
,
G.
,
2011
, “
Managing and Mentoring Capstone Design Teams: Considerations and Practices for Faculty
,”
Int. J. Eng. Educ.
,
27
(
6
), pp.
1192
1205
.
3.
Soni
,
V. D.
,
2020
, “
Importance and Strategic Planning of Team Management
,”
Int. J. Innov. Res. Technol.
,
7
(
2
), pp.
47
50
.
4.
Kirton
,
M. J.
,
2004
,
Adaptation-Innovation: In the Context of Diversity and Change
,
Routledge
,
New York
.
5.
Messick
,
S.
,
1976
,
Individuality in Learning
,
Jossey-Bass Publishers
,
Hoboken, NJ
.
6.
Urban
,
J. M.
,
Weaver
,
J. L.
,
Bowers
,
C. A.
, and
Rhodenizer
,
L.
,
1996
, “
Effects of Workload and Structure on Team Processes and Performance: Implications for Complex Team Decision Making
,”
Hum. Factors J. Hum. Factors Ergon. Soc.
,
38
(
2
), pp.
300
310
.
7.
Kirton
,
M. J.
,
1977
,
Manual of the Kirton Adaption-Innovation Inventory
,
National Foundation for Educational Research
,
London
.
8.
Samuel
,
P.
, and
Jablokow
,
K. W.
,
2011
, “
Toward an Adaption-Innovation Strategy for Engineering Design
,”
Proceedings of the 18th International Conference on Engineering Design
,
Washington, DC
,
Aug. 28–31
, pp.
377
386
.
9.
Jablokow
,
K.
,
Teerlink
,
W.
,
Yilmaz
,
S.
,
Daly
,
S.
,
Silk
,
E.
, and
Wehr
,
C.
,
2016
, “
Ideation Variety in Mechanical Design: Examining the Effects of Cognitive Style and Design Heuristics
,”
Proceedings of the ASME Design Engineering Technical Conference
,
Charlotte, NC
,
Aug. 21
.
10.
Stum
,
J.
,
2009
, “
Kirton’s Adaption-Innovation Theory: Managing Cognitive Styles in Times of Diversity and Change
,”
Emerg. Leadersh. Journeys
,
2
(
1
), pp.
66
78
.
11.
Lapp
,
S.
,
Jablokow
,
K.
, and
McComb
,
C.
,
2019
, “
KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving
,”
Des. Sci.
,
5
.
12.
Heininger
,
K.
,
Chen
,
H. E.
,
Jablokow
,
K.
, and
Miller
,
S. R.
,
2018
, “
How Engineering Design Students’ Creative Preferences and Cognitive Styles Impact Their Concept Generation and Screening
,”
Proceedings of the ASME Design Engineering Technical Conference
,
Quebec City, Canada
,
Aug. 26–29
.
13.
Radwan
,
N.
,
McComb
,
C.
,
Menold
,
J.
,
Jablokow
,
K. W.
, and
McTernan
,
J.
,
2022
, “
Towards Characterizing Cognitive Style Coping Behavior in Engineering Design
,”
Proceedings of the ASME Design Engineering Technical Conference.
,
St. Louis, MO
,
Aug. 14–17
.
14.
Buffington
,
K. W.
,
Jablokow
,
K. W.
, and
Martin
,
K. A.
,
2002
, “
Project Team Dynamics and Cognitive Style
,”
Eng. Manag. J.
,
14
(
3
), pp.
25
33
.
15.
Sonalkar
,
N.
,
Jablokow
,
K.
,
Edelman
,
J.
,
Mabogunje
,
A.
, and
Leifer
,
L.
,
2017
, “
Design Whodunit: The Relationship Between Individual Characteristics and Interaction Behaviors in Design Concept Generation
,”
Proceedings of the ASME Design Engineering Technical Conference, Vol. 7
,
Cleveland, OH
,
Aug. 6–9
.
16.
Hammerschmidt
,
P. K.
,
1996
, “
The Kirton Adaption Innovation Inventory Find Group Problem Solving Success Rates
,”
J. Creat. Behav.
,
30
(
1
), pp.
61
74
.
17.
Kurtzberg
,
T. R.
,
2010
, “
Feeling Creative, Being Creative: An Empirical Study of Diversity and Creativity in Teams
,”
Creat. Res. J.
,
17
(
1
), pp.
51
65
.
18.
Mannix
,
E.
, and
Neale
,
M. A.
,
2016
, “
What Differences Make a Difference?: The Promise and Reality of Diverse Teams in Organizations
,”
Psychol. Sci. Public Interest
,
6
(
2
), pp.
31
55
.
19.
Maier
,
T.
,
DeFranco
,
J.
, and
Mccomb
,
C.
,
2019
, “
An Analysis of Design Process and Performance in Distributed Data Science Teams
,”
Team Perform. Manag.
,
25
(
7–8
), pp.
419
439
.
20.
Chamakiotis
,
P.
,
Dekoninck
,
E. A.
, and
Panteli
,
N.
,
2013
, “
Factors Influencing Creativity in Virtual Design Teams: An Interplay Between Technology, Teams and Individuals
,”
Creat. Innov. Manag.
,
22
(
3
), pp.
265
279
.
21.
He
,
J.
,
Butler
,
B. S.
, and
King
,
W. R.
,
2007
, “
Team Cognition: Development and Evolution in Software Project Teams
,”
J. Manag. Inf. Syst.
,
24
(
2
), pp.
261
292
.
22.
Yost
,
C. A.
, and
Tucker
,
M. L.
,
2000
, “
Are Effective Teams More Emotionally Intelligent? Confirming the Importance of Effective Communication in Teams
,”
Delta Pi Epsil. J.
,
42
(
2
), p.
101
.
23.
Levitt
,
R. E.
,
2012
, “
The Virtual Design Team: Designing Project Organizations as Engineers Design Bridges
,”
J. Organ. Des.
,
1
(
2
), pp.
14
41
.
24.
Perišić
,
M. M.
,
Martinec
,
T.
,
Štorga
,
M.
, and
Kanduč
,
T.
,
2016
, “
Agent-Based Simulation Framework to Support Management of Teams Performing Development Activities
,”
DS 84: Proceedings of the DESIGN 2016 14th International Design Conference
,
Vienna, Austria
, pp.
1925
1936
.
25.
Herath
,
D.
,
Costello
,
J.
, and
Homberg
,
F.
,
2017
, “
Team Problem Solving and Motivation Under Disorganization—An Agent-Based Modeling Approach
,”
Team Perform. Manag. Int. J.
,
23
(
1–2
), pp.
46
65
.
26.
Crowder
,
R. M.
,
Robinson
,
M. A.
,
Hughes
,
H. P. N.
, and
Sim
,
Y. W.
,
2012
, “
The Development of an Agent-Based Modeling Framework for Simulating Engineering Team Work
,”
IEEE Trans. Syst. Man Cybern. Part A Syst. Humans
,
42
(
6
), pp.
1425
1439
.
27.
Xu
,
Y.
,
Liao
,
E.
,
Scerri
,
P.
,
Yu
,
B.
,
Lewis
,
M.
, and
Sycara
,
K.
,
2006
,
Coordination of Large-Scale Multiagent Systems
,
Springer
,
Boston, MA
, pp.
287
309
.
28.
Stolarski
,
V.
, and
Tilebein
,
M.
,
2009
, “
Diversity as a knowledge resource in top management teams–a framework for agent-based modeling
,”
42nd Hawaii International Conference on System Sciences
,
Big Island, HI
,
Jan. 5–8
, IEEE.
29.
McComb
,
C.
, and
Jablokow
,
K.
,
2022
, “
A Conceptual Framework for Multidisciplinary Design Research With Example Application to Agent-Based Modeling
,”
Des. Stud.
,
78
, p.
101074
.
30.
Hsu
,
S. C.
,
Weng
,
K. W.
,
Cui
,
Q.
, and
Rand
,
W.
,
2016
, “
Understanding the Complexity of Project Team Member Selection Through Agent-Based Modeling
,”
Int. J. Proj. Manag.
,
34
(
1
), pp.
82
93
.
31.
Martínez-Miranda
,
J
,
Aldea
,
A.
,
Bañares-Alcántara
,
R.
, and
Alvarado
,
M.
,
2006
, “
TEAKS: Simulation of Human Performance at Work to Support Team Configuration
,”
Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent Systems, 2006
,
Hakodate, Japan
,
May 8–12
, pp.
114
116
.
32.
Motamediyan
,
F.
,
Larsson
,
T.
,
Thompson
,
A.
, and
Motamediyan Dehkordi
,
F.
,
2012
, “
Impacts of Project-Overload on Innovation Inside Organizations: Agent-Based Modeling
,”
Int. J. Soc. Behav. Educ. Econ. Bus. Indus. Eng.
,
6
(
11
), pp.
2808
2813
.
33.
Kirkpatrick
,
S.
,
Gelatt
,
C. D.
, and
Vecchi
,
M. P.
,
1983
, “
Optimization by Simulated Annealing
,”
Science
,
220
(
4598
), pp.
671
680
.
34.
McComb
,
C.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2015
, “
Lifting the Veil: Drawing Insights About Design Teams From a Cognitively-Inspired Computational Model
,”
Des. Stud.
,
40
, pp.
114
142
.
35.
Cagan
,
J.
, and
Kotovsky
,
K.
,
1997
, “
Simulated Annealing and the Generation of the Objective Function: A Model of Learning During Problem Solving
,”
Comput. Intell.
,
13
(
4
), pp.
534
581
.
36.
Yu
,
B. Y.
,
Honda
,
T.
,
Sharqawy
,
M.
, and
Yang
,
M.
,
2016
, “
Human Behavior and Domain Knowledge in Parameter Design of Complex Systems
,”
Des. Stud.
,
45
, pp.
242
267
.
37.
Lapp
,
S.
,
Jablokow
,
K.
, and
McComb
,
C.
,
2019
, “
Collaborating With Style: Using an Agent-Based Model to Simulate Cognitive Style Diversity in Problem Solving Teams
,”
Proceedings of the ASME Design Engineering Technical Conference
,
Anaheim, CA
,
Aug. 18–21
.
38.
Carnabuci
,
G.
, and
Diószegi
,
B.
,
2015
, “
Social Networks, Cognitive Style, and Innovative Performance: A Contingency Perspective
,”
Acad. Manag. J.
,
53
(
3
), pp.
881
905
.
39.
Blackburn
,
J.
,
Lapre
,
M. A.
, and
van Wassenhove
,
L. N.
,
2006
, “
Brooks' Law Revisited: Improving Software Productivity by Managing Complexity
,” Available at SSRN 922768, pp.
1
24
.
40.
Patrashkova-Volzdoska
,
R. R.
,
McComb
,
S. A.
,
Green
,
S. G.
, and
Compton
,
W.
,
2003
, “
Examining a Curvilinear Relationship Between Communication Frequency and Team Performance in Cross-Functional Teams
,”
IEEE Trans. Eng. Manage.
,
50
(
3
), pp.
262
269
.
41.
Patrashkova
,
R. R.
, and
McComb
,
S. A.
,
2004
, “
Exploring Why More Communication Is Not Better: Insights From a Computational Model of Cross-Functional Teams
,”
J. Eng. Technol. Manag.
,
21
(
1–2
), pp.
83
114
.
42.
Austin-Breneman
,
J.
,
Yu
,
B. Y.
, and
Yang
,
M. C.
,
2015
, “
Biased Information Passing Between Subsystems Over Time in Complex System Design
,”
ASME J. Mech. Des.
,
138
(
1
), p.
011101
.
43.
Jablokow
,
K. W.
,
2008
, “
Developing Problem Solving Leadership: A Cognitive Approach
,”
Int. J. Eng. Educ.
,
24
(
5
), pp.
936
954
.
44.
Stempfle
,
J.
, and
Badke-Schaub
,
P.
,
2002
, “
Thinking in Design Teams—An Analysis of Team Communication
,”
Des. Stud.
,
23
(
5
), pp.
473
496
.
45.
Demirel
,
S.
,
Macek
,
L.
,
Bruijnen
,
H.
,
Hakimi
,
M.
,
Böckler
,
D.
, and
Attigah
,
N.
,
2012
, “
Eversion Carotid Endarterectomy is Associated With Decreased Baroreceptor Sensitivity Compared to the Conventional Technique
,”
Eur. J. Vasc. Endovasc. Surg.
,
44
(
1
), pp.
1
8
.
46.
Nachar
,
N.
,
2008
, “
The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come From the Same Distribution
,”
Tutor. Quant. Methods Psychol.
,
4
(
1
), pp.
13
20
.
47.
McHale
,
J.
, and
Flegg
,
D.
,
1986
, “
Innovators Rule OK- or Do They
,”
Train. Dev. J.
, pp.
10
13
.
48.
Silk
,
E. M.
,
Rechkemmer
,
A.
,
Daly
,
S. R.
,
Jablokow
,
K. W.
, and
McKilligan
,
S.
,
2021
, “
Problem Framing and Cognitive Style: Impacts on Design Ideation Perceptions
,”
Des. Stud.
,
74
, p.
101015
.
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