September  2020, 16(5): 2479-2494. doi: 10.3934/jimo.2019064

Does the existence of "talented outliers" help improve team performance? Modeling heterogeneous personalities in teamwork

System Dynamics Group, Sloan School of Management, MIT, Cambridge, MA 02139, USA

* Corresponding author: Tianyi Li

Received  November 2018 Revised  January 2019 Published  May 2019

Personality heterogeneity is an important topic in team management. In many working groups, there exists certain type of people that are talented but under-disciplined, who could occasionally make extraordinary contributions for the team, but often have less satisfactory overall performance. It is interesting to investigate whether the existence of such people in the team does help improve the overall team performance, and if it does so, what are the conditions for their existence to be positive, and through which channel their benefits for the team are manifested. This study proposes an analytical model with a simple structure that sets up an environment to study these questions. It is shown that: (1) feedback learning could be the mechanism through which outliers' benefits to the team are established, and thus could be a prerequisite for outliers' positive existence; (2) different types of teamwork settings have different outlier-positivity conditions: a uniform round-wise punishment for teamwork failures could be the key idea to encourage outliers' existence; for two specific types of teamwork, teamwork that highlights assistance in interactions are more outliers-friendly than teamwork that consists internal competitions. These results well match empirical observations and may have further implications for managerial practice.

Citation: Tianyi Li. Does the existence of "talented outliers" help improve team performance? Modeling heterogeneous personalities in teamwork. Journal of Industrial & Management Optimization, 2020, 16 (5) : 2479-2494. doi: 10.3934/jimo.2019064
References:
[1]

A. W. Astin, Competition or cooperation?: Teaching teamwork as a basic skill, Change: The Magazine of Higher Learning, 19 (1987), 12-19.   Google Scholar

[2]

A. V. Carron and L. R. Brawley, Group dynamics in sport and physical activity, 2008. Google Scholar

[3]

A. V. Carron, H. A. Hausenblas and M. A. Eys, Group dynamics in sport, Fitness Information Technology, 2005. Google Scholar

[4]

Y. Colas, The Meanings of Manu: Style, Race, and Globalization in the Culture of Basketball, In Sports and Nationalism in Latin/o America, Palgrave Macmillan, New York, (2015), 249–268. Google Scholar

[5]

L. T. Eby and G. H. Dobbins, Collectivistic orientation in teams: An individual and group-level analysis, Journal of Organizational Behavior, (1997), 275–295. Google Scholar

[6]

M. J. FrankJ. SamantaA. A. Moustafa and S. J. Sherman, Hold your horses: Impulsivity, deep brain stimulation, and medication in parkinsonism, Science, 318 (2007), 1309-1312.   Google Scholar

[7]

M. Gladwell, Outliers: The story of success, Hachette UK, 2008. Google Scholar

[8]

M. S. Grewal, Kalman filtering, In International Encyclopedia of Statistical Science, Springer Berlin Heidelberg, (2011), 705–708. Google Scholar

[9]

M. GundlachS. Zivnuska and J. Stoner, Understanding the relationship between individualism-collectivism and team performance through an integration of social identity theory and the social relations model, Human relations, 59 (2006), 1603-1632.   Google Scholar

[10]

D. Hounsell, Student feedback, learning and development, Higher Education and the Lifecourse, (2003), 67–78. Google Scholar

[11]

R. IliesD. T. Wagner and F. P. Morgeson, Explaining affective linkages in teams: Individual differences in susceptibility to contagion and individualism-collectivism, Journal of Applied Psychology, 92 (2007), 1140.   Google Scholar

[12] J. R. Katzenbach and D. K. Smith, The wisdom of teams: Creating the high-performance organization, Harvard Business Review Press, 2015.   Google Scholar
[13]

S. Kiffin-Petersen and J. Cordery, Trust, individualism and job characteristics as predictors of employee preference for teamwork, International Journal of Human Resource Management, 14 (2003), 93-116.   Google Scholar

[14]

J. KleinertJ. OhlertB. CarronM. EysD. FeltzC. HarwoodM. Sulprizio and et. al., Group dynamics in sports: an overview and recommendations on diagnostic and intervention, The Sport Psychologist, 26 (2012), 412-434.   Google Scholar

[15]

J. C. Lo andC. H. Yang, A heuristic error-feedback learning algorithm for fuzzy modeling, IEEE Transactions on Systems, Man, and Cybernetics-Part A, Systems and Humans, 29 (1999), 686–691. Google Scholar

[16]

S. Mohammed and L. C. Angell, Personality heterogeneity in teams: Which differences make a difference for team performance?, Small group research, 34 (2003), 651-677.   Google Scholar

[17]

E. Molleman, Diversity in demographic characteristics, abilities and personality traits: Do faultlines affect team functioning?, Group Decision and Negotiation, 14 (2005), 173-193.   Google Scholar

[18]

G. A. NeumanS. H. Wagner and N. D. Christiansen, The relationship between work-team personality composition and the job performance of teams, Group and Organization Management, 24 (1999), 28-45.   Google Scholar

[19]

D. M. Prue and J. A. Fairbank, Performance feedback in organizational behavior management: A review, Journal of Organizational Behavior Management, 3 (1981), 1-16.   Google Scholar

[20]

H. RahmandadN. Repenning and J. Sterman, Effects of feedback delay on learning, System Dynamics Review, 25 (2009), 309-338.   Google Scholar

[21]

J. D. Sterman, Business dynamics: Systems thinking and modeling for a complex world, (No. HD30. 2 S7835 2000), (2000). Google Scholar

[22]

J. D. StermanR. HendersonE. D. Beinhocker and L. I. Newman, Getting big too fast: Strategic dynamics with increasing returns and bounded rationality, Management Science, 53 (2007), 683-696.   Google Scholar

[23]

A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases, science, 185 (1974), 1124-1131.   Google Scholar

[24]

I. Van de VijverK. R. Ridderinkhof and M. X. Cohen, Frontal oscillatory dynamics predict feedback learning and action adjustment, Journal of Cognitive Neuroscience, 23 (2011), 4106-4121.   Google Scholar

[25]

J. A. Wagner and M. K. Moch, Individualism-collectivism: Concept and measure, Group and Organization Studies, 11 (1986), 280-304.   Google Scholar

[26]

C. WatanabeB. ZhuC. Griffy-Brown and B. Asgari, Global technology spillover and its impact on industry's R & D strategies, Technovation, 21 (2001), 281-291.   Google Scholar

[27]

L. ZhangF. LuA. LiuP. Guo and C. Liu, Application of K-means clustering algorithm for classification of NBA guards, International Journal of Science and Engineering Applications, 5 (2016), 1-6.   Google Scholar

show all references

References:
[1]

A. W. Astin, Competition or cooperation?: Teaching teamwork as a basic skill, Change: The Magazine of Higher Learning, 19 (1987), 12-19.   Google Scholar

[2]

A. V. Carron and L. R. Brawley, Group dynamics in sport and physical activity, 2008. Google Scholar

[3]

A. V. Carron, H. A. Hausenblas and M. A. Eys, Group dynamics in sport, Fitness Information Technology, 2005. Google Scholar

[4]

Y. Colas, The Meanings of Manu: Style, Race, and Globalization in the Culture of Basketball, In Sports and Nationalism in Latin/o America, Palgrave Macmillan, New York, (2015), 249–268. Google Scholar

[5]

L. T. Eby and G. H. Dobbins, Collectivistic orientation in teams: An individual and group-level analysis, Journal of Organizational Behavior, (1997), 275–295. Google Scholar

[6]

M. J. FrankJ. SamantaA. A. Moustafa and S. J. Sherman, Hold your horses: Impulsivity, deep brain stimulation, and medication in parkinsonism, Science, 318 (2007), 1309-1312.   Google Scholar

[7]

M. Gladwell, Outliers: The story of success, Hachette UK, 2008. Google Scholar

[8]

M. S. Grewal, Kalman filtering, In International Encyclopedia of Statistical Science, Springer Berlin Heidelberg, (2011), 705–708. Google Scholar

[9]

M. GundlachS. Zivnuska and J. Stoner, Understanding the relationship between individualism-collectivism and team performance through an integration of social identity theory and the social relations model, Human relations, 59 (2006), 1603-1632.   Google Scholar

[10]

D. Hounsell, Student feedback, learning and development, Higher Education and the Lifecourse, (2003), 67–78. Google Scholar

[11]

R. IliesD. T. Wagner and F. P. Morgeson, Explaining affective linkages in teams: Individual differences in susceptibility to contagion and individualism-collectivism, Journal of Applied Psychology, 92 (2007), 1140.   Google Scholar

[12] J. R. Katzenbach and D. K. Smith, The wisdom of teams: Creating the high-performance organization, Harvard Business Review Press, 2015.   Google Scholar
[13]

S. Kiffin-Petersen and J. Cordery, Trust, individualism and job characteristics as predictors of employee preference for teamwork, International Journal of Human Resource Management, 14 (2003), 93-116.   Google Scholar

[14]

J. KleinertJ. OhlertB. CarronM. EysD. FeltzC. HarwoodM. Sulprizio and et. al., Group dynamics in sports: an overview and recommendations on diagnostic and intervention, The Sport Psychologist, 26 (2012), 412-434.   Google Scholar

[15]

J. C. Lo andC. H. Yang, A heuristic error-feedback learning algorithm for fuzzy modeling, IEEE Transactions on Systems, Man, and Cybernetics-Part A, Systems and Humans, 29 (1999), 686–691. Google Scholar

[16]

S. Mohammed and L. C. Angell, Personality heterogeneity in teams: Which differences make a difference for team performance?, Small group research, 34 (2003), 651-677.   Google Scholar

[17]

E. Molleman, Diversity in demographic characteristics, abilities and personality traits: Do faultlines affect team functioning?, Group Decision and Negotiation, 14 (2005), 173-193.   Google Scholar

[18]

G. A. NeumanS. H. Wagner and N. D. Christiansen, The relationship between work-team personality composition and the job performance of teams, Group and Organization Management, 24 (1999), 28-45.   Google Scholar

[19]

D. M. Prue and J. A. Fairbank, Performance feedback in organizational behavior management: A review, Journal of Organizational Behavior Management, 3 (1981), 1-16.   Google Scholar

[20]

H. RahmandadN. Repenning and J. Sterman, Effects of feedback delay on learning, System Dynamics Review, 25 (2009), 309-338.   Google Scholar

[21]

J. D. Sterman, Business dynamics: Systems thinking and modeling for a complex world, (No. HD30. 2 S7835 2000), (2000). Google Scholar

[22]

J. D. StermanR. HendersonE. D. Beinhocker and L. I. Newman, Getting big too fast: Strategic dynamics with increasing returns and bounded rationality, Management Science, 53 (2007), 683-696.   Google Scholar

[23]

A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases, science, 185 (1974), 1124-1131.   Google Scholar

[24]

I. Van de VijverK. R. Ridderinkhof and M. X. Cohen, Frontal oscillatory dynamics predict feedback learning and action adjustment, Journal of Cognitive Neuroscience, 23 (2011), 4106-4121.   Google Scholar

[25]

J. A. Wagner and M. K. Moch, Individualism-collectivism: Concept and measure, Group and Organization Studies, 11 (1986), 280-304.   Google Scholar

[26]

C. WatanabeB. ZhuC. Griffy-Brown and B. Asgari, Global technology spillover and its impact on industry's R & D strategies, Technovation, 21 (2001), 281-291.   Google Scholar

[27]

L. ZhangF. LuA. LiuP. Guo and C. Liu, Application of K-means clustering algorithm for classification of NBA guards, International Journal of Science and Engineering Applications, 5 (2016), 1-6.   Google Scholar

Figure 1.  Summary of the model and major assumptions. Teamwork is conducted in a multi-round game setting. In the team, normal players (empty nodes) and "talented" outliers (the solid node) behave differently (orange box). Outliers have worse average performance but a greater performance potential than normal players. Moreover, unlike normal players, outliers do not adjust his performance according to feedbacks from past interactions
Figure 3.  Testing two utility types and two distributions of individual performance. Left: $ \Delta Q_g $ for individual MC runs; right: average $ \Delta Q_g $ for all 200 MC runs. Utility function: equation (3): U1; equation (14): U2. Performance distribution: uniform: D1; Gaussian: D2. $ \{\gamma, m, K, L\} $ is chosen such that $ H<0 $ (blue; D1U1) and $ H'>0 $ (red; D1U2). Proposition 3 is demonstrated since $ \Delta Q_g(\mathit{\boldsymbol{D\mathit{1}U\mathit{2}}})>0>\Delta Q_g(\mathit{\boldsymbol{D\mathit{1}U\mathit{1}}}) $. The Gaussian distribution of individual performance is more stringent for outlier's positive existence than the uniform distribution
Figure 2.  The significance of the feedback learning mechanism as a potential prerequisite for the positivity condition of outliers' existence. Left: no feedback. MC simulation results are consistent with equation (7). Right: the feedback mechanism activated. Inset: average all-round team performance as a function of the number of outliers in the team $ N_o $. Results show that $ N_o = 1 $ produces the best outcome of teamwork under this parameter set, which satisfies $ H'>0 $
Figure 4.  Feedback learning from multiple past rounds. Results show $ \Delta Q_g $ (as in Figure 2) as a function of $ w $, which ranges from 1 to 5. The results from a few individual runs are plotted together, for all four combinations of $ U $ and $ D $. No conclusion could be drawn from this test
[1]

Niklas Kolbe, Nikolaos Sfakianakis, Christian Stinner, Christina Surulescu, Jonas Lenz. Modeling multiple taxis: Tumor invasion with phenotypic heterogeneity, haptotaxis, and unilateral interspecies repellence. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 443-481. doi: 10.3934/dcdsb.2020284

[2]

Nicholas Geneva, Nicholas Zabaras. Multi-fidelity generative deep learning turbulent flows. Foundations of Data Science, 2020, 2 (4) : 391-428. doi: 10.3934/fods.2020019

[3]

Xin Guo, Lexin Li, Qiang Wu. Modeling interactive components by coordinate kernel polynomial models. Mathematical Foundations of Computing, 2020, 3 (4) : 263-277. doi: 10.3934/mfc.2020010

[4]

Min Chen, Olivier Goubet, Shenghao Li. Mathematical analysis of bump to bucket problem. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5567-5580. doi: 10.3934/cpaa.2020251

[5]

Philippe G. Ciarlet, Liliana Gratie, Cristinel Mardare. Intrinsic methods in elasticity: a mathematical survey. Discrete & Continuous Dynamical Systems - A, 2009, 23 (1&2) : 133-164. doi: 10.3934/dcds.2009.23.133

[6]

M. Dambrine, B. Puig, G. Vallet. A mathematical model for marine dinoflagellates blooms. Discrete & Continuous Dynamical Systems - S, 2021, 14 (2) : 615-633. doi: 10.3934/dcdss.2020424

[7]

Ilyasse Lamrani, Imad El Harraki, Ali Boutoulout, Fatima-Zahrae El Alaoui. Feedback stabilization of bilinear coupled hyperbolic systems. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020434

[8]

Bernold Fiedler. Global Hopf bifurcation in networks with fast feedback cycles. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 177-203. doi: 10.3934/dcdss.2020344

[9]

Leslaw Skrzypek, Yuncheng You. Feedback synchronization of FHN cellular neural networks. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2021001

[10]

Chun Liu, Huan Sun. On energetic variational approaches in modeling the nematic liquid crystal flows. Discrete & Continuous Dynamical Systems - A, 2009, 23 (1&2) : 455-475. doi: 10.3934/dcds.2009.23.455

[11]

Jean-Paul Chehab. Damping, stabilization, and numerical filtering for the modeling and the simulation of time dependent PDEs. Discrete & Continuous Dynamical Systems - S, 2021  doi: 10.3934/dcdss.2021002

[12]

Kengo Nakai, Yoshitaka Saiki. Machine-learning construction of a model for a macroscopic fluid variable using the delay-coordinate of a scalar observable. Discrete & Continuous Dynamical Systems - S, 2021, 14 (3) : 1079-1092. doi: 10.3934/dcdss.2020352

[13]

Vieri Benci, Sunra Mosconi, Marco Squassina. Preface: Applications of mathematical analysis to problems in theoretical physics. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020446

[14]

Urszula Ledzewicz, Heinz Schättler. On the role of pharmacometrics in mathematical models for cancer treatments. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 483-499. doi: 10.3934/dcdsb.2020213

[15]

Jakub Kantner, Michal Beneš. Mathematical model of signal propagation in excitable media. Discrete & Continuous Dynamical Systems - S, 2021, 14 (3) : 935-951. doi: 10.3934/dcdss.2020382

[16]

Yuan Tan, Qingyuan Cao, Lan Li, Tianshi Hu, Min Su. A chance-constrained stochastic model predictive control problem with disturbance feedback. Journal of Industrial & Management Optimization, 2021, 17 (1) : 67-79. doi: 10.3934/jimo.2019099

[17]

Wenjun Liu, Hefeng Zhuang. Global attractor for a suspension bridge problem with a nonlinear delay term in the internal feedback. Discrete & Continuous Dynamical Systems - B, 2021, 26 (2) : 907-942. doi: 10.3934/dcdsb.2020147

[18]

Jun Zhou. Lifespan of solutions to a fourth order parabolic PDE involving the Hessian modeling epitaxial growth. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5581-5590. doi: 10.3934/cpaa.2020252

[19]

Huijuan Song, Bei Hu, Zejia Wang. Stationary solutions of a free boundary problem modeling the growth of vascular tumors with a necrotic core. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 667-691. doi: 10.3934/dcdsb.2020084

[20]

Yining Cao, Chuck Jia, Roger Temam, Joseph Tribbia. Mathematical analysis of a cloud resolving model including the ice microphysics. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 131-167. doi: 10.3934/dcds.2020219

2019 Impact Factor: 1.366

Metrics

  • PDF downloads (111)
  • HTML views (707)
  • Cited by (0)

Other articles
by authors

[Back to Top]