Abstract
Optimizing the performance of teams in modern organizations is an important managerial function, and particularly so in contexts where new teams must continually be formed voluntarily, such as with software development, crowd-sourcing platforms, and even the formation of scientific collaborative teams. In many such cases, team performance is significantly influenced by the makeup of participant personalities and temperaments and goes beyond the analysis of individual skills. In this study, we present a team-assemblage model that is primarily influenced by knowledge of the past performance of team members and their personalities. Our goal is to provide a model, which can be parameterized for specific organizational contexts, for policy makers and managers to assess potential teams formed in dynamic circumstances. To provide real-world validation for our approach, we extracted data from the Python Enhancement Proposal (PEP) process, which involves the repeated self-assembly of software teams from a common pool of developers. We then used agent-based simulation to enact our model with PEP data to predict team grouping formation and resulting team performances.
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References
Contractor, N.: Some assembly required: leveraging Web science to understand and enable team assembly. Philos. Trans. A. Math. Phys. Eng. Sci. 371(1987), 20120385 (2013)
Ruef, M., Aldrich, H., Carter, N.: The structure of founding teams: Homophily, strong ties, and isolation among US entrepreneurs. Am. Sociol. Rev. (2003)
Guimerà , R., Uzzi, B., Spiro, J., Amaral, L.A.N.: Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722), 697–702 (2005)
Johnson, N., Xu, C., Zhao, Z., Ducheneaut, N., Yee, N., Tita, G., Hui, P.: Human group formation in online guilds and offline gangs driven by a common team dynamic. Phys. Rev. E 79(6), 066117 (2009)
Costa, R.R.M., Paul, T.: Professional manual: revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI). Odessa, FL Psychol. Assess. Resour. (1992)
Myers, I.: The myers-briggs type indicator. Consult. Psychol. Press (1962)
Jung, C.G.: Psychological types: or the psychology of individuation. Harcourt, Brace (1921)
Myers, I.B., McCaulley, M.H., Most, R.: Manual: A guide to the development and use of the Myers-Briggs Type Indicator. Consult. Psychol. Press (1985)
Tziner, A.: How Team Composition Affects Task Performance: Some Theoretical Insights. Psychol. Rep. 57(3f), 1111–1119 (1985)
Neuman, G.A., Wagner, S.H., Christiansen, N.D.: The Relationship between Work-Team Personality Composition and the Job Performance of Teams. Gr. Organ. Manag. 24(1), 28–45 (1999)
Wiersema, M., Bantel, K.: Top management team demography and corporate strategic change. Acad. Manag. J. (1992)
Bantel, K.: Strategic Planning Openness The Role of Top Team Demography. Gr. Organ. Manag. (1994)
Bradley, J.H., Hebert, F.J.: The effect of personality type on team performance. J. Manag. Dev. 16(5), 337–353 (1997)
Remondino, M., Correndo, G.: Data mining applied to agent based simulation. In: Proceedings of the 19th European Conference on Modelling and Simulation, Riga, Latvia (2005)
Rigby, P., Hassan, A.: What can oss mailing lists tell us? a preliminary psychometric text analysis of the apache developer mailing list. In: Fourth International Workshop on Mining Software Repositories (MSR 2007: ICSE Workshops 2007), pp. 23–23 (2007)
Pennebaker, J.: Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, p. 71 (2001)
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Farhangian, M., Purvis, M.K., Purvis, M., Savarimuthu, B.T.R. (2015). Modeling the Effects of Personality on Team Formation in Self-assembly Teams. In: Chen, Q., Torroni, P., Villata, S., Hsu, J., Omicini, A. (eds) PRIMA 2015: Principles and Practice of Multi-Agent Systems. PRIMA 2015. Lecture Notes in Computer Science(), vol 9387. Springer, Cham. https://doi.org/10.1007/978-3-319-25524-8_36
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DOI: https://doi.org/10.1007/978-3-319-25524-8_36
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