Computational Modeling in Industrial-Organizational Psychology

doi:10.1017/9781108755610.030

25 - Computational Modeling in Industrial-Organizational Psychology

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press: 21 April 2023

Jeffrey B. Vancouver

Edited by

Ron Sun

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Ron Sun: Affiliation:
Rensselaer Polytechnic Institute, New York

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Summary

This chapter describes computational models developed to represent basic and applied phenomena of interest to I-O psychology. The basic phenomena of interest relate to motivational, learning, and decision-making processes. The applied phenomena relate to selecting, training, evaluating, retaining, and managing employees. These employees may work in teams, be leaders of others, or engage in action, information sharing, and decision making relevant to organizational outcomes. A computational control systems architecture is used in many of the more basic models, and agent-based modeling as well as control systems modeling are used for the more applied models.

Keywords

motivation control systems leadership personnel decisions teams agent-based models

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Type: Chapter
Information: The Cambridge Handbook of Computational Cognitive Sciences , pp. 836 - 861

DOI: https://doi.org/10.1017/9781108755610.030 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

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References

Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.CrossRef Google Scholar

Anderson, J. A. (1995). An Introduction to Neural Networks. Cambridge, MA: MIT Press.Google Scholar

Antonakis, J. (2017). On doing better science: from thrill of discovery to policy implications. The Leadership Quarterly, 28 , 5–21.Google Scholar

Ashford, S. J., & Cummings, L. L. (1983). Feedback as an individual resource: personal strategies of creating information. Organizational Behavior and Human Performance, 32 (3), 370–398.CrossRef Google Scholar

Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: structure, process, and content. Psychological Bulletin, 120 (3), 338.CrossRef Google Scholar

Ballard, T., Vancouver, J. B., & Neal, A. (2018). On the pursuit of multiple goals with different deadlines. Journal of Applied Psychology, 103, 1242–1264.Google Scholar

Ballard, T., Vancouver, J. B., Yeo, G., & Neal, A. (2017). The dynamics of approach and avoidance goal striving: a formal model. Motivation and Emotion, 41, 698–707.Google Scholar

Ballard, T., Yeo, G., Loft, S., Vancouver, J. B., & Neal, A. (2016). An integrative formal model of motivation and decision making: the MGPM*. Journal of Applied Psychology, 101 , 1240–1265.CrossRef Google Scholar PubMed

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287.CrossRef Google Scholar

Barling, J., Christie, A., & Hoption, C. (2011). Leadership. In Zedeck, S. (Ed.), APA Handbook of Industrial and Organizational Psychology, Vol. 1: Building and Developing the Organization (pp. 183–240). Washington, DC: American Psychological Association.Google Scholar

Bauer, T. N., Bodner, T., Erdogan, B., Truxillo, D. M., & Tucker, J. S. (2007). Newcomer adjustment during organizational socialization: a meta-analytic review of antecedents, outcomes, and methods. Journal of Applied Psychology, 92 (3), 707.Google Scholar

Bauer, T. N., & Green, S. G. (1998). Testing the combined effects of newcomer information seeking and manager behavior on socialization. Journal of Applied Psychology, 83 (1), 72.Google Scholar

Beehr, T. A., & Gupta, N. (1978). A note on the structure of employee withdrawal. Organizational Behavior and Human Performance, 21 (1), 73–79.Google Scholar

Busemeyer, J., & Diederich, A. (2010). Cognitive Modeling. Thousand Oaks, CA: Sage.Google Scholar

Carver, C. S., & Scheier, M. F. (1998). On the Self-regulation of Behavior. New York, NY: Cambridge University Press.Google Scholar

Cronin, M., & Vancouver, J. B. (2020). The only constant is change: expanding theory by incorporating dynamic properties into one’s models. In Humphrey, S. E. & LeBreton, J. M. (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis. Washington, DC: American Psychological Association.Google Scholar

Cronin, M. A., Gonzalez, C., & Sterman, J. D. (2009). Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organizational Behavior and Human Decision Processes, 108 , 116–130.CrossRef Google Scholar

Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32 , 480–499.Google Scholar

Dionne, S. D., & Dionne, P. J. (2008). Levels-based leadership and hierarchical group decision optimization: a simulation. The Leadership Quarterly, 19 (2), 212–234.Google Scholar

Dionne, S. D., Sayama, H., Hao, C., & Bush, B. J. (2010). The role of leadership in shared mental model convergence and team performance improvement: an agent-based computational model. The Leadership Quarterly, 21 (6), 1035–1049.Google Scholar

Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51 (4), 380–417.Google Scholar

Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical Review Letters, 94 (1), 018102.Google Scholar

Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19 , 329–335.Google Scholar

Gibson, F. P., Fichman, M., & Plaut, D. C. (1997). Learning in dynamic decision tasks: computational model and empirical evidence. Organizational Behavior and Human Decision Processes, 71 (1), 1–35.CrossRef Google Scholar

Glebbeek, A. C., & Bax, E. H. (2004). Is high employee turnover really harmful? An empirical test using company records. Academy of Management Journal, 47 (2), 277–286.Google Scholar

Graen, G. B., & Uhl-Bien, M. (1995 ). Relationship-based approach to leadership: development of leader-member exchange (LMX) theory of leadership over 25 years: applying a multi-level multi-domain perspective. The Leadership Quarterly, 6 (2), 219–247.Google Scholar

Grand, J. A. (2017). Brain drain? An examination of stereotype threat effects during training on knowledge acquisition and organizational effectiveness. Journal of Applied Psychology, 102 (2), 115.Google Scholar

Grand, J. A., Braun, M. T., Kuljanin, G., Kozlowski, S. W., & Chao, G. T. (2016). The dynamics of team cognition: a process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101 , 1353–1385.Google Scholar

Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36, 341–355.CrossRef Google Scholar

Hanisch, K. A. (2000). The impact of organizational interventions on behaviors: an examination of models of withdrawal. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 33–68). Washington, DC: American Psychological Association.Google Scholar

Hanisch, K. A., Hulin, C. L., & Seitz, S. T. (1996). Mathematical/computational modeling of organizational withdrawal processes: benefits, methods, and results. Research in Personnel and Human Resources Management, 14, 91–142.Google Scholar

Hardy III, J. H. (2014). Dynamics in the self-efficacy–performance relationship following failure. Personality and Individual Differences, 71, 151–158.Google Scholar

Hardy III, J., Day, E. A., & ArthurJr, W. (2018). Exploration-exploitation tradeoffs and information-knowledge gaps in self-regulated learning: implications for training and development. Unpublished manuscript.Google Scholar

Harrison, J., & Carroll, G. (1991). Keeping the faith: a model of cultural transmission in formal organizations. Administrative Science Quarterly, 36 (4), 552–582.Google Scholar

Hill, J. M. M., & Trist, E. L. (1955). Changes in accidents and other absences with length of service: a further study of their incidence and relation to each other in an iron and steel works. Human Relations, 8 (2), 121–152.Google Scholar

Hough, L. M., & Furnham, A. (2003). Use of personality variables in work settings. In Hough, L. M. & Furnham, A. (Eds.), Handbook of Psychology: Industrial and Organizational Psychology (Vol. 12, pp. 131–169). New York, NY: John Wiley & Sons.CrossRef Google Scholar

Jagacinski, R. J., & Flach, J. M. (2003). Control Theory for Humans: Quantitative Approaches to Modeling Performance. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Kennedy, D. M., & McComb, S. A. (2014). When teams shift among processes: insights from simulation and optimization. Journal of Applied Psychology, 99 (5), 784.Google Scholar

Kerr, N. L. (2000). Getting tangled in one’s own (Petri) net: on the promises and perils of computational modeling. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 183–188). Washington, DC: American Psychological Association.Google Scholar

Lewin, K. (1951). Field Theory in Social Science: Selected Theoretical Papers. Oxford: Harpers.Google Scholar

Li, X. (2017). Dynamic goal choice when environment demands exceed individual’s capacity: scaling up the multiple-goal pursuit model. Ohio University.Google Scholar

Locke, E. (1997). The motivation to work: what we know. In Maehr, M. & Pintrich, P. (Eds.), Advances in Motivation and Achievement (Vol. 10, pp. 375–412). Greenwich, CT: JAI Press.Google Scholar

Locke, E. A., & Latham, G. P. (1990). A Theory of Goal Setting and Task Performance. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar

Lomi, A., & Larsen, E. R. (2001). Dynamics of Organizations: Computational Modeling and Organization Theories. Cambirdge, MA: MIT Press.Google Scholar

Lord, R. G., & Levy, P. E. (1994). Moving from cognition to action: a control theory perspective. Applied Psychology, 43 , 335–367.Google Scholar

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2 (1), 71–87.CrossRef Google Scholar

March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley.Google Scholar

Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26 (3), 356–376.Google Scholar

Martell, R. F., Lane, D. M., & Emrich, C. (1996). Male-female differences: a computer simulation. American Psychologist, 51, 157–158.Google Scholar

McGrath, J. E. (1962). The influence of positive interpersonal relations on adjustment and effectiveness in rifle teams. The Journal of Abnormal and Social Psychology, 65 (6), 365.Google Scholar

McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1 (1), 30.Google Scholar

McHugh, K. A., Yammarino, F. J., Dionne, S. D., Serban, A., Sayama, H., & Chatterjee, S. (2016). Collective decision making, leadership, and collective intelligence: tests with agent-based simulations and a field study. The Leadership Quarterly, 27 (2), 218–241.Google Scholar

Miller, G. A., Galanter, E., & Pribram, K. (1960). Plans and the Structure of Behavior. New York, NY: Holt.Google Scholar

Muller, P. (2006). Reputation, trust and the dynamics of leadership in communities of practice. Journal of Management and Governance, 10 , 381–400.Google Scholar

Neal, A., Ballard, T., & Vancouver, J. B. (2017). Dynamic self-regulation and multiple-goal pursuit. Annual Review of Organizational Psychology and Organizational Behavior, 4, 401–423.Google Scholar

O’BoyleJr, E., & Aguinis, H. (2012). The best and the rest: revisiting the norm of normality of individual performance. Personnel Psychology, 65 (1), 79–119.Google Scholar

Oh, W., Moon, J. Y., Hahn, J., & Kim, T. (2016). Research note – Leader influence on sustained participation in online collaborative work communities: a simulation-based approach. Information Systems Research, 27 (2), 383–402.Google Scholar

Peters, L. H., O’Connor, E. J., Pooyan, A., & Quick, J. C. (1984). The relationship between time pressure and performance: a field test of Parkinson’s Law. Journal of Occupational Behaviour, 5 , 293–299.Google Scholar

Phelps, K. C., & Hubler, A. W. (2006). Toward an understanding of membership and leadership in youth organizations: sudden changes in average participation due to the behavior of one individual. Emergence: Complexity & Organization, 8, 28–55.Google Scholar

Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.Google Scholar

Rees, J., & Koehler, G. J. (2000). Leadership and group search in group decision support systems. Decision Support Systems, 30 (1), 73–82.Google Scholar

Richardson, G. P. (1991). Feedback Thought: In Social Science and Systems Theory. Philadelphia, PA: University of Pennsylvania Press.Google Scholar

Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose and teleology. Philosophy of Science, 10 (1), 18–24.Google Scholar

Scherbaum, C. A., & Vancouver, J. B. (2010). If we produce discrepancies, then how? Testing a computational process model of positive goal revision. Journal of Applied Social Psychology, 40, 2201–2231.CrossRef Google Scholar

Schmidt, A. M., Beck, J. W., & Gillespie, J. Z. (2013). Motivation. In Schmitt, N. W. & Highhouse, S. (Eds.), Handbook of Psychology (2nd ed., Vol. 12, pp. 311–340). Hoboken, NJ: Wiley.Google Scholar

Schmidt, A. M., & DeShon, R. P. (2007). What to do? The effects of discrepancies, incentives, and time on dynamic goal prioritization. Journal of Applied Psychology, 92 (4), 928.Google Scholar

Schmidt, A. M., & DeShon, R. P. (2010). The moderating effects of performance ambiguity on the relationship between self-efficacy and performance. Journal of Applied Psychology, 95 , 572–581.Google Scholar

Senge, P. M. (1990). Catalyzing systems thinking within organizations. In Masaryk, F. (Ed.), Advances in Organization Development (Vol. 1, pp. 197–246). Westport, CT: Ablex Publishing.Google Scholar

Serban, A., Yammarino, F. J., Dionne, S. D., et al. (2015). Leadership emergence in face-to-face and virtual teams: a multi-level model with agent-based simulations, quasi-experimental and experimental tests. The Leadership Quarterly, 26 (3), 402–418.Google Scholar

Simon, H. A. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar

Sitzmann, T., & Yeo, G. (2013). A meta-analytic investigation of the within-person self-efficacy domain: is self-efficacy a product of past performance or a driver of future performance? Personnel Psychology, 66, 531–568.Google Scholar

Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review, 31 , 889–913.Google Scholar

Steel, P., & Weinhardt, J. M. (2018). The building blocks of motivation: goal phase system. In Ones, D. S., Anderson, N., Viswesvaran, C., & Sinangil, H. K. (Eds.), The SAGE Handbook of Industrial, Work & Organizational Psychology: Organizational Psychology (pp. 69–96). London: Sage Reference.Google Scholar

Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69 (5), 797.Google Scholar

Sterman, J. D. (1989). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43 (3), 301–335.Google Scholar

Sun, R. (2016). Anatomy of the Mind. New York, NY: Oxford University Press.Google Scholar

Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In Sun, R. (Ed.), Cambridge Handbook of Computational Psychology (pp. 23–58). New York, NY: Cambridge University Press.Google Scholar

Vancouver, J. B., & Purl, J. D. (2017). A computational model of self-efficacy’s various effects on performance: moving the debate forward. Journal of Applied Psychology, 102 , 599–616.Google Scholar

Vancouver, J. B., Putka, D. J., & Scherbaum, C. A. (2005). Testing a computational model of the goal-level effect: an example of a neglected methodology. Organizational Research Methods, 8 , 100–127.Google Scholar

Vancouver, J. B., & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two computational models. Computational and Mathematical Organizational Theory, 14 , 1–22.Google Scholar

Vancouver, J. B., Li, X., Weinhardt, J. M., Purl, J. D., & Steel, P. (2016). Using a computational model to understand possible sources of skews in distributions of job performance. Personnel Psychology, 69, 931–974.Google Scholar

Vancouver, J. B., More, K. M., & Yoder, R. J. (2008). Self-efficacy and resource allocation: support for a nonmonotonic, discontinuous model. Journal of Applied Psychology, 93 , 35v47.Google Scholar

Vancouver, J. B., Tamanini, K. B., & Yoder, R. J. (2010). Using dynamic computational models to reconnect theory and research: socialization by the proactive newcomer example. Journal of Management, 36 , 764–793.Google Scholar

Vancouver, J. B., Weinhardt, J. M., & Schmidt, A. M. (2010). A formal, computational theory of multiple-goal pursuit: integrating goal-choice and goal-striving processes. Journal of Applied Psychology, 95 , 985–1008.Google Scholar

Vancouver, J. B., Weinhardt, J. M., & Vigo, R. (2014). Change one can believe in: adding learning to computational models of self-regulation. Organizational Behavior and Human Decision Processes, 124, 56–74.Google Scholar

Vroom, V. R. (1964). Work and Motivation. New York, NY: Wiley.Google Scholar

Weinhardt, J. M., & Vancouver, J. B. (2012). Computational models and organizational psychology: opportunities abound. Organizational Psychology Review, 2 , 267–292.Google Scholar

Weiss, H. M. (1990). Learning theory and industrial and organizational psychology. In Dunnette, M. D. & Hough, L. M. (Eds.), Handbook of Industrial and Organizational Psychology (Vol. 1, 1st ed., pp. 171–221). Palo Alto, CA: Consulting Psychologists Press.Google Scholar

Wellman, N., Applegate, J. M., Harlow, J., & Johnston, E. W. (2020). Beyond the pyramid: alternative formal hierarchical structures and team performance. Academy of Management Journal, 63 (4), 997–1027.Google Scholar

Will, T. E. (2016). Flock leadership: understanding and influencing emergent collective behavior. The Leadership Quarterly, 27 (2), 261–279.Google Scholar

Zhou, L., Wang, M., & Vancouver, J. B. (2019). A formal model of leadership goal striving: development of core process mechanisms and extensions to action team context. Journal of Applied Psychology, 104 , 388–410.Google Scholar

Zickar, M. J. (2000). Modeling faking on personality tests. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 95–113). Washington, DC: American Psychological Association.Google Scholar

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