Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-10T10:53:20.923Z Has data issue: false hasContentIssue false

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

By
Jeffrey B. Vancouver
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

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

motivationcontrol systemsleadershippersonnel decisionsteamsagent-based models
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 836 - 861
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.CrossRefGoogle 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.CrossRefGoogle Scholar
Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: structure, process, and content. Psychological Bulletin, 120 (3), 338.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×