1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Organizational Psychology and Organizational Behavior
  4. Volume 3, 2016
  5. Article

Annual Review of Organizational Psychology and Organizational Behavior

Volume 3, 2016

Dynamic Modeling

Abstract

Recent effort in organizational psychology and organizational behavior (OPOB) research has placed increasing emphasis on understanding dynamic phenomena and processes. This calls for more and better use of dynamic modeling in OPOB research than before. The goals of this review are to provide an overview of the general forms of dynamic modeling in OPOB research, discuss three longitudinal data analytic techniques for conducting dynamic modeling with empirical data [i.e., time-series-based modeling, latent-change-scores-based modeling, and functional data analysis (FDA)], and introduce various dynamic modeling approaches for building theories about dynamic phenomena and processes (i.e., agent-based modeling, system dynamics modeling, and hybrid modeling). This review also highlights several OPOB research areas to which dynamic modeling has been applied and discusses future research directions for better utilizing dynamic modeling in those areas.

Keyword(s): computational modeling, dynamic modeling, longitudinal data analysis
Loading

Article metrics loading...

/content/journals/10.1146/annurev-orgpsych-041015-062553
2016-03-21
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/orgpsych/3/1/annurev-orgpsych-041015-062553.html?itemId=/content/journals/10.1146/annurev-orgpsych-041015-062553&mimeType=html&fmt=ahah

Literature Cited

  1. Aguinis H, Pierce CA, Bosco FA, Muslin IS. 2009. First decade of Organizational Research Methods: trends in design, measurement, and data-analysis topics. Organ. Res. Methods 12:69–112 [Google Scholar]
  2. Aguinis H, Vandenberg RJ. 2014. An ounce of prevention is worth a pound of cure: improving research quality before data collection. Annu. Rev. Organ. Psychol. Organ. Behav. 1:569–95 [Google Scholar]
  3. Beal DJ. 2015. ESM 2.0: state of the art and future potential of experience sampling methods in organizational research. Annu. Rev. Organ. Psychol. Organ. Behav. 2:383–407 [Google Scholar]
  4. Beal DJ, Trougakos JP, Weiss HM, Dalal R. 2013. Affect spin and the emotion regulation process at work. J. Appl. Psychol. 98:593–605 [Google Scholar]
  5. Beal DJ, Trougakos JP, Weiss HM, Green SG. 2006. Episodic processes in emotional labor: perceptions of affective delivery and regulation strategies. J. Appl. Psychol. 91:51053–65 [Google Scholar]
  6. Beal DJ, Weiss HM, Barros E, MacDermid SM. 2005. An episodic process model of affective influences on performance. J. Appl. Psychol. 90:1054–68 [Google Scholar]
  7. Box GEP, Jenkins GM. 1970. Time Series Analysis, Forecasting, and Control Oakland, CA: Holden-Day
  8. Box GEP, Jenkins GM, Reinsel GC. 1994. Time Series Analysis, Forecasting, and Control. Englewood Cliffs, NJ: Prentice Hall, 3rd ed..
  9. Brockwell PJ, Davis RA. 1991. Time Series: Theory and Methods. New York: Springer-Verlag, 2nd ed..
  10. Burton RM, Obel B. 2011. Computational modeling for what-is, what-might-be, and what-should-be studies—and triangulation. Organ. Sci. 22:1195–202 [Google Scholar]
  11. Carroll GR, Harrison JR. 1998. Organizational demography and culture: insights from a formal model and simulation. Adm. Sci. Q. 43:637–67 [Google Scholar]
  12. Christie AM, Barling J. 2009. Disentangling the indirect links between socioeconomic status and health: the dynamic roles of work stressors and personal control. J. Appl. Psychol. 94:1466–78 [Google Scholar]
  13. Coen C. 2013. Relative performance and implicit incentives in the intergroup prisoner's dilemma. Organ. Behav. Hum. Decis. Process. 120:181–90 [Google Scholar]
  14. Commandeur JJF, Koopman SJ. 2007. An Introduction to State Space Time Series Analysis New York: Oxford Univ. Press
  15. Cronin MA, Gonzalez C, Sterman JD. 2009. Why don't well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organ. Behav. Hum. Decis. Process. 108:116–30 [Google Scholar]
  16. Davis JP, Eisenhardt KM, Bingham CB. 2007. Developing theory through simulation methods. Acad. Manag. Rev. 32:480–99 [Google Scholar]
  17. DeShon RP. 2012. Multivariate dynamics in organizational science. The Oxford Handbook of Organizational Psychology SWJ Kozlowski 117–42 New York: Oxford Univ. Press [Google Scholar]
  18. DeShon RP, Rench TA. 2009. Clarifying the notion of self-regulation in organizational behavior. International Review of Industrial and Organizational Psychology 24 GP Hodgkinson, JK Ford 217–48 Chichester, UK: Wiley-Blackwell [Google Scholar]
  19. Dionne SD, Sayama H, Hao C, Bush BJ. 2010. The role of leadership in shared mental model convergence and team performance improvement: an agent-based computational model. Leadersh. Q. 21:1035–49 [Google Scholar]
  20. Durbin J, Koopman SJ. 2001. Time Series Analysis by State Space Methods New York: Oxford Univ. Press
  21. Edwards JR. 1992. A cybernetic theory of stress, coping, and well-being in organizations. Acad. Manag. Rev. 17:238–74 [Google Scholar]
  22. Edwards JR. 2002. Alternatives to difference scores: polynomial regression analysis and response surface methodology. Advances in Measurement and Data Analysis F Drasgow, NW Schmitt 350–400 San Francisco: Jossey-Bass [Google Scholar]
  23. Fioretti G. 2013. Agent-based simulation models in organization science. Organ. Res. Methods 16:227–42 [Google Scholar]
  24. Fuller JA, Stanton JM, Fisher GG, Spitzmüller C, Russell SS, Smith PC. 2003. A lengthy look at the daily grind: time series analysis of events, mood, stress, and satisfaction. J. Appl. Psychol. 88:1019–33 [Google Scholar]
  25. Gabriel AS, Diefendorff JM. 2015. Emotional labor dynamics: a momentary approach. Acad. Manag. J. 581804–25
  26. Galor O. 2007. Discrete Dynamical Systems Berlin: Springer
  27. Gilbert N, Troitzsch K. 2005. Simulation for the Social Scientist Berkshire, UK: McGraw-Hill
  28. Grimm KJ, Zhang Z, Hamagami F, Mazzocco MM. 2013. Modeling nonlinear change via latent change and latent acceleration frameworks: examining velocity and acceleration of growth trajectories. Multivar. Behav. Res. 48:117–43 [Google Scholar]
  29. Gu F, Preacher KJ, Ferrer E. 2014. A state space modeling approach to mediation analysis. J. Educ. Behav. Stat. 39:117–43 [Google Scholar]
  30. Gu F, Preacher KJ, Wu W, Yung Y-F. 2014. A computationally efficient state space approach to estimating multilevel regression models and multilevel confirmatory factor models. Multivar. Behav. Res. 49:119–29 [Google Scholar]
  31. Guimerà R, Uzzi B, Spiro J, Amaral LAN. 2005. Team assembly mechanisms determine collaboration network structure and team performance. Science 308:697–702 [Google Scholar]
  32. Guo W. 2002. Functional mixed effects models. Biometrics 58:121–28 [Google Scholar]
  33. Hanges P, Lord R, Dickson M. 2000. An information-processing perspective on leadership and culture: a case for connectionist architecture. Appl. Psychol. 49:133–61 [Google Scholar]
  34. Harrison JR, Lin Z, Carroll GR, Carley KM. 2007. Simulation modeling in organizational and management research. Acad. Manag. Rev. 32:1229–45 [Google Scholar]
  35. Harvey AC. 1989. Forecasting, Structural Time Series Models and the Kalman Filter Princeton, NJ: Princeton Univ. Press
  36. Hu Y, Boker S, Neale M, Klump KL. 2014. Coupled latent differential equation with moderators: simulation and application. Psychol. Methods 19:56–71 [Google Scholar]
  37. Huang GH, Zhao HH, Niu XY, Ashford SJ, Lee C. 2013. Reducing job insecurity and increasing performance ratings: Does impression management matter?. J. Appl. Psychol. 98:852–62 [Google Scholar]
  38. Hülsheger UR, Lang JW, Maier GW. 2010. Emotional labor, strain, and performance: testing reciprocal relationships in a longitudinal panel study. J. Occup. Health Psychol. 15:505–21 [Google Scholar]
  39. Ilgen DR, Hulin CL. 2000. Computational Modeling of Behavior in Organizations: The Third Scientific Discipline Washington, DC: Am. Psychol. Assoc.
  40. Jones KP, King EB, Gilrane VL, McCausland TC, Cortina JM, Grimm KJ. 2013. The baby bump managing a dynamic stigma over time. J. Manag. In press. doi: 10.1177/0149206313503012
  41. Kalman RE. 1960. A new approach to linear filtering and prediction problems. Trans. ASME D 82:35–45 [Google Scholar]
  42. Kammeyer-Mueller J, Wanberg C, Rubenstein A, Song Z. 2013. Support, undermining, and newcomer socialization: fitting in during the first 90 days. Acad. Manag. J. 56:1104–24 [Google Scholar]
  43. Kane GC, Alavi M. 2007. Information technology and organizational learning: an investigation of exploration and exploitation processes. Organ. Sci. 18:796–812 [Google Scholar]
  44. Kennedy DM, McComb SA. 2014. When teams shift among processes: insights from simulation and optimization. J. Appl. Psychol. 99:784–815 [Google Scholar]
  45. Kozlowski SW, Chao GT. 2012. The dynamics of emergence: cognition and cohesion in work teams. Manag. Decis. Econ. 33:335–54 [Google Scholar]
  46. Kozlowski SW, Chao GT, Grand JA, Braun MT, Kuljanin G. 2013. Advancing multilevel research design capturing the dynamics of emergence. Organ. Res. Methods 16:581–615 [Google Scholar]
  47. Kozlowski SW, Klein KJ. 2000. A multilevel approach to theory and research in organizations: contextual, temporal, and emergent processes. Multilevel Theory, Research and Methods in Organizations: Foundations, Extensions, and New Directions KJ Klein, SWJ Kozlowski 3–90 San Francisco, CA: Jossey-Bass [Google Scholar]
  48. Kreutzer W. 1986. System Simulation: Programming Styles and Languages. Sydney: Addison-Wesley
  49. Li W-D, Fay D, Frese M, Harms PD, Gao XY. 2014. Reciprocal relationship between proactive personality and work characteristics: a latent change score approach. J. Appl. Psychol. 99:948–65 [Google Scholar]
  50. Liu S, Wang M, Liao H, Shi J. 2014. Self-regulation during job-search: the opposing effects of employment self-efficacy and job search behavior self-efficacy. J. Appl. Psychol. 99:1159–72 [Google Scholar]
  51. Lomi A, Larsen ER. 2001. Dynamics of Organizations: Computational Modeling and Organization Theories Cambridge, MA: MIT Press
  52. Lord RG, Brown DJ, Harvey JL, Hall RJ. 2001. Contextual constraints on prototype generation and their multilevel consequences for leadership perceptions. Leadersh. Q. 12:311–38 [Google Scholar]
  53. Luenberger DG. 1979. Introduction to Dynamic Systems: Theory, Models, and Applications New York: Wiley
  54. March JG. 1991. Exploration and exploitation in organizational learning. Organ. Sci. 2:71–87 [Google Scholar]
  55. McArdle JJ. 2009. Latent variable modeling of differences and changes with longitudinal data. Annu. Rev. Psychol. 60:577–605 [Google Scholar]
  56. McArdle JJ, Hamagami F. 2001. Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. New Methods for the Analysis of Change LM Collins, M Sayer 139–75 Washington, DC: Am. Psychol. Assoc. [Google Scholar]
  57. McComb SA. 2007. Mental model convergence: the shift from being an individual to being a team member. Research in Multi-Level Issues 6 F Dansereau, FJ Yammarino 95–147 Amsterdam: Elsevier [Google Scholar]
  58. Meier LL, Spector PE. 2013. Reciprocal effects of work stressors and counterproductive work behavior: a five-wave longitudinal study. J. Appl. Psychol. 98:529–39 [Google Scholar]
  59. Molenaar PCM. 2004. A manifesto on psychology as idiographic science: Bring the person back into scientific psychology, this time forever. Measurement 2:201–18 [Google Scholar]
  60. Nesselroade JR. 1991. Interindividual differences in intraindividual change. Best Methods for the Analysis of Change LM Collins, JL Horn 92–105 Washington, DC: Am. Psychol. Assoc. [Google Scholar]
  61. Ramsay JO, Dalzell CJ. 1991. Some tools for functional data analysis. J. R. Stat. Soc. Ser. B 53:539–72 [Google Scholar]
  62. Ramsay JO, Silverman BW. 1997. Functional data analysis. New York: Springer [Google Scholar]
  63. Rudolph JW, Morrison JB, Carroll JS. 2009. The dynamics of action-oriented problem solving: linking interpretation and choice. Acad. Manag. Rev. 34:733–56 [Google Scholar]
  64. Rudolph J, Repenning N. 2002. Disaster dynamics: understanding the role of quantity in organizational collapse. Adm. Sci. Q. 47:1–30 [Google Scholar]
  65. Schmidt AM, DeShon RP. 2007. What to do? The effects of discrepancies, incentives, and time on dynamic goal prioritization. J. Appl. Psychol. 92:928–41 [Google Scholar]
  66. Selig JP, Preacher KJ. 2009. Mediation models for longitudinal data in development research. Res. Hum. Devel. 6:144–64 [Google Scholar]
  67. Shumway RH, Stoffer DS. 2011. Time Series Analysis and Its Applications. New York: Springer
  68. Smith LG, Amiot CE, Smith JR, Callan VJ, Terry DJ. 2013. The social validation and coping model of organizational identity development a longitudinal test. J. Manag. 39:1952–78 [Google Scholar]
  69. Sonnentag S. 2015. Dynamics of well-being. Annu. Rev. Organ. Psychol. Organ. Behav. 2:261–93 [Google Scholar]
  70. Sterman JD. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World New York: Irwin/McGraw-Hill
  71. Taber CS. 1992. POLI: An expert system model of U.S. foreign policy belief systems. Am. Polit. Sci. Rev. 86:888–904 [Google Scholar]
  72. Taber CS, Timpone RJ. 1996. Computational Modeling Thousand Oaks, CA: Sage
  73. Taylor SG, Bedeian AG, Cole MS, Zhang Z. 2014. Developing and testing a dynamic model of workplace incivility change. J. Manag. In press. doi: 10.1177/0149206314535432
  74. Toker S, Biron M. 2012. Job burnout and depression: unraveling their temporal relationship and considering the role of physical activity. J. Appl. Psychol. 97:699–710 [Google Scholar]
  75. Vancouver JB. 2008. Integrating self-regulation theories of work motivation into a dynamic process theory. Hum. Resour. Manag. Rev. 18:1–18 [Google Scholar]
  76. Vancouver JB, Tamanini KB, Yoder RJ. 2010a. Using dynamic computational models to reconnect theory and research: socialization by the proactive newcomer as example. J. Manag. 36:764–93 [Google Scholar]
  77. Vancouver JB, Weinhardt JM, Schmidt AM. 2010b. A formal, computational theory of multiple-goal pursuit: integrating goal-choice and goal-striving processes. J. Appl. Psychol. 95:985–1008 [Google Scholar]
  78. Vancouver JB, Weinhardt JM, Vigo R. 2014. Change one can believe in: adding learning to computational models of self-regulation. Organ. Behav. Hum. Decis. Process. 124:56–74 [Google Scholar]
  79. Walls TA, Schafer J. 2006. Models for Intensive Longitudinal Data New York: Oxford Univ. Press
  80. Wanberg CR, Zhu J, Kanfer R, Zhang Z. 2012. After the pink slip: applying dynamic motivation frameworks to the job search experience. Acad. Manag. J. 55:261–84 [Google Scholar]
  81. Wang M, Liu S, Liao H, Gong Y, Kammeyer-Mueller JD, Shi J. 2013. Can't get it out of my mind: employee rumination after customer mistreatment and negative mood in the next morning. J. Appl. Psychol. 98:989–1004 [Google Scholar]
  82. Zhou L, Wang M. 2015. Leader-member exchange and newcomer adjustment. The Oxford Handbook of Leader-Member Exchange T Bauer, B Erdogan 226–39 New York: Oxford Univ. Press [Google Scholar]
/content/journals/10.1146/annurev-orgpsych-041015-062553
Loading
Dynamic Modeling
Annual Review of Organizational Psychology and Organizational Behavior 3, 241 (2016); https://doi.org/10.1146/annurev-orgpsych-041015-062553
/content/journals/10.1146/annurev-orgpsych-041015-062553
/content/journals/10.1146/annurev-orgpsych-041015-062553
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error