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.
|Original language||English (US)|
|Number of pages||26|
|Journal||Annual Review of Organizational Psychology and Organizational Behavior|
|State||Published - 2016|
Bibliographical noteFunding Information:
We are grateful for the constructive comments provided by Fred Morgeson and Ben Schneider on an earlier version of this review. The preparation for this review was partially funded by the Singapore Ministry of Education Research grants R-317-000-085-112 and R-317-000-95-112, and by the National Natural Science Foundation of China grant 71072024. However, any opinions, findings, and conclusions or recommendations in this review are those of the authors and do not necessarily reflect the views of funding agencies.
© Copyright 2016 by Annual Reviews.
- computational modeling
- dynamic modeling
- longitudinal data analysis