TY - JOUR
T1 - Dynamic Modeling
AU - Wang, Mo
AU - Zhou, Le
AU - Zhang, Zhen
N1 - Funding 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.
Publisher Copyright:
© Copyright 2016 by Annual Reviews.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - computational modeling
KW - dynamic modeling
KW - longitudinal data analysis
UR - http://www.scopus.com/inward/record.url?scp=85015332464&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015332464&partnerID=8YFLogxK
U2 - 10.1146/annurev-orgpsych-041015-062553
DO - 10.1146/annurev-orgpsych-041015-062553
M3 - Review article
AN - SCOPUS:85015332464
SN - 2327-0608
VL - 3
SP - 241
EP - 266
JO - Annual Review of Organizational Psychology and Organizational Behavior
JF - Annual Review of Organizational Psychology and Organizational Behavior
ER -