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Computational Economics

, Volume 53, Issue 1, pp 289–313 | Cite as

Interactional Effects Between Individual Heterogeneity and Collective Behavior in Complex Organizational Systems

  • Xingguang ChenEmail author
  • Zhentao Zhu
Article
  • 99 Downloads

Abstract

Academics and practical practitioners increasingly recognize the complexity between individuals features,organizational structure and collective behavior. Yet research examining the effects between these elements remains fragmented and scarce. The purpose of this study is to explore the relationship between the components of individuals features and organizational behavior, with special focus on multi-hierarchy systems. A straightforward and comprehensive model named dynamic object interaction model is proposed to address these complex relations based on the idea of “near decomposability”. Computational simulation experiment shows that individual features have different impacts on the macro collective behavior. If the organizational members’s behavior are stable and its type will not change, then homogeneous networks can alleviate the fluctuation of organizational output better than heterogeneous networks, while this results are not stand in unstable members context. In contrast, if the organizational members’s type is varying constantly, then for specific networks structure, mixed type staff composition will be more beneficial for convergence of the organizational output than single type staff composition. Furthermore, optimal convergence interval of members is uncovered under dynamic context,there exist statistical significance between four typical organizational network models.Accordingly, the underlying mechanism are discussed to promote understanding for effective organizational redesign and managerial decision.

Keywords

Organizational structure Individuals features Collective behavior Complex networks Multi-hierarchy systems Near decomposability 

Notes

Acknowledgements

The authors thank the JEO editors and two anonymous referees for helpful comments. This work is supported by the Natural Science Foundation of China (Grant 71471084), Basic research program of Jiangsu province, P.R. China (Grant BK2012305), Open topic key foundation of Wuhan Research Institution of Jianghan University (Grant jhunwyy 20151204).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of BusinessJianghan UniversityWuhanPeople’s Republic of China
  2. 2.Big Data Research CenterJianghan UniversityWuhanPeople’s Republic of China
  3. 3.School of Economics and ManagementNanjing Institute of TechnologyNanjingPeople’s Republic of China

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