Hierarchies, ties and power in organizational networks: model and analysis

An organizational structure consists of a network where employees are connected by working and social ties. Analyzing this network, one can discover valuable insights into information flow within the organization. We develop this idea and propose a simple network model that is consistent with management theory, and that captures main traits of large corporations. The carcass of the model is an organizational hierarchy. We extend it by allowing additional types of connections such as collaboration, consultation, and friendship. Having both formal and informal interpersonal ties, our model supports a multilevel approach to social networks. Using a centrality-based definition of power, we are able to identify important individuals in the network. Our model provides novel insights into a range of organizational properties: (1) organizations have limited hierarchy height. (2) Flattening, the process when a business changes from a tall to a flat hierarchy by delayering, is intimately related to changes in the power of employees. (3) Informal relations significantly impact power of individuals. (4) Leadership styles could be reflected and analyzed through understanding weights on the ties in an organizational network. We implement our model and tools in a stand-alone application \(<\mathsf>\) , which provides functions for generating synthesized organizational networks, analyzing and visualizing interpersonal relations, and computing network measures.

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$$\mathsf $$ : Towards a Decision Support System for Organizational Network Analysis Using Multiplex Interpersonal Relations

On Networked Organizations

Social Networks: Connections in Structures

Notes

A negative value of \(\beta\) implies a negative exchange power where connections to nodes with smaller power results in a bigger power. See e.g. Bonacich and Lloyd (2001) and Franceschet and Bozzo (2015).

A prototype of \(<\mathsf>\) and its source code can be downloaded from https://github.com/mourednik/corpnet.

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Acknowledgements

The second author is financially supported by the Marsden Fund of the Royal Society of New Zealand. The authors would like to thank Michael Ouředník for help with implementing software tools and running experiments. The authors would also like to thank the anonymous reviewers for providing helpful comments and invaluable suggestions.

Author information

Authors and Affiliations

1. Department of Computer Science, The University of Auckland, Auckland, New Zealand Jiamou Liu
2. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand Anastasia Moskvina

1. Jiamou Liu