Repository logo
 

Power and Network Integration: Structural and Algorithmic Analysis of Organizational Networks

Files

Whole thesis(5.4 MB)

Date

2016

Authors

Supervisor

Narayanan, Ajit
Liu, Jiamou

Item type

Thesis

Degree name

Doctor of Philosophy

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

This thesis constitutes the results of our research towards structural and algorithmic analysis of organizational networks. We study how interpersonal ties become crucial empowerment channels that shape organizational structure. We develop an organizational network model that is consistent with management studies; moreover, by incorporating both formal and informal ties into the model, we build a promising theory that is capable to explain several organizational phenomena including flattening, workplace homophily, and loss of control. Through rigorous analysis, we demonstrate that our theoretical framework can be used to reflect general properties of organizations. Understanding how different departments and employees of an organization interact with one another leads to comprehension of how well the organization operates. Studying an organizational structure often reveals critical positions that may require additional attention. It is the organizational structure from which one may extract hidden clues about concealed communication obstacles. In this thesis, we consider organizational structures from the network perspective. We see the following problems: (1) There is a lack of mathematical analysis on the dual-structure of formal and informal organizations. (2) Existing formal definitions of power only deal with networks whose edges have a single interpretation of social links, while not incorporating formal roles and levels. (3) Network evolution represents a substantial direction of the structural analysis of social networks but yet there is a lack of models suitable for joining two networks as an outcome of strategic calculations. The aim of this thesis, therefore, is to challenge the problems by developing a mathematical model that sits at the confluence of algorithmic and structural analyses. Our investigation unfolds in two main directions: the first covers individual power in organizations; the second lies in integrating two disjoint organizational networks. The first focal point of this thesis is our centrality-based definition of power which is accompanied with comprehensive and deep analysis, case studies and experiments. Our power based model provides novel insights into a range of organizational properties: 1) Organizations have limited hierarchy height. 2) Flattening is closely 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. 5) The model endorses a natural interpretation of the loss of managerial control. Our second research direction concerns computational and algorithmic aspects of network integration. The integration process amounts to the fundamental question that arises in numerous social, political, and physical domains. We study the algorithmic nature of network integration, analyze the corresponding computational problems, apply a formal framework to tackle the problems and employ various heuristics that reflect natural intuition. To compare the methods, we perform thorough experimental analysis on both synthesized and real-world data. The significance of this thesis lies in theoretical models, simulations and analysis. Our novel, structural approach to organizational analysis provides new insights, explanations and potentially predictive guidelines for organizational decision making.

Description

Keywords

Social network analysis, Organizational network analysis, Power in organizarions, Network integration, Togetherness, Bonacich power, Formal and informal relations

Source

DOI

Publisher's version

Rights statement

Permanent link

https://hdl.handle.net/10292/10444

Collections

Doctoral Theses