Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Educational Leadership

Committee Chair/Advisor

Marion, Russ

Committee Member

Christiansen , Jon

Committee Member

Granberg , Ellen

Committee Member

Hanson , Bill

Committee Member

Havice , Pam


This study addresses the question, 'how do network dynamics and leadership behavior influence community college faculty job satisfaction?' Using ORA's dynamic network analysis (DNA) tools, this study investigates how network interactions relate to faculty job satisfaction, how beliefs about leader-member exchange (LMX) relationships relate to network interactions, and how beliefs about LMX relationships relate to job satisfaction. A faculty network is analyzed as a whole, then clusters are identified and analyzed using standard network measurements and a belief propagation algorithms.
Results indicate that job satisfaction and perceptions of relationship with leaders are co-created within networks. Cluster which have high network density (tightly coupled) and clusters which have low network density (loosely coupled) have lower co-created realities of job satisfaction and perceptions of quality of relationships with leaders than clusters with moderate network density (moderate coupling). Network theory asserts that networks which have moderate density also respond more adaptively to internal and external challenges, are more creative, and allow for more appropriate flow of information into and out of the network than those with low or high density. In other words, clusters with moderate density are not only adaptive systems, but also that members of moderately dense clusters have high levels of job satisfaction and perceive high quality relationships with leaders.
An additional finding is that larger, co-located clusters of agents are likely to have moderate network density. Agents within larger clusters are likely to have high job satisfaction and perceptions of high-quality relationships with leaders.
Furthermore, this study offers a new approach to studying job satisfaction though the use of in-depth analysis of the co-created network conditions under which satisfaction occurs. Changes in satisfaction are projected through modeling using a belief propagation algorithm.



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