Systems engineering teams' value-creation for enterprises is slower than possible due to inefficiencies in communication, learning, common knowledge collaboration and leadership conduct. This dissertation outlines the surrounding people, process and technology dimensions for higher performing engineering teams. It describes a true experiment investigation of opportunities to improve communication, learning and common knowledge collaboration.
The art and practice of Systems Engineering contributes business value by orchestrating large numbers of knowledge-workers as engineering teams in the achievement of complex goals. During the creation of new systems, engineering team performance modulates business efficiencies to realize those complex goals. Higher performing engineering teams share a vision providing purpose, rely on personal knowledge convolved with collaborators knowledge to unleash potential, leverage common knowledge in their team mental models, and execute synergistically. Why do non-high performing teams exist? Culture change is hard. Humans prefer the familiar. Without Leadership and systematic enablement, teams usually do not naturally find the high performing team traits.
This research investigates a unique Information Technology based Systems Engineering Knowledge Asset (SEKA) management mechanism. The selected mechanism integrates multiple techniques for improved collaboration efficacy. The research methodology was a modified true experiment with dual-posttest only, using an A and B group for comparative controls. Research findings reflect, with 99% confidence, that SEKA represented in 3-way Multiple Informational Representations Required of Referent (MIRRoR) knowledge constructs improves systems engineering teams' consumption of a common knowledge base.
Engineering teams can consume a set of information, which generates knowledge common with their collaborators, in a shorter period. More knowledge that is common facilitates increased ability to collaborate. Increased collaboration accelerates team learning, leading to shorter systems delivery schedules, lower cost to produce and earlier actionable intelligence. Shorter delivery times increase customer satisfaction; lower costs improve profit margin potential, and earlier actionable intelligence supports "left of boom" intervention.
|Advisor:||Sarkani, Shahram, Mazzuchi, Thomas A.|
|Commitee:||Fomin, Pavel, Murphree, E. Lile, Tanju, Bereket|
|School:||The George Washington University|
|Department:||School of Engineering and Applied Science|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 75/05(E), Dissertation Abstracts International|
|Subjects:||Management, Information Technology, Systems science|
|Keywords:||High performance engineering teams, Knowledge asset management, Knowledge workers|
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