CCE Theses and Dissertations

Date of Award

2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

College of Engineering and Computing

Advisor

Yair Levy

Committee Member

Amon Seagull

Committee Member

Mark E. Nissen

Keywords

collaborative environment, knowledge management systems, knowledge sharing, trust, willingess

Abstract

Since September 11, 2001, the United States Government (USG) has possessed unparalleled capability in terms of dedicated intelligence and information collection assets supporting the analysts of the Intelligence Community (IC). The USG IC has sponsored, developed, and borne witness to extraordinary advances in technology, techniques, and procedures focused on knowledge harvesting, knowledge sharing, and collaboration. Knowledge, within successful (effective & productive) organizations, exists as a commodity; a commodity that can be created, captured, imparted, shared, and leveraged. The research problem that this study addressed is the challenge of maintaining strong organizational effectiveness and productivity through the use of an information technology-based knowledge management system (KMS). The main goal of this study was to empirically assess a model testing the impact of the factors of rewards, power, centrality, trust, collaborative environment, resistance to share, ease-of-using KMS, organizational structure, and top management support to inducement, willingness to share, as well as opportunity to contribute knowledge to a KMS on knowledge-sharing in a highly classified and sensitive environment of the USG IC. This study capitalized on prior literature to measure each of the 15 model constructs. This study was conducted with a select group of USG Departments and Agencies whose primary interest is Intelligence Operations. This study solicited responses from more than 1,000 current, as well as former, Intelligence Analysts of the USG IC, using an unclassified anonymous survey instrument. A total of 525 (52.5%) valid responses were analyzed using a partial least squares (PLS) structural equation modeling (SEM) statistical technique to perform model testing. Pre-analysis data screening was conducted to ensure the accuracy of the data collected, as well as to correct irregularities or errors within the gathered data. The 14 propositions outlined in this research study were tested using the PLS-SEM analysis along with reliability and validity checks. The results of this study provide insights into the key factors that shed light onto the willingness of US intelligence community analysts to contribute knowledge to a KMS in a highly classified and sensitive environment. Specifically, the significance of a knowledge worker’s willingness to contribute his/her knowledge to a KMS along with the opportunity to contribute knowledge, while inducement was not a significant factor when it comes to knowledge sharing using KMS in highly classified environments.

Download
Share Feedback

Share

COinS