CCE Theses and Dissertations

Date of Award


Document Type


Degree Name

Doctor of Philosophy in Information Systems (DISS)


College of Engineering and Computing


Timothy J. Ellis

Committee Member

Ling Wang

Committee Member

Lynette Ralph


Decision-Support Systems, KMS Usage, Knowledge, Knowledge Management, Knowledge Management Systems, Socio-technical Systems, Information technology, Computer science


Existing literature indicates that although both academics and practitioners recognize knowledge management (KM) as a source of competitive advantage, users are not always willing to use a knowledge management system (KMS). Because of the social nature of knowledge transfer, a KMS can be considered a socio-technical system. Many explanations have been presented for this failure to utilize the KMS. These explanations include a number of the socio-technical factors relating to people, processes, and technologies. While these factors may have significant explanatory power when examined independently, existing studies have not sufficiently addressed the interactions among all three socio-technical factors or their impacts on KMS usage.

The goal of this study was to develop a comprehensive understanding of socio-technical factors that impact KMS usage within decision support systems (DSS). A comprehensive framework was presented that will be helpful in developing and improving KMS initiatives and thus improving KM across the organization. This study identified factors of people (self-efficacy, social ties, and ease of use), processes (leadership, culture/climate, and governance), and technologies (system & information quality, and technology fit) and their influence on KMS system usage. Analysis for this problem required a causal, non-contrived field study employing structural equation modeling.

Founded on socio-technical systems theory, nine hypotheses were proposed. Data was collected using a 36 item survey distributed to KMS users from a variety of industries in the United States. Confirmatory factor analysis and an eight-stage structural equation modeling procedure were used to analyze 97 usable responses. The results confirmed that technology-oriented factors predicted knowledge seeking and contributing in DSS. Furthermore, significant positive relationships were confirmed between certain sociotechnical factors including: (1) people and process, (2) people and technology, (3) processes and technology, (4) processes and people, (5) technology and people, and (6) technology and processes. These findings extend the relevance and statistical power of existing studies on KMS usage.

This study indicated that the most important concerns for increasing KMS usage were system quality, information quality, and technology fit. Results also confirmed that in the context of this study, people-oriented factors (self-efficacy, social ties, and ease of use/usefulness) and organizational process factors (leadership, organizational culture/climate, and governance) were not critical factors directly responsible for increasing KMS usage. However, the relationships among socio-technical factors all had positive significant relationships. Therefore, investments in people and process-oriented factors will create a more favorable perspective on technology-oriented factors, which in turn can increase KMS usage.

On a practical front, this study provided indicators to managers regarding a number of desirable and undesirable conditions that should be taken into consideration when developing or implementing knowledge management initiatives and the systems to support them. This study offered an original contribution to the existing bodies of knowledge on socio-technical factors and KMS usage behavior. The constructs presented in this study highlighted the significance of social and technical relationships in understanding knowledge seeking and contribution in a decision-driven organization.