Knowledge Management & E-Learning: An International Journal (KM&EL), Vol 1, No 2 (2009)

Font Size:  Small  Medium  Large

"KMS-Fit": a case-based exploration of task/technology fit in an applied knowledge management context

Jason M Turner, David P Biros, Michael W Moseley


The notion of Task/Technology Fit (TTF) posits that as the degree of overlap increases between the task domain, and the ways in which the capabilities of an information system (IS) are suited to activities within that domain, performance gains experienced via use of the IS should also increase. This research proposes an expanded TTF model that is applicable to the context of Knowledge Management (KM) and Knowledge Management Systems (KMS). In particular, additional individual, technological, and social factors and interrelationships between these factors could provide greater explanatory power of IS user behaviors, perceptions, and outcomes within the realm of knowledge work.
A mixed-method field study approach was employed at a large government organization, currently in the process of developing and fielding a new KMS to support knowledge-intensive work, to investigate the underlying factors and relationships described within an expanded ―KMS Fit‖ model. Results suggest that the foundational mechanisms described by the TTF model may in fact change within KM contexts. In particular, the inherently social characteristics of knowledge-based work were found to play a very important role in determining the degree of fit relative to a KMS. Moreover, the social ecology within the organization was found to have significant impact on KMS Fit. Results of this research further reinforce the notion that KMS may be a unique subset of IS and that traditional IS models (such as TTF) should be updated or tailored to reflect the social nature of knowledge-based work and knowledge management.

Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Knowledge Management & E-Learning: An International Journal (KM&EL)
ISSN 2073-7904


Maintained and Developed by:

Laboratory for Knowledge Management & E-Learning

Faculty of Education, The University of Hong Kong