Knowledge management systems (KMS) provide access to both codified and personalized knowledge so that knowledge workers can perform with higher expertise. Yet the knowledge management literature is not clear on how accessing both kinds of knowledge influence one another and thus in combination influence KMS success. In this research I utilize a learning-based theory focused on the development of expertise to investigate outcomes from the temporal use of codification-based KMS and personalization-based KMS in the domain of technical problem-solving support, using Hierarchical Linear Modeling (HLM) as my method of analysis. Contributions to knowledge from this research include: (1) investigating how the use over time of both codified and personalized knowledge from KMS complement one another and influence KMS success; (2) clarifying the conceptual structure underlying the use of KMS in problem-solving knowledge work to better-include the human element in the immediate nomological net of the IT artifact, while at the same time suggesting that IT artifacts – such as personalized and codified knowledge contained within KMS – can have a significant impact on human performance; (3) explaining one approach – HLM – to the analysis of KMS use over time data in a unique setting; and (4) providing insights on how knowledge worker experience may be conceptualized in regards to knowledge worker use of information technologies.
University of Minnesota Ph.D. dissertation. September 2015. Major: Business Administration. Advisor: Mani Subramani. 1 computer file (PDF); 120 pages.
Exploring the Interdependence of Codified and Personalized Knowledge Use on Knowledge Management System Success.
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