Paper Type
Research-in-Progress Paper
Abstract
Given the importance of knowledge transfer in individual performances, we assess the effect of knowledge flows complexity on knowledge transfer performance in a simulation model. In this regard this paper seeks to contribute to knowledge literature by proposing a new knowledge complexity framework, in which we explore the structural (diversity of knowledge type and depth of knowledge) and dynamic (loss of knowledge, knowledge creation pace) dimensions of knowledge flow complexity. Using an exploratory simulation study we propose the four aspects of knowledge flow complexity and test its effects on learners’ occupation and learning system queues. As a research-in-process, our preliminary results support that knowledge creation pace with both dependent variables (busy time proportion of the learner and queue length of knowledge processing) is the strongest among all the relationships in sensitivity analysis comparison. The least change exists in the relationship from the percentage of knowledge loss to the dependent variables.
Recommended Citation
Tang, Xiao; Parameswaran, Srikanth; Kishore, Rajiv; and Herath, Tejaswini, "Simulation Model of Knowledge Complexity in New Knowledge Transfer Performance" (2013). AMCIS 2013 Proceedings. 5.
https://aisel.aisnet.org/amcis2013/BusinessIntelligence/RoundTablePresentations/5
Simulation Model of Knowledge Complexity in New Knowledge Transfer Performance
Given the importance of knowledge transfer in individual performances, we assess the effect of knowledge flows complexity on knowledge transfer performance in a simulation model. In this regard this paper seeks to contribute to knowledge literature by proposing a new knowledge complexity framework, in which we explore the structural (diversity of knowledge type and depth of knowledge) and dynamic (loss of knowledge, knowledge creation pace) dimensions of knowledge flow complexity. Using an exploratory simulation study we propose the four aspects of knowledge flow complexity and test its effects on learners’ occupation and learning system queues. As a research-in-process, our preliminary results support that knowledge creation pace with both dependent variables (busy time proportion of the learner and queue length of knowledge processing) is the strongest among all the relationships in sensitivity analysis comparison. The least change exists in the relationship from the percentage of knowledge loss to the dependent variables.