Abstract
Purpose - This paper aims to analyze the exchange and reciprocal mechanism behind individual knowledge transfer activities as well as their impact on the individual knowledge transfer networks.Design/methodology/approach - The authors conducted a theoretical and simulation research. Agent-based technology is employed to construct an agent dynamics agent-based model that simulates and explains how an individual initiates the evolution of a knowledge network through knowledge transfer activities.Findings - The results demonstrate that the two mechanisms can improve the knowledge levels of the network members; the exchange mechanism is more efficient as it can improve the values of both sides. Individual knowledge transfer networks evolve from random networks to small-world networks.Research limitations/implications - The research model must include of more variables.Computer simulation research will be cross confirmed by other research methods in future studies.Practical implications - Individual Knowledge transfer networks form and subsequently evolve as a result of social interaction. Our research findings will contribute to the policy making for knowledge management in organizations.Originality/value - Little has been published about the dynamics of individual knowledge transfer networks. The authors believe that they are the first to analyze the internal mechanisms behind individual knowledge transfer activities and test them with agent-bsed technologies.
Purpose - This paper aims to analyze the exchange and reciprocal mechanism behind individual knowledge transfer activities as well as their impact on the individual knowledge transfer networks.Design/methodology/approach - The authors conducted a theoretical and simulation research. Agent-based technology is employed to construct an agent dynamics agent-based model that simulates and explains how an individual initiates the evolution of a knowledge network through knowledge transfer activities.Findings - The results demonstrate that the two mechanisms can improve the knowledge levels of the network members; the exchange mechanism is more efficient as it can improve the values of both sides. Individual knowledge transfer networks evolve from random networks to small-world networks.Research limitations/implications - The research model must include of more variables.Computer simulation research will be cross confirmed by other research methods in future studies.Practical implications - Individual Knowledge transfer networks form and subsequently evolve as a result of social interaction. Our research findings will contribute to the policy making for knowledge management in organizations.Originality/value - Little has been published about the dynamics of individual knowledge transfer networks. The authors believe that they are the first to analyze the internal mechanisms behind individual knowledge transfer activities and test them with agent-bsed technologies.