A Simulation Study On Knowledge Sharing,Learning By Doing And Knowledge Evolution In Research And Development Organization

Research and development(R&D)can bring new knowledge and new products for enterprise,and it is also an engine for firm’s sustainable development.The key to enhance organization development ability is to improve individual and collective knowledge level and their ability to create new knowledge.Since organization members can obtain new knowledge constantly by many approaches,organization development ability is dynamically changing.In this paper,computational experiment method is used to study the dynamic changing mechanism of knowledge in R&D organization simultaneously driven by knowledge sharing and learning by doing.This may help to learn the discipline of knowledge obtaining,evolution and updating in R&D organization and improve manager’s management efficiency for R&D activities.In the first,the evolution and catastrophe characteristic of R&D members’ knowledge sharing behavior are researched.Knowledge sharing is an important channel to acquire knowledge for development members.By establishing a knowledge sharing model,the boundary condition of knowledge sharing is discovered.Based on this,computational experiments on decision making methods of knowledge sharing,unit knowledge value and knowledge sharing cost are executed.Experiment results reveal that making decision according to Nash equilibrium will produce better knowledge sharing performance compared with decision making method based on expectation return.If all members make decision according to Nash equilibrium,rewarding knowledge sharing behavior or punishing no sharing behavior can both improve knowledge sharing performance.In addition,catastrophe theory is leveraged to analyze the catastrophic phenomenon in knowledge sharing evolution and analysis results indicate that the catastrophe phenomenon of knowledge sharing may take place only if knowledge sharing cost is close to the benefit of knowledge sharing.And increasing knowledge sharing cost in a certain boundary will increase uncertainty of knowledge sharing performance.Second,the influence of learning by doing on individual knowledge evolution and collective knowledge evolution in R&D organization is explored.Except for knowledge sharing,R&D members can also achieve new knowledge through learning by doing.According to the theory of learning by doing and some related researches,a member-task-knowledge matching model is built and computational experiment method is used to do simulation experiments on organization size,employee flow,knowledge dependence and workload.Results of simulation experiments reveal that bigger organization size can contribute to individual task performance and individual learning but organization size has no significant effect on collective learning.Employee flow may cause some loss of individual professional knowledge,though,it can also benefit collective learning and collective task performance.In addition,the smaller the difference of dependence of task on different professional knowledge,the better individual task performance,individual learning and collective learning.But a little difference between the dependences of task on different knowledge can benefit collective task performance.Besides,experiment results also indicate that workload has a positive influence on individual task performance and collective task performance,however,high workload will hinder collective learning.Thirdly,the influence mechanism of interaction between knowledge sharing and learning by doing on knowledge evolution in R&D organization is studied.Knowledge sharing and learning by doing is integrated into one research model by leveraging task.The interaction between knowledge sharing and learning by doing and its influence on knowledge evolution in R&D organization are explored with computational experiment method.Results of experiments reveal that there is indeed an interaction effect between knowledge sharing and learning by doing.At the beginning of knowledge evolution,enhancing individual knowledge sharing willingness can greatly promote individual learning performance.In the middle and later stages,the way organization members gain knowledge changes from knowledge sharing to learning by doing.Based on this model and research findings,task characteristics and strategy of task assignment are also simulated.Simulation results indicate that increasing task arrival rate in a range can promote individual learning and the relationship between task complexity and learning performance shows U-inverted shape.In addition,tasks are randomly assigned to organization members will produce better learning effect than output efficiency strategy,minimum requirements strategy and minimum distance strategy.Finally,the influence mechanism of knowledge sharing and learning by doing on innovation performance in R&D organization is also studied.Based on existing researches,an innovation model driven by both knowledge sharing and learning by doing is built.And simulation experiments on network structure,learning ability by knowledge sharing and learning ability from experience are complished with computational experiment method.Experiment results show that the interaction between knowledge sharing and learning by doing can promote incremental innovation rate in early stage but it also produces a negative effect on innovation rate in later stage.Fortunately,the interaction will improve the disruption of incremental innovation.As to radical innovation,the interaction between knowledge sharing and learning by doing significantly contribute to innovation rate,however,the interaction negatively affects innovation disruption.When the relationship between organization members shows a small-word network structure,innovation rate and innovation disruption are influenced neither by learning ability by knowledge sharing nor by learning ability from experience.While the relationship between members is scale-free,only learning ability by knowledge sharing can affect incremental innovation rate.If the relationship between members has random network characteristics,learning ability by knowledge sharing will affect the disruption of incremental innovation and radical innovation,and learning ability from experience has influence on the rate of radical innovation,the disruption of incremental innovation and radical innovation.