任务驱动的隐性知识学习研究

A research on task-driven learning of tacit knowledge

针对隐性知识的隐蔽性、非结构化等特点,借鉴经验学习和知识转移理论,从任务视角出发用仿真方法研究了任务分配策略、任务到达速率、不同复杂任务比重对隐性知识学习的作用机制。仿真结果表明,以知识增长为目的的学习策略最有利于隐性知识学习;以当前时间成本最小化为目的的策略会引起原有知识的损失;随机分配策略对隐性知识学习无显著作用。任务到达速率衡量了员工的任务压力,适当增加员工任务压力能够极大地促进隐性知识学习。不同复杂任务的比重通过影响整体任务复杂度而作用于隐性知识学习,且任务复杂度与隐性知识学习之间呈现倒U型关系,无论各种任务之间的比重如何,只要整体任务复杂度维持在适中水平,就可以显著促进隐性知识学习。

Due to the characteristics of concealment and non- construction for tacit knowledge,we utilize the theory of learning by doing,knowledge transfer and simulation method to study the impact of strategy of task assignment,task arrival rate,proportion of different complex task on learning process for tacit knowledge from the perspective of task. The simulation results reveal that the learning strategy for purpose of knowledge increase can promote tacit knowledge learning best,the strategy for purpose of minimizing time cost will cause loss for original knowledge,and the strategy of random task assignment can little affect tacit knowledge learning. Task arrival rate measures the employees＇ work pressure,and appropriately increasing work pressure can greatly contribute to tacit knowledge learning. The proportion of different complex task will affect tacit knowledge learning by influencing global task complexity,and it appears an inverted U- shape between task complexity and learning effect of tacit knowledge. No matter what proportion of different complex tasks is,it may significantly contribute to tacit knowledge learning if only global task complexity is maintained at a moderate level.