Management Review ›› 2021, Vol. 33 ›› Issue (2): 153-163.

• Technology and Innovation Management • Previous Articles     Next Articles

Interactive Effects of Knowledge Sharing Intention and Tacit Knowledge on Organizational Learning Performance

Dong Jiamin, Liu Renjing, Yan Jie, Yang Jingyu   

  1. School of Management, Xi'an Jiaotong University, Xi'an 710049
  • Received:2018-01-10 Online:2021-02-28 Published:2021-03-08

Abstract: Organizational learning has been regarded as a sustainable competitive advantage of an organization in the rapidly volatile environment. Taking into account the decline phenomena of organization members' knowledge sharing intentions, we extend Miller's (2006) multi-agent simulation to figure out how knowledge sharing intention and tacit knowledge influence the organizational performance within a closed or open system. The simulation results indicate that a slight decline of knowledge sharing intention makes almost no difference to organizational performance in a closed system, while a sharp decline would impede the interpersonal learning, which results in a lower performance. As to an open system with personnel turnover, turnover plays a negative role under the circumstance of a slight decline of knowledge sharing intention. However, an appropriate personnel turnover is beneficial to the organization with a sharp decline of knowledge sharing intention. Intriguingly, our findings indicate that the positive effect of tacit knowledge which was found by Miller (2006) only exists in the closed system with a slight decline of knowledge sharing intention, otherwise, the effects of the tacit knowledge can be negative. Finally, in an open system with environment turbulence, neither the decline of knowledge sharing intention nor the proportion of tacit knowledge is important to the organizational learning. So, it appears that organizations should pay more attention to acquiring external knowledge for achieving better performance.

Key words: knowledge sharing intention, tacit knowledge, organizational learning, multi-agent simulation