Simulating the impacts of mutual trust on tacit knowledge transfer using agent-based modelling approach

Authored by Hong Li, Changhong Li, Zhan Wang, Xinlan Li

Date Published: 2019

DOI: 10.1080/14778238.2019.1601506

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: Mathematical description Other Narrative

Model Code URLs: Model code not found

Abstract

Effective transfer of tacit knowledge within an organization is the key issue to ensure its sustainable competitive advantage. However due to the asymmetric nature of information when transferring the tacit knowledge, there is a tendency for the moral hazard of information hiding to emerge, which hinders the effective transfer of tacit knowledge. According to the norm of reciprocity, we assume that the primary motivation of suppliers to transfer tacit knowledge is that the individuals who share their tacit knowledge trust the recipients can reciprocate in the future. In this paper, we build a simulation model based on trust and mutual benefit through the agent-based modeling and simulation. From the perspective of theoretical quantification, our simulation result shows that, the endowment effect, the initial trust between members and the minimum honesty are very important, which provides relevant practical guidance for organization managers in the context of tacit knowledge transfer.
Tags
Intention stability Reciprocity Performance Cooperation preferences transmission Empirical-evidence Population biology Management approach Norm of reciprocity Trust and honesty Agent-based modelling and simulation Tacit knowledge