Simulating the impacts of mutual trust on tacit knowledge transfer using agent-based modelling approach
Hong Li,
Changhong Li,
Zhan Wang and
Xinlan Li
Knowledge Management Research & Practice, 2019, vol. 17, issue 2, 227-244
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.
Date: 2019
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/14778238.2019.1601506 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tkmrxx:v:17:y:2019:i:2:p:227-244
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tkmr20
DOI: 10.1080/14778238.2019.1601506
Access Statistics for this article
Knowledge Management Research & Practice is currently edited by Giovanni Schiuma
More articles in Knowledge Management Research & Practice from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().