Influence of a range of interaction among agents on efficiency and effectiveness of knowledge transfer within an organisation

TL;DR

This paper investigates how different sizes of local interaction groups among organizational members affect knowledge transfer efficiency and effectiveness, using cellular automata models to simulate various neighborhood structures.

Contribution

It introduces a cellular automata model to analyze the impact of different neighborhood sizes on knowledge transfer within organizations.

Findings

Larger neighborhoods can enhance knowledge transfer efficiency.

Local interaction structures significantly influence transfer effectiveness.

Simulation results highlight optimal neighborhood configurations for knowledge sharing.

Abstract

In this study we examined how the size of non-formal groups between organization members affect the transfer of knowledge in the context of the efficiency and effectiveness of this process. To analyse the dynamics of the transfer of knowledge the cellular automata model was used. The model is based on local interactions between members of the organization, that take place in the nearest neighbourhood. These groups of close neighbours are represented by von Neumann's neighbourhood (four nearest-neighbours) and Moore's neighbourhood (four nearest-neighbours and four next-nearest neighbours) and complex neighbourhood (four nearest neighbours, four next-nearest neighbours and four next-next-neighbours). The results of the simulation show the influence of the size of the neighbourhood on the efficiency of knowledge transfer.