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Cited by 15Knowledge Management and SharingInnovation and Knowledge ManagementComplex Network Analysis Techniques

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Model of Knowledge Transfer Within an Organisation

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Kowalska-Styczeń, Agnieszka, Malarz, Krzysztof, Paradowski, Kamil·2018·Journal of Artificial Societies and Social Simulation
DOI 10.18564/jasss.3659
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Abstract

Many studies show that the acquisition of knowledge is the key to building the competitive advantage of companies.We propose a simple model of knowledge transfer within the organisation and we implement the proposed model using the cellular automata technique.In this paper the organisation is considered in the context of complex systems whereby the main role in the organisation is played by the network of informal contacts (informal communication).The goal of this paper is to ascertain which factors influence the efficiency and effectiveness of knowledge transfer.Our studies indicate a significant role of initial distribution of chunks of knowledge for the knowledge transfer process, and the results suggest taking action in the organisation to shorten the distance (social distance) between people with different levels of knowledge, or determining incentives to share knowledge.

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Image 1
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Image 2
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Image 3
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Figure 2 The time evolution of the fraction n ( k ) of agents having k chunks of knowledge for K = 4 andvarious systems sizes ( L =5, 20) and initial concentration and distribution of chunks of knowledge ( p =0.2, 0.5, 0.8). The values of n ( k ) are averaged over M = 100 independent simulations.
Figure 2 The time evolution of the fraction n ( k ) of agents having k chunks of knowledge for K = 4 andvarious systems sizes ( L =5, 20) and initial concentration and distribution of chunks of knowledge ( p =0.2, 0.5, 0.8). The values of n ( k ) are averaged over M = 100 independent simulations.CC-BY · From the PDF
Image 5
CC-BY · From the PDF
Figure 4 The time τ necessary for reaching the stationary state as dependent on initial concentration of chunks of knowledge p . The results are averaged over M = 10 3 independent simulations. The error bars are smaller than the symbols.
Figure 4 The time τ necessary for reaching the stationary state as dependent on initial concentration of chunks of knowledge p . The results are averaged over M = 10 3 independent simulations. The error bars are smaller than the symbols.CC-BY · From the PDF
Image 7
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Figure 6 The fraction of agents having total knowledge ( c 1 , c 2 , · · · , c K ) = (1 , 1 , · · · , 1 ) vs. initial probability p for various system sizes L andvarioussizesoftotalknowledge K . The results are averaged over M independent simulations. The system reaches the stationary state in t < 200 ( t < 1000 ) steps for L < 400 ( L > 400 ). In sub-figures (c) and (d) the upper part of sub-figures (a) and (b) are displayed ( 90% < n ( K ) < 100% ).
Figure 6 The fraction of agents having total knowledge ( c 1 , c 2 , · · · , c K ) = (1 , 1 , · · · , 1 ) vs. initial probability p for various system sizes L andvarioussizesoftotalknowledge K . The results are averaged over M independent simulations. The system reaches the stationary state in t < 200 ( t < 1000 ) steps for L < 400 ( L > 400 ). In sub-figures (c) and (d) the upper part of sub-figures (a) and (b) are displayed ( 90% < n ( K ) < 100% ).CC-BY · From the PDF
Image 9
CC-BY · From the PDF
Figure 8 The average coverage of chunks of knowledge 〈 f 〉 = K -1 ∑ K k =1 f ( k ) of agents having any chunk of knowledge c k for L =5, 20 and long simulations time T → ∞ and various values of K ( K =2, 3, 4, 8, 16) as dependent on initial concentration of chunks of knowledge p . The values of 〈 f 〉 are averaged over M = 100 independent simulations.
Figure 8 The average coverage of chunks of knowledge 〈 f 〉 = K -1 ∑ K k =1 f ( k ) of agents having any chunk of knowledge c k for L =5, 20 and long simulations time T → ∞ and various values of K ( K =2, 3, 4, 8, 16) as dependent on initial concentration of chunks of knowledge p . The values of 〈 f 〉 are averaged over M = 100 independent simulations.CC-BY · From the PDF
Figure 9 The estimated probabilities of acts of knowledge transfer (dotted and dashed lines) and lack of it (solid line) in the first step of simulation as dependent on the initially concentration of chunks of knowledge p .
Figure 9 The estimated probabilities of acts of knowledge transfer (dotted and dashed lines) and lack of it (solid line) in the first step of simulation as dependent on the initially concentration of chunks of knowledge p .CC-BY · From the PDF
http://www.zis.agh.edu.pl/knowledge_transfer/
http://www.zis.agh.edu.pl/knowledge_transfer/CC-BY · From the PDF

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Citations

Cites

Show all 9 references

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Metadata

Authors
Kowalska-Styczeń, A., Malarz, K., & Paradowski, K.
Affiliations
  • Silesian University of Technology (PL)
  • AGH University of Krakow (PL)
ORCID
Venue
Journal of Artificial Societies and Social Simulation
Publisher
Journal of Artificial Societies and Social Simulation
Published
2018
Volume
21
Issue
2
ISSN
1460-7425 (ISSN-L)
DOI
10.18564/jasss.3659
OpenAlex
W2608037719
Cited by
15
References
9
Citations by year
2026: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 5 · 2020: 1 · 2019: 4
FWCI
5.47
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Artificial Societies and Social Simulation (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Infrastruktura PL-Grid
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Knowledge Management and Sharing
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
knowledge transfer, organizational knowledge, cellular automata, complex systems, informal communication, social networks, knowledge distribution, social distance, knowledge sharing incentives, competitive advantage

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