<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model (IKM). The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.</p>
</sec>
<sec>
<h3>Design/Methodology/Approach</h3>
<p>The IKM model enhances the preferential attachment and growth observed in scale-free BA networks, while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network. To compare its performance, the BA and DMS models are also employed for simulating the network. Pearson coefficient analysis is conducted on the simulated networks generated by the IKM, BA and DMS models, as well as on the actual network.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network. It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm. The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.</p>
</sec>
<sec>
<h3>Research Limitations</h3>
<p>This study has some limitations. Firstly, it primarily focuses on the evolution of knowledge flow networks within the field of physics, neglecting other fields. Additionally, the analysis is based on a specific set of data, which may limit the generalizability of the findings. Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.</p>
</sec>
<sec>
<h3>Practical Implications</h3>
<p>The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions. It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations. The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks. The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions. Moreover, the model has the potential to be applied to other knowledge networks, which are formed by knowledge organizations as node units.</p>
</sec>
</abstract>

Purpose
This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model (IKM). The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.


Design/Methodology/Approach
The IKM model enhances the preferential attachment and growth observed in scale-free BA networks, while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network. To compare its performance, the BA and DMS models are also employed for simulating the network. Pearson coefficient analysis is conducted on the simulated networks generated by the IKM, BA and DMS models, as well as on the actual network.


Findings
The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network. It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm. The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.


Research Limitations
This study has some limitations. Firstly, it primarily focuses on the evolution of knowledge flow networks within the field of physics, neglecting other fields. Additionally, the analysis is based on a specific set of data, which may limit the generalizability of the findings. Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.


Practical Implications
The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions. It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations. The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.


Originality/value
This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks. The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions. Moreover, the model has the potential to be applied to other knowledge networks, which are formed by knowledge organizations as node units.

A new evolutional model for institutional field knowledge flow network

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A new evolutional model for institutional field knowledge flow network"}

Purpose
This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional...

A new evolutional model for institutional field knowledge flow network

Article Category: Research Papers

Online veröffentlicht: 06. Feb. 2024

Seitenbereich: 101 - 123

Eingereicht: 12. Dez. 2023

Akzeptiert: 23. Jan. 2024

DOI: https://doi.org/10.2478/jdis-2024-0009

Schlüsselwörter
Knowledge flow networks, Evolutionary mechanism, BA model, Knowledge units

© 2024 Jinzhong Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A new evolutional model for institutional field knowledge flow network

Article Category: Research Papers

Online veröffentlicht: 06. Feb. 2024

Seitenbereich: 101 - 123

Eingereicht: 12. Dez. 2023

Akzeptiert: 23. Jan. 2024

DOI: https://doi.org/10.2478/jdis-2024-0009

SchlüsselwörterKnowledge flow networks, Evolutionary mechanism, BA model, Knowledge units

© 2024 Jinzhong Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Knowledge flow networks, Evolutionary mechanism, BA model, Knowledge units