Knowledge sharing in a dynamic, multi-level organization: an agent-based modeling approach

Bianica Pires (), Joshua Goldstein (), Emily Molfino (), Kathryn Ziemer (), Mark Orr () and José Jiménez ()
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Bianica Pires: The MITRE Corporation
Joshua Goldstein: University of Virginia
Emily Molfino: U.S. Census Bureau
Kathryn Ziemer: Old Town Psychology
Mark Orr: University of Virginia
José Jiménez: Army-Baylor University

Computational and Mathematical Organization Theory, 2024, vol. 30, issue 1, No 3, 75-100

Abstract: Abstract Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. Studying organizations as complex systems is a challenge as we must account for hierarchically nested structures, multi-level processes, and changes over time. Informal structures interact with individual attitudes to influence organizational processes such as knowledge sharing, a process vital to organizational performance and innovation. To explore such organizational dynamics, we integrate dynamic social networks, a cognitive model of attitude formation and change, and a physical environment into an agent-based model, the combination of which represents a novel way to study organizations. We use a hospital in southwest Virginia as our case study. The agents in the model are the healthcare workers within the hospital and agent movement occurs over the physical environment of the hospital. Results show that the simulated hospital is resilient to impacts from employee attrition but that communication approaches must be thought through strategically so as not to hinder knowledge sharing. For managers, this type of modeling approach can provide resource and planning guidance in regards to attrition-based strategies and communication approaches.

Keywords: Agent-based modeling; Social network analysis; Organizations; Knowledge sharing; Artificial neural networks; Theory of reasoned action (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10588-023-09373-8

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