Applying network analysis to measure organizational behaviors using R software

Menezes, Igor, Zwiegelaar, Jeremy, Moraes, Elton and Mendy, John (2019) Applying network analysis to measure organizational behaviors using R software. In: European Academy of Management, Lisbon, Portugal, 26 - 28 June 2019, Lisbon.

Applying network analysis to measure organizational behaviors using R software
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


The amount of research investigating psychological networks has grown substantially over the last decade but to our knowledge this is the first study applying network analysismethodology to the fields of OB/HRM. As such, this study aims to provide researchers and practitioners with an easy-to-use syntax to conduct network analysis for the exploration of relationships among organizational behaviors. Unlike the mainstream techniques used in psychometrics (e.g., principal component analysis and structural equation modelling), which are constrained by the number of associations among variables or assumptions regarding dimensionality, network analysis is able to analyze the whole set of items at once in order to find the most representative associations among them. A step-by-step guide is provided with an example showing how to test potential relationships between engagement and authentic leadership using the R package bootnet. Besides information on edge-weights and centrality measures, this paper covers a bootstrapping procedure to test their accuracy and stability when small sample sizes are used. The possibilities of applications of psychological networks to organizational behavior and HRM practices are endless and can help overcome some of the limitations of the traditional statistical techniques applied to these fields.

Keywords:psychological networks, organizational behavior, Human Resource Management practices, R software, bootnet package
Subjects:N Business and Administrative studies > N600 Human Resource Management
Divisions:Lincoln International Business School
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ID Code:36492
Deposited On:23 Jul 2019 10:07

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