Unsupervised Learning of Organizational Learning Patterns: The Roles of Knowledge Sharing and Cognitive Flexibility
Keywords:
Organizational Learning, Knowledge Sharing, Cognitive Flexibility, Unsupervised Machine LearningAbstract
Objective: The objective of this study was to autonomously identify latent patterns of organizational learning among corporate professionals and examine the differentiating roles of knowledge sharing and cognitive flexibility within these profiles.
Methods and Materials: A descriptive, quantitative cross-sectional design was employed, collecting data from a sample of corporate professionals in Egypt via a validated, self-administered online questionnaire. The analytical framework utilized unsupervised machine learning, specifically Principal Component Analysis for dimensionality reduction and K-means clustering to extract distinct learning profiles. Subsequently, Multivariate Analysis of Variance (MANOVA) and Tukey’s post-hoc tests were conducted to evaluate how knowledge sharing and cognitive flexibility differed across the autonomously generated clusters.
Findings: The K-means clustering algorithm identified an optimal three-cluster solution ( ): a Passive Learning Profile ( , ), an Adaptive Learning Profile ( , ), and a Proactive Learning Profile ( , ). The MANOVA results revealed a highly significant omnibus effect across the clusters, Wilks’ , . Univariate and post-hoc analyses demonstrated that individuals in the Proactive Learning Profile exhibited significantly higher levels of knowledge sharing ( ) and cognitive flexibility ( ) compared to those in the Adaptive Profile ( and , respectively; ) and the Passive Profile ( and , respectively; ).
Conclusion: Fostering a highly proactive and generative organizational learning environment fundamentally depends on deliberately cultivating both interpersonal knowledge exchange networks and individual cognitive adaptability.
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