Human resource management and performance: A neural network analysis

• Published in: European journal of operational research Vol. 181; no. 1; pp. 453 - 467
• Main Authors: Stavrou, Eleni T., Charalambous, Christakis, Spiliotis, Stelios
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.08.2007; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Artificial neural network (ANN); Competitive advantage; Exact sciences and technology; Human resource management; Innovations; Kohonen self-organizing map (SOM); Neural networks; Operational research. Management science; Organization development; Studies; Europe; Artificial neural network (ANN); Kohonen self-organizing map (SOM); Performance; Human resource management; Overlap; Neural network; Kohonen algorithm; Modeling; Public sector; Self organization; European Union; Network analysis; Private sector
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2006.06.006

This study utilizes an innovative research methodology (Kohonen’s Self-Organizing Maps) to explore a subject relatively understudied in Europe. It focuses on the connection between human resource management as a source of competitive advantage and perceived organizational performance in the European Union’s private and public sectors. While practices in these two sectors did not differ significantly, three diverse but overlapping HRM models did emerge, each of which involved a different set of EU member states. Training & Development practices were strongly related to performance in all three models and Communication practices in two. These results show the usefulness of an innovative technique when applied to research so far conducted through traditional methodologies, and brings to the surface questions about the universal applicability of the widely accepted relationship between superior HRM and superior business performance.