مطالعات منابع انسانی (Jul 2022)

Present a Human Resource Functions Model for Employee Separation Management by Mixed Research Approach

  • Seyed Mohammad Reza Torabipour,
  • Reza Taghvaei,
  • Kambiz Hamidi

DOI
https://doi.org/10.22034/jhrs.2022.342202.1927
Journal volume & issue
Vol. 12, no. 2
pp. 78 – 103

Abstract

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Background & Purpose: The high rate of voluntary and involuntary separation of employees in most cases signals organizational issues. The separation of experienced and efficient personnel, especially for sensitive positions, may have irreparable consequences. For leading organizations, therefore, the concept of the "Employee separation Management" is of particular importance, such that they strive to maintain a stable rate between incoming and outgoing human forces. level of stability between in the input and output of the work force. Therefore, considering the direct and indirect costs of employee separation and the importance of employee role in the performance of organizations, the present study aims to provide a model of "Human Resource Functions for Employee Separation Management" in government organizations. Methodology: Current research is applied in term of purpose and using a mixed research approach (qualitative-quantitative). In the first step and using a qualitative approach, data was acquired by conducting semi-structured interview with human resource experts and managers. Using thematic analysis method an initial research model was extracted. In the second step and based on the structural equation modeling methodology, the research model developed in the first step was validated. Findings: For human resource functions, this research identifies a number of ten main components/themes, namely human resource planning, employee recruitment, compensation, performance management, career development, training, organizational discipline, motivation, succession, and social support. This research, furthermore, reveals 51 subsidiary themes for human resource functions. Conclusion: Having GOF values of 0.561, the structural equation modeling indicates a strong modelling fit for the initial research model.

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