Proper selection and positioning of employees is an important issue for
strategic human resources management. Within this framework, the aim of the
research conducted, was to investigate the most efficient machine learning
techniques to support employees’ recruitment and positioning evaluation.
Towards this aim, a series of tests were conducted based on classification
algorithms concerning employees of the public sector, seeking to predict best
fit in workplaces and allocation of employees. Based on the outcome of the
administered tests, an algorithm model was built to assist the decision support
system of employees’ recruitment and assessment. The primary findings of the
present research could lead to the argument that the adoption of the Employees’
Evaluation for Recruitment and Promotion Algorithm Model (EERPAM) will
significantly improve the objectivity of employees’ recruitment and positioning
procedures.
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