Evaluating the Employment Efficiency of IT Candidates Using Data Envelopment Analysis

Abstract

This study aims to identify efficient Information Technology (IT) candidates for a specific position and highlight areas for improvement using Data Envelopment Analysis (DEA). By streamlining the selection process and reducing costs, the findings can assist companies in making better-informed hiring decisions. Additionally, the results provide candidates with valuable feedback on areas for development, increasing their chances of securing employment in their desired company. The DEA model offers a unique advantage in this context by generating reference units for each candidate, enabling precise determination of the necessary changes in inputs or outputs for achieving efficiency. The Charnes, Cooper, and Rhodes (CCR) model served as the baseline, with parallel comparisons drawn against the Banker, Charnes, and Cooper (BCC) and categorical models to identify the most effective approach. The findings reveal the efficient candidates based on the assessed criteria, demonstrating that less experienced candidates can be evaluated as efficient compared to their more experienced counterparts. The hypothesis that the BCC model, with its more flexible efficiency frontier, results in poorer candidate differentiation was confirmed. This study highlights the value of adopting the DEA method in evaluating the employment efficiency of IT candidates, offering practical implications for both hiring organizations and job-seekers.