E-ARIE: A Novel Hybrid Entropy and ARIE Method for Best Employee Selection in the Banking Sector
Keywords:
E-ARIE, Entropy Weighting, ARIE, Employee Selection, Multi-Criteria Decision-MakingAbstract
Employee selection is a critical decision-making process in the banking sector because employee performance directly influences service quality, operational efficiency, and organizational competitiveness. However, conventional selection approaches often rely on subjective criterion weighting and may produce rankings that are sensitive to changes in criterion importance. This study proposes E-ARIE (Entropy-based Adaptive Ranking with Ideal Evaluation), a Multi-Criteria Decision-Making (MCDM) approach that integrates entropy-based objective weighting with the Adaptive Ranking with Ideal Evaluation (ARIE) method. The proposed approach determines criterion weights based on the discriminatory information contained in the assessment data and ranks employee candidates according to their relative closeness to ideal and anti-ideal conditions. The method was evaluated using data from 15 employee candidates across eight criteria: performance score, sales achievement, customer service, attendance, work experience, disciplinary record, teamwork, and competency. The results show that work experience (CBE-5) obtained the highest weight of 0.2605, followed by competency (CBE-8) with 0.1728 and performance score (CBE-1) with 0.1418. Employee 7 achieved the highest relative closeness score of 0.5513, followed by Employee 11 (0.5433) and Employee 3 (0.5363). Sensitivity analysis demonstrated that the ranking remained unchanged under criterion-weight perturbations of ±0.05 and ±0.10, indicating high ranking stability and robustness. Overall, E-ARIE provides an objective, adaptive, and robust framework for supporting transparent and data-driven employee selection in the banking sector.
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Copyright (c) 2026 Mesran Mesran, Desyanti Desyanti, Temi Ardiansah Temi (Author)
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