A Fuzzy-TOPSIS Approach for Personnel Selection in Nigerian University

Favoritism, personal and political influence embedded in the personnel selection/recruitment for administrative roles in Nigerian universities has undermined public's trust in the system. This study focuses on proposing and demonstrating an MCDM method, fuzzy-TOPSIS to promote fairness, transparency and merit-based selection process. Subjective judgments and uncertainty are embedded in the recruitment process and can be quantified by using multi-criteria decision-making (MCDM) method which uses linguistic variables mapped to triangular fuzzy numbers (TFNs) to assess alternatives performances. A simulated scenario involving 12 candidates, 10 criteria, and 3 experts was designed for selecting a candidate to fill a vacant Registrar office in a Nigerian university. The model ranked qualified candidates based on their Closeness Coefficient (CCi) to an ideal solution by aggregating expert opinions. The model's results generated a definitive ranking, and identified Candidate 5 (A5) with CCi value of 0.6066 as the most suitable followed by Candidate 10 (A10) with CCi value of 0.5527 in the second place and Candidate 12 (A12) with CCi value of 0.5427 in the third place. Also, Candidates 11 (A11), 7 (A7) and 1 (A1) were ranked tenth with CCi value of 0.4623, eleventh with CCi value of 0.4616, and twelfth with CCi value of 0.3872 respectively. Subsequent sensitivity analyses were performed to confirmed the model's stability and robustness in which the top candidates' rankings A5 (80% appearance), A10 (40% appearance), A12 (50% appearance) remained largely consistent. The study confirms the fuzzy-TOPSIS framework is a dependable tool for selecting leaders in Nigerian universities. While it doesn't entirely remove bias, it significantly improves the process by boosting transparency and accountability.