The study aims to understand the concept of boomerang hiring: the rehiring of previous employees, within the automotive industry. It seeks to recognize and approve the key criteria that impact rehire choices and offer an organized, data-driven approach to assist HR experts in making educated choices. The consideration highlights the significance of coordinating human knowledge into AI-based decision models for a way better talent management.
This study embraces a two-phase MCDM approach, using Fuzzy AHP to weigh ten key rehire criteria and Fuzzy TOPSIS to rank previous workers. The model’s outputs are validated by comparing them with expert HR judgments. This integrated system improves objectivity in boomerang hiring choices and offers an organized method for ability re-evaluation. It aims to support key HR planning within the automotive industry. The study also conducted a consistency ratio (CR) test to verify the logical reliability of expert pairwise judgments within FAHP. Further, a ±10% sensitivity analysis and bootstrap resampling (B = 2000) were applied to assess the robustness and stability of the FAHP-derived weights and FTOPSIS rankings. These validation steps ensure that the model results are consistent and statistically reliable under expert judgment uncertainty.
The study identifies attitude, domain knowledge and break period as the most compelling criteria in rehire choices. It also reveals that while intelligent models like FAHP-TOPSIS offer organized assessment, they cannot fully replace the subtlety of human judgment. Moreover, practices such as positive offboarding and maintaining alumni networks were found to upgrade the success rate of rehires. The results of the sensitivity and bootstrap analyses confirmed that these key criteria remained stable even when the weights were perturbed or resampled, validating the robustness of the FAHP–FTOPSIS framework. This research also contributes to the existing knowledge of performance management by showing that using combined MCDM methods in HR decisions can make judgments more correct, with lower risks of bias, and allow for strategic assessments of returning employees. The study introduces a clear way of measuring talent value by embedding FAHP–FTOPSIS into the HR processes, thus supporting better and more reliable data-driven methods for performance management in the car industry.
This study provides valuable insights, but its limited emphasis on the automobile sector may restrict its generalizability. Furthermore, the examination of HR specialists may not reflect different views. Future studies should include a wider range of industries and use different samples. Subjective evaluations by experts may also influence the findings. Future research might combine longitudinal data to increase reliability and depth. Although consistency, sensitivity and bootstrap validations were performed to enhance reliability, the model remains dependent on expert-based judgments, which introduces an element of subjectivity. Future studies can integrate larger datasets or multi-sectoral validations to further strengthen generalizability.
By pushing for a more inclusive approach to representative rehiring, this paper contributes to the broader discussion around workforce stability, engagement and the significance of positive organizational culture in retaining talented individuals within the industry.
This research fills a gap within the existing literature on boomerang hiring by offering an organized decision-making model utilizing FAHP and Fuzzy TOPSIS within the HR domain. It gives a novel, data-driven approach to rehire decision-making, merging analytical thoroughness with real-world HR insights. The study is particularly valuable for businesses like automotive where retaining skilled talent is critical. Furthermore, this study enhances methodological originality by incorporating statistical validation through CR, sensitivity and bootstrap analyses, establishing the FAHP–FTOPSIS model as a robust decision-support tool for HR applications.
