Breaking the Subjective Bias: Rethinking the Talent Management Model in the Ministry of Home Affairs
Abstract
Objectives: The objective of this research is to address subjective elements in talent management assessment in civil service organizations, focusing particularly on the Ministry of Home Affairs. By investigating the effectiveness of the Sikerja application and proposing solutions to enhance objectivity, the study seeks to contribute practical insights for advancing talent management practices.
Methodology: This research employed qualitative research, utilizing a case study approach. Through in-depth interviews with key personnel and a thorough literature review, data is collected and analyzed comprehensively.
Finding: The study reveals significant shortcomings in Sikerja's ability to address subjectivity in performance appraisal at the Ministry of Home Affairs. It lacks rigorous testing and is influenced by personal biases, undermining its reliability. To develop a more effective talent management framework, the study proposes adopting a Data-Driven Decision-Making (DDDM) approach. This involves establishing an independent team to analyze performance data objectively, ensuring fairness and transparency. Finally, the study recommends developing a talent management model based on Berger's concept and data for continuous improvement.
Conclusion: Developing a talent management model for the Ministry of Home Affairs requires an understanding of the current situation. The review of the Sikerja application highlights its limitations; subjectivity in assessments and the lack of scientific validation in its methods, which hinder its efficacy. Therefore, the author suggests adopting a Data-Driven Decision-Making (DDDM) approach in talent management. DDDM offers a robust solution by establishing an independent data analysis team, ensuring objectivity, and identifying areas for improvement. Integrating DDDM principles into all talent management stages will establish a framework promoting objectivity, transparency, and fairness, ultimately optimizing the workforce's potential.
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DOI: http://dx.doi.org/10.22441/jurnal_mix.2024.v14i2.008
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