AI-Powered Performance Appraisal: Balancing Automation with Human Appraisal in Performance Management Systems - Mantong, Joni (PT. Multidisciplinary Press Indonesia, 2024)
This study investigates the effectiveness and perceived fairness of an AI-powered performance appraisal system in Gojek’s regional office in Padang. The study aims to explore how automation affects the performance appraisal process, focusing on the role of human judgment as a complementary factor. A quantitative approach was adopted, with a sample of 35 employees selected using a saturated sampling technique. The study analyzes the relationships between automation, perceived fairness, human judgment, and effectiveness using Smart PLS. The findings reveal that automation significantly improves the efficiency and objectivity of performance appraisals. However, perceived fairness and human judgment are critical in ensuring employee acceptance and trust in the system. Mediation analysis suggests that perceived fairness of the AI system further enhances its effectiveness, and human judgment provides valuable context to complement AI data-driven insights. The study concludes that a balanced integration of automation and human input is necessary to achieve an effective and fair performance appraisal system. This study fills the gap by highlighting the importance of local and contextual factors in the implementation of AI-based assessments in a decentralized workplace.