Machine Learning and the Transformation of Performance Management Models

Abstract

The study aims to examine the role and mechanisms of machine learning in transforming performance management models and to explore their technological, organizational, and ethical dimensions. This research employed a qualitative systematic review using a thematic analysis approach. Data were collected through a literature review of scholarly articles published between 2018 and 2023 in Scopus, Web of Science, and Google Scholar. After screening and quality assessment, 10 relevant articles were selected. The data were analyzed and coded using NVivo version 14 to identify major and subthemes. The thematic analysis revealed four main themes: (1) the role of machine learning in transforming performance evaluation models, (2) the evolution of managerial structures in the era of artificial intelligence, (3) challenges and ethical-technical considerations in implementing machine learning, and (4) foresight and developmental pathways of performance management based on machine learning. Findings indicated that machine learning enhances intelligent metrics, predictive analytics, personalized feedback, and shifts managers’ roles from controllers to data interpreters. However, algorithmic bias, privacy concerns, and organizational resistance remain key challenges. Machine learning fundamentally transforms performance management systems, creating an integrated paradigm between artificial intelligence, behavioral science, and data ethics. Successful implementation requires a transparent, ethical, and human-centered approach to ensure both efficiency and fairness in organizational decision-making.