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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Mohammad Ridhwan
Universitas Riau Kepulauan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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THE INFLUENCE OF AI-DRIVEN PERFORMANCE APPRAISAL SYSTEMS ON EMPLOYEE MOTIVATION AND JOB SATISFACTION: A REVIEW Mohammad Ridhwan; Ramon Zamora; Lukmanul Hakim
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 11 (2026): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.23291528

Abstract

Artificial intelligence (AI) has fundamentally restructured performance management, shifting organizational appraisals from episodic subjective evaluations to real-time, data-driven algorithmic monitoring. This systematic review critically evaluates the impact of AI-driven performance appraisal systems on employee motivation and job satisfaction across modern enterprise settings. Synthesizing 16 benchmark empirical studies published between 2021 and 2026, this paper examines how core AI attributes—such as continuous telemetry tracking, natural language sentiment processing, predictive rating algorithms, and real-time feedback loops—shape worker perceptions of organizational justice and psychological safety. Findings indicate a dual socio-technical paradox: while algorithmic appraisal objectivity and reduced supervisory bias enhance distributive justice and extrinsic performance drive, black-box opacity, algorithmic surveillance, and automated rating errors significantly degrade procedural justice, intrinsic motivation, and job satisfaction. The review highlights perceived algorithmic fairness and human-in-the-loop (HITL) oversight as crucial mediating pathways that determine whether AI appraisals foster empowerment or techno-stress. Based on structural synthesis, a multi-level governance model is proposed to guide ethical AI integration, balanced feedback, and employee-centered performance management.
Co-Authors Lukmanul Hakim Ramon Zamora