Utilizing Artificial Intelligence for Employee Performance Evaluation in Human Resource Management

Thesis Abstract

Abstract
This thesis explores the utilization of Artificial Intelligence (AI) in the context of employee performance evaluation within the field of Human Resource Management (HRM). The integration of AI technologies in HRM practices has gained significant attention in recent years, offering promising opportunities to enhance the efficiency and accuracy of performance evaluation processes. This research aims to investigate the impact of AI on employee performance evaluation, focusing on its benefits, challenges, and implications for HRM professionals. The study begins with a comprehensive introduction that highlights the background of the research, identifies the problem statement, outlines the objectives, discusses the limitations and scope of the study, underscores the significance of the research, and presents the structure of the thesis. The introduction sets the stage for the subsequent chapters by providing a clear overview of the research framework and the rationale behind the study. Chapter two presents a detailed literature review that delves into existing research on AI applications in HRM, employee performance evaluation, and related theories and frameworks. The review synthesizes key findings from scholarly articles, books, and reports to establish a theoretical foundation for the study and identify gaps in the current literature that the research seeks to address. Chapter three focuses on the research methodology employed in this study, detailing the research design, data collection methods, sampling techniques, data analysis procedures, and ethical considerations. The chapter provides a transparent and structured approach to conducting the research, ensuring the reliability and validity of the findings. Chapter four presents an in-depth discussion of the research findings, analyzing the impact of AI on employee performance evaluation practices in HRM. The chapter examines the advantages and challenges associated with AI implementation, explores the implications for HR professionals, and discusses potential strategies to optimize the use of AI in performance evaluation processes. Finally, chapter five offers a conclusive summary of the research findings, highlighting the key insights, implications, and recommendations for practice and future research. The conclusion underscores the significance of AI in transforming employee performance evaluation practices and its potential to revolutionize HRM processes in the digital age. Overall, this thesis contributes to the growing body of knowledge on the role of AI in HRM and provides valuable insights for practitioners, researchers, and policymakers seeking to leverage AI technologies for enhancing employee performance evaluation in organizations.