Project Abstract

Abstract
This research project delves into the innovative use of Artificial Intelligence (AI) in the realm of Human Resource Management (HRM), specifically focusing on employee performance evaluation. In a rapidly evolving digital age, organizations are increasingly leveraging AI technologies to streamline and enhance their HR processes, with performance evaluation being a critical aspect of managing human capital efficiently. This study aims to explore the potential benefits, challenges, and implications of integrating AI into the traditional employee performance evaluation systems. The introduction section provides an overview of the research topic, highlighting the growing importance of AI in HRM and the specific focus on performance evaluation. The background of the study discusses the evolution of performance appraisal methods and the need for more objective, data-driven approaches. The problem statement identifies the gaps and limitations of current performance evaluation practices, paving the way for the proposed AI solution. The objectives of the study outline the specific goals to be achieved, including assessing the effectiveness of AI in performance evaluation, identifying potential implementation challenges, and exploring the impact on employees and organizational outcomes. The limitations of the study acknowledge potential constraints and constraints that may affect the research findings, while the scope delineates the boundaries and focus areas of the investigation. The significance of the study underscores the potential contributions to both academic research and practical HRM applications, emphasizing the need for evidence-based insights into AI-driven performance evaluation. The structure of the research outlines the organization of the study, providing a roadmap for the reader to navigate through the various chapters and sections. Lastly, the definition of terms clarifies key concepts and terminology used throughout the research project. The literature review in Chapter Two critically examines existing literature on AI in HRM, performance evaluation methods, and the intersection of technology and human capital management. Drawing on a wide range of scholarly sources, this section offers a comprehensive analysis of the theoretical foundations and empirical evidence related to the research topic. Chapter Three details the research methodology, encompassing the research design, data collection methods, sample selection, and data analysis techniques. By employing a mixed-methods approach, this study aims to gather both quantitative and qualitative data to provide a holistic understanding of AI-enabled performance evaluation in HRM. Chapter Four presents the findings of the research, showcasing the empirical results, data analysis outcomes, and key insights derived from the study. Through a detailed discussion of the findings, this section elucidates the implications for HR practitioners, organizational leaders, and policymakers seeking to implement AI technologies in performance evaluation processes. In the concluding Chapter Five, the research culminates in a comprehensive summary of the key findings, implications, and recommendations for future research and practice. By synthesizing the research outcomes, this section offers valuable insights into the potential benefits and challenges of utilizing AI for employee performance evaluation in HRM. In conclusion, this research project contributes to the evolving discourse on AI applications in HRM, shedding light on the transformative potential of technology-driven performance evaluation systems. By bridging the gap between theory and practice, this study aims to inform strategic decision-making and foster innovation in HRM practices to optimize employee performance and organizational effectiveness in the digital era.

Project Overview