Utilizing Artificial Intelligence in Recruitment and Selection Processes: A Comparative Analysis

Thesis Abstract

Abstract
This thesis explores the utilization of artificial intelligence (AI) in recruitment and selection processes, offering a comparative analysis of traditional methods versus AI-driven approaches. The study delves into the increasing trend of integrating AI technologies in human resource management practices, specifically focusing on recruitment and selection processes within organizations. The research aims to provide insights into the benefits, challenges, and implications of AI adoption in these critical HR functions. The introductory chapter sets the stage by presenting the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. It also defines key terms to provide clarity and understanding for readers. The literature review chapter critically examines existing studies and theories related to AI in recruitment and selection, highlighting key factors influencing the adoption and effectiveness of AI technologies in HR practices. The research methodology chapter outlines the approach, research design, data collection methods, sample selection, data analysis techniques, and ethical considerations employed in conducting the study. This chapter also discusses the rationale behind choosing specific research methodologies to achieve the objectives of the study effectively. The following chapter presents a detailed discussion of the findings derived from the analysis, comparing the outcomes of AI-driven recruitment and selection processes with traditional methods. The conclusion and summary chapter provide a comprehensive overview of the research findings, drawing conclusions on the effectiveness and implications of utilizing AI in recruitment and selection. The study underscores the potential of AI technologies to enhance efficiency, accuracy, and fairness in HR processes while also recognizing the challenges and ethical considerations associated with their implementation. Recommendations for practitioners and suggestions for future research are provided to guide further exploration in this evolving field of study. In conclusion, this thesis contributes to the growing body of knowledge on AI applications in HR management, specifically in the context of recruitment and selection. By offering a comparative analysis, this study sheds light on the opportunities and challenges presented by AI technologies in reshaping traditional HR practices. The findings of this research aim to inform HR professionals, policymakers, and scholars on the evolving landscape of AI-driven HR processes and the implications for organizational decision-making and talent management strategies.