Utilizing Artificial Intelligence in Recruitment and Selection Processes: A Comparative Analysis of Traditional versus AI-driven Methods

Thesis Abstract

Abstract
This thesis investigates the utilization of Artificial Intelligence (AI) in recruitment and selection processes, with a specific focus on comparing traditional methods with AI-driven approaches. The research aims to explore how AI technologies can enhance the efficiency, accuracy, and effectiveness of recruitment and selection practices within organizations. The study further seeks to identify the advantages and limitations of AI in these processes, as well as its impact on various stakeholders such as job applicants, recruiters, and HR professionals. The introduction sets the context for the research by highlighting the increasing importance of AI in transforming HR functions, particularly in the area of talent acquisition. The background of the study provides an overview of the evolution of recruitment and selection practices, leading up to the integration of AI technologies in these processes. The problem statement identifies the gaps and challenges faced by organizations in adopting AI for recruitment and selection, while the objectives of the study outline the specific goals and research questions to be addressed. The literature review critically examines existing studies and theories related to AI in recruitment and selection, covering topics such as machine learning algorithms, natural language processing, bias and fairness in AI, and the candidate experience. Through a comprehensive analysis of ten key themes, this chapter synthesizes current knowledge and identifies trends and best practices in the field. The research methodology chapter outlines the design and approach of the study, including the research design, data collection methods, sampling techniques, and data analysis procedures. By incorporating both quantitative and qualitative research methods, the study aims to provide a holistic understanding of the impact of AI on recruitment and selection processes. The discussion of findings chapter presents the results of the empirical research, highlighting the key findings, trends, and implications for practice. This chapter also compares traditional recruitment and selection methods with AI-driven approaches, discussing the benefits, challenges, and ethical considerations associated with each approach. The findings are analyzed in relation to the research objectives and existing literature, providing insights into the potential of AI in transforming HR practices. In conclusion, this thesis summarizes the main findings and contributions of the study, highlighting the implications for theory, practice, and future research. The research underscores the potential of AI technologies to revolutionize recruitment and selection processes, offering organizations innovative tools to enhance decision-making and improve the overall quality of hires. By conducting a comparative analysis of traditional and AI-driven methods, this study contributes to the ongoing discourse on the role of technology in shaping the future of HR practices.