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The Impact of Artificial Intelligence on Recruitment and Selection Processes in Human Resource Management

 

Table Of Contents


Chapter ONE

: Introduction 1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objectives of Study
1.5 Limitations of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter TWO

: Literature Review 2.1 Overview of Human Resource Management
2.2 Recruitment and Selection Processes
2.3 Artificial Intelligence in HR Management
2.4 Impact of AI on Recruitment
2.5 Impact of AI on Selection
2.6 Challenges of Implementing AI in HR
2.7 Benefits of AI in HR Management
2.8 Integration of AI with Traditional HR Practices
2.9 Ethical Considerations in AI-driven HR
2.10 Future Trends in AI and HR

Chapter THREE

: Research Methodology 3.1 Research Design
3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Analysis Procedures
3.5 Research Instrumentation
3.6 Ethical Considerations
3.7 Validity and Reliability
3.8 Limitations of the Methodology

Chapter FOUR

: Discussion of Findings 4.1 Analysis of Recruitment Processes
4.2 Analysis of Selection Processes
4.3 Comparison of AI and Traditional Methods
4.4 Impact on HR Professionals
4.5 Employee Perspectives on AI
4.6 Organizational Implications
4.7 Recommendations for Practice

Chapter FIVE

: Conclusion and Summary 5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions to HR Management
5.4 Implications for Future Research
5.5 Final Remarks

Thesis Abstract

Abstract
The integration of artificial intelligence (AI) in various organizational functions has transformed traditional practices and introduced new approaches to enhance efficiency and effectiveness. In the realm of Human Resource Management (HRM), the utilization of AI technologies in recruitment and selection processes has gained significant attention due to its potential to streamline operations, improve decision-making, and enhance candidate experience. This research investigates the impact of AI on recruitment and selection processes in HRM, focusing on its implications for organizations, HR professionals, and job seekers. The study begins with a comprehensive review of existing literature on AI in HRM, exploring key concepts, theories, and empirical studies related to the topic. Through a systematic analysis of ten relevant studies, the literature review highlights the benefits, challenges, and best practices associated with the implementation of AI technologies in recruitment and selection processes. In the research methodology section, the study details the research design, data collection methods, and analysis techniques employed to investigate the impact of AI on recruitment and selection processes. The methodology incorporates both qualitative and quantitative approaches, including interviews with HR professionals, surveys of job seekers, and analysis of recruitment data from AI-enabled platforms. Findings from the study reveal that the integration of AI in recruitment and selection processes has led to increased efficiency, reduced bias, and improved decision-making in HRM. However, challenges such as data privacy concerns, algorithmic bias, and resistance to change have also emerged as key issues that organizations must address when adopting AI technologies. The discussion of findings delves into the implications of the study results for HR professionals, organizations, and job seekers, highlighting the opportunities and challenges associated with the use of AI in HRM. Recommendations are provided to guide practitioners in leveraging AI technologies effectively to optimize recruitment and selection processes while ensuring fairness, transparency, and ethical practices. In conclusion, this thesis underscores the transformative impact of AI on recruitment and selection processes in HRM, emphasizing the need for continuous learning, adaptation, and ethical considerations in the adoption of AI technologies. By embracing AI as a tool to enhance decision-making and improve candidate experience, organizations can navigate the evolving landscape of HRM with greater agility and effectiveness in the digital age.

Thesis Overview

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