Artificial Intelligence in Hiring and Recruitment: A Comprehensive Bibliometric Analysis of Trends and Innovations

Abstract

Purpose: This research aims to provide a structured understanding of the field by combining existing knowledge. The main goal is to study how AI has changed hiring, how it is currently used in recruitment, and what trends and challenges are shaping AI-driven hiring methods.

Methodology: A total of 168 papers from the Scopus database (2000–2024) were analyzed through a bibliometric study on AI in recruitment. The research utilized Biblioshiny, an online tool within the R-language Bibliometric package, to systematically identify influential journals, leading authors, key countries, prominent articles, and emerging themes. Additionally, social, intellectual, and conceptual network analyses were conducted. The study also incorporated quantitative metrics to evaluate published AI-related research in talent acquisition.

Findings: The study revealed that AI significantly enhances recruitment efficiency by streamlining key hiring processes such as candidate matching, resume screening, and interview scheduling. Additionally, AI reduces human bias, promoting diversity and inclusivity in hiring. This research provides valuable insights and highlights areas that require further exploration.

Practical Implications: The research shows that AI can make hiring more efficient by reducing administrative tasks, allowing HR professionals to focus on important decisions. It highlights key concerns in AI recruitment and suggests new research topics. By exploring the social and intellectual structure of the field, the study helps researchers understand challenges, real-world applications, and opportunities for teamwork.

Originality/Value: By conducting a thorough bibliometric review, this report explores the latest trends and developments in AI technologies for recruitment, providing valuable insights into their integration. While prior conceptual and empirical studies have examined AI across various domains, this research consolidates the fragmented literature, highlighting influential authors, key sources, and significant publications in AI-driven talent acquisition.