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Prerna Ahuja, Meenu Gupta, Jinesh Jain, Kiran Sood, Luan Vardari: HR Analytics Research
Landscape (2003–2024): A Systematic, Bibliometric, and Content Analysis

Researchers have utilised a spectrum of methods, including statistical analysis of applicant
databases, predictive modelling via machine learning techniques, and experimental
investigations comparing different recruitment strategies [36]. Emerging trends in talent
acquisition analytics highlight the adoption of AI-powered tools for screening and
assessing candidates [Gonzalez M, Capman J, Oswald F, Theys E, Tomczak D. 2019].
The application of natural language processing and machine learning is revolutionising
the process of resume analysis, job descriptions, and social media profiles, enabling the
quick identification of potential candidates and forecasting their suitability for specific
roles [Zimmermann T, Kotschenreuther L, Schmidt K. 2016]. Furthermore, research has
also drawn attention to the possible risks of technology integration in human resource
management, emphasising the need to preserve data integrity and mitigate biases in
algorithms, while also underscoring the requirement to carefully address the ethical
implications of deploying AI in hiring decisions. Employers must ensure that AI-driven
tools operate transparently, allowing candidates to understand how decisions are made.
Accountability is paramount, requiring clear attribution of responsibility for AI-generated
results [Hunkenschroer AL, Luetge C. 2022].

Performance Management
Data-driven methodologies for performance management systems have emerged as a key
area of interest in the intellectual landscape of HR Analytics [Patel S. 2025; Hangal MrA,
Duraipandian. 2020; Meijerink J, Boons M, Keegan A, Marler J. 2021]. Research shows
that by leveraging employee performance data, organisations can improve the
effectiveness of feedback and development interventions for their workforce [Sharma A,
Sharma T. 2017; Zebua NDK, Santosa NTA, Putra NFD. 2024]. Analysis in this domain
generally entails analysing data collected from different sources, including performance
evaluations, project management systems, and employee feedback surveys. Statistical and
data visualisation techniques are frequently utilised to find out significant patterns and
trends, providing valuable insights for organisational improvement.

The usage of wearable technologies and sensor-based data in performance management
analytics is an emerging trend, facilitating real-time monitoring of workforce activity
and productivity [Yuan J. 2019; Gaur B, Shukla VK, Verma A. 2019]. Researchers
underscore the growing impact of AI tools in delivering personalised feedback and
targeted training within performance management systems. Although HR analytics is
acknowledged for its potential to significantly improve organisational decision-making,
its erroneous application, such as biased evaluations, excessive monitoring, or punitive
measures, may adversely affect employee morale and engagement [Buck B, Morrow J.
2018]. The effort should be on the adoption of a more balanced approach, where the

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