AI Human Resource Analytics Linked to Sustainable Job Performance in IT Workers

Artificial intelligence is increasingly used to analyse workforce data and support human resource decisions, and a new study suggests this technology may be associated with more sustainable employee performance in the information technology sector. The research, published open access in Discover Sustainability, examined how AI-based human resource analytics relate to sustainability-driven employee job performance, and whether employee engagement helps explain that connection.

The study was grounded primarily in Job Demands-Resources theory, which frames workplace conditions as demands and resources that shape employee well-being and output. The authors also drew on the Resource-Based View, treating AI-based human resource analytics as an organizational capability rather than simply a set of software tools.

Data came from 350 employees in the IT industry. Of these, 69.4 percent were male (n = 243) and 30.6 percent were female (n = 107). The researchers analysed the survey responses using Partial Least Squares Structural Equation Modelling, or PLS-SEM, carried out with the SmartPLS 3 software package.

The findings indicate significant positive relationships between AI-based human resource analytics and both employee engagement and sustainability-driven job performance. Employee engagement itself also showed a significant positive relationship with sustainable job performance, supporting the idea that engaged employees are more likely to perform consistently over time.

A central result concerns mediation. Employee engagement partially mediated the relationship between AI-based analytics and sustainable performance, meaning that part of the association between the technology and performance ran through how engaged employees felt at work. The remaining portion of the association was direct, suggesting engagement explains some but not all of the link.

The authors report that both the measurement model, which assesses how well survey items capture their intended concepts, and the structural model, which tests the relationships between constructs, demonstrated acceptable levels of reliability, validity, predictive relevance and model fit.

The study contributes to a growing body of evidence on AI-enhanced human resource management by connecting an organizational analytics capability with employee-level engagement and sustainable job performance. In practical terms, the results point to the value of pairing AI-based analytics adoption with employee engagement interventions, rather than relying on technology alone.

The researchers are careful about interpretation. They describe the results as positive associations within the sampled context, not as proof that AI-based human resource analytics automatically improve employee outcomes.

The study offers a measured signal: the apparent benefits of AI-based human resource analytics for sustainable performance appear to travel, at least in part, through the engagement of the employees the technology is meant to support.

Subject of Research: AI-based human resource analytics, employee engagement and sustainability-driven job performance in the information technology sector

Article Title: Artificial intelligence based human resource analytics and sustainability driven employee job performance through the mediating role of employee engagement in the information technology sector

Article References: Marella, V. S., & Swamy, T. N. V. R. L. (2026). Artificial intelligence based human resource analytics and sustainability driven employee job performance through the mediating role of employee engagement in the information technology sector. Discover Sustainability. https://doi.org/10.1007/s43621-026-05052-1

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05052-1

Keywords: artificial intelligence, human resource analytics, employee engagement, job performance, sustainability, information technology sector, PLS-SEM, Job Demands-Resources theory, Resource-Based View, HR management, Artificial, intelligence