This study aims to use quantile regression methodology to examine the differential impacts of artificial intelligence (AI) adoption in human resource management practices across the conditional distribution of organizational effectiveness in Gulf organizations, providing nuanced insights beyond traditional average treatment effects.
A cross-sectional survey of 500 HR professionals across six Gulf Cooperation Council nations (UAE, Saudi Arabia, Qatar, Oman, Kuwait and Bahrain) was conducted. Quantile regression models at the 25th, 50th and 75th percentiles were estimated to analyze AI adoption patterns in hiring and performance evaluation, with comparative analysis against Ordinary Least Squares and robust regression methods.
Results reveal significant heterogeneity in AI adoption effects across organizational effectiveness levels. High-performing organizations (75th percentile) demonstrated 34% higher AI adoption benefits in hiring processes compared to median performers, while lower-performing entities (25th percentile) showed minimal adoption advantages. UAE and Saudi Arabia exhibited the highest integration rates, with Qatar displaying elevated algorithmic bias concerns across all performance quantiles.
The heterogeneous effects suggest that uniform AI implementation strategies are inadequate. High-performing organizations require advanced AI applications, while lower-performing entities need foundational capability building and targeted support mechanisms before AI deployment.
This research uses quantile regression methodology to study AI adoption in HRM across the Gulf region. It reveals that AI benefits vary across organizational effectiveness levels, with high-performing organizations experiencing 34% higher benefits. The study challenges traditional “one-size-fits-all” implementation paradigms by demonstrating that AI benefits are conditional on organizational readiness. This provides an evidence-based framework for differentiated AI implementation strategies, bridging the gap between academic research and practical implementation guidance in AI-driven HRM in emerging economies.
