Artificial Intelligence Adoption in Talent Acquisition: Effects on Recruitment Efficiency, Algorithmic Fairness Perceptions, and Employee Experience

The deployment of artificial intelligence in talent acquisition has accelerated rapidly, yet empirical research examining the full pathway from adoption determinants to downstream effects on perceived recruitment efficiency, algorithmic fairness perceptions, and HR-reported employee experience remains limited. This study develops and tests an integrated theoretical framework combining the Technology Acceptance Model and the Technology-Organization-Environment framework to examine AI adoption in talent acquisition across mid-to-large United States enterprises. Using cross-sectional survey data from 523 human resource professionals and hiring managers representing 184 organizations across multiple industries, the analysis relies on partial least squares path modeling (PLS-PM) to test eight predictions linking technological perceptions, organizational factors, and adoption outcomes. Results indicate that perceived usefulness and perceived ease of use significantly predict AI adoption intention, with perceived usefulness exhibiting a stronger effect. Top management support and HR digital readiness are both positively associated with organizational AI adoption, though top management support demonstrates greater explanatory power. AI adoption is positively associated with recruitment efficiency across all three metrics examined: time-to-hire reduction, cost-per-hire reduction, and quality-of-hire improvement. Algorithmic transparency emerges as a strong predictor of procedural fairness perceptions, which in turn positively predict employee experience outcomes including organizational commitment, job satisfaction, and employer trust. Organizational size moderates the adoption-efficiency relationship such that larger firms realize proportionally greater efficiency gains. These findings contribute to the human resource management and information systems literatures by providing empirical evidence linking AI adoption antecedents to a chain of recruitment efficiency and employee experience outcomes, while highlighting the central role of algorithmic transparency in sustaining perceived fairness. Practical implications for HR leaders, technology vendors, and policymakers are discussed.