Antecedents of the Effective Implementation of Artificial Intelligence in Talent Acquisition: A Quantitative Study

Abstract

Artificial Intelligence (AI) is transforming business operations across industries, with talent acquisition (TA) experiencing some of the most dramatic changes. These transformations are highly visible in the shifts they are bringing to recruitment and related human resource tasks. In this quantitative study, the impact of four core variables, including the level of AI adoption, digital policies and systems, workforce readiness, and organizational culture are examined with respect to their influence on the effectiveness of AI in recruitment processes. The research is grounded in the Technology Acceptance Model (Davis, 1985), Resource-Based View (Barney, 1991), and Institutional Theory (Scott, 1995, 2013), integrating theoretical insights with practical applications. Data were collected from 229 HR professionals using a combination of purposive and convenience sampling, undertaken primarily through social networking-based snowballing. Using principal component analysis and multiple regression, the findings reveal that the level of AI adoption (β = 0.519, p < .001), digital policies and systems (β = 0.209, p = .015), and organizational culture (β = 0.151, p = .045) are statistically significant predictors of AI effectiveness in TA, whereas workforce readiness did not emerge as a significant factor in the final model. These findings suggested that effective AI deployment is dependent on organizational commitment and a culture that supports innovation. The study offers valuable implications for the practice of HR, encouraging strategic AI integration and cultural alignment. Also, it contributes to policy discussions by underscoring the need for future frameworks to support organizational readiness and digital transformation in HR functions.