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Title: EXAMINING THE EFFECT OF TRUST ON BEHAVIORAL INTENTION AND AI-HRIS ADOPTION: A STUDY OF HR PROFESSIONALS IN PAKISTAN
Authors: Dr. Sundus Khalid , Sardar Ammar Khan , Dr. Asif Shahzad
Journal: Journal of Business and Management Research (JBMR)
Publisher: GO GREEN RESEARCH AND EDUCATION
Country: Pakistan
Year: 2025
Volume: 4
Issue: 3
Language: en
This study examines the role of trust in artificial intelligence (AI) in influencing the adoption of AI-driven Human Resource Information Systems (HRIS). It focuses on how HR professionals’ perceptions of AI’s reliability, fairness, and transparency affect their behavioral intention to use AI-based HRIS tools, and how this intention mediates actual adoption. Grounded in the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, a conceptual model is developed to link trust, behavioral intention, and AI-HRIS adoption. Survey data were collected from HR professionals working in Pakistan’s various IT based firms and analyzed using Structural Equation Modeling (SEM). The results indicate that greater trust in AI significantly enhances the intention to adopt, and this intention serves as a key mediator between trust and actual use. In particular, when AI systems are viewed as dependable and transparent, HR professionals demonstrate stronger willingness to adopt and use AI-HRIS solutions. This study extends the TAM and DOI frameworks by integrating trust as a central construct and applying them to the under-researched Pakistani HR context. The findings offer valuable insights for practitioners and policymakers by underscoring the importance of transparent algorithms, ethical AI governance, and targeted user training in building trust and facilitating successful AI integration in HR practices. By emphasizing trust as a foundational element, the study provides a practical and theoretical basis for advancing AI adoption in human resource management.
Keywords: Artificial Intelligence; Human Resource Information Systems (HRIS); Trust in AI; Behavioral Intention; Technology Adoption.
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