journal-of-information-and-optimization-sciences Journal of Information and Optimization Sciences 2169-0103 0252-2667 Taru Publications 10.47974/JIOS-2439 Examining trust, transparency and AI adoption on LinkedIn : Evidence from an extended UTAUT framework Rathod Chiragkumar B. Department of Business Management, Faculty of Management Studies, Smt. S. B. Patel Institute of Business Management, Sankalchand Patel University, Visnagar, Gujarat, 384315, India Mistri Vinitkumar M. Department of MBA, LDRP Institute of Technology & Research, Kadi Sarva Vishwavidyalaya, Gandhinagar, Gujarat, 382015, India Vagadia Abhipsa Department of MBA, Sunshine Group of Institutions, Affiliated with Gujarat Technological University, Rajkot, Gujarat, 360005, India Patel Chirag Rasikbhai Department of Business Management, Faculty of Management Studies, Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar, Gujarat, 384315, India Karnavat Bhagvan M. Department of Management Studies, National Forensic Sciences University, Delhi Campus, Delhi, 110085, India 47 9-B 3425 3433 30 09 2026

This study examines how artificial intelligence (AI) features on LinkedIn—job matching, connection recommendations, and career suggestions—shape user satisfaction and engagement. Extending the Unified Theory of Acceptance and Use of Technology (UTAUT), we surveyed 300 active users, including HR professionals, job seekers, students, and recruiters. Perceived usefulness and accuracy positively predicted satisfaction and engagement, while transparency raised trust and privacy concerns lowered it. Trust partially mediated the usefulness–satisfaction link, and transparency strengthened that link. Recruiters rated AI effectiveness higher than general users did. The findings underscore the need for ethical, transparent AI integration in professional networking.

 Artificial intelligence LinkedIn Professional networking UTAUT Algorithmic transparency User trust Recruitment technology access open retracted no