Harnessing Artificial Intelligence for Learning Effectiveness in the Banking Sector: The Mediating Roles of Technological Self-Efficacy and Learning Orientation

The rapid integration of artificial intelligence (AI) technologies within Pakistan’s banking sector has significantly transformed workplace dynamics, reshaping employee roles, learning processes, and organizational expectations. Grounded in social cognitive theory (SCT), this study examines the influence of artificial intelligence usage on perceived learning effectiveness (PLE) and their capacity to effectively adapt to such technological changes. This study specifically investigates the mediating roles of technological self-efficacy (TSE) and learning orientation (LO) in this process. Using quantitative research design, data were collected via a structured questionnaire administered to managerial-level employees from various banks. The purpose sampling technique was used to collect the responses. The data were collected from a sample of 348 managers employed in public, commercial, and Islamic banks located in Punjab (e.g., Bahawalpur, Multan, and Lahore), and were subsequently analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that AI usage has a positive and statistically significant influence on technological self-efficacy and learning orientation. Likewise, technological self-efficacy and learning orientation have positive impact on perceived learning effectiveness. Furthermore, the relationship between AI usage and perceived learning effectiveness is mediated by two psychological constructs: technological self-efficacy and learning orientation. The findings indicate that the relationship between AI usage and perceived learning effectiveness is contingent upon both technological self-efficacy and learning orientation. These findings provide both theoretical and practical contributions, enriching the existing literature on technology adoption, workplace learning, and self-efficacy theory. Furthermore, this study provides empirically grounded recommendations for managers and organizational leaders operating within technology-intensive industries.