Fostering employees' adoption of machine learning-enabled technologies in asset pricing

Purpose This research aims to explore how financial professionals perceive and apply machine learning (ML) in asset pricing, identifying key determinants of its adoption by examining the role of institutional pressures, individual and organizational factors. In addition, the study examines the moderating roles of technology interconnectivity and technology interoperability in the association between employees' ML adoption intention and actual usage behavior. Design/methodology/approach Using a quantitative approach, data were gathered from 689 financial professionals in Vietnam through an online survey targeting those with experience or knowledge in asset pricing. The research model was analyzed using structural equation modeling (PLS-SEM) via SmartPLS 4 software. Findings The results indicate that ML knowledge, perceived cognitive capabilities, personal innovativeness and leadership vision positively influence employees' intention to adopt ML. However, contrary to expectations, concerns about job displacement and organizational innovativeness were found to negatively affect ML adoption. Moreover, adoption intention strongly predicts the actual use of ML in asset pricing practices. Originality/value This study pioneered insights into how individual and organizational factors, such as leadership vision and cognitive abilities, shape ML adoption in finance. It highlights the interplay between digital transformation, employee perceptions and organizational culture in emerging markets. Policymakers and businesses can use these findings to drive innovation and enhance competitiveness in the ML-driven financial sector.