Artificial Intelligence Adoption, AI Trust, and Employee Performance: Evidence of a Mediation Model among Corporate Employees

Abstract

As artificial intelligence (AI) has swiftly entered the organizational landscape, there has been a rising interest in comprehending the association it has on employee productivity. The adoption of AI has been studied in the context of its association on employee performance, statistically mediated by AI trust, based on the Technology Acceptance Model (TAM) and Social Exchange Theory (SET). The study adopted the quantitative approach to a cross-sectional survey with a structured questionnaire and primary data were collected from 231 corporate employees in Bangladesh. Hayes' PROCESS Macro (Model 4) was used to perform the mediation analysis (5,000 bootstrap samples). Results validate that there is a positive effect of the use of AI on employee performance (β = 0.459, p < .001) and on AI trust (β = 0.489, p < .001) and that AI trust is an independent predictor of employee performance (β = 0.413, p < .001). The bootstrapped mediation analysis results show that AI trust is significantly mediated in the relationship between AI adoption and employee performance (indirect effect B = 0.180, 95% CI [0.116, 0.255]), meaning that AI adoption influences employee performance both directly and indirectly through AI trust. These findings emphasize the importance of trust as a key psychological factor in technology adoption and its impact on workforce performance, and the need for organizations to invest in transparency, AI awareness training, and ethical guidelines to harness the potential of AI to the fullest. This study offers empirical data from an emerging economy context to complement the AI – performance literature, which is mainly from the Western context.