Generative AI acceptance linked with service performance through employee cognition

Employees who report greater acceptance of generative artificial intelligence are more likely to elaborate on the knowledge it provides, and a two-wave study of 890 supervisor–employee dyads links that cognitive adaptation with better supervisor-rated service performance. The study, reported in BMC Psychology, found that generative AI acceptance was positively associated with knowledge elaboration, which was itself positively related to service performance. The estimated indirect association through knowledge elaboration was significant, while the direct association between acceptance and performance also remained significant.

The research examined psychological adaptation to generative AI in service work, focusing on employees’ acceptance of the technology and its relationship with their engagement with AI-generated knowledge. The investigators tested whether knowledge elaboration could serve as a cognitive mechanism connecting acceptance with service performance. They also examined whether future orientation altered the relationship between generative AI acceptance and knowledge elaboration. The analysis was based on a two-wave, multi-source survey design rather than an experiment or a direct test of workplace interventions.

At Time 1, employees reported their generative AI acceptance, future orientation and demographic information. At Time 2, employees reported their knowledge elaboration, while supervisors assessed service performance. This arrangement placed the major measures in sequence and used different sources for employee perceptions and performance evaluation. The researchers tested the proposed mediation and moderated mediation models with regression analysis and bootstrapping procedures. Their results supported the estimated indirect association between generative AI acceptance and service performance through knowledge elaboration, as well as the moderating role of future orientation: the relationship between acceptance and knowledge elaboration was stronger among employees reporting higher future orientation. The significant moderated-mediation index indicated that the estimated indirect association through knowledge elaboration was stronger at higher levels of future orientation.

These results identify knowledge elaboration as a measured pathway associated with service performance and future orientation as a psychological boundary condition for the relationship between generative AI acceptance and that pathway. The findings do not show that acceptance or knowledge elaboration caused improved service performance. The observational survey design cannot establish causal direction or rule out other factors that may influence technology acceptance, cognitive engagement and performance. Scope was also limited to service-sector employees and their supervisors, so the supplied findings do not establish that the same relationships hold in other occupations, industries or with different AI systems. The reported model therefore offers evidence about statistical associations in AI-integrated service work, not proof that changing a worker’s level of acceptance, knowledge elaboration or future orientation will produce measurable performance gains.

Subject of Research: Associations among generative AI acceptance, knowledge elaboration, future orientation and service performance

Article Title: Psychological adaptation to generative AI in service work: knowledge elaboration and future orientation

Article References: Li, X., Ou, Y., & Wang, J. (2026). Psychological adaptation to generative AI in service work: knowledge elaboration and future orientation. BMC Psychology. https://doi.org/10.1186/s40359-026-05477-3

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05477-3

Keywords: generative AI, service work, knowledge elaboration, future orientation, employee adaptation, service performance, survey research, mediation analysis, workplace psychology, Psychological, adaptation, generative