The impact of AI service quality on researchers' work wellbeing: a dual-path moderated mediation model

As AI tools permeate scientific research, a critical question emerges: does better AI actually make researchers happier? Grounded in the Job Demands-Resources (JD-R) model, this study proposes and tests a dual-path moderated mediation framework in which perceived AI service quality influences researcher work well-being through two parallel psychological routes. The motivational gain pathway posits that higher quality is positively related to research self-efficacy, while the loss-alleviation pathway posits that higher quality is negatively related to cognitive verification load. AI trust is introduced as a moderator of both first-stage paths. Using data from 439 researchers and PROCESS macro models 4 and 7, we found that AI service quality did not directly affect well-being (β = 0.07, p = 0.260), but exerted a fully mediated total effect. The loss pathway via cognitive verification load reduction was the sole significant mediating mechanism (indirect effect = 0.08, 95% CI [0.028, 0.135]). AI trust significantly moderated only the loss pathway (β = -0.21, p < 0.001): the cognitive-relief effect became significant only at medium and high trust levels, remaining inoperative for low-trust researchers. These findings reframe the value of AI quality in research settings as cognitive offloading rather than competence enhancement, and position trust as a threshold condition rather than a continuous amplifier of AI benefits.