This article examines how knowledge workers manage generative AI adoption in ethically charged workplace contexts, focusing on how perceived usefulness and necessity coexist with ethical and societal concerns.
We use a multi-phase mixed-method design combining qualitative interviews, a quantitative survey, and supplementary qualitative follow-up interviews. Study 1 draws on 22 semi-structured interviews and abductive, theory-informed qualitative analysis, informed by constructivist grounded theory principles, to develop a process account of acceptance under moral tension. Study 2 surveys 543 knowledge workers and uses latent-variable structural equation modelling to examine selected relationships suggested by the qualitative mechanism.
Study 1 shows that acceptance is shaped by tension between “must-use” necessity beliefs and ethical/societal risk beliefs. Knowledge workers manage this tension through strategies such as input sanitization, selective “safe-task” use, verification routines, responsibility shifting, trivialization, and bolstering. Study 2 provides partial quantitative support: perceived risk and perceived necessity strongly predict experienced cognitive dissonance, while the dissonance–intention relationship is specification-sensitive, significant in the direct model but non-significant in the mediated specification. The high HTMT value between perceived usefulness and behavioural intention further requires caution in interpreting PU-related paths.
The study contributes a mechanism-oriented account of generative AI adoption under ethical tension. Cognitive dissonance is theorized as a complementary process lens, not as a validated replacement for TAM3.
