Hidden Generative AI Use in Consulting: Social-Cognitive and Organizational Factors in AI Disclosure and Concealment

Generative artificial intelligence (GenAI) is increasingly embedded in consulting and professional-service work, yet employees may not always disclose its contribution to work outputs. This study examines hidden GenAI use as a social-cognitive, organizational, and human-AI interaction phenomenon and investigates how individual and organizational factors are associated with responsible AI disclosure and concealment. Survey data were collected from 564 consulting and professional-service employees in Türkiye. The analytical framework combined PLS-SEM with covariance-based SEM robustness checks, cross-validated predictive modeling with out-of-fold SHAP interpretation, and exploratory Choquet decision support. The structural results showed that AI disclosure anxiety and competence penalty fear were positively associated with hidden GenAI use, whereas social intelligence, official AI tool adequacy, and responsible AI disclosure were negatively associated with concealment. AI metacognition, human-AI trust, AI policy clarity, social intelligence, and official AI tool adequacy were positively associated with responsible AI disclosure. In the predictive analysis, Ridge regression achieved the strongest average cross-validated performance, while XGBoost was used for nonlinear interpretation through out-of-fold SHAP values; AI disclosure anxiety and competence penalty fear emerged as the strongest predictors. The exploratory Choquet analysis consistently prioritized a non-punitive AI-use disclosure system under alternative weighting and uncertainty scenarios. The study contributes by shifting attention from whether professionals adopt GenAI to whether, why, and under what organizational and social conditions they disclose or conceal its substantive contribution to professional work. The cross-sectional design supports association-based rather than causal interpretation.