Why Hide AI Use? Psychological Configurations and Explainable Machine Learning Evidence from Marketing Work

Area of Science:

• Workplace psychology
• Human-computer interaction
• Marketing technology

Background:

• Artificial intelligence (AI) is increasingly integrated into marketing roles.
• Employees may intentionally conceal AI's contribution to their work outputs.
• AI disclosure silence is a post-adoption behavior distinct from AI avoidance or general employee silence.

Purpose of the Study:

• To investigate the factors associated with AI disclosure silence among marketing employees.
• To examine the roles of threat-based conditions, safety and governance, and AI capability.
• To understand the complex pathways leading to AI disclosure silence.

Main Methods:

• A two-wave survey of 635 marketing employees using AI tools.
• Employed measurement validation, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA).
• Utilized explainable machine learning (SHAP) for predictive relevance interpretation.

Main Results:

• AI disclosure silence emerges through multiple pathways, not a single bottleneck.
• Key contributing factors include AI anxiety, fear of negative evaluation, perceived creativity threat, job insecurity, low trust in management, weak psychological safety, and unclear AI policy.
• Fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management showed the strongest predictive relevance.

Conclusions:

• AI disclosure silence is an emerging post-adoption disclosure construct in workplace AI research.
• Clear AI disclosure norms, supportive management, authorship guidelines, and safe AI governance are crucial.
• Findings provide configurational and predictive insights, encouraging cross-sector and cross-cultural validation.