AI Governance as a Cultural System: How National Cultural Configurations Shape Organizational Governance of Generative AI
“How do national cultural configurations shape the legitimacy, effectiveness, and evolution of organizational generative AI governance systems?”
Theoretical Logic & Inquiry Overview
Current AI governance frameworks often emphasize universal principles of accountability, transparency, human oversight, and responsible use. This research investigates whether the legitimacy and effectiveness of these mechanisms depend on culturally embedded expectations concerning authority, trust, uncertainty, accountability, and autonomy. Rather than treating national culture as a set of independent dimensions, the study conceptualizes culture as an integrated governance orientation and develops the construct of Cultural AI Governance Fit: the degree to which organizational AI governance mechanisms align with culturally embedded expectations. The framework further considers governance as a dynamic sociotechnical process in which sustained interaction with generative AI may itself reshape organizational expectations and governance norms over time.
The study examines cultural variation in expectations surrounding AI accountability, explainability, human oversight, algorithmic authority, autonomy, and governance legitimacy, and investigates how governance-cultural alignment affects trust, adoption, resistance, and compliance.
The research aims to develop a culturally contingent theory of generative AI governance, moving beyond universal governance prescriptions toward governance models that account for cultural context. It further proposes that AI governance systems are not merely shaped by organizational culture, but may themselves participate in the evolution of organizational governance norms.
Mixed-method, multi-country research design combining qualitative interviews, comparative thematic analysis, cross-cultural survey research, and configurational analysis. Potential methods include fsQCA, latent profile analysis, structural equation modeling, and longitudinal analysis across culturally diverse organizational settings.
Open to collaboration with researchers in AI governance, Information Systems, organizational theory, cross-cultural management, responsible AI, computational social science, and technology policy, particularly those working with multinational organizations or comparative cross-cultural datasets.