Algorithmic Institutionalism: How AI Systems Create and Enforce New Organizational Norms

Building on Li et al.'s (2021) work on trust in AI systems, this study asks: What happens when AI becomes not just an institutional tool but an institutional actor? Traditional institutional theory assumes human actors create and maintain norms, but AI systems now independently generate organizational policies, evaluate performance, and enforce compliance. This research would examine how AI algorithms create "algorithmic institutional pressures" that operate alongside regulative, normative, and cognitive pressures. Unlike existing studies that focus on AI adoption barriers, I'd investigate how AI systems generate autonomous institutional logics that reshape power dynamics within organizations. This challenges the human-centric assumptions in institutional theory while connecting to Schildt's (2022) work on digitalization's institutional logic.

References:

1. The Institutional Logic of Digitalization. H. Schildt (2022).
2. An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory. Jia Li, Yiwen Zhou, Junping Yao, Xuan Liu (2021). Scientific Reports.