The Same New Story? - A Quantitative Analysis of Artificial Intelligence’s Impact on Organizational Isomorphism

• In Event: Economic Sociology Section Roundtables
  In Refereed Roundtable (60 minute): Roundtable 3: AI and Algorithms

Abstract

This paper investigates whether and how Artificial Intelligence (AI) functions as an accelerator of institutional isomorphism across organizational fields. Building on the classic framework of DiMaggio and Powell (1983), the study reconceptualizes AI not merely as a productivity-enhancing tool but as an emerging form of institutional infrastructure that embeds standardized models of decision-making, evaluation, and organizational rationality. While recent theoretical work has suggested that AI may intensify mimetic, normative, and coercive pressures (Hein-Pensel et al., forthcoming), systematic large-scale empirical evidence remains limited.
To address this gap, the study develops a quantitative research design that measures organizational similarity using a multidimensional similarity vector combining structural, textual, and relational indicators. Using panel data on publicly listed firms between 2015 and 2025, the analysis constructs an AI intensity index based on patent activity, AI-related job postings, and disclosure-based text measures. Fixed-effects panel regressions and difference-in-differences models are employed to estimate whether increases in AI adoption are associated with convergence within industries.
The paper advances three main arguments. First, higher levels of AI adoption are expected to be associated with greater organizational similarity, particularly through mimetic and normative mechanisms. Second, AI-driven convergence should be strongest in digitally intensive sectors. Third, the relationship is likely nonlinear, with early adopters differentiating and later adopters converging.
By introducing scalable computational measures of isomorphism and linking them to AI diffusion, the study contributes to organizational sociology’s core debates on institutional change, technological infrastructures, and field-level dynamics. Substantively, the findings speak to growing concerns that AI—while often framed as a driver of innovation—may also reinforce dominant organizational templates and reduce diversity within organizational fields.

Authors

• Sebastian Schuhmann, Institute for Applied Informatics

• Franziska Hein-Pensel, Institut für Angewandte Informatik (InfAI) e. V.

• Sabine Hartig, InfAI (University Leipzig)

• Amit Kirschenbaum