When Corporations Are People: Agent Talk and the Development of Organizational Actorhood, 1890–1934

Abstract

Research in organizational theory takes as a key premise the notion that organizations are “actors.” Organizational actorhood, or agency, depends, in part, on how external audiences perceive organizations. In other words, organizational agency requires that external audiences take organizations to be agents. Yet little empirical research has attempted to measure these attributions: when do audiences assume that organizations are agents and how have these attributions changed over time? In this article, I suggest that scholars can triangulate across computational methods—including named entity recognition, dependency parsing, topic models, and dictionary methods—to analyze attributions of agency in text, discourse that I term “agent talk.” I demonstrate the utility of this approach by analyzing how business organizations were discussed as agents during a key period of organizational development, the turn of the twentieth century. Analyzing articles from two of the leading national newspapers, the Wall Street Journal and New York Times, I examine agent talk in everyday business discourse. I find that agent talk generally increased over the early twentieth century, as organizations were depicted as active subjects in text and personified as speakers. Moreover, I find that this discourse was concentrated in social and legal semantic contexts: in particular, contexts relating to labor, regulation, and railroads. Finally, I show the uneven growth of this rhetoric over time, as organizations across different semantic arenas were personified as speakers. Overall, these results show how measures of discourse can provide a window into how and when audiences endow organizations with actorhood.

Cultural Matching in Networks: A Combination of Machine Learning and Exponential Random Graph Models to Examine Homophily in Identity Performance

“…With the rise of machine learning and other computationally intensive methods, there is a growing body of work applying computational methods to sociological analysis of cultural meaning, beliefs, and networks (Arseniev-Koehler and Foster 2022; Boutyline and Vaisey 2017;Goldberg 2011;Knight 2022;Kozlowski, Taddy and Evans 2019;Snijders 2011;Voyer et al 2022;Zhou 2022). This paper contributes to this line of work by applying machine learning techniques to the study of identity and identification (Long and So 2015;So, Long, and Zhu 2019;So and Roland 2020).…”

“…First, there is no constraint regarding the sources of data. In addition to applying computational methods to text data (Arseniev-Koehler and Foster 2022, Knight 2022, Kozlowski, Taddy and Evans 2019, Nelson 2021, Voyer et al 2022, Zhou 2022, such methods have also been applied to social survey data for a long time (Boutyline and Vaisey 2017, Brensinger and Sotoudeh 2022, DellaPosta 2020, Goldberg 2011. Second, while the computational method is a quantitative technique, its methodology is compatible with qualitative methodologies (Nelson 2017, Nelson 2021 and can be combined with several important theoretical insights coming from qualitative analyses (Arseniev-Koehler andFoster 2022, Nelson 2021).…”

Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research

“…The applications of LLMs for computational text analysis extend well beyond classification tasks. The flexibility of the approach dovetails with the way that computational sociologists are increasingly conducting what Bonikowski and Nelson (2022) term "methodological bricolage," concatenating multiple techniques into their analyses (e.g., Nelson 2017;Knight 2022;Pardo-Guerra and Pahwa 2022). Conventional machine learning methods are often dichotomized into supervised or deductive models, like text classifiers, and unsupervised or inductive models, like topic modeling (Molina and Garip 2019).…”

The Sociology of Interpretation

“…Text parsers provide the possibility of inferring de-pendency relationships between words in specific sentences (Stuhler 2022). Knight (2022a) uses such an approach to demonstrate an increasing prevalence of agentic metaphors applied to corporate actors in the media.…”