A Language-Based Model of Organizational Identification Demonstrates How Within-Person Changes in Identification Relate to Network Position

Oct 10, 2022, 12:00 pm1:30 pm
Aaron Burr Hall 219
  • The Center for Statistics and Machine Learning
  • Department of Sociology
Event Description


Shifting attachments to social groups are a constant in the modern era.They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification? Whereas prior work has mostly focused on explaining variation between individuals, we develop a network-analytic theory of within-person changes in identification. We hypothesize that identification is positively related to occupying positions characterized by local clustering—having contacts who are mutually interconnected—and global bridging—having contacts who are disproportionately connected to individuals beyond a focal actor's direct reach. We use the tools of computational linguistics to develop a language-based measure of identification and find support for the theory using pooled data of internal communications from three disparate organizations.


Professor Goldberg received bachelor’s degrees in computer science and film studies from Tel Aviv University, and an MA in sociology from Goldsmith’s College, University of London. Before pursuing a PhD in sociology at Princeton University, he worked for several years as a software programmer, an IT consultant, and a technology journalist. An associate professor of organizational behavior at Stanford Graduate School of Business, his research projects all share an overarching theme: the desire to understand the social mechanisms that underlie how people construct meaning, and consequently pursue action. His work has been published in the American Journal of Sociology, the American Sociological Review, Management Science and the Review of Financial Studies.