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Data Sciences Speaker Series – A Language-Based Model of Organizational Identification Demonstrates how Within-Person Changes in Identification Relate to Network Position – Prof. Amir Goldberg
Session Description
October 20, 2022 @ 12:00 pm - 1:15 pm
A Language-Based Model of Organizational Identification Demonstrates how Within-Person Changes in Identification Relate to Network Position – Prof. Amir Goldberg
Join us for the Data Sciences Speaker Series with Prof. Amir Goldberg, Associate Professor of Organizational Behavior and Sociology (by courtesy), Stanford University. This talk is co-sponsored by the Data Sciences Institute and the Department of Sociology, University of Toronto Scarborough, and launches a new initiative: Computational and Quantitative Social Sciences, DSI@UTSC.
Date: October 20 , 2022
Time: 12:00 pm – 1:15 pm ET
Format: Hybrid (In-person & Virtual)
In person location – Room AA160, 1265 Military Trail, Scarborough, ON M1C 1A5, Arts & Administration Building, University of Toronto Scarborough Campus
Doors will open at 11:45 am and a light lunch will be available
Virtual link – Zoom: https://utoronto.zoom.us/j/88911252361, Meeting ID: 889 1125 2361, Passcode: dsss2022
No registration required
Talk Title: A Language-Based Model of Organizational Identification Demonstrates how Within-Person Changes in Identification Relate to Network Position
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.
About the speaker: Amir Goldberg’s research lies at the intersection of cultural sociology, data science and organization studies. He is interested in understanding how social meanings emerge and solidify through social interaction, and what role network structures play in this process. As co-director of the computational culture lab, Amir uses and develops computationally intensive network- and language-based methods to study how new cultural categories take form as people and organizational actors interact.
