Paper Type
Complete
Paper Number
PACIS2025-1554
Description
Generative artificial intelligence is causing automation anxiety (AA) amongst some workers. One way to cope with that emotion is through discourse. This research examines AA conversations using a Netnographic study of Reddit data science community We develop a research model that (1) identifies how AA emerges and is appraised by affected individuals, (2) uncovers the discursive mechanisms—particularly discursive closures—that shape community responses to these expressions, and (3) explains how these interactions lead to discursive stagnation, preventing the development of shared understanding or adaptation strategies. Our model reveals that AA is not experienced equally: less active community members are more likely to be dismissed, reinforcing their marginal status. This creates a cycle of exclusion, where concerns remain unaddressed and dominant perspectives are amplified. We contribute to the scholarly conversation on AA and the future of work by demonstrating how these internal community dynamics hinder meaningful dialogue and collective adaptation.
Recommended Citation
Retamales, Jorge; Asadi Someh, Ida; Indulska, Marta; and Rinta-Kahila, Tapani, "Automation Anxiety and Discursive Closures in Online Data Science Communities" (2025). PACIS 2025 Proceedings. 17.
https://aisel.aisnet.org/pacis2025/general_topic/general_topic/17
Automation Anxiety and Discursive Closures in Online Data Science Communities
Generative artificial intelligence is causing automation anxiety (AA) amongst some workers. One way to cope with that emotion is through discourse. This research examines AA conversations using a Netnographic study of Reddit data science community We develop a research model that (1) identifies how AA emerges and is appraised by affected individuals, (2) uncovers the discursive mechanisms—particularly discursive closures—that shape community responses to these expressions, and (3) explains how these interactions lead to discursive stagnation, preventing the development of shared understanding or adaptation strategies. Our model reveals that AA is not experienced equally: less active community members are more likely to be dismissed, reinforcing their marginal status. This creates a cycle of exclusion, where concerns remain unaddressed and dominant perspectives are amplified. We contribute to the scholarly conversation on AA and the future of work by demonstrating how these internal community dynamics hinder meaningful dialogue and collective adaptation.
Comments
General