Make way for the algorithms: symbolic actions and change in a regime of knowing
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Authors
Pachidi, S
Berends, H
Faraj, S
Huysman, M
Publication Date
2021-01Journal Title
Organization Science
ISSN
1047-7039
Publisher
Institute for Operations Research and the Management Sciences
Volume
32
Issue
1
Pages
18-41
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Pachidi, S., Berends, H., Faraj, S., & Huysman, M. (2021). Make way for the algorithms: symbolic actions and change in a regime of knowing. Organization Science, 32 (1), 18-41. https://doi.org/10.1287/orsc.2020.1377
Abstract
When actors deem technological change undesirable, they may act symbolically by pretending to comply while avoiding real change. In our study of the introduction of an algorithmic technology in a sales organization, we found that such symbolic conformity led unintendedly to the full implementation of the suggested technological change. To explain this surprising outcome we advance a regime-of-knowing lens that helps to analyze deep challenges happening ‘under the surface’ during the process of technology introduction. A regime of knowing guides what is worth knowing, what actions matter to acquire this knowledge, and who has the authority to make decisions around those issues. We found that both the technologists who introduced the algorithmic technology, as well as the incumbent workers whose work was affected by the change, used symbolic actions to either defend the established regime of knowing or to advocate a radical change. While the incumbent workers enacted symbolic conformity by pretending to comply with suggested changes, the technologists performed symbolic advocacy by presenting a positive side of the technological change. Ironically, because the symbolic conformity enabled and was reinforced by symbolic advocacy, reinforcing cycles of symbolic actions yielded a radical change in the sales' regime of knowing: from one focused on a deep understanding of customers via personal contact and strong relationships, to one based upon model predictions from the processing of large datasets. We discuss the theoretical implications of these findings for the introduction of technology at work and for knowing in the workplace.
Sponsorship
Cambridge Judge Business School internal grant
Identifiers
External DOI: https://doi.org/10.1287/orsc.2020.1377
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301651
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