4.3 Article

Make Way for the Algorithms: Symbolic Actions and Change in a Regime of Knowing

期刊

ORGANIZATION SCIENCE
卷 32, 期 1, 页码 18-41

出版社

INFORMS
DOI: 10.1287/orsc.2020.1377

关键词

knowledge; knowing; symbolic action; algorithmic technologies; artificial intelligence; analytics; technology introduction; digital transformation; work

资金

  1. Judge Business School internal grants scheme
  2. Canada Research Chairs program

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By engaging in symbolic conformity and advocacy, employees and technologists inadvertently brought about a radical change in the sales regime of knowing, shifting it from customer understanding through personal contact to model predictions based on processing large datasets.
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, and 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. Although 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 on model predictions fromthe 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.

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