Isomorphism through algorithms: Institutional dependencies in the case of Facebook

dc.contributor.author

Caplan, Robyn

dc.contributor.author

boyd, danah

dc.date.accessioned

2023-09-27T15:39:04Z

dc.date.available

2023-09-27T15:39:04Z

dc.date.issued

2018-01

dc.date.updated

2023-09-27T15:39:03Z

dc.description.abstract

<jats:p> Algorithms and data-driven technologies are increasingly being embraced by a variety of different sectors and institutions. This paper examines how algorithms and data-driven technologies, enacted by an organization like Facebook, can induce similarity across an industry. Using theories from organizational sociology and neoinstitutionalism, this paper traces the bureaucratic roots of Big Data and algorithms to examine the institutional dependencies that emerge and are mediated through data-driven and algorithmic logics. This type of analysis sheds light on how organizational contexts are embedded into algorithms, which can then become embedded within other organizational and individual practices. By investigating technical practices as organizational and bureaucratic, discussions about accountability and decision-making can be reframed. </jats:p>

dc.identifier.issn

2053-9517

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2053-9517

dc.identifier.uri

https://hdl.handle.net/10161/29029

dc.language

en

dc.publisher

SAGE Publications

dc.relation.ispartof

Big Data & Society

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10.1177/2053951718757253

dc.title

Isomorphism through algorithms: Institutional dependencies in the case of Facebook

dc.type

Journal article

pubs.begin-page

205395171875725

pubs.end-page

205395171875725

pubs.issue

1

pubs.organisational-group

Duke

pubs.organisational-group

Sanford School of Public Policy

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Institutes and Provost's Academic Units

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Initiatives

pubs.organisational-group

Duke Science & Society

pubs.publication-status

Published

pubs.volume

5

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