Isomorphism through algorithms

Institutional dependencies in the case of Facebook

Bibliographic Data

ID5260690
AuthorsRobyn Caplan (0000-0002-6407-3116, Rutgers, the State University of New Jersey, corresponding author), Drick Boyd (0000-0002-7722-7778, Microsoft (United States)), Danah Boyd
Year2018
Volume5
Issue1
Publication date2018-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/2053951718757253
OpenAlexW2788918314
LanguageEN
Citations received129
References cited19

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

Accountability · Big data · Bureaucracy · Data mining · Data science · Economics · Knowledge management · Management · Organizational analysis · Organizational theory · Political science · Politics · Sociology · Blockchain Technology Applications and Security · Computer Science · Ethics and Social Impacts of AI · Information Systems Theories and Implementation · Artificial Intelligence

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