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  • 标题:Isomorphism through algorithms: Institutional dependencies in the case of Facebook:
  • 本地全文:下载
  • 作者:Robyn Caplan ; danah boyd
  • 期刊名称:Big Data & Society
  • 电子版ISSN:2053-9517
  • 出版年度:2018
  • 卷号:5
  • 期号:1
  • DOI:10.1177/2053951718757253
  • 语种:English
  • 出版社:SAGE Publications
  • 摘要: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.
  • 关键词:Algorithms; accountability; Facebook; institutional theory; isomorphism; bureaucracy
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