Isomorphism through algorithms: Institutional dependencies in the case of Facebook

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.

Duke Scholars

Author Robyn Caplan Sanford School of Public Policy