Published In

Academy of Management Global Proceedings

Document Type

Conference Proceeding

Publication Date

2018

Subjects

Big data, Data Science -- methods

Abstract

Currently most of the managerial literature on big data analytics assumes a straightforward, unidirectional relationship between data and phenomena they describe. Drawing from critical perspectives on big data, this paper posits that a bidirectional view of causality in big data analytics is needed. Relying on the theory of reactivity by Espeland and Sauder, the authors designed a mixed-methods case study involving both interviewing and a computational analysis of a big data set to lay bare the mechanisms at play behind the intended and unintended consequences in a learning analytics system deployed at a major UK business school. The authors argue that such a fuller view of causality in big data analytics sheds light on digital organising and managing in digital organisations.

Rights

This is the accepted manuscript version of a conference paper.
Final version copyrighted © by Academy of Management.

Description

Marta Stelmaszak was affiliated with London School of Economics and Political Science at the time of authorship.

Persistent Identifier

https://archives.pdx.edu/ds/psu/37038

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