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Academy of Management Global Proceedings

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Conference Proceeding

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Big data, Data Science -- methods


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.


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


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

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