Academy of Management Global Proceedings
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.
Stelmaszak, M., & Aaltonen, A. (2018) Toward a Bidirectional View of Causality in Big Data Analytics: The Case of Learning Analytics. Academy of Management Global Proceedings, Vol. Surrey, No. 2018