Uncovering Causality in Narratives of Collaboration: Actor-Network Theory and Event Structure Analysis

  • Marisa Ponti University of Gothenburg
Keywords: actor-network theory, case study, causal interpretation, collaboration, event structure analysis, narrative

Abstract

Uncovering the underlying order in organizational change narratives to determine event causalities is a long-standing methodological problem. The order emerged within a narrative from the reconstruction of sequences of events can be taken as evidence of the causal relations between specified aspects of reality. This evidentiary status of causality attributed to narratives may be taken for granted when using actor-network theory (ANT) as a methodology, because ANT descriptions and explanations cannot be separated. This article suggests that the use of ANT benefits from merging CALLON's processes of translation and event structure analysis (ESA). Proposed is an approach for merging the two, which provides an interpretation of main ESA concepts in ANT terms. This article describes the application of this approach in a case study, and argues that the conceptual tools offered by ANT and ESA tap into the potential of narratives to be simultaneously descriptive and explanatory by fostering an explicit deployment of temporal order, connectedness, and unfolding of events.

URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1201117

Downloads

Download data is not yet available.

Author Biography

Marisa Ponti, University of Gothenburg

Marisa PONTI (Ph.D., University of Gothenburg) is Senior Lecturer in the Department of Applied Information Technology, Chalmers University of Technology ǀ University of Gothenburg. Her intellectual interests include digital media and collaboration in education and science, virtual ethnography, actor-network theory

Published
2011-11-14
How to Cite
Ponti, M. (2011). Uncovering Causality in Narratives of Collaboration: Actor-Network Theory and Event Structure Analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 13(1). https://doi.org/10.17169/fqs-13.1.1659