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Process mining refers to a family of algorithms used to computationally reconstruct, analyze and visualize business processes through event log data. While process mining is commonly associated with the improvement of business processes, we argue that it can be used as a method to support theorizing about change in organizations. Central to our argument is that process mining algorithms can support inductive as well as deductive theorizing. Process mining algorithms can extend established theorizing in a number of ways and in relation to different research agendas and phenomena. We illustrate our argument in relation to two types of change: endogenous change that evolves over time and exogenous change that follows a purposeful intervention. Drawing on the discourse of routine dynamics, we propose how different process mining features can reveal new insights about the dynamics of organizational routines.
|Titel||Proceedings of the 53rd Hawaii International Conference on System Sciences|
|Redakteure/-innen||University of Hawaii|
|Seiten||5492 - 5501|
|Publikationsstatus||Veröffentlicht - 2020|