Bringing Context Inside Process Research with Digital Trace Data

Abstract

Context is usually conceptualized as external to a theory or model and treated as something to be controlled or eliminated in empirical research. We depart from this tradition and conceptualize context as permeating processual phenomena. This move is possible because digital trace data are now increasingly available, providing rich and fine-grained data about processes mediated or enabled by digital technologies. This paper introduces a novel method for including fine-grained contextual information from digital trace data within the description of process (e.g., who, what, when, where, why). Adding contextual information can result in a very large number of fine-grained categories of events, which are usually considered undesirable. However, we argue that a large number of categories can make process data more informative for theorizing and that including contextual detail enriches the understanding of processes as they unfold. We demonstrate this by analyzing audit trail data of electronic medical records using ThreadNet, an open source software application developed for the qualitative visualization and analysis of process data. The distinctive contribution of our approach is the novel way in which we contextualize events and action in process data. Providing new, usable ways to incorporate context can help researchers ask new questions about the dynamics of processual phenomena.