Theorizing Routines with Computational Sequence Analysis: A Critical Realism Framework

Abstract

We develop a methodological framework to develop process theories on routines by leveraging large volumes of digital trace data following critical realism principles. Our framework starts with collecting and pre-processing digital trace data, corresponding to the empirically observed experience of critical realism. At the second and third steps of the framework, we identify a finite set of similar repetitive patterns (routines) through computational analysis. We accomplish this by combining frequent sub-sequence mining and clustering analysis to transform empirical observation into a set of routines that correspond to actual happening in critical realism. Then we employ a retroduction approach to identify generative mechanisms of the routines. At the final step, we validate the generative mechanisms by evaluating proposed processual explanations and/or eliminating alternatives. We provide an illustrative example of developing a process theory with regards to the collaboration pattern in Wikipedia.