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 begins with collecting and preprocessing digital trace data, corresponding to the empirically observed experience of critical realism. In 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 subsequence 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. In 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 in relation to the collaboration pattern in Wikipedia.
Zhang, Zhewei; Lee, Habin; Yoo, Youngjin; and Choi, Youngseok Thomas
"Theorizing Routines with Computational Sequence Analysis: A Critical Realism Framework,"
Journal of the Association for Information Systems, 23(2), 589-630.
Available at: https://aisel.aisnet.org/jais/vol23/iss2/1
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