STUDYING DYNAMICS AND CHANGE WITH DIGITAL TRACE DATA: A SYSTEMATIC LITERATURE REVIEW

Digital trace data offer promising opportunities to study dynamics and change of various socio-technical phenomena over time. While we see a surge of empirical and conceptual articles, we lack a systematic understanding of why, how, and when digital trace data are or can be used to study dynamics and change. In this article, we present the findings of a systematic literature review to uncover common approaches, motivations, findings, and general themes in the existing literature. We systematically reviewed 40 studies that were published in premium outlets in the information systems field. Our review sheds light on (1) underlying purposes of such studies, (2) utilized data sources, (3) research contexts, (4) socio-technical phenomena of interest, (5) applied analytical methods, and (6) measures that are being used. Building on our findings, we point to several implications for research and shed light on avenues to advance this field in the future

Using Action Networks to Detect Change in Repetitive Patterns of Action

We report on a simple method for detecting changes in repetitive patterns of action. The method involves computing the correlation of action networks before and after a focal date that is moved through an event log that represents the history of a process. Unlike process mining methods that focus on identifying an accurate model of a process at one point in time, we use a simplified model of the process to detect changes over time. The method can be used to objectively identify the date on which a process change occurred and also the relative magnitude of the change. We demonstrate the method with longitudinal data from two different organizations