Digital trace data derived from organizations’ information systems represent a wealth of possibilities in analyzing decision-making processes and organizational performance. While data-mining methods have advanced considerably over recent years, organizational process research has rarely analyzed this type of trace data with the objective of better understanding organizations’ decision-making processes. However, accurately tracking decision-making actions via digital trace data can produce numerous applications that represent new and unexplored opportunities for IS research.
The paper presents a novel method developed to combine quantitative process mining approaches with a variance perspective. Its viability is demonstrated by looking at teams’ decision patterns from a dynamic business-simulation game. This exploratory data-driven method represents a promising starting point for translating complex raw process data into interesting research questions connected with dynamic decision-making environments.
Kallio, H., Malo, P., Lainema, T., Bragge, J., Seppälä, T., & Penttinen, E. (2022). Generating Research Questions from Digital Trace Data: A Machine-Learning Method for Discovering Patterns in a Dynamic Environment. Communications of the Association for Information Systems, 51, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol51/iss1/12
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