Abstrakti
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.
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.
Alkuperäiskieli | Englanti |
---|---|
Artikkeli | 12 |
Julkaisu | Communications of the Association for Information Systems |
Vuosikerta | 51 |
Tila | Julkaistu - 2022 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |