Advancing Process Research with Digital Trace Data

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Type and Duration

PhD-Thesis, since July 2023

Coordinator

Hilti Chair of Business Process Management

Main Research

Business Process Management

Description

Digital trace data research is becoming more and more ubiquitous in information systems (IS) research and offers prodigious opportunities to study temporal dynamics, processes, and change underlying various socio-technical phenomena. Prior research leveraged the large quantity, fine-granular nature, and temporal features of digital trace data to study several process-related phenomena such as organizational change or routines. Yet, research still lacks a deep understanding of how process dynamics and change can be explained, influenced, and shaped over time by relying on digital trace data. Hence, this research project uses digital trace data, along with adequate computational methods to analyze it, to study how processes take shape, unfold, and evolve over time. For this purpose, the research project draws on digital trace data sets (i.e., event log data from various processes) from financial institutions in Liechtenstein. From a methodological perspective, systematic literature reviews, qualitative approaches (e.g., interviews or observations), and computational methods (e.g., process mining) are employed. Current studies particularly highlight the role of complexity and IS design interventions for process and routine dynamics. All in all, this research project aims to contribute to process research with digital trace data and to provide valuable implications for practice that can lead to process improvements.

PhD-Student

Sandro Franzoi, MSc

Supervisor

Prof. Dr. Jan vom Brocke

Co-Supervisor

Prof. Dr. Nicholas Berente