This book provides a comprehensive overview of digital trace data research in information systems and reflects on its idiosyncrasies throughout all phases of a research project. It delivers important advice on how to construct, manage, and execute research with digital trace data, it clarifies the complex relationship of digital trace data to the reality it purportedly represents, it helps devising research programs that systematically explore key phenomena, and it assists in how to leverage insights from digital trace data for theory building.

To this end, the book is organized into three interrelated parts. The first part focuses on the foundations of digital trace data research . It offers guidance on how to design and manage research projects that employ digital trace data and how it can be employed for theory development. The second part focuses on methods for digital trace data research and covers computational research methods and analysis techniques that support sense-making of digital trace data. Eventually, the third part presents selected examples for applications of digital trace research . It not only offers empirical insights into emerging phenomena but also showcases and reflects on the use of digital trace data.

While the main target audiences for the book are researchers and graduate students that want to learn about or aim to apply digital trace data research in their own projects, professionals in industry may also apply digital trace data research to improve their understanding of how their organization works and implement improvements accordingly.

Delivers guidance on how to design and manage research projects that employ digital trace data Covers computational research methods and analysis techniques that support the exploitation of digital trace data Provides empirical insights as well as case studies reflecting on the proper usage of digital trace data

Autorentext

Bastian Wurm is an assistant professor and research group leader at the Institute for Digital Management and New Media at LMU Munich School of Management. His group investigates various topics related to process and algorithmic management. His dissertation entitled “Organizational Complexity: Insights from Digital Trace Data Research” was awarded the Stephan Koren-Award for outstanding dissertations by WU Vienna.

Jan Mendling is the Einstein Professor of Process Science at Humboldt University, Berlin, Germany, and adjunct professor with Vienna University of Economics and Business, Austria, Principle Investigator at the Weizenbaum Institute, Berlin, and Co-Founder of Noreja Intelligence GmbH. His research interests include business process management and information systems. He has published more than 500 research papers and articles. He is one of the founding editors-in-chief of Process Science and Chair of the Steering Committee of the International Conference on Business Process Management.

Inhalt

1. Introduction.- Part 1: Foundations.- 2. Research Management for Digital Trace Data Research Project.- 3. Navigating the Meanders of the Digital Trace Data Research Process: Some Observations and Recommendations.- 4. Building Research Programs in the Computationally Intensive Theory Construction Genre.- 5. From Digital Trace Data to Theory: Pathways towards Theory Construction.- Part 2: Methods.- 6. Trace Methods: Probing the Apparatus.- 7. Visual Analytics.- 8. Encoding Techniques for Digital Traca Data.- 9. Process Mining.- 10. Text Mining.- 11. Digital Traces as Measurement Instruments for Variance-Theoretic Research in Information Systems.- Part 3: Applications.- 12. When Chatbots meet Process Mining: Conversation Mining in the Era of Digital Trace Data.- 13. The Role of Firm Interaction in Shaping Online Community User Networks.- 14. Leveraging Digital Trace Data in Teaching for Improving Students' Technology Use and Well-Being.