Computationally-intensive theorizing is an emerging genre in organizational research, information systems research and the management sciences. Because actors in organizations use all kinds of digital technologies to do their work, an increasing share of work-related activities are stored as digital trace data. Such digital trace data can reflect communication patterns (e.g., from e-mails or collaboration platforms), workflow activities (e.g., from ERP systems), decision-making outcomes (e.g., from work record systems), physical movements (e.g., from GPS sensors), among others. Because researchers can analyze such digital trace data with all kinds of computational tools (e.g., sequence analysis, machine learning), they encounter novel opportunities to theorize about organizational phenomena. 

Various top journals, including Academy of Management Journal, Organization Science, Management Science, MIS Quarterly, Information Systems Research and others, encourage authors to use digital trace data and computational methods to develop new or refine existing theories. The basic argument is that digital data offer granular views into organizational phenomena, and their collection is non-intrusive, allowing for unbiased insights into how actors behave in work settings. 

Despite the promises, however, conducting computationally-intensive research comes with challenges. These pertain to all major steps in a research project, including  data collection, data analysis, interpretation of findings, and presenting results in a convincing manner. In particular, since digital data often lack the context in which they were produced and collected, considerable attention has to be placed on the computational tools that are used to analyze them, along with the integration of other data sources, such as qualitative-inductive insights through interviews or observations.

This course provides a comprehensive introduction into digital trace-data based theorizing, enabling PhD students to pursue related research design within their projects.

This course will familiarize PhD students from the management and organizational sciences with the basics of computationally-intensive research. The goal is two-fold.

1. On a theoretical level, students will learn about the opportunities of such research studies, as well as their threats and how to mitigate them. To this end, we will discuss the role of (novel) data for theory development, and we will reflect on previous research and how studies can be positioned to contribute to top journals in the management and organizational fields.

2. On a practical level, students will be empowered to conduct their own computationally-intensive research. Students will plan and conceptualize a study design involving computationally intensive research using their current PhD topic (if existing). We will work on the following questions: What kinds of data could you use? What computational tools can you use to analyze these data? How will insights help you to contribute to existing research?

Attendance according to WU regulations.

Theoretical Input

Discussions

Hands-on Exercises

Presentation of assigned paper 20%

Presentation of own research idea 40%

Final Paper 40%

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thomas.grisold@wu.ac.at