Research Methods in Information Systems
| Instructors: | Frederik Ahlemann Jens Pöppelbuß Stefan Stieglitz Manuel Wiesche |
| Dates: (details will be shared approx. four weeks ahead of class) | 08.02.2022 KickOff (M. Wiesche + Alle) 24.02.2022 Qualitative Research (M. Wiesche) 03.03.2022 Data- und Network Analysis (S. Stieglitz) 14.04.2022 Structural Equation Modeling (F. Ahlemann) 06.05.2022 Design Science Research (J. Pöppelbuß) 27.06.2022 TBA (R. Schütte) |
| Location: | Online or at University of Duisburg-Essen/Ruhr-University Bochum/TU Dortmund University |
| Audience: | UA Ruhr PhD students in the first year of their PhD who are interested in conducting research in the Information Systems (Wirtschaftsinformatik) discipline. |
| Registration: | Please apply with a one-page summary of your profile (work history, experience with research methods, research focus, specific interests) via email to the secretariat of the Chair of Digital Transformation |
Course content
1. Introduction (Frederik Ahlemann, Manuel Wiesche)
- fundamentals of science
- overview of research and research methods in IS
- initial understanding of science and how a research discipline creates scientific process
- overview of the information systems field and which forms of scientific knowledge exist in the discipline
- overview of the publication process and a short introduction into reviewing
2. Qualitative Research (Manuel Wiesche)
- development of an understanding of qualitative exploratory research methods (incl. case studies & grounded theory methodology)
- understand when such methods should be applied
- understand which critical decisions and which challenges arise
- learn how the methods can be applied to research projects in the Information Systems (IS) field
- understand the espoused goal of theory development for novel phenomena but also other contexts in which qualitative-exploratory approaches can be helpful
- key methodological procedures and their applications
3. Data- and Network Analysis (Stefan Stieglitz)
- insights about different types of data (e.g. structured and unstructured data)
- learn how to collect, process and analyse data
- introduction of different methods (e.g. social network analysis, sentiment analysis & topic clustering)
- use real-world data sets to apply these methods to answer research questions
- examples will be provided about how to link theories and data analytics in a meaningful way
- critical reflection of data analytics (ethical implications, data protection & data management)
4. Structural Equation Modeling (Frederik Ahlemann)
- general approach of structural equation modeling and how it links to theory testing
- basic elements of a structural equation model & alternative approaches to measurement (reflective versus formative)
- insights into the basic steps of model development, testing & interpretation of results
- exploration of fundamental quality criteria for structural & measurement models
- different approaches to model validation (component based versus covariance-based)
- application of the learned knowledge via SmartPLS and a small demo dataset
5. Design Science Research (Jens Pöppelbuß)
- understanding of design science research as a main research paradigm in Information Systems
- differences to and reasonable integration with/complementation by behavioral research
- learn about different types of artifacts – including constructs, models, methods, and instantiations (e.g., software and hardware) – and discuss under what circumstances these can be considered valuable knowledge contributions to academia
- recognized process models & guidelines for conducting design science research
- importance of & approaches to artifact evaluation
- concepts of design theory & design principles
- abstract from specific artifact design projects towards generalizable design knowledge
- publication schema for design science studies & the anatomy for design theories