The Generation of Qualitative Data in Information Systems Research: The Diversity of Empirical Research Methods

• Published in: Communications of the Association for Information Systems 2019, Vol.44, p.572-599
• Main Author: Goldkuhl, Göran
• Format: Article
• Language: English
• Subjects: Data analysis; Data Collection; Data Generation; Empirical analysis; Information Systems; Qualitative analysis; Qualitative Research; Research Method; Research methodology
• ISSN: 1529-3181; 1529-3181
• DOI: 10.17705/1CAIS.04428

This paper investigates the concept of data collection in information systems qualitative research. In this text, I replace the term “data collection” with “data generation” to emphasize that the researcher arranges situations that produce rich and meaningful data for further analysis. Data generation comprises activities such as searching for, focusing on, noting, selecting, extracting, and capturing data. This paper analyzes and compares a repertoire of empirical research methods for generating qualitative data. It describes and visualizes (through a common data-generation template) 12 research methods: interviewing, questionnaire study, document study, artifact study, observation study, participant observation, intervention study, practice-based design study, lab-based design study, focus group study, test study, and self-reporting. I compare these data-generation methods according to 1) the researcher’s role in data generation, 2) data generation’s influence on everyday life reality, 3) each data-generation method’s relationship to everyday life reality, 4) what parts/mediators of everyday life reality each data-generation method addresses, 5) the expected value of generated data and 6) possible shortcomings in generated data. As a basis for investigating data generation, I ontologically clarify (based on a practice-theoretical perspective) the empirical landscape of information systems (the kinds of phenomena and sources of data that exist). A concluding discussion contains 1) analyses concerning relationships between data-generation methods and compound research methods/strategies such as case study research, action research, and design science research and 2) the role of interpretation in data generation versus data analysis.