When is enough enough? A critical assessment of data adequacy in IS qualitative research

Abstract

Qualitative researchers across disciplines, including information systems (IS), face new pressures to ensure the transparency of their studies and their accountability for knowledge claims. As qualitative research becomes more scrutinized, researchers need to demonstrate transparency in their methods. However, the methods sections in published articles may not provide enough details to meet the changing expectations and policies of journals. This raises the issue of how to judge a qualitative study without imposing inappropriate criteria, such as quantitative metrics (e.g., volume of data) or standard templates that may not match the diversity of qualitative approaches. Based on these concerns, we clarify the status of data and their adequacy for achieving research objectives. We show how data adequacy can support theoretical reasoning in three modes of inference: induction, deduction, and abduction. We include illustrative practices for researchers wishing to adopt more transparent practices for judging and reporting data adequacy.