Enhancing the Rigor of Qualitative Research: Application of a Case Methodology to Build Theories of IT Implementation
Bibliographic record
Abstract
This paper presents and illustrates how the approach proposed by Eisenhardt (1989) for building theories from intensive qualitative research, more precisely case study research, can help information systems and medical informatics researchers understand and explain the inherently dynamic nature of IT implementation. The approach, which adopts a positivist view of research, relies on past literature and empirical data as well as on the insights of the researcher to build incrementally more powerful theories. We describe in some detail how this methodology was applied in a particular case study on IT implementation in the health care context and how the use of this approach contributed to the discovery of a number of new perspectives and empirical insights. Furthermore, we provide insights into the many choices that a researcher must make when adopting this methodological approach. Overall, using Eisenhardt's approach as a starting point, our objective is to provide a rigorous, step-by-step methodology for using case studies to build theories within the information systems and medical informatics fields. We provide several insights to the nature of case research, information on and concrete examples of specific techniques and tools, and guidance on how to improve intensive case research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.356 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".