Enhancing the Rigor of Qualitative Research: Application of a Case Methodology to Build Theories of IT Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it