Finding Theory-Method Fit: A Comparison of Three Qualitative Approaches to Theory Building
Bibliographic record
Abstract
This article, together with a companion video, provides a synthesized summary of a Showcase Symposium held at the 2016 Academy of Management Annual Meeting in which prominent scholars—Denny Gioia, Kathy Eisenhardt, Ann Langley and Kevin Corley—discussed different approaches to theory building with qualitative research. Our goal for the symposium was to increase management scholars’ sensitivity to the importance of theory-method “fit” in qualitative research. We have integrated the panelists’ prepared remarks and interactive discussion into three sections: an introduction by each scholar, who articulates their own approach to qualitative research; their personal reflections on the similarities and differences between approaches to qualitative research, and answers to general questions posed by the audience during the symposium. We conclude by summarizing insights gleaned from the symposium about important distinctions among these three qualitative research approaches and their appropriate usages.
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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 | 0.464 | 0.576 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".