Long Interviews in Organizational Research: Unleashing the Power of “Show and Tell”
Bibliographic record
Abstract
Long interviews represent a powerful method for generating rich qualitative data, yet they are rarely used in organizational research. To address their untapped potential, our paper provides methodological clarity on long interviews and illustrates how they can help researchers do and see more. We reflect in depth on our own experiences using long interviews to produce a theme analysis that illuminates the nature, role, and benefits of long interviews in organizational research. Our key insight is that long interviews are more than simply “speech events”; they offer a unique opportunity to unleash the power of “show and tell,” both by integrating material objects and spaces into the conversation and by making them the locus of interaction. Importantly, such interactions can elicit rich narrative details that inspire conceptual leaps, which, in turn, can lead to the development of impactful theories. We conclude with detailed recommendations for conducting long interviews and engaging in show-and-tell.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".