Challenges and Best‐practice Recommendations for Designing and Conducting Interviews with Elite Informants
Bibliographic record
Abstract
Abstract Elite informants (i.e., those in the upper echelon of organizations) are a key data source for building and testing theories in management research. We offer best‐practice recommendations to overcome challenges in designing and conducting interviews with elite informants (EIs) based on a comprehensive and multidisciplinary literature review and information provided by subject matter experts (i.e., authors of recently published articles that included EI interviews). Given unique characteristics of EIs and differences compared to interviewing non‐EIs, we provide recommendations on how to address challenges related to: (1) research design (e.g., what is the best order for the interviews?); (2) data collection (e.g., how can researchers access EIs? what is the best format for the interview? how can researchers obtain more honest responses?); and (3) reporting of results (i.e., what information should researchers report and to whom?). Finally, we offer suggestions for future EI research focusing on methodological issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.567 | 0.634 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.016 | 0.018 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".