Miles and Snow Typology: most influential journals, articles, authors and subject areas
Bibliographic record
Abstract
Purpose The purpose of this paper is to find out the most influential journals, articles, authors and the subject areas where Miles and Snow typology is used. The study identifies the opportunities for future research as well. Design/methodology/approach Review is based on 196 journal articles selected through a systematic and rigorous search process from the four databases: ProQuest, Business Source Complete, Willy and Science Direct. Total Citation, threshold citations, fractional citation and citation per year techniques are used for analyses. Findings Strategic Management Journal (SMJ), Academy of Management Journal (AMJ) and Journal of Marketing (JOM) are the most influential Journals. The most influential and prolific articles on the subject are from Hambrick (1983), Conantet al.(1990), Dotyet al.(1993), Sabherwalet al.(2001), Desarboet al.(2005) and Fiss (2011). Management, strategic management and marketing are the most studied subject areas. Originality/value Although there have been many reviews of the literature on this typology, the systematic review on Miles and Snow typology to find out the most influential journals, authors, articles and subject area has not been done before.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.080 | 0.047 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".