History‐informed strategy research: The promise of history and historical research methods in advancing strategy scholarship
Bibliographic record
Abstract
Abstract Research Summary The last decade has witnessed an increasing interest in the use of history and historical research methods in strategy research. We discuss how and why history and historical research methods can enrich theoretical explanations of strategy phenomena. In addition, we introduce the notions of “history‐informed strategy research,” distinguishing between the dimensions of “history to theory” and “history in theory” and discussing various under‐utilized methods that may further work on history‐informed strategy research. We then discuss how contemporary research contributes to history‐informed research within the strategy field, examine key methodological and empirical challenges associated with such research, and develop an agenda for future research. Managerial Summary Firms are increasingly making use of their historical past as they reflect on their identities and how these can be used strategically. At the same time, strategy researchers are paying increasing to the use of historical research methods, as well as to how firms use history strategically. We take stock on the role of history in strategy research, outline the key strategic issues that can be informed by a historical way of doing research, discuss the available historical methods, and offer suggestions for future research in the history/strategy intersection.
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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 | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".