Research in Cognition and Strategy: Reflections on Two Decades of Progress and a Look to the Future
Bibliographic record
Abstract
This review of cognition in strategic management research takes as its starting point the appreciation of the seminal paper, ‘Competitive groups as cognitive communities: the case of Scottish knitwear manufacturers’, by Porac, Thomas and Baden-Fuller on cognitive categorization of competition, published in the Journal of Management Studies only 20 years ago. In this paper, I reflect on the context in which their paper emerged, the impact it has had, and the future paths that research on cognition in strategy might take. In doing so, I highlight the challenges associated with establishing cognition as a legitimate factor in strategic management (alongside the traditional explanations of capabilities and incentives) and of showing the causal relationship between cognition and strategic outcomes. Subsequent work in cognition explored the dynamic relationship between cognition, capabilities, and incentives, and, in process models of framing, linked cognition with political action. Rather than managerial cognition becoming its own independent field, cognitive concepts have diffused throughout work in many different managerial fields, leading to a proliferation of terms, concepts, and approaches. I conclude by exploring some of the paths that research in cognition and strategy is taking in the present day – particularly those involving studies of the construction of markets and categories, each of which are themes that the work by Porac, Thomas and Baden-Fuller brought to our attention.
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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.019 | 0.043 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".