History and the micro‐foundations of dynamic capabilities
Bibliographic record
Abstract
Abstract Research Summary The capacity to manage history is an important but undertheorized component of dynamic capabilities. We argue that the capacity to manage the interpretation of the past, in the present for the future, is a critical ability that informs a firm's ability to successfully enact changes needed to adapt to disruptive technology. We identify and elaborate three specific cognitive interpretations of history—history as objective fact, history as interpretive rhetoric, and history as imaginative future‐perfect thinking—and demonstrate how these different views of history can be mobilized by managers to sense, seize, and reconfigure around opportunities made available by understanding the invisible thread of technology. Managerial Summary History is typically understood to be a constraint on a manager's ability to effect change. A firm's past is assumed to create inertia in routines and structures that compromise a firm's ability to change. We show how acquiring a broader understanding of the role of history can improve a manager's ability to enact organizational change. Studying the evolution of technology over time and across products allows managers to sense opportunities created by technological change. Using different narrations of the past as continuous or disruptive can improve a manager's ability to motivate or resist change. Using the past to construct convincing scenarios of the future, managers can enroll key stakeholders in the industry to support a strategic direction that advances the firm's strategic goals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".