Does deliberate learning lead to dynamic capability? The role of organizational schema for Kodak, 1993-2011
Bibliographic record
Abstract
Purpose The process of building dynamic capabilities remains understudied, although deliberate learning is posited to be the key to developing and maintaining dynamic capabilities in turbulent environments. Based on the case study of Kodak’s responses to the shift from traditional to digital technology in the imaging industry (1993-2011), the purpose of this paper is to examine the role of managerial cognition in building dynamic capabilities. Design/methodology/approach The paper employs case study and qualitative method approach. Findings The results reveal that, when facing environmental turbulence, deliberate learning is subject to routine disruptions through entrepreneurial activities, and these organizational routines and activities are determined by organizational schema. Organizational schema itself is updated as a result of managers’ ongoing interpretation of the organization’s fit with the environment. The study findings contribute to the organizational studies and management literature by highlighting the role of managerial cognition into the microfoundation of dynamic capabilities. Originality/value The results demonstrate managerial cognition, and organizational schema in particular, as a microfoundation of dynamic capability.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".