Learning to Build a Car: An Empirical Investigation of Organizational Learning
Bibliographic record
Abstract
abstract This study provides a longitudinal empirical examination of the basic elements of Nonaka's (1994 ) dynamic theory of organizational knowledge creation. First, the data illustrate the notion that knowledge creation in organizations proceeds through an intertwined four‐phase process: (1)socialization(tacit knowledge amplification); (2)externalization(tacit knowledge is transformed into explicit knowledge); (3)combination(explicit knowledge amplification); and (4)internalization(explicit knowledge is transformed into tacit knowledge). Second, the study extends Nonaka's theory by comparing the relative amount of intra‐organizational knowledge transfer occurring during periods of product redesign with the amount of knowledge transfer occurring during steady‐state periods. Thequestionnairedata suggest that the overall level of knowledge transfer is higher during periods of product redesign than it is during the steady state, whereas theinterviewdata indicate that there were more mentions of knowledge transfer during the steady state. Third, the data suggest that there may be benefit in addingtacit error correctionas a fifth phase in the learning cycle. This phase is characterized by a dual emphasis on externalization and internalization. Implications of these findings are discussed.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".