Bibliographic record
Abstract
Despite increased recognition of the importance of knowledge creation, understanding how organizations transfer knowledge is limited. Organizations use greenfields to experiment with and expand the implementation of new work practices. Greenfields are new plants, typically but not exclusively manufacturing, that belong to an existing organization. Hyundai learned from its initial greenfield failures in Canada and Turkey to build successful ventures in India, China and the U.S. Greenfields are important to understand because they offer advantages to organizations expanding into new economic and labor markets. Greenfields typically require a lower upfront investment than acquisitions or mergers and a greenfield minimizes the downside risks. Thus a greenfield can start as a relatively smaller operation and expand at the most optimal time. But the challenges starting up a greenfield cannot be overlooked. Despite potential, greenfields have not been thoroughly explored in their own right. As a result, we really don’t understand greenfield implementations that well. This is increasingly important as firms migrate new operations outside their country of origin. Greenfield managers provided insight from their experience in this exploratory study. Eight themes are identified and discussed based on follow-up interviews with these managers. The overarching goal is to better understand how knowledge transfer occurs in these interesting team operations.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".