Bibliographic record
Abstract
Purpose Recent research on intra‐organizational knowledge‐transfer showed that new capability development within multinational corporations shifts from parent companies to foreign subsidiaries. This paper seeks to identify antecedents and barriers for reverse capability‐transfer in multinational corporations. Design/methodology/approach The paper adopts a multiple case study approach based on active interviews at six subsidiaries of a multinational manufacturing company. Findings The results suggest that subsidiary autonomy, environmental heterogeneity, and managerial initiatives are all necessary antecedents of unique capability development at the subsidiary level, but that companies do not utilize foreign subsidiary‐originated capabilities in their home‐country operations. The results also show that person‐to‐person communication is required for intra‐MNC capability‐transfer in any direction, and that other forms of communication seem to be inefficient. Research limitations/implications A logical next step is the investigation of the phenomenon at the headquarters level with the goal to identify specific barriers for reverse capability‐transfer. Practical implications The findings support the idea that managers of multinational corporations should recognize that new unique capabilities originate not only at the parent company level but also at the foreign subsidiary level, and that it could be beneficial for the company as a whole to transfer these new capabilities back to the home country operation. Originality/value The study shows that in‐depth interviews provide the richest form of data for this type of research. Moreover, it provides a counter‐intuitive perspective on intra‐organizational knowledge and capability‐transfer in multinational corporations.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".