海外子会社の企業者活動 : インタビュー調査に基づいて <論説>
Bibliographic record
Abstract
The number of subsidiaries of Japanese multinational enterprises (JMNEs) is increasing in the past two decades. In this situation, in order to be competitive in a global market, JMNEs need to deploy resources and capabilities of their foreign subsidiaries, not solely dependent on parent companies' capabilities with which we are quite familiar and they also need to encourage entrepreneurship of them. In this paper, we made interviews with senior executives of Japanese automobile subsidiary and its first-tier Japanese parts makers in Canada to examine how an automobile transplant is encouraging or discouraging its parts suppliers' initiative to make full use of their resources and capabilities. Through the study, we found that entrepreneurship in the Japanese parts maker is impacted by automobile maker, especially automobile parent as well as parts parent both in Japan. Under the Keiretsu system, the entrepreneurial discretionary power of first-tier parts subsidiary is constrained. Whether or not the finding of the limited entrepreneurship is applicable also to the relationship between other Japanese automobile transplant and its captured parts subsidiary or to the relationship between other Japanese industrial MNEs and their supplier is still to be seen.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.076 | 0.027 |
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".