R&D subsidiary embedment: a resource dependence perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate researchers' operationalization of the construct of embedment of overseas R&D subsidiaries. Design/methodology/approach First, the paper provides a systematic literature review of subsidiary embedment research. Second, it draws on resource dependence theory (RDT) and argues how embedment of overseas R&D subsidiaries should be treated as a more multi‐faceted and complex phenomenon than has been apparent in the literature to date. Findings The authors find a large variation in the operationalization of embedment (e.g. frequency of communication versus depth of integration versus direction of communication). They also find scant attention to the nature of differences between external actors (types of actors, including local and international). These represent weaknesses that inhibit the advancement of theory and policy within the context of the globalization of innovation. Research limitations/implications Researchers should treat R&D subsidiary embedment as a multi‐level phenomenon consisting of resource‐dependence interactions between collective entities internal and external to the subsidiary. R&D subsidiary embedment research design can be improved by being: formative; multiple‐actor; bi‐directional; and longitudinal. Practical implications Managers should treat external R&D subsidiary embedment as pattern of resource dependencies in which the actors that matter most to R&D subsidiary performance are a function of the importance and availability of the innovation‐specific resources they contain. This involves building a capability in multi‐level networking with R&D resource providers in the external environment. Originality/value The contribution of the current paper is to provide a critical evaluation of scholarly treatment of the construct of R&D subsidiary embedment, and to develop a foundation for operationalizing and analyzing the external embedment of R&D subsidiaries.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".