Impact of nationality composition in foreign subsidiary on its performance: a case of Korean companies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores how the nationality compositions of management teams and employee groups in foreign subsidiaries can affect subsidiary performance. By analyzing firm-level data on 401 South Korean subsidiaries across 35 countries in the period between 2005 and 2007, we found that balanced compositions in both subsidiary management teams (SMTs) and subsidiary employee groups (SEGs) were positively associated with subsidiary performance. The results suggest that the benefits of balanced composition are higher for both innovative and coordinative tasks conducted by management teams and for simple computational tasks conducted by employee groups. The effect of the SMT and SEG compositions on subsidiary performance, however, may depend on the host country's institutional conditions. These findings have practical implications for multinational staffing strategies in order to ensure high performance in subsidiaries and for host country policies used to attract high quality foreign direct investments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it