Innovation Offshoring, Institutional Context and Innovation Performance: A Meta‐Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Innovation offshoring (IO) has become a widespread management practice. Yet, evidence on the performance implications is inconsistent, and scattered across disciplines and contexts. We argue that the benefits firms can derive from IO depend on the institutional environment at home. Drawing on recent work on institutional theory in international business, we explore institutions that facilitate reverse knowledge transfer and/or institutional arbitrage with respect to innovation‐related activities. The results of our meta‐analysis that synthesizes evidence from 48 samples show that IO is related positively to innovation performance. As predicted, this relationship is moderated by differences in the institutional environments across countries. Specifically, when national innovation systems are weak at home, IO appears to enable institutional arbitrage strategy whereas Confucian cultures enable more effective reverse knowledge transfer. However, contrary to our expectations, the beneficial effects of IO appear to have diminished over time.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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