A Meta-analysis of Innovation Offshoring and Firm Innovation Performance
Bibliographic record
Abstract
Innovation offshoring (IO) has become a widespread strategic practice over the last two decades. One of the most crucial outcomes of IO is innovation performance; yet, prior research shows a significant variation of empirical results regarding the direction and magnitude and of the IO-performance link. As a consequence, to date we know little as to how IO is related to innovation performance, and under which conditions IO unfolds their benefits or drawbacks. By drawing on neo-institutional theory, we examine how offshoring firms’ task environment and their home country regulative, normative and cognitive institutional dimensions shape the IO-innovation performance relationship. Our meta-analysis that combines 52 samples and 66,308 observations shows that IO is overall positively related to innovation performance. Furthermore, our results imply that the regulative (rule of law), normative dimension (openness of the market), and cognitive dimension (culture) of the institutional environment in which the offshoring firm is embedded are moderators of the IO-innovation performance relationship.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".