Does organizational innovation moderate technical innovation directly or indirectly?
Bibliographic record
Abstract
We find a divergence in the literature regarding the treatment of how organizational innovation affects innovation and performance. One point of view suggests that organizational innovation impacts performance only, while the other suggests that it impacts technical innovation and firm performance. We use the framework of Crepon-Duguet-Mairesse (CDM) to control for endogeneity; we also use two different measures for organizational innovation. Our contributions to the literature are: the CDM framework in this context is novel; prior research either did not/could not control for endogeneity whereas the CDM framework mitigates this. To discriminate between the direct and indirect approach, we implemented AIC and BIC tests. We find that for the innovation equations in all cases and regardless of which organizational innovation variable is used the direct model is preferable. In contrast, for the productivity equations, we find that in all cases the indirect model is preferable. Thus we do not have a definitive statistical test for which model is superior. Yet, it is our contention that organizational innovation is a new routine within the firm that should impact technical innovation. Furthermore, organizational design theories deduce that organizational innovation should impact technical innovation-implying that the direct model is indeed preferable.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".