Impact of organizational innovation on product and process innovation
Bibliographic record
Abstract
The purpose of this paper was to test the effect of organizational innovation on product and process innovation (while controlling for endogeneity). Our hypothesis was that organizational innovation should have a significant and positive impact on technical (product or process) innovation. We control for endogeneity by using a Poisson estimator that accommodates a binary endogenous regressor. We test 10 potential instruments using a battery of test criteria and settle on five. All results are presented using the five instruments to avoid expectation bias. In general we find that organizational innovation does impact technical innovation positively. With the 2009 data we find that the mean of the average treatment effect for product innovation is roughly 1.7 times that of process innovation. For the 2009–2012 data we find that the impact on product innovation is roughly 1.5 times that of process innovation. For the 2012 data, we had anomalous results for process innovation, such that organizational innovation reduced the number of process innovations by 2.3 per year. In terms of Canadian government policy, the results lend support to the view that technical innovation is not the only innovation that matters. The right policy mix may encourage firms to experiment with and adopt more organizational innovations to enhance technical innovation.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".