Business practices of highly innovative Japanese firms
Bibliographic record
Abstract
Highly innovative firms are more competitive and achieve greater performance than their less innovative counterparts. Innovation orientation has been commonly used to assess an organization's innovative culture. To date, most innovation orientation research has explored its relationship with performance. However, the literature is unclear as to what innovative companies do differently to achieve superior performance. This study advances innovation orientation research by examining differing business practices of high versus low innovative Japanese firms. The various business practices include culture management, open innovation, analytics, innovation management software, crowdsourcing, design thinking, measuring innovation, stage-gate, and scientific discovery. Using data from 261 Japanese firms, this study finds that high innovators, as compared to low innovators, are more likely to engage in many of these business practices. Until this study, some of these business practices were not empirically shown to be correlated with high innovators, much less explored in the same study. This paper also offers a stepwise approach for executives seeking to enhance competitiveness via innovation. Specifically, executives should first look to creating an innovation orientation and subsequently implement such business practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".