Integrating corporate foresight with open innovation: enhancing competitiveness of equipment and technology suppliers to the US forest sector
Bibliographic record
Abstract
Equipment and technology suppliers to the United States (US) forest sector are confronting challenges and opportunities to respond to disruptive changes in an increasingly challenging business environment. Integrating corporate foresight (CF) with open innovation (OI) may contribute to enhanced competitiveness of these equipment and technology suppliers in today’s complex business context. Due to its collaborative and interdisciplinary nature, open innovation is crucial for success. Corporate foresight may enhance the ability of firms to implement open innovation, in turn enhancing innovation in the US forest sector through collaboration. This study uses data from a questionnaire-based study to investigate the current state of OI and CF activities that are applied by equipment and technology suppliers to the US forest sector. The results present strengths and weaknesses of OI and CF maturity among respondents and indicate that improved collaboration is necessary for OI management and a culture change must be facilitated for CF management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".