Work climate, innovativeness, and firm performance in the US forest sector: in search of a conceptual framework
Bibliographic record
Abstract
Innovativeness can help companies differentiate themselves, with the ultimate goal of securing survival and improving performance. Modern theories in organizational behavior look at innovation as something that starts with individual creativity but that is also affected by the work environment. Using one broad industry sector, the US forest products industry, this study attempts to integrate into a unifying model the concepts of work climate, innovativeness, and firm performance using structural equation modeling. Results support the proposed theoretical model, with some modifications, finding a positive and significant relationship among all factors. Having innovation as a core part of a company’s strategy and fostering a climate for innovation positively affects the degree of innovativeness and performance of a company. This is especially true for secondary or value-added wood products manufacturers. A climate for innovation is characterized by high levels of autonomy and encouragement, team cohesion, openness to change and risk taking, and sufficient resources available to people. Lack of a validation sample suggests treating the model as tentative until further testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".