Environmental factors impacting the motivation to innovate: a systematic review
Bibliographic record
Abstract
The environments where innovation occurs are often as varied as the areas of endeavors that aspiring innovators could pursue. This systematic review followed the guidelines of the Campbell Collaboration and PRISMA to consolidate the findings of 74 studies into the Expectancy-Value-Cost motivation theoretical framework as a means of usefully isolating for decision-makers the environmental factors that impact the motivation to innovate. The results of this review reveal that additional study of interdisciplinary samples is needed to gather deep narrative and case-driven data that considers the experiences of innovators in addition to organizations. Leaders, including decision-makers, teachers, and supervisors, can set a precedent for their learners and workers to use their past experiences and to feel safe to take intelligent risks and make reasonable mistakes in pursuit of innovating. Ensuring that project teams have a mix of experiences and backgrounds can make for more productive collaborations. Proactively addressing costs can increase workplaces' psychological safety and stability, which enables workers and learners to better focus on the endeavors at hand. The articles' evaluation illustrates that conversation about innovation promotion is dominated by business, which reduces the opportunity to learn from other innovation-driven disciplines or take truly interdisciplinary approaches.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".