What Factors and Experiences Motivate Innovators? An Expectancy-Value-Cost Approach to Promoting Student Innovation
Bibliographic record
Abstract
This multi-manuscript dissertation integrated a systematic literature review, interviews, and a developed survey instrument to investigate the expectancies, values, and costs that are involved in motivating current Canadian innovators. Using the Expectancy-Value-Cost framework, the research investigated individual innovators' motivations, but also considered the relationship between individual motivations and the environments (e.g., climates, contexts, and the surroundings of innovators) and strategies (e.g., approaches, interventions, and decisions made by figures of importance within contexts) that they experience. This research offers unique insights in alignment with innovation education that address paucities within the innovation literature at large, particularly the relative lack of research addressing motivations of the innovative individual. The findings of this research nuance and advance the knowledge of promotive and hindering motivational factors that can inform the design of innovation promotion efforts. Innovator participants also identified specific strategies that they use to make their innovating more likely and gave advice to future innovators regarding maximizing expectancies and values, whilst mitigating perceived costs of innovation. Innovators also reflected on their educational experiences to identify the mechanisms that formal and informal education can provide in increasing the prevalence of innovation among Canadian students.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".