Leader strategies for motivating innovation in individuals: a systematic review
Bibliographic record
Abstract
= 375) search through EBSCOhost completed on April 6th, 2019 in addition to search engine use. Three hundred three studies were full-text reviewed yielding 82 final studies eligible for the inclusion in findings extraction. The findings were synthesized and then organized into the Expectancy-value-cost (EVC) motivation framework to isolate promotive and hindering factors. It is clear that there is an unbalanced primacy in the innovation literature in favor of business and corporate settings with very little representation from the arts or social justice sectors. There is also a common trend of using surveys of individuals in organizations within a single discipline, while interviews are rare. The paucity of studying costs of innovation in the literature is symptomatic of the primarily positive psychology approach taken by studies, rather than a framework like EVC which also considers detractive factors like costs. Numerous studies provide support for the notion that more internal motivations like intrinsic (e.g., interest) and attainment (e.g., importance, fulfillment) were more influential than external motivators like rewards as targets of strategies. Leaders should focus, whenever possible, on topics that engaged curiosity, interest, and satisfaction and, if they choose to use rewards, should focus their strategies to give related rewards; otherwise, they risk sundering the internal motivation to innovate for already interested workers.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".