Bibliographic record
Abstract
The relationship between slack resources and innovation is complex, with the literature linking slack to both breakthrough innovations and resource misallocation. We reconcile these conflicting views by focusing on a novel mechanism: the role slack time plays in the endogenous allocation of time and effort to innovative projects. We develop a theoretical model that distinguishes between periods of high- (work weeks) versus low- (break weeks) opportunity costs of time. Low-opportunity cost time during break weeks may induce (1) lower quality ideas to be developed (a selection effect); (2) more effort to be applied for any given idea quality (an effort effect); and (3) an increase in the use of teams because scheduling is less constrained (a coordination effect). As a result, the effect of an increase in slack time on innovative outcomes is ambiguous, because the selection effect may induce more low-quality ideas, whereas the effort and coordination effect may lead to more high-quality, complex ideas. We test this framework using data on college breaks and on 165,410 Kickstarter projects across the United States. Consistent with our predictions, during university breaks, more projects are posted in the focal regions, and the increase is largest for projects of either very high or very low quality. Furthermore, projects posted during breaks are more complex, and involve larger teams with diverse skills. We discuss the implications for the design of policies on slack time. The online appendices are available at https://doi.org/10.1287/orsc.2018.1215 .
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".