A Dynamic Perspective on Slack Resources and Innovation in Challenging Institutional Contexts
Bibliographic record
Abstract
As new ventures grow and mature, they must navigate current demands and anticipate future challenges. One mark of venture success is the accumulation of slack resources. Slack resources are seen as a double-edged sword for commitment to innovation. We argue that understanding the slack resources-innovation relationship is incomplete without integrating a dynamic and firm environment perspective. We examine the relationship between slack change over time and innovation and propose that this relationship is contingent on slack change speed (i.e., the dynamic slack trajectory) and institutional instability. We test our hypotheses using a sample of 127 recently IPOed Chinese firms covering the 2010-2015 period. Our results help to reconcile mixed findings reported in prior research regarding the slack resources-innovation relationship.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".