Exploring the Impact of an External Crisis on R&D Expenditures of Innovative New Ventures
Bibliographic record
Abstract
What is the impact of an exogenous crisis on research and development expenditures of innovative new ventures? Existing literature does not provide a clear answer. One view suggests that shrinking revenues and constrained funding reduce firms’ R&D intensity. The opposite view argues for amplified risk-seeking and innovative behavior of organizations in crisis, leading to higher commitment to R&D with resulting additional investments. We unite these opposing views in a generalized behavioral framework based on the premise that the impact of a crisis on a venture’s R&D expenditures is contingent on its pre-crisis R&D intensity. When facing a crisis, R&D-intensive companies reduce their R&D commitment, while non-R&D-intensive companies do not alter their R&D expenditure budgets, or even increase R&D spending to innovate themselves out of the adversity. We empirically test our behavioral framework using the longitudinal data from the Kauffman firm survey. The results strongly support our theoretical reasoning: during the 2008 financial crisis R&D-intensive ventures tended to substantively decrease their R&D investments (on average more than 10 percentage points decrease in R&D to sales), while their non-R&D-intensive counterparts demonstrated positive (although statistically insignificant) change in R&D investments. In other words, a crisis strikes most deeply the R&D activity of the most innovative ventures, despite the rational need to sustain R&D funding in industries with rapid technological change and short product life cycles. We conclude by positing underlying reasons for the observed behavioral patterns, and then suggest avenues for further research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".