The role of firm innovativeness in the time of Covid-19 crisis: Evidence from Chinese manufacturing firms
Bibliographic record
Abstract
Can being innovative help firms to shield themselves from the detrimental effects of a crisis? This study employed a mixed-methods approach using empirical analysis based on firm-level secondary data of China's manufacturing sector and multi-case analysis to provide evidence on whether and how innovativeness could help businesses to survive the Covid-19 crisis and thrive afterward. We find that innovativeness empowers firms to withstand the negative financial consequences of the crisis. The first quarter 2020 analysis based on a sample of 606 manufacturing firms reveal that innovative firms appear more efficient and profitable and have significantly higher chances of survival than less innovative firms. Furthermore, the second-quarter results based on a sample of 582 firms show that innovative firms exhibit higher operating efficiency and a greater probability of survival relative to others. The results remain consistent even after controlling for common firm characteristics and sector fixed effects. From additional analyses, we further find that the innovativeness-performance association is even stronger than the one found during pre-crisis periods, suggesting that a firm's innovation capabilities have greater utility in the rapidly changing situation rather than a stable environment. The paper contributes to knowledge that will be of use to managers, researchers, and policymakers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".