Development of Innovation Capabilities in The Covid-19 Pandemic Era and Its Effect on Financial Performance
Bibliographic record
Abstract
Background: The COVID-19 pandemic has profoundly affected multiple aspects of life, particularly the business sector, which has struggled with the challenges posed by decreasing consumer spending. Globally, an economic crisis has occurred in many countries, including Indonesia, which experienced a recession in the third quarter of 2020 with a GDP decline of 3.49%. As the country with the highest number of COVID-19 cases in Southeast Asia and a high mortality rate, Indonesia faces major challenges in economic recovery. The government has implemented various measures to support people affected by the pandemic. In this situation, companies must develop adaptive strategies not only to survive but also to seize opportunities to improve their performance, even surpassing pre-pandemic conditions.Purpose: To explain how manufacturing companies in Indonesia invest in R&D to develop their innovation capabilities and financial performance during the crisis caused by the COVID-19 pandemic, to prove the differences between the innovation capabilities of Indonesian manufacturing companies before and during the crisis and its effect on financial performance.Design/methodology/approach: The research was conducted on 37 Indonesian manufacturing companies that disclosed expenditures for research and development (R&D) activities. Observational data are financial reports from 2018 to 2021. Data analysis techniques use the Wilcoxon Signed-Rank Test and Linear Regression.Findings/Result: The R&D intensity and ROA before and during the crisis did not differ significantly, whereas ROE showed a significant difference. Innovation capabilities show a significant effect on financial performance, both in ROA and ROE.Conclusion: Only 22% of Indonesian manufacturing companies allocate funds for R&D activities. Nevertheless, the Indonesian manufacturing companies under study demonstrated greater efforts to innovate during the COVID-19 pandemic. Investment in R&D increased from an average of 0.67% before the pandemic to 1.06% during the pandemic. The company's financial performance showed a decline during the pandemic.Originality/value (State of the art): This research has the novelty of revealing the development of innovation capabilities of Indonesian manufacturing companies during the COVID-19 pandemic and using a financial approach to prove the influence of innovation capabilities on company financial performance. Keywords: financial performance, innovation capabilities, manufacturing companies, pandemic era; research and development (r&d)
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".