Factors Affecting the Performance of Micro, Small and Medium Enterprises (MSMEs) in Indonesia during COVID 19 Pandemic
Bibliographic record
Abstract
Due to the Covid 19 pandemic, Indonesia's economic growth in the first quarter of 2020 has fallen to 2.97; it recorded -5.35 in the second quarter and -3.49 in the third quarter (Mulyani, 2020). This decline in growth has undoubtedly shaken micro, small and medium enterprises (MSMEs). According to the Ministry of Cooperatives for Micro, Small and Medium Enterprises (2020), 18.83% of MSMEs suffered hampered production, 22.9% experienced decreased demand, 18.87% faced difficulties in obtaining raw materials and 20.01% encountered hampered distribution. MSMEs in Indonesia contribute 60.4% of GDP and 97% of employment (Economic Indicator, 2019). However, they were severely affected by the Covid 19 pandemic. Therefore, examining the performance of MSMEs during the period of COVID 19 pandemic is crucial. Moreover, the pandemic has resulted chaotic economic conditions and changes in social order. Economic chaos and changes in social order could either strengthen or weaken the resilience of MSMEs. According to Tencer & Cadoso (2014), innovation arises because of chaos and unhealthy market domination. Ivanus & Repanovici (2016) mentioned that MSMEs need to have clear innovation strategy, adjust to market demand, make changes in production costs and show product quality in order to increase the economic growth. As supported by Christensen et al. (2018), MSMEs innovate to ensure their business continuity is maintained. Thus, innovation is particularly important for business survival in the era of Covid 19 pandemic. However, Martinez-Vergara and Vall-Pasola (2020) found that some businesses did not innovate. As such, there is a need to scrutinize further on the influence of business owners' characteristics on innovation and its effect on performance. Keywords: Characteristics, Innovation, Micro small and medium enterprises (MSMEs), Performance
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".