TECHNOLOGY ADOPTION: A SOLUTION FOR SMES TO OVERCOME PROBLEMS DURING COVID- 19
Bibliographic record
Abstract
Unfortunately, SMEs expect to provide a significant share in the economic growth of the nations, but the organizations are facing the problem of resource limitations Most of the consumers were spending their disposable income on buying products, but due to COVID-19, most of the consumer is facing a job loss or salary cut, so the spending power is decreasing In the United Kingdom, 69% of SMEs are facing severe cash flow problems with 35% are facing the fear of not reopen again Petropoulos, (2020) ;in China, 80% of SMEs have stopped their operation during February' 2020;in the United States 70% of SMEs are expecting disruptions in supply chain nearly 80% SMEs are facing destructive impact directly or indirectly (OECD, 2020);78% of Canadian SMEs have reported a drop in sales;in Greece, SMEs have experienced 60% decline in sales;in Thailand, 90% of SMEs are expecting drop in revenue;in New Zealand, approximately 71% SMEs have experienced gain hit, and in India, cash situation is awful for SMEs (OECD, 2020) [ ]they are not ordering the products in Figure 1
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".