Competitive Performance of the Ethiopian Flower Industry from a Pre-to Post COVID-19 Pandemic Era (2003-2022): A Comparative Study
Bibliographic record
Abstract
The purpose of this study was to examine the competitive performance of the Ethiopian flower industry in the world market from the pre-to post COVID-19 pandemic era in comparison with the Netherlands, Colombia, Ecuador, Kenya, Italy, China, Malaysia, Israel, and Canada. The study collected secondary data from the International Trade Centre (ITC) database for the years between 2003 and 2022. The data collected was analyzed by NRCA (Normalized Revealed Comparative Advantage) and RTA (Relative Trade Advantage) indexes as a measure of competitive performance as well as using both descriptive and inferential statistical analysis tools: mean, standard deviation, coefficient of variation, Pearson’s correlation test, and paired sample t-test. The findings show that Ethiopia, as well as the Netherlands, Colombia, Ecuador, and Kenya, have a strong competitive advantage in the flower industry in the pre-to post COVID-19 pandemic era (2003-2022), with relatively consistent competitive performance trend in the world market; while other countries with fluctuating competitive advantage and self-balancing (Israel, Malaysia, and China) and also countries such as Italy and Canada have a competitive disadvantage in flower in the world market. Ethiopia’s flower competitive advantage has achieved a steady improvement in the post COVID-19 pandemic era while other countries are facing inconsistent and huge decline, particularly, Colombia, as the finding revealed. Also, paired sample t-test result shows that statistically significant difference was found between Ethiopia’s competitive advantage and those of the top competing countries with a p-value ˂ .05. The finding also indicated that with average values of the relative import advantage (RMA = .48) and relative trade advantage (RTA = 128.80) indexes, Ethiopia is a net exporter of flowers. Therefore, the findings of this study give managers robust evidence of how their flower industry’s competitive performance changes over time and the rank of their respective country. The flower competitive performance of Ethiopia, compared to the Netherlands, Colombia, Ecuador, Kenya, Italy, China, Malaysia, Israel, and Canada, is believed to inform the practitioners in the industry to make necessary adjustments aimed at generating comparative and competitive advantages for the country.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".