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Record W4386004270 · doi:10.33844/ijol.2023.60370

Competitive Performance of the Ethiopian Flower Industry from a Pre-to Post COVID-19 Pandemic Era (2003-2022): A Comparative Study

2023· article· en· W4386004270 on OpenAlex
Muluken Gemechu Worku, Kenenisa Lemmi Debela, Huub L. M. Mudde

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Organizational Leadership · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageRevealed comparative advantageComparative advantageChinaPandemicDescriptive statisticsCoronavirus disease 2019 (COVID-19)DisadvantageGeographyBusinessEconomicsDemographyInternational tradeMarketingStatisticsPolitical scienceMedicineMathematicsSociology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.086
GPT teacher head0.304
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it