Impacts Of COVID-19 On Sustainable Business Performance : A Case of Zara in Saudi Arabia
Bibliographic record
Abstract
The recent pandemic of the Covid-19 has impacted the performance of businesses and corporations badly. Companies had to undergo a devastating situation that they did never experience before the pandemic. Restricted environment, uncertainty and lockdowns have imposed serious threats to the supply chain and logistics of businesses and it became difficult for them to ship their products into the store where consumers could easily avail themselves. Fashion retailers that entered into the new year of 2020 with effective strategies and strong positions experienced a substantial shock due to the pandemic. This present thesis uses secondary data to support its findings. This data is collected directly from the website of Zara in the form of financial reports to see the sales and other valuable numeric and quantitative data. Additionally, articles that are analyzed for the results are taken from Google scholars and other databases. \n \nAnalysis of the data has found that in the first quarter of 2020, due to the pandemic of the Covid-19 Zara has suffered losses. Similar to the other businesses fashion retailers have also found first period of the pandemic difficult. Zara has suffered a loss of almost 229 million US dollars in the first quarter, however in the second quarter it has recovered itself. However, most of the sales of the company have been driven from online sales as the data analysis reveals. Analysis of the results has found that the recent pandemic of Covid-19 has badly impacted all the fashion brands across the globe including Zara. Company has witnessed a shortfall in the late 2019 however; it has been able to recover later in the second quarter of 2020.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".