Managing Financial Performance toward Achievements in Sustainability Prospects: Comparative Analysis of the e-Commerce and Hospitality Industries
Bibliographic record
Abstract
The recent pandemic has been identified as a driver of one of the most severe socio-economic crises over the last few decades. While some sectors have experienced an expansion, others have struggled with a changed business environment. The aim of this research is to simultaneously examine the financial performance and sustainability of the e-commerce and hospitality industries, applying asset and debt ratio analysis to the top five companies in the world from each sector in the time period from 2017 to 2022. The results indicate that the assessed companies demonstrated the ability to successfully manage some of their assets. The debt ratio analysis implies that the assessed companies in the hotel industry have reshaped their capital structure, increasing their reliance on debt in 2020 and 2021 to finance their assets. On the contrary, the selected e-commerce companies were found on average to rely less on debt to finance assets. In accordance with expectations, the differences across the examined sectors and companies that have been observed are mostly in regard to the lower scale of utilization of fixed assets to generate turnover, and in terms of the increased share of debts used to finance assets in the hotel industry, which was among the first and hardest hit by the pandemic. Consequently, the study allows policy makers to identify distinctive strategies for each area of economic activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".