An Empirical Study on Usage of Business Jets and Financial Performance of Chinese Firms
Bibliographic record
Abstract
This paper analyses sustainable financial performance of 242 Chinese companies, to see if the companies that utilize business aviation produce better results compared with non-users. This type of research has been done in North America and Europe but has not yet been done in China. The companies were analyzed using the economic performance metric Economic Value Added (EVA). This indicator was used to assess if the companies added or destroyed economic value through their operations. Primary data was gathered from published financial statements of all companies for the six-year period 2011-2016. This paper contributes to quantify the argument that the use of business jets helps companies to be more productive. The main finding of this study is that companies that utilize Business Jets added more collective value to EVA than non-users. In line with similar studies in the USA, Canada and Europe, it is concluded that companies that use business jets produce superior economic growth to non-users. The results were mixed as to whether the companies utilizing business jets experience better financial performance than non-users. The overall growth in EVA by business jet users was very significant. The 80 companies that made up this group grew their EVA by a collective average of 26.4%. When compared with some of the biggest companies on a world stage, business jet users performed outstandingly. The 81 Chinese companies from the Forbes Global 2000 destroyed-17.8% in economic value over the period studied. Any injection of government money into these companies does not seem to be helping them produce added value. However, the number of companies producing positive EVA for the ‘Business Jet User’ group fell from 68% in 2011 to 56% in 2016.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".