Empirical Assessment of Factors Influencing Corporate Performance of China’s Independent Brand Automobile Companies
Bibliographic record
Abstract
China’s independent brand automobile is an infant industry in China. It is characterized by a small share in the world market and low price with unreliable quality. However, continuous expansion of China’s population, an increase in the number of high-end consumers, coupled with rapid economic development, serve altogether to promise China’s independent brand automobile companies a bright future. In October 2014, China introduced a favorable policy that low-exhaust models (1.6L and below) pay half of previous purchase tax. Afterwards, China’s independent brand automobile industry experienced a booming period. Nevertheless, the gradual degradation of policy dividend, changes in market environment, and consumer awareness, have compelled China’s independent brand automobile companies to adjust their original business model and to innovate accordingly so as to meet new market demand.This paper empirically investigates factors influencing corporate performance of China’s independent brand automobile companies. It utilizes data of all 18 listed China’s independent brand automobile companies between 2012 and 2016. Factors assessed include capital scale, enterprise growth rate, capital turnover rate, enterprise solvency, research and development expenditure input intensity, sales channel, human resources etc. Empirical results find that the scale of the company, the growth rate of total assets, the growth rate of net profit, the profit margin of core business, the operating profit margin, the current ratio, the quick ratio, the intensity of research and development investment, and the ratio of highly educated employees, are the key factors which enhance corporate performance of China’s independent brand automobile companies.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".