Performance Evaluation and Determinant Factors of China’s Logistics Enterprises Based on Careersmart Balanced Score Card
Bibliographic record
Abstract
China has become the second-largest market for logistics worldwide. However, its logistics performance index (LPI) is ranked 27th, which is far below the average of East Asia and Central Asia (World Bank, 2016). This paper empirically tests determinant factors of China’s logistics enterprises based on the Careersmart Balanced Score Card. The data are gathered from 42 listed logistics enterprises spanning from 2012 to 2016. Empirical results reveal that corporate performance on the part of China’s logistics enterprises is positively correlated with the factors of human capital investment, long-term liability, research and development expenses, the number of employees with higher education preferably a postgraduate degree, and ownership concentration, while factors negatively correlated with the proportion and cost of core business include management, delay rate, company scale, and other factors. The paper also considers the influence of operational management, customer service, asset structure, and innovation. Policy implications based on empirical results are proposed accordingly.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".