Analysing firm performance in Chinese IT industry: DEA Malmquist productivity measure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chinese IT industry has become more important and maturity after development for tens of years and come up quickly in global IT market. They may have huge influence on Chinese IT market or even the world. This paper is concerned with the study on exploring the performance of Chinese IT industry, including the managerial, technical and scale efficiencies and their changes over time. We employ data envelopment analysis (DEA)-based Malmquist method to measure the performance of listed IT firms in China, in the period of 2005 to 2007 and reveal the detailed technology and efficiency changes over time by analysing decomposed components of Malmquist index. Furthermore, the technical diffusion of Chinese IT industry is tested by efficiency convergence analysis. Accordingly, the IT companies can make decisions on the functions and strategies shifts that are beneficial to the performance improvement and achieving competitive advantages.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it