The Impact of Operational Capabilities on Corporate Performance: Evidence from Listed Companies in the Agriculture, Forestry, Livestock Farming, Fishery Industry
Bibliographic record
Abstract
In China, the agriculture, forestry, livestock farming, fishery (AFLF) industry is the basis of all industries. However, the overall development and performance level of listed companies in the AFLF industry is lower than the overall market level. According to previous literature, there is generally a positive impact of operational capabilities on the corporate performance of listed companies, but the impact on listed companies in the AFLF industry has not been investigated. This study attempts to fill in the gap by empirically analyzing the impact of operational capabilities on the corporate performance of listed companies in the AFLF industry in China. Based on a panel data set of 43 listed companies, this study performs regressions using a fixed effect model and a threshold panel model. The results show that there is a positive correlation between the operational capabilities and the corporate performance of listed companies in the AFLF industry, but different indicators that represent operational capabilities have different impacts on corporate performance. Based on the empirical results, this study puts forward corresponding suggestions for listed companies in the AFLF industry and policy makers.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".