Research on the Influence Mechanism of Digital Transformation on the Development of New Quality Productive Forces in Manufacturing Enterprises – Based on the Spatial Perspective
Bibliographic record
Abstract
As digital transformation (Digital) accelerates globally, conventional enterprise production models are proving increasingly insufficient to meet the demands of today’s dynamic market landscape. China has innovated the concept of New Quality Productivity (NQPF), and exploring its functioning is critical to promoting high-quality enterprise development. This study examines the impact mechanism of Digital on NQPF in manufacturing firms by applying spatial econometric models—including the spatial Durbin model, spatial mediation model, and spatial threshold model—to panel data from A-share listed manufacturers (2013–2022). The results indicate that digital transformation significantly influences the level of NQPF, exhibiting spatial spillover effects and spatial attenuation boundaries. This influence initially promotes and subsequently inhibits productivity. The analysis of the spatial mediation effect reveals that Digital affects enterprise productivity levels by influencing total factor productivity. Furthermore, the spatial threshold effect analysis indicates that higher total enterprise assets enhance the positive impact of Digital on NQPF. These results provide robust micro-level empirical evidence to inform manufacturing enterprise development strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".