Does Digital Transformation Promote Sustainable Development of Enterprises: An Empirical Analysis of A-Share Listed Companies
Bibliographic record
Abstract
This article examines the interplay between digital technology and enterprise development in China, with a specific focus on the efficacy of digital transformation within enterprises.Our research draws on data from A-share listed companies in China between 2012 and 2021 to investigate the influence of digital transformation on an enterprise's capacity for sustainable development and the underlying mechanisms at work.Our findings indicate that digital transformation can significantly enhance an enterprise's sustainable development capabilities.The mechanism testing results reveal that advancements in digital transformation have led to improved internal control quality and boosted total factor productivity, thereby strengthening the capacity for sustainable development.Moreover, our heterogeneity analysis uncovers several intriguing trends.We found that digital transformation more notably improves the sustainable development capacity of non-state-owned enterprises compared to their stateowned counterparts.Similarly, high-tech enterprises experience a more pronounced enhancement in sustainable development capabilities through digital transformation compared to non high-tech enterprises.Furthermore, we discovered that enterprises located in regions with high levels of marketization benefit more significantly from digital transformation in terms of sustainable development capacity than those in regions with lower levels of marketization.Through this study, we provide valuable empirical evidence that aids in evaluating the effectiveness of digital transformation in enterprises and in promoting highquality sustainable development.Additionally, our findings offer a theoretical foundation for devising relevant policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".