Enterprise digital transformation and production efficiency: mechanism analysis and empirical research
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
In the post-industrial period, traditional Chinese enterprises are facing the awkward situation of being ‘big but not strong’, with their core technologies being controlled by others. The digital transformation of enterprises has not only reshaped business models and industrial boundaries, but also boosted the high-quality development of China’s economy. This paper reviews the existing literature and discovers that digital technology promotes enterprise production efficiency through cost reduction, efficiency improvement, and innovation. Based on the data of listed manufacturing companies in the Shanghai and Shenzhen stock exchanges from 2009 to 2017, this paper constructs a differences in differences (DID) model to empirically study the relationship between digital transformation and production efficiency. The results revealed that the implementation of digital transformation plays a significant role in promoting economic benefits and the results of the lag regression method are still robust. Based on this, combined with the actual situation of Chinese enterprises, this paper proposes countermeasures and suggestions to promote the development of enterprise digital transformation. The conclusion is of great significance for Chinese enterprises to occupy a dominant position in the new wave of global industrial revolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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