Research on the Trends and Features of Enterprise Digital Transformation: Based on the WOS Database
Bibliographic record
Abstract
In the context of the digital economy, in order to systematically understand the overall characteristics of the digital transformation of Chinese enterprises, this article conducts a statistical analysis of hotspots and trends in this field, which can provide a useful directional reference for subsequent scholars. Using literature statistical methods, processing literature data based on CiteSpace software, using data mining thinking to analyze scientific metrology knowledge graphs, selecting the enterprise digital transformation research papers published in the Web of Science database from 2008 to 2021 as the research object, Analyze the changes in the volume of articles, research institutions, authors, and keywords. The research shows that after 2018, it is a period of rapid development of enterprise digital transformation; paper publishing institutions are mainly concentrated in institutions and universities that are excellent in engineering and management. Most institutions conduct corresponding research independently, but cooperative research is Future trends; five author research communities constitute the main research strength; the research mainly focuses on digital government, intelligent manufacturing, industrial Internet and supply chain management, and has important theoretical reference value for the development of digital transformation of Chinese enterprises.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.084 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".