Digital Marketing Transformation of China’s SMEs in the Post-epidemic Era: Crisis, Trends and Strategies
Bibliographic record
Abstract
The influence of the coronavirus pandemic on China is complicated. A great deal of small and medium-sized enterprises (SMEs) are struggling economically. As a substantial amount of customer demand transfers online, people are beginning to see the hidden economic opportunities brought by the epidemic. In the post-epidemic period, digital initiatives have helped small and medium-sized enterprises adapt to the shifting market conditions, recover, and succeed. The use of digital technology has emerged as an essential component of SME growth strategies, and the deployment of digital marketing represents the initial step in their digital transformation process. SMEs contribute significantly to the growth of the national economy, the alleviation of employment-related stress, and the promotion of social stability. Based on the existing research, this study will focus on the impact of the epidemic on the operation of China’s SMEs and the difficulties and demands faced by them. This paper will investigate the new potential for China’s SMEs to contribute to the growth of the digital economy using digital marketing as an entrance point. This study aims to help SMEs discover potential opportunities in the complex environment; nonetheless, they should still adopt the most appropriate strategies for their particular circumstances.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".