The Effects of Social Media on Consumer Engagement in Chinese Women’s Football
Bibliographic record
Abstract
As the Chinese women’s national football team won the women’s Asian Cup in 2022, it attracted people’s attention back to Chinese women’s football. However, this attention did not last long. Insufficient attention has always been a challenge for Chinese women’s football, especially from a marketing perspective. This study aimed to explore how the social media promotion of the Chinese Women’s Super League (CWSL) impacts consumer engagement. This paper applied the theory based on several literature reviews on the social media effect as an influential factor in consumer engagement. SWOT analysis was used to identify strengths, weaknesses, opportunities, and threats for the CWSL. This research concluded that deficiency in social media promotion is the reason that leads to low attention, utilizing social media to promote CWSL affects consumer engagement to a larger extent if appropriate strategies are used. Thus, it revealed the importance of social media’s effect on CWSL’s consumer engagement. These findings can provide considerable strategies for the development of CWSL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".