The Impact of Influencer Marketing Compared with Celebrity Endorsement on the Chinese Market
Bibliographic record
Abstract
This article discusses the impact of influencer marketing in the Chi-nese market. Due to the rapid growth of online shopping, influencer promotion has become an important marketing method in the Chinese market. The leading social media in China are Douyin, Red Book, and WeChat. And all of this software comes with an online shopping mall component. The presence of Taobao has also made online shopping more convenient. Social media professionals often have their shops on Taobao. We surveyed consumers of all ages across China using a ques-tionnaire and obtained a sample of 241. The questionnaire was de-signed to compare the impact of online marketing and celebrity en-dorsement on consumers in the Chinese market. Our survey showed that Chinese consumers are likelier to choose products promoted by internet celebrities, with female consumers being the majority. And the majority prefer beauty and garment products. Based on the ques-tionnaire analysis, it can be concluded that influencer marketing is more prevalent in the Chinese market. Due to the development of technology, people do not need to go to offline shops to buy products. Online shopping is more convenient for consumers. And influencer promotion just makes up for the lack of online shopping. Influencer demonstrations of products allow consumers to visualize the effec-tiveness of the products and thus feel confident in their purchases. This is also the reason why most consumers choose influencer market-ing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".