RESEARCH: THE COMPARISON OF SOCIAL MEDIA AND MARKETING RELATIONSHIPS IN THE EAST AND THE WEST
Bibliographic record
Abstract
Prevalent social media use has resulted in many new forms of relationships between and among businesses and consumers around the world. We investigate the difference in, business-to-business (B2B), business-to-customer (B2C), and customer-to-customer (C2C) relationships in the U.S. versus in China. We propose that since Guanxi has a nuanced difference than the Western concept of networking (Lee, Pae, & Wong, 2001; Luo, 1997), in the B2B context, social media will strengthen pre-existing Guanxi in China. Contrarily, U.S. business will use social media to initiate B2B network. For B2C relationship, U.S. companies use social media more directly to communicate with customers when compared to Chinese companies. Finally for C2C relationship, C2C brand communities in China are more likely to be formed from pre-existing Guanxi while U.S. brand communities are initiated using social media. Our conceptualizations inform marketers on how to enter global market places using social media.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".