The Impact of Co-branding on Consumers’ Purchase Intention-A Case Study of NAIXUE
Bibliographic record
Abstract
In recent years, as competition in China's new-style tea drink market intensifies and the consumer base becomes younger, tea brands have begun to adopt co-branding as a marketing strategy to improve their competitiveness. This study takes co-branding of tea brand as the research object. The study firstly combed the literature related to brand co-branding and consumers’ purchase intention and reviewed the existing research results. On this basis, this study proposes hypotheses and constructs a conceptual model based on SOR theory and customer value theory. To better understand how the five factors are connected in the model, this study will use surveys and statistics to analyze them. This will help us learn more about how they relate to one another. First, the questionnaire was developed by drawing on well-established scales from existing studies and combining the co-branding characteristics of tea brands. Subsequently, the questionnaire was distributed on social platforms, and a total of 308 usable responses were collected. The analysis was conducted using SPSS26.0 and AMOS26.0 to conduct mathematical statistics and test hypotheses and results were discussed to draw management suggestions. Finally, the limitations of the study are analyzed and prospects on future research are envisioned. The study found that: (1) co-brand awareness positively affects consumers' perceived value; (2) the matching degree of co-brands positively affects consumers' perceived value; (3) the scarcity of co-branded products positively affects consumers' purchase intention; (4) consumers' perceived value positively affects consumers' purchase intention. Based on the results of the study, the following recommendations are made for tea beverage brands: (1) choose the right co-brands; (2) improve the brand value; (3) increase consumers’ perceived value towards the products.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".