Branding strategies in transitional economy: The case of Aimer
Bibliographic record
Abstract
The purpose of the study was to investigate the branding strategies implemented by the Beijing Aimer Lingerie Company Ltd. that support its success. A case study method was used which included on-site visits and in-depth interviews with Aimer executives, mid-level managers, and frontline employees. Additionally, a review of the company’s website and internal documents, as well as an extensive external search of relevant news reports, social media contents, industry information, and academic literature contributed to the data source. The study adopted a deductive content analysis strategy by integrating Balmer and Gray’s (2003) C2ITE framework and the luxury fashion brand dimensions, and used triangulation as a validation strategy. The most important element of Aimer’s success is strategic planning and continuous brand development. In every step of Aimer’s business development, the executive team strategically planned its next step and had a clear vision for the future. Aimer executed well for each dimension of the analytical framework (Cultural, Marketing Communication, Tangible Branding, Intangible Branding, and Commitment) except one: brand signature (Cultural dimension). The findings provide valuable implications for other Chinese apparel manufacturers striving to establish their own brands, as well as global companies that compete in the Chinese marketplace.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".