Bibliographic record
Abstract
Purpose The paper aims to highlight the importance of corporate rebranding in branding practice, which is neglected in theoretical treatment, so an extended theory is to be developed. Design/methodology/approach From the literature, the existing state of the theory of corporate rebranding is articulated. That theory is extended by the development of six principles and by case research. The principles are illustrated in the case of a Canadian leather goods retailer which has implemented a major corporate rebranding strategy. The paper demonstrates the value of organisational single case studies as a precursor to further research. Findings The single case enables a more in‐depth analysis of how branding principles were applied to corporate rebranding. All six principles were supported, indicating the need for maintaining core values and cultivating the brand, linking the existing brand with the revised brand, targeting new segments, getting stakeholder “buy‐in”, achieving alignment of brand elements and the importance of promotion in awareness building. Originality/value Although corporate rebranding is often used narrowly in practice as renaming, this paper redresses the limited attempts to build theory in this area of marketing. It attempts to build a more sophisticated and substantial theory of corporate rebranding.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".