A Sectorial Validation and Application of a Conceptual Framework for Creating a Brand Management Strategy
Bibliographic record
Abstract
Brands can be one of a company’s most valuable intangible assets and a lever to generate value. As a source of added value, a brand should be strategically built and managed. To fully take advantage of the benefits that the brand provides, it is necessary to propose a brand management strategy. A conceptual framework was developed by the authors as an alternative to propose a brand management strategy according to a specific business scenario. The objective of this study is to validate this conceptual framework and apply it to propose a brand management strategy in a specific business scenario. For this purpose, a sectorial cross-validation was developed by triangulating the application of the framework to two data collection methods: (1) interviews and (2) a literature review. The results suggested that decomposing a complex business scenario into single-dimensioned business scenarios can help to propose, enhance, or reframe a brand strategy. The results also suggested that some brand dimensions can be used to lever other brand dimensions, such as brand relationship, which is at the top of CEO/CMO priorities in this field. This work contributes to theory by cross-validating the conceptual framework for creating brand management strategies through triangulation.
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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 | 0.072 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".