Rethinking brand architecture: a study on industry, company- and product-level drivers of branding strategy
Bibliographic record
Abstract
Purpose – This study aims to compare academic prescriptive models on how to choose a branding strategy on the continuum from a “branded house” to a “house of brands” with real-life branding strategies of leading companies. Design/methodology/approach – Data from an executive survey, observations and desk research on 75 leading companies in Austria are analysed with multilevel weighted least squares (WLS) regression. Findings – Branding strategies for products are determined by industry (23 per cent of variance), the overall strategy of the company (28 per cent), the remaining variance being product-level decisions deviating from both. Service and consumer durables companies lean more towards corporate branding than consumer nondurables. On the company level, synergies in advertising, e-commerce and e-CRM (customer-relationship management) increase the usage of shared brands. A higher company age leads to brand proliferation. On the product level, quality differences between products, the emphasis on and differences in experiential product positioning and, marginally, the symbolic differences between products favour individual brands. Research limitations/implications – Future research should investigate additional markets, additional drivers, small and medium-sized entreprises (SMEs) and employ additional measures. Practical implications – The study informs brand-architecture audits with benchmarks from leading companies, calls for a view of brand architecture more flexible than ideal-type categories proposed in literature and cautions against management inertia, industry standards and trends in designing branding strategies. Originality/value – This study is the first quantitative cross-industry multi-level study on real-life branding strategies. It also applies a new conceptualisation and measurement of branding strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".