A cross-product category CBBE study: item response theory perspective
Bibliographic record
Abstract
Purpose – The purpose of this study is to propose a new item response theory-based model to facilitate brand equity comparison among brands in different product categories. Brand equity has been defined as the value a brand adds upon a product. This definition provides the theoretical basis for comparing brands across product categories. Researchers have measured brand equity from three major approaches: finance, economics and psychology. Unlike the first two approaches that have developed methods to facilitate cross-product-category brand equity comparison, no methodology has been identified in the psychology approach (consumer-based brand equity, CBBE), and this study will fill this gap. Design/methodology/approach – We used survey method and collected data from both soft drink and car product categories to empirically demonstrate our method. Findings – A new item response theory-based model to facilitate brand equity comparison among brands in different product categories is proposed. Originality/value – Considering consumers are the most widely considered stakeholder group in the existing brand equity literature, the lack of cross-product category research in consumer-based brand equity (CBBE) area constrains the use of CBBE for firms managing multiple brands across product categories. This proposed model is the first one to address this limitation.
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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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".