Epistemological Jangle and Jingle Fallacies in the Consumer–Brand Relationship Subfield: A Call to Action
Bibliographic record
Abstract
Abstract For more than 20 years, the consumer–brand relationship (CBR) subfield has flourished with scores of constructs being employed. We provide an epistemological examination of its 14 most commonly measured relational constructs (e.g., Brand Love, Self-Brand Connection) collected from 767 research articles, reflecting 1,753 scales and approximately 9,200 items. We demonstrate that constructs overlap an average of 43% across all journals and 21% in top journals due to assessing highly similar or synonymous ideas (i.e., jangle). We use a combination of text and cluster analyses to show that measures of allegedly the same construct are polysemic, having an average of 5.3 different meanings (i.e., jingle). The results document in the CBR subfield the types of measurement inconsistencies and ambiguities that have sown confusion and frustration in other academic fields. We discuss the roots of these problems and offer recommendations aimed at helping scholars to improve measurement practices and to limit the presence of jingle and jangle in the CBR subfield.
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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 | 0.283 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.014 | 0.093 |
| Scholarly communication | 0.034 | 0.058 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".