Brand hypocrisy from a consumer perspective: scale development and validation
Bibliographic record
Abstract
Purpose There is increasing interest in understanding negative consumer reactions to brands and the nature of negative brand perceptions. The purpose of this paper is to conceptualize the construct of brand hypocrisy from a consumer perspective and develop a scale to measure it. Design/methodology/approach A multiphase scale development process involving 559 consumers was conducted. Study 1 pertains to item generation and reduction phases. Study 2 reports on scale purification and validation through confirmatory factor analyses and model comparisons. Study 3 focuses on discriminant and predictive validity, while Study 4 further investigates predictive validity using real brands with differences in brand hypocrisy. Findings A 12-item scale measuring four dimensions of brand hypocrisy is developed: image hypocrisy (brand failing to put words into action), mission hypocrisy (brand exerting an unacknowledged negative impact on society or consumer well-being), message hypocrisy (brand conveying unrealistic or unattainable images) and social hypocrisy (brand supporting social responsibility initiatives for strategic purposes only). Results indicate that brand hypocrisy is distinguishable from similar constructs in the literature and that it is a significant predictor of negative word-of-mouth and brand distance. Practical implications This conceptualization provides managers with a detailed understanding of what constitutes a hypocritical brand in the eyes of consumers as well as insights about how to prevent consumer perceptions of brand hypocrisy. Originality/value Findings enrich the understanding of negative consumer inferences related to brands and provide a conceptualization of an understudied but increasingly relevant form of brand judgment.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".