The brand authenticity effect: situational and individual-level moderators
Bibliographic record
Abstract
Purpose This paper aims to examine under what conditions consumers develop emotional attachment toward authentic brands. It proposes that authentic brands’ ability to evoke attachment is contingent upon situational (i.e. need to belong and need to express the authentic self) and consumer individual difference variables (i.e. brand engagement in self-concept [BESC] and personal authenticity). Design/methodology/approach Two experiments empirically test the effects of brand authenticity on emotional brand attachment. Experiment 1 considers the moderating roles of social exclusion and BESC. Experiment 2 examines the moderating roles of situationally induced feelings of self-inauthenticity and enduring personal authenticity. Findings Consumers with a high level of BESC show greater emotional brand attachment to authentic (versus less authentic) brands when they feel socially excluded. Consumers with a high level of enduring personal authenticity show greater emotional brand attachment to authentic (versus less authentic) brands when they experience situations that make them feel inauthentic. Practical implications This paper has implications for brand communication strategies adopted by brands that are positioned strongly on authenticity. Originality/value This paper is one of the few to examine the effect of brand authenticity on brand attachment taking into account the moderating role of situational and individual difference variables. The findings contribute to the brand attachment and brand authenticity literatures.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".