The dynamic nature of brand authenticity for a new brand: Creating and maintaining perceptions through iconic, indexical, and existential cues
Bibliographic record
Abstract
Abstract Although brand authenticity has been studied across multiple domains, the development and maintenance of brand authenticity in new brands has never been explored. This study provides the first evidence of the evolving nature of brand authenticity (i.e., the creation and maintenance phases) through the interplay of iconic (impression‐based brand characteristics), indexical (evidence‐based brand characteristics), and existential (self‐referential brand characteristics) cues for a new brand. Sixteen season ticket holders for a new sports team brand were interviewed two times each (during and after the team's inaugural season). The analysis shows the interplay of authenticity cues in the development and maintenance of authenticity perceptions, such that indexical and existential cues replace iconic cues as the consumer‐brand relationship evolves. The results reveal the critical roles of existential cues in creating a self‐relevant relationship with consumers as well as the underlying dimensions (i.e., virtuousness, proximity, and transparency) and outcomes (e.g., brand attitude and emotional brand attachment) of authenticity for a new brand. This study provides evidence that new brands can benefit from authenticity perceptions and offers insights into the underlying process in terms of antecedents and outcomes, contributing to authenticity and branding literature.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".