The Role of Psychological Distance in Enhancing Identity-Relevant Brand Awareness
Bibliographic record
Abstract
Advertisers are increasingly prioritizing brand awareness, as it has become a top marketing objective, yet market trends suggest that much of consumers’ brand knowledge is not easily retrieved from memory. To inform advertising strategies designed to address this issue, we examine how self-identity–driven psychological distance affects the type of brand information that consumers are more readily able to retrieve from memory. Study 1 demonstrates that the psychological distance associated with a self-identity influences consumers’ ability to recall concrete (vs. abstract) identity-relevant brand knowledge. For identity-congruent brands related to a current (future) self-identity, consumers elaborate on and process more concrete (abstract) brand information, resulting in stronger brand associations that are more easily recalled from memory. Study 2 leverages this effect to enhance the recall of brand knowledge by matching the psychological distance of a self-identity with a construal mindset. Study 3 demonstrates that retrieval of newly learned brand information can be enhanced by matching the psychological distance of a self-identity with the concreteness of advertising messaging and identifies consumers for whom this effect may not occur (i.e., those with high product involvement). The research makes important theoretical contributions and suggests actionable advertising strategies for enhancing the retrieval of brand knowledge.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".