Reimagining Brand Positioning in the Age of Artificial Intelligence: A Conceptual Framework for Cognitive Anchoring in Consumer Minds
Bibliographic record
Abstract
Purpose – This study aims to explore how artificial intelligence (AI) technologies are reshaping the strategic process of brand positioning by influencing the way consumers mentally perceive, store, and recall brand information. It introduces a novel conceptual framework for understanding the role of AI in cognitive anchoring and positioning strength in the consumer psyche. Design/methodology/approach – This is a conceptual research paper that integrates insights from brand management, cognitive psychology, and AI-driven marketing technologies. The study synthesizes recent literature and proposes a multidimensional model that captures the dynamic interaction between AI personalization mechanisms and consumer perception processes. Findings – The research proposes that AI acts as an active positioning agent by personalizing sensory and emotional brand cues based on real-time data. Through micro-targeted and adaptive communication strategies, AI deepens mental imprinting of brand attributes, thereby enhancing consumer memory, emotional attachment, and positioning distinctiveness. Originality/value – While AI’s role in personalization is well documented, its theoretical connection to long-term brand positioning remains underexplored. This paper addresses that gap by developing an original model that aligns traditional branding theory with AI-powered consumer insight technologies. The framework provides a valuable foundation for future empirical testing and strategic application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".