Bibliographic record
Abstract
Purpose Imagination is a complex mental process where consciousness departs from reality to create new content not currently found in existence. Imagination is key to marketing persuasion, but studies that examine consumer imagination in response to marketing messages illustrate confusing and sometimes contradictory perspectives about consumers’ mental processing. This paper aims to provide a review of the existing literature on consumer imagination relevant to marketing scholarship, and builds a new theoretical framework to organize and explain these papers. Design/methodology/approach A systematic review of the marketing literature was undertaken to identify all papers related to consumer imagination and its role in marketing persuasion. A focus was placed on empirical papers, review papers and meta-analyses. Findings A new conceptual framework was created to classify the consumer imagination literature based on both the characteristics and the content of imagination. The existing marketing literature was then organized into the framework. The framework helps to explain seeming contradictions between different studies as well as helps to collect similar studies together to summarize schools of thought. Originality/value The imagination framework presents an entirely new way of conceptualizing imagination research in marketing. This new categorization structure not only clarifies consumers’ use of imagination in response to marketing messages but also identifies questions for future research in this area of marketing theory.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".