Bibliographic record
Abstract
Consumer research generally focuses on the consumption of tangible objects and experiences, which are concrete. However, consumers often consume in their minds by fantasizing, dreaming, or imagining that they possess some desired object or that they are living some experience. In this article, the term consumption dreams is used to refer to mental representations of consumption objects that consumers desire and experiences that they want to realize. These are distinguished from uncontrolled mental activities that occur when asleep. The results of two exploratory studies that examined consumption dreams are presented. In the first study, five adult consumers were asked about their most important consumption dream, as well as the factors that influenced this dream and the behaviors that ensued. The second study consisted of a survey of 195 adult consumers where the determinants and consequences of consumption dreaming were probed. It was found that indulging in consumption dreaming is a common activity among most consumers and that consumption dreams and their characteristics depend on general as well as dream-based variables. In addition, those dreams were found to impact on several consumer behaviors. A causal model involving a subset of the variables examined in this exploratory research was put forward and tested with the survey data. The results showed the value of a proposed conceptual framework to generate theoretical propositions about consumption dreaming. © 2005 Wiley Periodicals, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".