The Impact of Perceived Self-Skill Levels on Product Choice: An Exploratory Study of the Moderating Influence of Mood
Bibliographic record
Abstract
In view of previous research on consumers’ single-peaked product preference and “matching” process, this paper examines the effect of price as a necessary condition for consumers’ ideal-point formation process in making choices in a product array that has monotonically increasing values on the benefit dimension. Building on the “mood-as-information” perspective and incorporating the emerging stream of mood study, this paper also studies the moderating impact of mood on the effects of perceived self-skill levels on consumers’ purchase intentions. Using experiments, this research generates and replicates consumers’ single-peaked product preferences in a consumer behavioral lab. Results show that subjects always go for the best product option in the absence of price information (i.e., preference is monotonically increasing, rather than single-peaked), and subjects exhibit different product preference patterns when they are in different moods. This paper extends previous research by explicitly including and testing the effect of price as a cost dimension, and incorporating the emerging trend of mood study, and therefore, this paper gains a deeper understanding of consumers’ single-peaked preference function.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".