Understanding the Role of Consumer Heterogeneity in the Formation of Satisfaction Uncertainty
Bibliographic record
Abstract
ABSTRACT Prior empirical research has focused on the antecedents and consequences of attitude uncertainty. Drawing on regulatory focus theory and need for closure theory, this research examines the role of individual difference variables in shaping satisfaction uncertainty. This empirical work seeks to explore the interplay of individual difference variables, cognition and affect, in shaping satisfaction uncertainty. The proposed model maintains that need for closure and regulatory focus shape satisfaction uncertainty through their influence on cognitive and affective processes. The model was tested on 192 participants in an experiment using a restaurant scenario. Satisfaction uncertainty is estimated, rather than measured, using the Judgment Uncertainty and Magnitude Parameters (JUMP) model. The results show that prior expectation, pleasure, and arousal have positive effects on satisfaction uncertainty, while perceived performance has a negative impact. Furthermore, regulatory focus is found to moderate the effects of cognition and affect on satisfaction uncertainty, while need for closure moderates the impact of affect on satisfaction uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".