Perceived deception and online repurchase intention: The moderating effect of product type and consumer regulatory orientation
Bibliographic record
Abstract
Abstract Limited by e‐tailers' ability to present accurate information about their products, consumers often misunderstand and misinterpret product messaging, which can heighten their perception of deception among e‐tailers and result in unfavorable consumer behaviors and actions. Integrating perceived deception and existing consumer behavior theories, this research examines the intricate relationships between product type (hedonic vs. utilitarian), consumer regulatory orientation (promotion vs. prevention), and their interactive effect on the relationship between perceived deception and consumer repurchase intention in online settings. Across three studies, we identified a less negative impact due to perceived deception for online repurchase intention of hedonic products than for utilitarian products. After perceived deception occurs, promotion‐oriented individuals show a higher online repurchase intention compared to prevention‐oriented individuals. Furthermore, the fit between promotion orientation and hedonic products works best to attenuate perceived deception's unfavorable impact on online repurchase intention. In contrast, the fit between prevention orientation and utilitarian products leads to the lowest online repurchase intentions. Also, e‐tailers can increase repurchase intentions by emphasizing the hedonic attribute, and instigating promotion intention would help mitigate the negative effects of perceived deception.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".