“Holiday effect” in online product Returns: Evidence from negative expectation disconfirmation and post-purchase dissonance
Bibliographic record
Abstract
Product returns pose significant challenges for online retailers, particularly during holiday seasons when shopping behaviors are influenced by heightened emotions and promotional incentives. This study examines the impact of holidays on consumers’ online product return intentions using a two-phase repeated online survey conducted before and after major holiday periods. It examined the role of negative expectation disconfirmation and post-purchase dissonance (including both cognitive and emotional dissonance) as key psychological mechanisms driving return intentions. The findings confirm that negative expectation disconfirmation and emotional dissonance significantly influence return intentions, with emotional dissonance playing a stronger role than cognitive dissonance. Additionally, holidays moderate the relationships between price, negative expectation disconfirmation, and return intentions, suggesting that consumer decision-making during holiday shopping differs from the non-holiday period. This research contributes to the literature on consumer post-purchase behavior by empirically validating a dual psychological mechanism underlying product returns and highlighting the nuanced effects of holidays. Managerial implications suggest that retailers should develop strategies to mitigate emotional dissonance during holidays. • A two-phase survey study before and after holiday. • Two psychological mechanisms influence consumers’ online return intentions. • Negative expectation disconfirmation influences online return intentions. • Emotional dissonance significantly influences online return intentions. • The holiday effect exists in online product returns.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".