Does digital hoarding lead to nostalgic consumption? The dual mediating effects of cognitive dissonance and emotional memory linkage
Bibliographic record
Abstract
Purpose Based on cognitive dissonance theory and emotional memory linkage theory, this study proposes a serial mediation model of “digital hoarding → cognitive dissonance → emotional memory linkage → nostalgic consumption”, aiming at exploring how digital hoarding can drive individual's nostalgic consumption behaviors through psychological mechanisms. Design/methodology/approach We conducted two scenario experiments to systematically examine these relationships. Experiment 1, which exposed participants to a digital hoarding context through situational manipulation, revealed that digital hoarding significantly and positively predicts nostalgic consumption intention. Experiment 2 introduced scales for cognitive dissonance and emotional memory linkage, with the bootstrap method applied to examine mediating effects. Findings The results showed that digital hoarding significantly increases nostalgic consumption intention. Cognitive dissonance and emotional memory linkage independently mediate this relationship, concurrently forming a sequential pathway where cognitive dissonance activates nostalgic memory retrieval, ultimately amplifying nostalgic consumption desire. Originality/value This study expands the positive value dimensions of digital hoarding research from the interdisciplinary perspective of consumer behavior and psychology, establishes a novel paradigm for understanding emotional compensation mechanisms under technology-induced stress, and provides a theoretical foundation for businesses to design nostalgia-oriented marketing strategies and refine digital asset management tools.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".