Hedonism, hedonistic shopping experiences and compulsive buying tendency: a demographics-based model approach
Bibliographic record
Abstract
Although consumer and marketing research has focused on identifying various precursors of compulsive buying behavior, little attention has been paid to more complex relationships examined from the perspective of hedonism as a personal value, hedonic shopping experiences, and consumer demographics. Thus, the present study postulates a mediation model in which the extent of hedonism s relationship to compulsive buying via hedonistic shopping experiences is diagnosed, and proceeds to moderation effects based on consumer demographic characteristics (i.e gender, age, education). Using data (N = 1,245) from a representative survey, and based on structural equation modeling, results revealed that hedonism significantly influences compulsive buying via hedonistic shopping experiences, while moderation effects indicated that these relationships were stronger in younger individuals, mostly women. In contrast, these effects were nonsignificant with regard to consumers’ education level. The study s findings are discussed in terms of the theoretical and practical insights to better understand and prevent contemporary consumerism trends related to hedonism, hedonistic shopping, and compulsive buying tendencies. The research also offers important public policy and retailing implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".