Impulse buying: the role of affect, social influence, and subjective wellbeing
Bibliographic record
Abstract
Purpose The purpose of this research is to examine predictors of impulse buying. Although moderate levels of impulse buying can be pleasant and gratifying, recent theoretical work suggests that chronic, high frequency impulse buying has a compulsive element and can function as a form of escape from negative affective states, depression, and low self‐esteem. Design/methodology/approach The present research empirically tests a theoretical model of impulse buying by examining the associations between chronic impulse buying tendencies and subjective wellbeing, affect, susceptibility to interpersonal influence, and self‐esteem. Findings Results indicate that the cognitive facet of impulse buying, associated with a lack of planning in relation to purchase decisions, is negatively associated with subjective wellbeing. The affective facet of impulse buying, associated with feelings of excitement and an overpowering urge to buy, is linked to negative affect and susceptibility to interpersonal influence. Practical implications Given the link to negative emotions and potentially harmful consequences, impulse buying may be viewed as problematic consumer behavior. Reductions in problematic impulse buying could be addressed through public policy or social marketing. Originality/value This study validates and extends the Verplanken et al. model by examining the relationship between impulse buying and other psychological constructs (i.e. subjective wellbeing, positive and negative affect, social influence, and self‐esteem).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".