Assessment of heterogeneity of compulsive buyers based on affective antecedents of buying lapses
Bibliographic record
Abstract
Although compulsive buying has been predominantly viewed as the chronic need to manage negative affective states, other emotions, such as positive affect and boredom, have also been reported to precede buying lapses among compulsive buyers. The main objectives of this article were to: (1) empirically examine the centrality of the frequent experience of negative affect prior to buying lapses in compulsive buying, and (2) assess the heterogeneity of compulsive buyers based on the frequency of experiencing negative affect, boredom, and positive affect that precede buying lapses. To examine these issues, we used survey data provided by individuals with excessive buying tendencies (N = 419). Latent profile analysis of the frequency of the three types of affective states extracted three clusters of buyers: (1) the “escape seeker” cluster with a strong propensity to buy in excess in negative emotions, (2) the “excitement seeker” cluster that reported having lapsed when feeling boredom more frequently than negative affect, and (3) the “low affect management buyer” cluster whose frequency of experiencing the three types of emotions was lower than the other clusters. The majority of escape seekers and excitement seekers exceeded the diagnostic cut-off for compulsive buying. Clinical implications of the findings are also discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".