Assessment of heterogeneity of compulsive buyers based on affective antecedents of buying lapses
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it