Shopping Outcome Expectancies Questionnaire – development and psychometric properties
Bibliographic record
Abstract
Introduction: Outcome expectancies are cardinal determinants of undertaking and continuing intentional activities.This study aimed to develop a questionnaire measure of shopping outcome expectancies and to understand their significance with respect to the risk of compulsive buying. Material and methods:Based on literature studies and an open survey of 150 participants, 42 statements describing shopping outcome expectancies were developed.As a result, an experimental version of the Shopping Outcome Expectancies Questionnaire (SOEQ) was produced.The research was then conducted on a sample of 595 participants (68% women) aged from 18 to 84.Exploratory factor analysis was performed on data Streszczenie Wprowadzenie: Oczekiwania efektów są kardynalnym wyznacznikiem podejmowania i kontynuowania czynności celowych.Celem badania było opracowanie kwestionariuszowej miary oczekiwanych efektów kupowania i poznanie ich znaczenia dla ryzyka kompulsywnego kupowania.Materiał i metody: Na podstawie analizy literatury i otwartej ankiety na próbie 150 osób opracowano 42 stwierdzenia opisujące oczekiwane efekty kupowania, które utworzyły eksperymentalną wersję Kwestionariusza Oczekiwanych Efektów Kupowania (KOEK).Następnie przeprowadzono badania na próbie 595 osób od 18. do 84.roku życia (68% stanowiły kobiety).Dane uzyskane od 215 osób posłużyły do eksploracyjnej analizy czynnikowej, is useful for explaining shopping behaviour and its functions for buyers, predicting compulsive buying risks as well as planning and evaluating preventive and therapeutic interventions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".