Thinking fast and slow: a revised SOR model for an empirical examination of impulse buying at a luxury fashion outlet
Bibliographic record
Abstract
Purpose Despite the extensive stimulus–organism–response (SOR) literature, little attention has been paid to the role of marketing activity as a key environmental stimulus, and there is a dearth of research examining the interplay between emotions and cognition on consumer behaviour, as well as the sequential effects of emotions on cognition. To address these gaps, this study aims to develop a revised SOR model by incorporating Kahneman’s fast and slow thinking theory to investigate the impulse buying of affordable luxury fashion (ALF). Design/methodology/approach The authors use outlet stores at Bicester village (BV) in England as the research context for ALF shopping. Partial least squares structural equation modelling was used to analyse a survey sample of 633 consumers with a BV shopping experience. Findings The authors find that impulse buying of ALF arises from the interplay of emotional and cognitive factors, as well as a sequential and dual process involving in-store stimuli affecting on-site emotion and in-store browsing. Research limitations/implications This study reveals that brand connection has a significant and negative influence on the relationship between on-site emotion and in-store browsing, advancing the SOR paradigm and reflecting the interactive effect of human emotion and reasoning on the impulse buying of ALF items. Practical implications Insights into consumers’ impulse buying offer practical implications for luxury brand management, specifically for ALF outlet retailers and store managers. Originality/value The results suggest a robust sequential effect of on-site emotion towards in-store browsing on impulse buying, providing updated empirical support for Kahneman’s theory of System 1 and System 2 thinking.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".