The Influence of Impulsive Personality Traits and Store Environment on Impulse Buying of Consumer in Karachi
Bibliographic record
Abstract
This study was aimed to understand and assess the role of store environment, impulsive buying personality traits, impulsive buying tendency, and urge to buy on impulsive buying. Data was collected from 250 respondents and analyzed using Structural Equation Modeling technique, findings suggested that impulse buying was positively associated with impulse buying tendency, impulsively personality trait and urge to buy. The most important finding of the study is the insignificant effect of store environment on consumers that are instinctive buyers. The study also suggests that impulsive traits of the consumer directly lead to impulse buying. It actually don’t need some drive such as store environment that would stimulate their impulse buying tendency. However, this study didn’t find any effect of demographic variables (gender and income) on impulse buying tendency. The overall store environment has insignificant effect on consumer impulse purchase but different attributes such as cleanliness and arrangement of product has relative impact on impulse buying that’s why the retailers should focus on store environment elements such as crowd, sales employee, entertainment, lighting, aroma and display etc. to stimulate impulse buying. This study confirmed the role of personality in encouraging impulse buying at retail outlets. Marketers should identify ways to reach out open ended and extrovert people to target their promotional offers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".