The prevalence of impulsive, compulsive and innovative shopping behaviour in the economic retail hub of South Africa: A marketing segmentation approach
Bibliographic record
Abstract
To date, research on impulsive, compulsive and innovative retail shopping behaviour motivated by the desire to obtain emotional, social and identity-related benefits, has largely been limited to Europe (Germany), Canada and western developed economies (the UK and USA). This article provides an initial analysis of the prevalence of impulsive, compulsive and innovative shopping traits of consumers in the economic retail hub of South Africa. More precisely, the article explores the demographic diversity in the purchase and innovative traits of Gauteng consumers. Overall, the article reveals clear disparity in impulsive, compulsive and innovative shopping behaviour across selected demographic and socioeconomic consumer segments. From a practical perspective, the marketing segmentation approach applied in this study is ideally suited for retail segmentation and marketing strategy development and serves to identify key marketplace buying behaviours. For retail, insight into buying and innovative shopping behaviour is essential for building long-term relationships with consumers and to serve their specific needs within an extremely complex and competitive retail landscape. Key words: Impulsive shopping, compulsive shopping, Mavenism, Oniomania,shopping addiction, retail therapy, recreational shopping, innovative shopping, behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".