Chocolate, Colour and Consideration: An Exploratory Study of Consumer Response to Packaging Variation in the South African Confectionery Sector
Bibliographic record
Abstract
Color has become increasingly influential as marketers seek to unlock the potential this factor has on behavioural decisions, in part due to colour being the first product attribute a consumer’s brain registers when looking at a product. However, minimal academic research has been undertaken to investigate this function in an emerging market context. This exploratory study sets out to examine how thecolourof packaging of chocolate products affects consumers’ purchase intent, both in terms of spontaneous response and brand loyalty. A consumer survey was conducted through a mall-intercept approach using a sample of 161 respondents. The Friedman and Mann Whitney U tests were conducted in order to compute the data generated within this study. The research concludes that colour holds the potential to influence a consumer’s purchase behaviour of chocolate, with purple far more influential than orange, for example. Moreover, this effect was found to be consistent across low and high income groups. The results underscore the importance of colour and suggest marketers and retailers would be well advised to consider the associations between packaging colour and consumer response.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".