Flat or gradient? The impact of packaging color palette presentation on consumer purchase intent for utilitarian versus hedonic products
Bibliographic record
Abstract
The current research examines two distinct color palette presentations frequently featured in packaging design: flat versus gradient. Gradient color palettes feature a smooth, gradual transition from one color to another, while flat color palettes feature discrete static blocks of colors placed next to one another. Across four experimental studies using both self-report measures (Studies 1 and 3a-3b) and eye-tracking technology (Study 2), the authors show that the use of gradient color palette in the brand’s packaging enhances consumer purchase intent for hedonic products, while the use of flat color palette enhances consumer purchase intent for utilitarian products. Further, the underlying mechanism for this shift in purchase intent is due to the higher arousal level elicited by gradient (vs. flat) color palettes (Studies 3a and 3b). The present research contributes novel theoretical insights to the color research stream by demonstrating how subtle changes in color palette presentations can influence consumer purchase intent for specific product categories. Managerially, this research reveals how brands can strategically affect consumers’ arousal levels through different color palette presentations and highlights the possible increase in overconsumption or indulgence of hedonic products via color gradients, which consumers and policymakers should caution.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".