Psychophysical models of consumer expectations and colour harmony in the context of juice packaging

The aim of this study was to develop psychophysical models that predict the influence of pack colours on consumers' psychological responses of fruit juices, such as visually perceived expectations of freshness, quality, liking, and colour harmony. Two existing colour harmony models derived from...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Color research and application 2015-04, Vol.40 (2), p.157-168
Hauptverfasser: Wei, Shuo-Ting, Ou, Li-Chen, Ronnier Luo, M., Hutchings, John
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The aim of this study was to develop psychophysical models that predict the influence of pack colours on consumers' psychological responses of fruit juices, such as visually perceived expectations of freshness, quality, liking, and colour harmony. Two existing colour harmony models derived from experiments involving only uniform colour plaques were tested using the juice packaging experimental data. Both models failed to predict the visual results obtained. Nevertheless, two parameters relevant to chromatic difference and hue difference were somewhat associated with the visual results. This suggested that, among all colour harmony principles for uniform colours, only the equal‐hue and the equal‐chroma principles can be adopted to describe colour harmony of packaging used for juice. This has the implication that the principles of colour harmony may vary according to the context in which the colours are used. A new colour harmony model was developed for juice packaging, and a predictive model of freshness was derived. Both models adopted CIELAB colour attributes of the package colour and the fruit image colour to predict viewers' responses. Expected liking and juice quality can be predicted using the colour harmony model while expected freshness can be predicted using the predictive model of freshness. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 157–168, 2015
ISSN:0361-2317
1520-6378
DOI:10.1002/col.21867