Consumer preferences in food packaging: CUB models and conjoint analysis

Packaging features have been shown to be of great importance for the consumer final choice of fresh products (Silayoi and Speece, 2007). Packaging is an extrinsic attribute, which consumers tend to rely on, when relevant intrinsic attributes of the product are not available. In the current literature, studies on the influences of packaging features on consumer preferences are mainly related to classical preference evaluation methods like conjoint analysis (CA). The purpose of this paper is to apply both CA and the less known combination of uniform discrete and shifted binomial distributions (CUB) models to food packaging evaluations.

Starting from a real case study in this field, along with CA, the author apply CUB models (Iannario and Piccolo, 2010) as a useful tool to evaluate preferences. CUB models can grasp some psychological characteristics of consumers related to the “feeling” toward packaging attributes and related to an inherently “uncertainty” that affects the consumers’ choices. Both psychological characteristics “feeling” and “uncertainty” can be linked to relevant subject’s information. At first we detect preferred packaging attributes of fresh food by means of CA, then we apply CUB models to some relevant attributes from the CA study.

Results show that attributes like packaging material and size/shape of packaging are the most important attributes and that biodegradable packaging, reclosable trays/bags and long “best by” date are also valuable features for consumers. The introduction of covariates showed that specific demographic characteristics are linked to both feeling and uncertainty.

The “data driven” segmentation results give to the integrated approach “CUB models and Conjoint Analysis” the most important added value.