Consumer preferences for eggs using conjoint analysis

Abstract

In the UK consumers are becoming more aware of issues related to food quality. Food marketers face the problem of responding to these developments by offering products which are consistent with changing consumer preferences. It is therefore increasingly important for marketers to understand the nature of consumers' preferences. This study focuses on the preferences of egg purchasers and uses conjoint analysis to identify consumer preference segments in the market. The analysis reveals that the preferences of consumers are very heterogeneous, but that it is possible to identify segments with distinct preferences for particular egg attributes.

UK consumers' willingness to pay for laying hen welfare

“…Both were listed as important attributes in Parrott (2001) and within the present study. The importance of freshness concurs with results from existing literature (Gerhardy and Ness, 1995; Gerini et al , 2016) as this attribute is also synergistic with other egg attributes such as taste and nutrition (Karoui et al , 2006; Surai and Sparks, 2001). In terms of egg size, respondents revealed a preference for large-size eggs.…”

Functional and organic eggs as an alternative to conventional production: a conjoint analysis of consumers' preferences

“…These attributes must reflect the product's characteristics and dimensions that are of most importance for the consumer 23. In the present study the attributes and levels to use were selected after reviewing previous studies on consumer preferences for eggs1, 11, 24, 25 and by direct observation of the information included in egg labelling and found in the actual products offered on shop shelves. These attributes and levels are listed in Table 1.…”

Quality labels as a marketing advantage

“…For this purpose, a series of hierarchical and k ‐means cluster analyses were conducted. The average importance of the “price” and “existence of a regional or commercial indication (Zagorin or PDO)” attributes for each of the 206 persons of the sample, expressed as percentages, are used as segmentation variables (Green and Krieger, 1991; Gerhardy and Ness, 1995). Owing to the relatively limited size of the sample, the solution of three clusters is considered as the most realistic, easily describable and suitable to our objectives.…”