A Conjoint-Based Approach to Analyze the Importance of Brand Choice Attributes: Pizza Restaurant Cases.
- Author:
In Sook CHAE
1
;
Min A LEE
;
Seo Young SHIN
;
Il Sun YANG
;
Jin A CHA
Author Information
1. Department of Tourism and Foodservice Industry, Tonghae University, Donghae, Korea.
- Publication Type:Original Article
- Keywords:
pizza restaurant;
brand choice;
trade off;
conjoint analysis
- MeSH:
Atmosphere;
Focus Groups;
Korea;
Restaurants*
- From:Korean Journal of Community Nutrition
2002;7(3):354-360
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
The purposes of this study were (1) to understand how customers trade off one attribute against another when they choose a pizza restaurant, (2) to compare the importance of individual attributes with their relative importance and (3) to compare customers' brand choice patterns with the prediction of pizza restaurant operators. Empirical data for this study were collected from the customers (n = 307) and operators (n = 273) of four famous pizza franchise restaurants in Korea, Pizza Hut, Mister Pizza, Domino's Pizza and Pizza Mall. The attributes and attribute levels for the hypothetical profiles were decided from the focus group discussion. A total of 16 profiles was selected from fractional factorial designs. The SPSS conjoint procedure was used to calculate utility scores and simulate profiles. The overall group statistics showed the relative importance of all attributes compared with one other. Taste was the most important attribute (32.48%) in choosing a pizza restaurant, followed by service (21.87%), atmosphere (17.23%), price (15.17%) and speed of delivery (13.26%). There was a difference between the customers' ratings of the importance of the individual attributes and the ranking of the same attributes' relative importance as derived from the conjoint analysis. The operators rated service (26.54%) as also being important, as well as taste (27.76%), in choosing a pizza restaurant. The rankings of relative importance for pizza taste, service and price were statistically different in the customers' and operators' data (p < .001, p < .01, p < .05). Operators who want to differentiate themselves from their competitors should make decisions based on an increased understanding of their customers' brand choice decision process and measure the hidden needs of their customers.
