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학술논문외식경영연구2011.12 발행KCI 피인용 7

컨조인트 분석을 이용한 외식 소비자의레스토랑 선택 유형에 관한 연구

Types of Restaurant Selections by Consumers Using Conjoint Analysis

홍종숙(여주대학); 정유경(세종대학교)

14권 5호, 93~111쪽

초록

The purpose of this study is to determine the relative importance of restaurant attributes, preference in choosing a restaurant and to find out differences between segments. The web survey was conducted among 151 customers(aged from 20~49years old) who lived in Seoul and Kyonggi, from the period of Sep 17 to Oct 4, 2008. The statistical data analyses were performed using SPSS/WIN 12.0 for the conjoint analysis. The main results of this study were as follows : according to the analysis on the attributes and the levels of restaurants, the relative importance of restaurant attributes was assessed in restaurant type(37.6%), price(29.6%), atmosphere(19.14%) and place(13.66%). Simulation to predict market share as a virtual dining seafood restaurant in price between 10,000 to 30,000 won exclusive space within the interpersonal environment-friendly atmosphere was the most preferred.

Abstract

The purpose of this study is to determine the relative importance of restaurant attributes, preference in choosing a restaurant and to find out differences between segments. The web survey was conducted among 151 customers(aged from 20~49years old) who lived in Seoul and Kyonggi, from the period of Sep 17 to Oct 4, 2008. The statistical data analyses were performed using SPSS/WIN 12.0 for the conjoint analysis. The main results of this study were as follows : according to the analysis on the attributes and the levels of restaurants, the relative importance of restaurant attributes was assessed in restaurant type(37.6%), price(29.6%), atmosphere(19.14%) and place(13.66%). Simulation to predict market share as a virtual dining seafood restaurant in price between 10,000 to 30,000 won exclusive space within the interpersonal environment-friendly atmosphere was the most preferred.

발행기관:
한국외식경영학회
분류:
관광학

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컨조인트 분석을 이용한 외식 소비자의레스토랑 선택 유형에 관한 연구 | 외식경영연구 2011 | AskLaw | 애스크로 AI