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成都历史街区美食旅游者聚类研究 被引量:2

Cluster Analysis of Food-tasting Tourists Visiting Historical Districts in Chengdu
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摘要 以成都历史街区国内美食旅游者为调研对象,揭示美食动机、态度与满意度特征,探索基于动机的美食旅游者聚类及其差异性特征。结果表明:美食感知评价总体趋于正面,仅"与我日常饮食差异较大"(M=2.85)低于量表中值(M=3)。"社交动机""品味动机""尝鲜动机"3项美食旅游公因子逐步聚类为"社交品味型""社交动机型""微弱尝鲜型"3类人群。"微弱尝鲜型"多为女性,其美食态度与美食满意度评价值相对较低。通过揭示成都历史街区美食旅游者类型及其差异,有望为四川省打造以成都为中心的世界美食旅游目的地提供决策参考。 This study investigates motivations,attitudes and satisfaction of food-tasting tourists visiting historical districts in Chengdu,a rising international destination renowned for its unique food culture.The study focuses on clusters of food-tasting tourists by analyzing tourism motivations.The results indicate that respondents are inclined to express positive attitudes towards the food in Chengdu and are generally satisfied with their food-tasting experiences.Most of them confess that the food is different from what they eat every day,as suggested by the mean score(2.85)lower than the median value of the scale(M=3).Thirteen food-tasting motivations are reduced into three factors:social motives,taste motives,and novelty motives,which are further clustered into three groups,and these include group one who likes to taste local food with friends and relatives;group two who aims to socialize with people,and group three who merely wants to discover something new.Interestingly,female respondents from group three have a relatively lower food-tasting satisfaction and are reluctant to take positive towards the local food.The findings with full practical implications will help making Chengdu a world famous food-tasting destination.
作者 汪嘉昱 梁越 何莉 唐勇 WANG Jiayu;LIANG Yue;HE Li;TANG Yong(College of Tourism and Urban-rural Planning,Chengdu University of Technology,Chengdu,Sichuan 610059,China;Gushi County Cultural Broadcasting and tourism bureau,Henan,Gushi 465200 China)
出处 《乐山师范学院学报》 2021年第11期53-58,75,共7页 Journal of Leshan Normal University
基金 成都理工大学中青年骨干教师发展资助计划(10912-KYGG2019-00940) 国家留学基金(201908510015)。
关键词 旅游动机 美食旅游 历史街区 聚类分析 Tourism motivation Food-tasting Tourism Historic Districts Cluster Analysis
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