An Empirical Study of Consumer Characteristics and Buying Behavior of Small Varieties of Edible Oil---Take Tea oil for example
In this study, the use of Camellia oil as an example, Fuzhou, Guangzhou and Changsha three cities 664 questionnaires data using binary Logistic regression model confirmed consumer internal factors and external environmental factors have a significant impact on the buying behavior of small va-rieties of oil. The conclusions are as follows: Consumer education, family structure, the degree of at-tention on health, the smaller varieties of oil having a degree of recognition of high nutritional value, degree of attention to the nutritional value of edible oil, edible oil taste emphasis on small oil varieties emphasis price, and claims that the ability to distinguish small variety oil is good or bad, on the rec-ommendation of friends and relatives recognized degree, the degree of distance from the small variety oil sales to the regional environment is an important factor affecting people buy a small variety oil. Wherein small recognition of the extent of oil varieties with high nutritional value, degree of attentionto the nutritional value of edible oil, consider themselves to distinguish good and bad small oil varieties ability to relatives and friends recommended acceptable levels, consumer buying behavior and the ex-tent of their own culture was positive small oil varieties to the relationship. Emphasis on small breed price of oil, the emphasis on taste and the extent of oil from the oil sales to a small variety of small va-rieties of edible oil on distance buying behavior has a negative impact. In addition, consumers individ-ual characteristic variables the impact of gender, age, income level, health status of small varieties of oil was not significant. Residents of different regions are different varieties of small oil purchases. Fi-nally, policy implications on the basis of the conclusions of the study.
small varieties of edible oilCamellia oilconsumer behaviorconsumer characteris-ticsinfluencing factorsLogistic regression model
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