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Research on Influencing Factors of Consumption and Purchase Intention of Camellia Oil in Coastal Areas Based on Logistics Model.

Authors :
Wu, Xue
Source :
Mathematical Problems in Engineering. 9/1/2022, p1-10. 10p.
Publication Year :
2022

Abstract

Camellia oil contains a variety of active substances, which have the functions of strengthening the heart and lowering cholesterol, and can prevent a variety of cardiovascular and cerebrovascular diseases caused by vascular sclerosis edible oil. Based on the logistics model, this paper builds a research model on the influencing factors of consumption and purchase intention and analyzes the coastal areas of camellia oil. Using this model, it makes an empirical analysis and research on the differences in the purchase intention and influencing factors of different consumers of camellia oil. Based on the results of empirical analysis, the main research conclusions of this paper are extracted, including the influencing factors of consumers' camellia oil purchase willingness and the differences in the influencing factors of consumers' camellia oil purchase willingness under different income levels, and put forward some corresponding measures and policy suggestions in a targeted manner. Based on the research model of the influencing factors of purchase intention established by logistic algorithm, this paper analyzes the coastal areas of Chashan oil and finds that the factors affecting consumers' purchase intention are positively related to education and personal monthly income and negatively related to gender. "Educational education" is significant at 1% and has a positive correlation with a coefficient of 0.864; personal income is significant at 5% with a coefficient of 0.762; for gender, it is negatively correlated with a coefficient of -0.259, indicating that other variables remain unchanged. Education can promote consumers' willingness to buy camellia oil, and consumers with higher education are more likely to buy camellia oil. In the same regression prediction model, the regression prediction efficiency of this model is better. In general, this shows that the model in this paper has certain superior performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1024123X
Database :
Academic Search Index
Journal :
Mathematical Problems in Engineering
Publication Type :
Academic Journal
Accession number :
158867811
Full Text :
https://doi.org/10.1155/2022/7028499