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Identifying Worldwide Interests in Organic Foods by Google Search Engine Data

文献类型： 外文期刊

作者： Liu, Xu 1 ; Zhong, Meiying 1 ; Li, Bin 2 ; Su, Ye 3 ; Tan, Jinglu 4 ; Gharibzahedi, Seyed Mohammad Taghi 5 ; Guo, Ya 1 ;

作者机构： 1.Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Univ Nebraska Kearney, Dept Econ, Kearney, NE 68847 USA

4.Univ Missouri, Dept Bioengn, Columbia, MO 65211 USA

5.Islamic Azad Univ, Lahijan Branch, Young Researchers & Elites Club, Lahijan 4416939515, Iran

6.Wuxi Inst Technol, Sch Foreign Languages & Tourism, Wuxi 214121, Jiangsu, Peoples R China

关键词： Organic food; search engine; search interest; neural network; data modeling; deep learning; consumer behavior

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2019 年 7 卷

页码：

收录情况： SCI

摘要： Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers' interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people's interests in major organic foods over time.

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