The Analysis of Agricultural Products Consumers' Purchase Behavior under the Background of Big Data

Author(s):  
Xu Chen ◽  
Hua Fang
IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49990-50002 ◽  
Author(s):  
Qian Tao ◽  
Chunqin Gu ◽  
Zhenyu Wang ◽  
Joseph Rocchio ◽  
Weiwen Hu ◽  
...  

2020 ◽  
Vol 4 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Jinghuan Zhang ◽  
Wenfeng Zheng ◽  
Shan Wang

Purpose The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method. Design/methodology/approach This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior. Findings Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity. Originality/value Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.


Big Data Analytics is one of the most cutting edge technology in the world. Big Data Analytics will provide data management to store, process and analyze the huge amount of data. Agricultural is one of the domains that assuring big data analytics used to make a change in the field. With the consistent enhancements of the peoples living lifestyle, step by step peoples concentrate on the demand of perishable agricultural products. However, the periodic eruption of food quality and safety issues affected the concern of the end-users. To enrich the distribution performance of agricultural product logistics and to provide the freshness, quality, and safety of the agricultural products has become a thread of the current agricultural domain. Big Data Analytics in Agricultural Products Logistics has an essential prospect of optimizing the distribution path of products, prediction of product demands in the market, traceability of the products, analyzing customer feedbacks and increasing overall performance of logistics in agriculture. This paper investigates the key challenges, methods used, technologies used, algorithms for distribution of products and future of Big Data Analytics in agro-logistics.


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