ObjectiveWith consumers’ concerns about food safety and the environment growing, the interest in organic food has increased. However, due to the late start of the organic food market in China, the market size of the Chinese organic food industry is still relatively small. This study aims to examine whether organic food credence attributes have an impact on consumers’ attitudes and willingness to pay a premium (WTPP), in order to provide valuable information to facilitate the development of the organic food market in China.MethodsA questionnaire survey was conducted with 647 respondents in China. Structural equation modeling (SEM) was utilized to verify the model and test the relationships among the constructs.ResultsSEM analyses showed that credence attributes stimulate consumers’ attitudes and increase consumers’ WTPP. Utilitarian attitudes and hedonistic attitudes play a partially mediating role in the relationship between credence attributes and WTPP. Uncertainty negatively moderates the role between utilitarian attitudes and WTPP, while it positively moderates the role between hedonistic attitudes and WTPP.DiscussionThe findings reveal the motivations and barriers for Chinese consumers to purchase organic food at a premium, providing a theoretical basis for companies to gain a deeper understanding of consumer groups and develop organic food marketing strategies.

The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources.

At this stage, countries around the world have their own operating management model for the procurement system of emergency equipment. This article analyzes the influencing factors affecting the operation of the emergency procurement system through a convolutional neural network analysis method, and the contract management of the emergency procurement system is realized. Management and monitoring and balance of interests on supply and demand also meet the requirements of the construction and improvement of emergency procurement systems at this stage. During the construction and improvement of the emergency procurement system, through the monitoring and management of the procurement system, standardize the management of emergency procurement contracts, and implement the management of the memorandum of emergency procurement contracts to maximize the benefits of supply and demand of emergency equipment, and meet the requirements of different emergency levels in the future equipment procurement requirements.