Xiaxia Niu

Purpose: Blockchain technology (BT) plays an important role in enhancing operational efficiency and consumer trust within the agricultural supply chain (ASC). The purpose of this paper is to investigate how risk-averse attitudes of e-ASC members affect the adoption decision of BT under the consideration of demand uncertainty. Design/methodology/approach: This paper simultaneously considers the improvement of consumer trust and concerns of privacy information leakage within the context of BT. We constructed a game model of BT adoption under different risk attitudes consisting of agricultural supplier, third-party logistic (3PL) and e-retailer and introduced the mean-variance theory to analyze the impact of risk attitudes on equilibrium pricing, profits and consumer surplus. We further considered an extended analysis of risk-averse consumers. Findings: The equilibrium pricing and equilibrium profit of supply chain members will increase with the heightened consumer trust degree for blockchain products and decrease as the consumer privacy concerns intensify regardless of risk attitudes. In the shift from risk-neutral to risk-averse, supply chain members have an incentive to reduce retail and wholesale prices to maintain their market size, which helps to mitigate the risk of profit volatility associated with demand uncertainty. Suppliers tend to reduce the profit risk from demand uncertainty at the cost of lower expected profits. It is interesting to note that considering risks is beneficial for e-retailer, as it may increase their expected profits. Consumers will also benefit from the risk aversion of supply chain members. Practical implications: Practitioners can flexibly adjust pricing strategies based on their own risk preferences and market demand uncertainty to achieve maximum profit and minimum risk. To reduce consumers' privacy concerns, security measures such as digital encryption and digital summarization can be integrated at the technical level to ensure the confidentiality of privacy information. Originality/value: This study enriches the research on the application of BT in ASCs and provides insights for practitioners on how to develop effective strategies under different risk attitudes.

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations.

In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e‐commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e‐commerce platform, this paper uses an improved RFM model to extract user features and uses the K‐means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time (R), purchase frequency (F), and total consumption amount (M). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the K‐means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e‐commerce platform to realize the accurate marketing strategy based on big data.