Sales data sharing to improve product development efficiency in cross-border e-commerce

Niu, Baozhuang; Dong, Jian; Zhi-peng, Dai; Liu, Yaoqi

doi:10.1016/j.elerap.2021.101112

Cited by 23 publications

(9 citation statements)

References 46 publications

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“…They provided case studies on the successful utilization of the intelligent knowledge-based conversational agent system. Niu et al (2022) examined the impacts of sales data sharing on product development efficiency in cross-border e-commerce. In crossborder e-commerce, the e-commerce platforms have big data of sales, and may decide to share the data vertically with their upstream multinational firm (MNF) to improve the product development efficiency.…”

Section: Insights Into Emerging Technologies In E-commerce Operations...mentioning

confidence: 99%

Emerging technologies in e-commerce operations and supply chain management

Shen

Dong

Tong

et al. 2022

Electronic Commerce Research and Applications

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Section: Insights Into Emerging Technologies In E-commerce Operations...mentioning

confidence: 99%

Emerging technologies in e-commerce operations and supply chain management

Shen

Dong

Tong

et al. 2022

Electronic Commerce Research and Applications

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“…e key to building a product recommendation system is to build a user model, and building a user model requires determining the algorithm for the recommendation [12]. In order to generate reasonable recommendations and ensure the real-time nature of the recommendation system and the application requirements in different fields, researchers have proposed a variety of different recommendation algorithms, such as collaborative filtering algorithms, Bayesian network technology, clustering technology, correlation rule technology, and graph-based Hunting graph technology [13].…”

Section: State Of the Artmentioning

confidence: 99%

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

Shao

2022

Advances in Multimedia

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In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

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“…However, existing relevant studies have not provided effective solutions. For example, Niu et al [24] pointed out that the sharing of cross-border e-commerce sales data benefits all parties in the supply chain, but there is no further analysis of how to share data. Markovict et al [25] only pointed out that the open innovation of B2B enterprises should focus on the selection of business partners, innovation process, and innovation results.…”

Section: Realization Mode Of Open Innovation Enterprise Wishing To Ga...mentioning

confidence: 99%

Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

Zhang

Guo

2022

Mathematical Problems in Engineering

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Product development and innovation is the key issue for cross-border e-commerce operation. It is of great importance to build an intelligent open innovation system and maximize its proximity to market demand with the internationalization and digital advantages of cross-border e-commerce. Cross border e-commerce data is widely distributed among each node enterprise in the supply chain. But the enterprises will not share private data for intelligent learning for the sake of data security, which has become a difficulty problem in intelligent decision-making of open innovation. This paper analyzes the research status of open innovation and the technical basis of federated learning, builds an open innovation intelligent decision-making model of cross-border e-commerce based on federated learning, trains and tests the model using data from two participants, compares the intelligent prediction effects among whole learning, local learning and federated learning, and puts forward the collaborative promotion strategy of open innovation and intelligent optimization of cross-border e-commerce based on federated learning.

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Sales data sharing to improve product development efficiency in cross-border e-commerce

Cited by 23 publications

References 46 publications

Emerging technologies in e-commerce operations and supply chain management

Emerging technologies in e-commerce operations and supply chain management

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

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