Data sharing between financial service providers (FSPs) can promote the reduction of financial risks, but the reality is that the FSPs may be unwilling to share data with each other due to data security problems. The emergence of federated data technology provides a convenient way to address this issue. In this article, the author proposes using a federated data sharing model for FSPs and two dynamic evolutionary games for evaluating influencing factors. Cooperation results when both FSPs reach a certain level of mutual payoff. The reward and punishment mechanisms are important means by which FSPs actively implement real data sharing. The interaction between the FSP and the federated technology service provider (FTSP) demonstrates the practicality of this new technology. The results show that the data sharing improved the data trust and supported regulatory mechanisms.

Privacy in the information systems (IS) context is becoming increasingly challenging and complicated. This study conducted a systematic review of the information privacy literature in the IS context to identify theories to understand and explain online information privacy. We reviewed eight journals from the Association for Information Systems over the 10-year period from 2013 to 2023. These journals were identified as mainstream in the IS discipline by senior scholars in the field. We identified and explored 20 theories in depth. Our findings have important implications for academics and practitioners. Scholars can build on our findings to identify and adopt promising theories from other disciplines to be contextualized and applied to examining information privacy. New theories can enrich our understanding of information privacy and provide in-depth insights from different perspectives, thereby enhancing our understanding of information privacy issues.

This paper aims to develop accounting methods for data assets helpful to developing sustainable regional economic development within the digital economy. By considering data-asset characteristics such as virtuality and sharing, it proposes accounting treatments and processes for data-asset evaluation and management. Through a case study of Guangdong Power Grid Co., Ltd., the optimized method is evaluated. Results show a 70% increase in data-asset valuation compared to intangible assets, with a 17.8% growth in total profits. The method reduces asset-management costs by 3.67% and amortization by CN¥98,600. These findings highlight the importance of optimizing accounting methods for data assets able to foster sustainable regional economic development in the digital-economy era.

Prefabricated assemblies are popular in the construction industry due to their minimal carbon footprint, enhanced safety, and reliability. A combination of software, including Revit, Navisworks, and Practical Structural Design and Construction (PKPM) software, is used to reduce costs by refining the specifications and dimensions of components, streamlining the variety of molds, enhancing design performance through rigorous component collision inspections and structural optimization, and ensuring cost-effectiveness. The integration of building integration modeling (BIM) visualization significantly diminishes errors attributable to information asymmetry and minimized material wastage stemming from production inaccuracies. The implementation of a Radio Frequency Identification (RFID) information exchange platform enables real-time component tracking, provides insights into the transportation dynamics of these components, and facilitates cost optimization during the transportation phase. Moreover, simulations conducted using Fuzor software preemptively identify potential construction site issues. There is a substantial cost savings of 710,000 yuan.

Traditional cross-domain recommendation models make it difficult to deeply mine users' aspect-level preferences from comment information due to existing problems such as polysemy of comment text, sparse comment data, and user cold start. A Cross-Domain Recommender (CDR) model that integrates comment knowledge enhancement and aspect-level user preference transfer (C-KE-AUT) was proposed to address the above issues. Firstly, an aspect-level user preference extraction model was constructed by combining the RoBERTa word embedding model, high-level feature representation based on Transformer, and aspect-level attention-learning methods. Then, a user aspect-level preference cross-domain transfer model was constructed based on a two-stage generative adversarial network that can transfer the aspect-level interest preferences of users in the source domain to the target domain with sparse data. The experimental results on the Amazon 2018 comment dataset indicated that the recommendation performance of the proposed C-KE-AUT model was significantly superior to other advanced comparative models.

In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.

In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

Expanding industrial digitization is vital for a thriving digital economy. This article delves into the application of interspecies competition models within the digital economy ecosystem. Such models aid in analyzing competitive dynamics among enterprises and understanding innovation trends. Interspecies competition models offer insights into enterprise competition within the digital economy ecosystem. They illuminate competitive landscapes, enabling strategic planning for enterprises facing peer and cross-industry competition. Moreover, these models facilitate the study of innovation and evolution, predicting market trends and aiding decision-making for enterprises and policymakers. Empirical studies across e-commerce, fintech, and sharing economy sectors validate the effectiveness of interspecies competition models. Analysis using these models elucidates competitive dynamics and provides actionable insights for enterprises and policymakers. Furthermore, addressing data migration bandwidth issues in cloud processes fortifies digital ecosystem construction.

This paper studies a new integrated product development (IPD) process at an automobile parts company. The process includes benchmarking and offers four optimization strategies for the new product development process: (1) re-structuring the new product development organization astraddle department teams, (2) reshaping the new product development and selection process already at the planning and conceptual design stages, (3) optimizing the design review process at the engineering stage, and (4) establishing a product development team with large project manager responsibilities in the industrialization stage so as to give full play to the guiding role of expert resources. This paper can help enhance the competitiveness of domestic auto parts enterprises compared to leading foreign auto parts enterprises.

In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.