Research on Development Stage of Service Model in Big Data Industry

Yoo, Soonduck; Choi, Kwangdon

doi:10.1007/978-3-662-45402-2_81

Cited by 5 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The big data industry system needs to cover the upstream, midstream and downstream of the industry [17], consisting of infrastructure, core technologies, services and industry applications, talent development, government regulation and guidance, industry associations, enterprises and other elements [18]. In the early stages of big data development, European and American countries usually carried out the construction of several key projects in key industries and gradually formed key technologies, management and business models, etc., with a view of promoting the rapid development of big data.…”

Section: ) Industry Support Governancementioning

confidence: 99%

A Framework for Big Data Governance to Advance RHINs: A Case Study of China

et al. 2019

View full text Add to dashboard Cite

The emergence of big data presents a serious challenge to the fast growth of regional health information networks (RHINs) globally. In China, many constructors of RHINs have spontaneously and independently created governance measures, which may be valuable as a point of reference for other countries. This paper aimed to propose a big data governance framework for healthcare data based on the governance activities associated with the processing of RHINs in China. Typical methodology for RHIN case studies in China, including rich personal experience in nationwide consulting, literature review, expert consultation, and interpretative structural modeling methods, was adopted. Based on the analysis of ten typical RHIN case studies, healthcare big data governance practices in China were summarized. A framework with 3 domains and 12 elements was proposed, which include a drive domain (big data strategy planning, laws and regulations, open transaction, and industry support), capability domain (healthcare big data organization, collection, storage, process and analysis, and usage), and support domain (healthcare big data resource planning, standards system, and privacy and security protection). We obtained 12 guidelines for healthcare big data governance. A big data governance framework with 3 domains and 12 elements was presented based on Chinese practice, which might serve as valuable references for the cross-dimensional development of RHINs, provide overall guidance for the sustainable development of regional health informatization, and contribute to realizing the business value of healthcare big data. INDEX TERMS Big data governance, framework, regional health information networks (RHINs). I. BACKGROUND 50330

show abstract

Section: ) Industry Support Governancementioning

confidence: 99%

A Framework for Big Data Governance to Advance RHINs: A Case Study of China

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the course of long-term economic development, a large amount of capital has gradually accumulated. As mentioned earlier in this article, small and medium-sized enterprises require a large amount of credit funds as a support during the development process to provide an output path for the accumulated capital (Yoo and Choi, 2015 ; Zhou et al, 2017 ). In addition, private finance has information, geographical, and cost advantages, to a certain extent, guarantees that private finance can more accurately determine the credit risk, asset status, and solvency of borrowing objects, so it can make potential risks for borrowing behavior.…”

Section: Introductionmentioning

confidence: 99%

Regional Private Financing Risk Index Model Based on Private Financing Big Data

Zhao

2022

Front. Psychol.

View full text Add to dashboard Cite

With the rapid development of China's economy in recent decades, and the decentralization of the country's economic regulation and legal support, private financing has developed rapidly due to its simple, flexible and unique advantages. Some SMEs can solve it to some extent through private financing. The company's own financing issues have also helped the local financial market's effectiveness. Based on the “Yantai Private Financing Interest Rate Index,” this paper constructs a private financial risk index model from three perspectives of interest rate risk, scale risk and credit risk, and conducts a case simulation analysis of the private financing risk index. The characteristic indicators of the early warning system are screened from the macro, micro and stability dimensions, and subjective and objective adjustment coefficients are set for each indicator from both subjective and objective perspectives. This article takes the Yantai Index as the representative of China's private financing interest rate index. Based on the term structure of Yantai's private lending rate, this paper studies its response to macroeconomic shocks and analyzes the information value it contains. And use the private financing interest rate index to build a financial risk monitoring model. Through the system transformation model, the article finds that there is a significant asymmetry in the response of private lending to macroeconomic shocks. When private lending rates are higher, inflation has a greater effect on interest rates; when private lending rates are lower, monetary policy has a stronger regulatory effect on private lending rates. In the data processing, the principal component analysis method and the Bayesian vector autoregressive model were established. Through the study of this article, it is concluded that the interest rate decreases with the increase of the term, and the risk comparison is performed for the 1-month period, 3-month period, June period, 1-year period, and more than 1-year. The risks in the previous period are greater, and the risks in the March and June periods are relatively small. This model can be used to calculate the comprehensive evaluation value and its fluctuation in the historical risk market and historical equilibrium market, so as to determine the risk range of the comprehensive evaluation value. Thus, the early warning system is verified to be feasible.

show abstract

“…(2014),Yoo and Choi (2015),Zhang et al (2023), using network readiness (infrastructure construction), information and communication technology application index, and big data application benefit index as the primary indicators to measure the level of big data development. Based on these, each primary indicator is further divided into secondary indicators, including fixed (mobile) telephone penetration rate, number of internet domain names, energy-saving and consumption-reducing index, etc.…”

mentioning

confidence: 99%

Enhancing environmental management through big data: spatial analysis of urban ecological governance and big data development

Lei

2024

Front. Environ. Sci.

View full text Add to dashboard Cite

Introduction: This research focuses on exploring the impact of Big Data Development (BDD) on Urban Ecological Governance Performance (EGP), with a particular emphasis on environmental dimensions within and among various regions. It aims to understand the complex interplay between technological advancements, urbanization, and environmental management in the context of urban ecological governance.Methods: Employing the Spatial Durbin Model (SDM), the study rigorously investigates the effects of BDD on EGP. It also examines the mediating role of Industrial Structure Level (ISL) and the moderating effects of both Level of Technological Investment (LTI) and Urbanization Level (URB), to provide a comprehensive analysis of the factors influencing urban ecological governance.Results: The findings reveal that big data significantly strengthens urban ecological governance, characterized by pronounced spatial spillover effects, indicating interregional interdependence in environmental management. Urbanization level notably amplifies the influence of BDD on EGP, whereas the magnitude of technological investments does not show a similar effect. Moreover, the industrial structure acts as a partial mediator in the relationship between BDD and EGP, with this mediating role demonstrating variability across different regions.Discussion: The research highlights the critical role of big data in enhancing urban ecological governance, particularly in terms of environmental aspects. It underscores the importance of technological advancements and urbanization in augmenting the effectiveness of ecological governance. The variability of the mediating role of industrial structure across regions suggests the need for tailored strategies in implementing big data initiatives for environmental management.

show abstract

Research on Development Stage of Service Model in Big Data Industry

Cited by 5 publications

References 4 publications

A Framework for Big Data Governance to Advance RHINs: A Case Study of China

A Framework for Big Data Governance to Advance RHINs: A Case Study of China

Regional Private Financing Risk Index Model Based on Private Financing Big Data

Enhancing environmental management through big data: spatial analysis of urban ecological governance and big data development

Contact Info

Product

Resources

About