This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examine...This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examines the impact of data assetization on the supply chain resilience of SRDI SMEs,and examines the impact of data assetization on the supply chain resilience of SRDI SMEs using the role of the mechanism model.Through the mechanism model,the mediating effects of financing constraints and technological innovation are examined,and a path of action is drawn,which provides theoretical evidence and policy recommendations for promoting the digital transformation of SRDI SMEs and improving supply chain resilience.展开更多
As data is incorporated into production factors,the Accounting discipline should intensively study the new data asset.Starting from the analysis of the value attribute,right attribute,and relationship attribute of the...As data is incorporated into production factors,the Accounting discipline should intensively study the new data asset.Starting from the analysis of the value attribute,right attribute,and relationship attribute of the data,it is found that under certain conditions,the data has complete Accounting attributes and can be included in the assets.The Accounting discipline shall establish the research direction of Data Asset Accounting and focus on the research,including the recognition and measurement of data assets,the value evaluation of data assets,information disclosure,and Data Asset Accounting standards.Data assets are facing the major challenge of integrating into the Accounting discipline.We can carry out the Accounting professional reform and textbook construction facing the practice of data assets management from the aspects of theoretical construction,talent training,and industry research cooperation.展开更多
In order to realize the effective management of massive data resources brought by the comprehensive informatization of power system, it can better provide scientific basis for production decisions and better solve the...In order to realize the effective management of massive data resources brought by the comprehensive informatization of power system, it can better provide scientific basis for production decisions and better solve the data asset management problems of enterprises. First of all, this paper analyzes the present situation and demand of data resources, puts forward a data asset management system suitable for the power industry, discusses it deeply, and designs its function, data structure and technical structure. Provide efficient, safe, shared and comprehensive data services and data centers for electric power enterprises. On this basis, the data asset management system constructed can not only effectively guarantee the use of data resources, but also accelerate the digital transformation, cultivate digital economy and build digital ecology.展开更多
This paper explores the audit risks associated with the recognition of data assets on financial statements,focusing on the complexities arising from their replicability,unique valuation patterns,and contextual depende...This paper explores the audit risks associated with the recognition of data assets on financial statements,focusing on the complexities arising from their replicability,unique valuation patterns,and contextual dependencies.It identifies major misstatement risks at both the financial statement and assertion levels,including the potential for management to exaggerate data asset values,uncertainties in valuation methods,and deficiencies in data governance and internal controls.Additionally,auditors’lack of professional knowledge and inappropriate audit methods can lead to inspection risks.The paper emphasizes the urgent need for enhanced accounting standards for data assets,effective guidelines for their recognition and measurement,and robust internal controls.Furthermore,it advocates for the exploration of effective valuation methods and the incorporation of advanced technologies,such as big data and AI,into auditing practices.By improving auditor training and methodologies,organizations can better manage the inherent risks associated with data asset auditing.展开更多
With the advancement of technologies such as the Internet,cloud computing,and artificial intelligence,data has evolved into data assets,which hold significant economic value.Recently,China has introduced a series of a...With the advancement of technologies such as the Internet,cloud computing,and artificial intelligence,data has evolved into data assets,which hold significant economic value.Recently,China has introduced a series of accounting standards for valuing data assets on balance sheets.These standards define the conceptual scope and categories of data assets,establishing an institutional foundation for their recognition as capital contributions.As data assets are controllable,integral,and transferable,they qualify as non-monetary capital contributions under article 48 of the newly-revised Company Law of China.Within this context,this article aims to refine the analytical framework for data assets as capital contributions under the newly-revised Company Law,balancing the protection of individual privacy rights with the realization of data's economicvalue.展开更多
This paper explores the challenges and opportunities related to the activation of data assets in the maritime industry.This study sheds light on the evolving landscape of data asset monetization in the maritime sector...This paper explores the challenges and opportunities related to the activation of data assets in the maritime industry.This study sheds light on the evolving landscape of data asset monetization in the maritime sector.The successful activation of these data assets has the potential to generate substantial economic benefits.By addressing ownership,pricing,and security concerns,maritime enterprises can unlock the true potential of their data assets and contribute to the growth and development of the industry.Maritime enterprises possess extensive and long-standing data assets,which have the potential for substantial value extraction.The first part highlights the global trend of data asset monetization and provides an overview of data accumulation by leading maritime companies.It also underscores the unique characteristics of maritime data,including relatively straightforward ownership and ease of utilization.The second part delves into the practical experiences and pros and cons of data asset monetization in various regions.The third part examines the main risks associated with data asset monetization in maritime enterprises.These risks include issues related to ownership and profit distribution after data rights are established,the possibility of data idling leading to a bubble effect,and escalating concerns about data security.In the fourth part,the paper offers specific regulatory pathways and recommendations to address these challenges.This includes resolving ownership attribution issues,implementing market-oriented pricing strategies with legal safeguards,and embracing technological measures for robust data security,such as distinguishing between information and raw data and ensuring the anonymization of original data.展开更多
Artificial intelligence(AI)has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes.This study proposes a subject-strategy-process(SSP)framework for b...Artificial intelligence(AI)has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes.This study proposes a subject-strategy-process(SSP)framework for business intelligence(BI)for big data-driven product innovation through logical deduction,drawing on the theory of big data cooperative assets and an adaptive innovation perspective on enterprise-user interaction.The aim is to explore new mechanisms through which AI influences product innovation in manufacturing.This study indicates three aspects.Firstly,the two-way involvement of humans and AI forms a dual feedback-enhancement mechanism of factor combination and knowledge accumulation.This mechanism drives structural changes in innovation subjects and forms a new foundation for strategic and process transformations in product innovation.Secondly,the alignment between an enterprise’s cognitive strategy about AI,competitive strategy,organizational culture,business model,and ecosystem jointly shapes the integrated application of AI in innovation processes.Thirdly,the new features of the big data-driven product innovation process include full-process diffusion from the fuzzy front end,nonlinear iteration of demand-solution pairs,and generative self-testing in intelligent manufacturing.Taken together,the study demonstrates that the SSP framework is well-suited to analyzing the new mechanisms of BI for big data-driven product innovation,which offers a fresh lens for examining the relationship between AI and product innovation.展开更多
Blockchain is commonly considered a potentialdisruptive technology. Moreover, the healthcareindustry has experienced rapid growth in the adoption ofhealth information technology, such as electronic healthrecords and e...Blockchain is commonly considered a potentialdisruptive technology. Moreover, the healthcareindustry has experienced rapid growth in the adoption ofhealth information technology, such as electronic healthrecords and electronic medical records. To guarantee dataprivacy and data security as well as to harness the value ofhealth data, the concept of Health Data Bank (HDB) isproposed. In this study, HDB is defined as an integratedhealth data service institution, which bears no “ownership”of health data and operates health data under the principalagentmodel. This study first comprehensively reviews themain characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in theareas of health insurance, pharmacy, and medical treatment.Then, we analyze the fundamental principles ofHDB and point out four challenges faced by HDB’ssustainable development: (1) privacy protection andinteroperability of health data;(2) data rights;(3) healthdata supervision;(4) and willingness to share health data.We also analyze the important benefits of blockchainadoption in HDB. Furthermore, three application scenariosincluding distributed storage of health data, smart-contractbasedhealthcare service mode, and consensus-algorithmbasedincentive policy are proposed to shed light on HDBbasedhealthcare service mode. In the end, this study offersinsights into potential research directions and challenges.展开更多
Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted colle...Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.展开更多
基金National Undergraduate Training Program for Innovation and Entrepreneurship(D202410120257422558)。
文摘This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examines the impact of data assetization on the supply chain resilience of SRDI SMEs,and examines the impact of data assetization on the supply chain resilience of SRDI SMEs using the role of the mechanism model.Through the mechanism model,the mediating effects of financing constraints and technological innovation are examined,and a path of action is drawn,which provides theoretical evidence and policy recommendations for promoting the digital transformation of SRDI SMEs and improving supply chain resilience.
文摘As data is incorporated into production factors,the Accounting discipline should intensively study the new data asset.Starting from the analysis of the value attribute,right attribute,and relationship attribute of the data,it is found that under certain conditions,the data has complete Accounting attributes and can be included in the assets.The Accounting discipline shall establish the research direction of Data Asset Accounting and focus on the research,including the recognition and measurement of data assets,the value evaluation of data assets,information disclosure,and Data Asset Accounting standards.Data assets are facing the major challenge of integrating into the Accounting discipline.We can carry out the Accounting professional reform and textbook construction facing the practice of data assets management from the aspects of theoretical construction,talent training,and industry research cooperation.
文摘In order to realize the effective management of massive data resources brought by the comprehensive informatization of power system, it can better provide scientific basis for production decisions and better solve the data asset management problems of enterprises. First of all, this paper analyzes the present situation and demand of data resources, puts forward a data asset management system suitable for the power industry, discusses it deeply, and designs its function, data structure and technical structure. Provide efficient, safe, shared and comprehensive data services and data centers for electric power enterprises. On this basis, the data asset management system constructed can not only effectively guarantee the use of data resources, but also accelerate the digital transformation, cultivate digital economy and build digital ecology.
文摘This paper explores the audit risks associated with the recognition of data assets on financial statements,focusing on the complexities arising from their replicability,unique valuation patterns,and contextual dependencies.It identifies major misstatement risks at both the financial statement and assertion levels,including the potential for management to exaggerate data asset values,uncertainties in valuation methods,and deficiencies in data governance and internal controls.Additionally,auditors’lack of professional knowledge and inappropriate audit methods can lead to inspection risks.The paper emphasizes the urgent need for enhanced accounting standards for data assets,effective guidelines for their recognition and measurement,and robust internal controls.Furthermore,it advocates for the exploration of effective valuation methods and the incorporation of advanced technologies,such as big data and AI,into auditing practices.By improving auditor training and methodologies,organizations can better manage the inherent risks associated with data asset auditing.
基金supported by the National Social Science Foundation ofChina(GrantNo.21BFX079).
文摘With the advancement of technologies such as the Internet,cloud computing,and artificial intelligence,data has evolved into data assets,which hold significant economic value.Recently,China has introduced a series of accounting standards for valuing data assets on balance sheets.These standards define the conceptual scope and categories of data assets,establishing an institutional foundation for their recognition as capital contributions.As data assets are controllable,integral,and transferable,they qualify as non-monetary capital contributions under article 48 of the newly-revised Company Law of China.Within this context,this article aims to refine the analytical framework for data assets as capital contributions under the newly-revised Company Law,balancing the protection of individual privacy rights with the realization of data's economicvalue.
基金National Social Science Fund of China,21BFX077,Xiaolan Yu。
文摘This paper explores the challenges and opportunities related to the activation of data assets in the maritime industry.This study sheds light on the evolving landscape of data asset monetization in the maritime sector.The successful activation of these data assets has the potential to generate substantial economic benefits.By addressing ownership,pricing,and security concerns,maritime enterprises can unlock the true potential of their data assets and contribute to the growth and development of the industry.Maritime enterprises possess extensive and long-standing data assets,which have the potential for substantial value extraction.The first part highlights the global trend of data asset monetization and provides an overview of data accumulation by leading maritime companies.It also underscores the unique characteristics of maritime data,including relatively straightforward ownership and ease of utilization.The second part delves into the practical experiences and pros and cons of data asset monetization in various regions.The third part examines the main risks associated with data asset monetization in maritime enterprises.These risks include issues related to ownership and profit distribution after data rights are established,the possibility of data idling leading to a bubble effect,and escalating concerns about data security.In the fourth part,the paper offers specific regulatory pathways and recommendations to address these challenges.This includes resolving ownership attribution issues,implementing market-oriented pricing strategies with legal safeguards,and embracing technological measures for robust data security,such as distinguishing between information and raw data and ensuring the anonymization of original data.
基金supported by the key project of the National Natural Science Foundation of China“Research on the Theory,Methods,and Applications of Innovation via Enterprise-User Interaction Driven by Big Data in the Internet Environment”(No.71832014)the key project of the National Natural Science Foundation of China“Research on the Digital Transformation and Adaptive Management Changes of Manufacturing Enterprises”(No.72032009)the major project of the National Social Science Fund of China“Research on the Impact of Artificial Intelligence on the Transformation and Upgrading of the Manufacturing Industry and Its Governance System”(No.23&DA091).
文摘Artificial intelligence(AI)has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes.This study proposes a subject-strategy-process(SSP)framework for business intelligence(BI)for big data-driven product innovation through logical deduction,drawing on the theory of big data cooperative assets and an adaptive innovation perspective on enterprise-user interaction.The aim is to explore new mechanisms through which AI influences product innovation in manufacturing.This study indicates three aspects.Firstly,the two-way involvement of humans and AI forms a dual feedback-enhancement mechanism of factor combination and knowledge accumulation.This mechanism drives structural changes in innovation subjects and forms a new foundation for strategic and process transformations in product innovation.Secondly,the alignment between an enterprise’s cognitive strategy about AI,competitive strategy,organizational culture,business model,and ecosystem jointly shapes the integrated application of AI in innovation processes.Thirdly,the new features of the big data-driven product innovation process include full-process diffusion from the fuzzy front end,nonlinear iteration of demand-solution pairs,and generative self-testing in intelligent manufacturing.Taken together,the study demonstrates that the SSP framework is well-suited to analyzing the new mechanisms of BI for big data-driven product innovation,which offers a fresh lens for examining the relationship between AI and product innovation.
基金the National Natural Science Foundation of China(Grant No.71671039).
文摘Blockchain is commonly considered a potentialdisruptive technology. Moreover, the healthcareindustry has experienced rapid growth in the adoption ofhealth information technology, such as electronic healthrecords and electronic medical records. To guarantee dataprivacy and data security as well as to harness the value ofhealth data, the concept of Health Data Bank (HDB) isproposed. In this study, HDB is defined as an integratedhealth data service institution, which bears no “ownership”of health data and operates health data under the principalagentmodel. This study first comprehensively reviews themain characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in theareas of health insurance, pharmacy, and medical treatment.Then, we analyze the fundamental principles ofHDB and point out four challenges faced by HDB’ssustainable development: (1) privacy protection andinteroperability of health data;(2) data rights;(3) healthdata supervision;(4) and willingness to share health data.We also analyze the important benefits of blockchainadoption in HDB. Furthermore, three application scenariosincluding distributed storage of health data, smart-contractbasedhealthcare service mode, and consensus-algorithmbasedincentive policy are proposed to shed light on HDBbasedhealthcare service mode. In the end, this study offersinsights into potential research directions and challenges.
基金the National Natural Science Foundation of China(No.62272480 and 62072470)。
文摘Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.
