Data value creation activities are divided into fundamental and incremental value activities.Data value chain facilitates the creation of fundamental data value and the value enhancement during the circulation process in the various chains of data collection,transmission,storage,analysis,and application.In light of the absence of a scientific and systematic research paradigm in the current academic research in this field,this paper focuses on the data value chain,systematically elucidating its conceptual framework,evolutionary stages,operational mechanisms,value assessment,and the value chain ecosystem.Additionally,it provides insights into future research directions.Initially,the paper conducts a systematic examination of the conceptual framework and evolutionary stages inherent in the data value chain.Diverse professional backgrounds contribute to the formulation of diverse definitions for the data value chain,resulting in three typical conceptual perspectives based on data flow,data processing technology,and value augmentation.The evolutionary stages of the data value chain originate from various welfare effects brought by information(the information economy stage),undergo gradual embedding of digital technologies(the digital technology embedding stage),and ultimately take shape through the convergence of dual chains(the data value dual-chain fusion stage).Subsequently,the paper analyzes the mechanism of data value creation,focusing on the data production and usage process chains and the physical chains integrating data with industries.The data value chain demonstrates typical dual-chain characteristics,where one chain encompasses the process from data production to usage,while the other involves the integration of data with industries.Research on value creation during data operations primarily analyzes its mechanism based on the functioning of these chains,while value creation in physical chains emphasizes the value generated through the integration of data with industries.Furthermore,the paper systematically summarizes methods for assessing data value,elaborating on the welfare effects of data on stakeholders from both micro and macro perspectives.According to different assessment perspectives,data value assessment methods are categorized into asset-oriented and resource-oriented approaches.Participants in the data value chain are mainly divided into two major categories:macro and micro-level ones.Originating from the theory of firms in a market economy,the micro-level participants in the market include both suppliers and demanders of products,namely,businesses and consumers.The micro-level welfare effects of data primarily focus on the benefits that data brings to various entities in the market.The macro-level welfare effects of data concentrate more on the impact of data elements on the overall economy.Moreover,the paper provides an overview of research progress in the ecological aspects of the data value chain,covering its models and governance.The data value chain's ecological models describe how data flows,transforms,and is utilized throughout its lifecycle,featuring three typical patterns of the digital technology model,online platform model,and comprehensive integration model.Ecological governance refers to the guidance and control on the data value chain ecosystem,encompassing data management,data security supervision,and data rights confirmation.In conclusion,the paper outlines future research directions for the data value chain.Although existing literature has made certain achievements in various aspects of the data value chain,several issues still warrant further investigation,such as the characteristics of the data value chain and its related content system construction,the analysis of the value chain pattern and its effects along with data integration,the identification and measurement of data value chain empowerment,the integration of multi-chain coordinated control mechanisms,and the risk management and propagation in the data value chain.
Data Value ChainValue CreationValue AssessmentData Ecosystem
NETL
NSTL
万方数据
维普
