Research Hotspots and Cutting-Edge Discussions on Data Element Configuration: Analysis Based on Bibliometric Method and Knowledge Graph

Dandan Li; Zheng He

doi:10.62177/amit.v1i5.613

Authors

Dandan Li Xi’an Polytechnic University
Zheng He Xi’an Polytechnic University

DOI:

https://doi.org/10.62177/amit.v1i5.613

Keywords:

Data Elements, Data Element Configuration, Bibliometrics, Knowledge Graph, Hotspots and Trends

Abstract

In the era of digital economy, the strategic value of data as a key production factor has become increasingly prominent, and its efficient allocation has become a core issue driving economic growth and social governance innovation. This paper employs bibliometric analysis and knowledge mapping techniques to analyze literature from the Web of Science and CNKI databases, constructing a topic co-occurrence network, keyword evolution path, and author collaboration map. It systematically reviews the research trajectory and frontier trends in the field of data element allocation both domestically and internationally. The research findings are as follows: (1) data governance, data ownership confirmation, production factors, frontal crash reconstruction, and federal interagency are the current core focuses of research. (2) Data assetization, scenario-based configuration models, and digital economy governance systems are emerging as cutting-edge exploration directions. (3) the field is showing a trend of interdisciplinary integration, with a multitude of innovative achievements emerging in the intersection of economics, law, and computer science. This study provides theoretical references for policymakers to improve the market-oriented allocation system of data elements.

Downloads

Download data is not yet available.

References

Autor, D. H. (2021). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30.

Cheng, Y. J., & Duan, X. (2022). Research on the Impact and Transmission Mechanism of Digital Inclusive Finance on High Quality Development of Urban Economy: Empirical Data from 79 Prefecture level Cities in Central China. *Research World, (6), 23–37.

David, P. A. (2021). The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox. *The American Economic Review, 80(2), 355–361.

Gao, F. P. (2023). The Allocation of Rights for Data Holders - Legal Implementation of Structural Separation of Data Property Rights. *Comparative Law Research, (3), 26–40.

Gereffi, G. (2019). Global Value Chains and International Development Policy: Bringing Firms, Networks and Policy-Engaged Scholarship backing. Journal of International Business Policy, 2(3), 195–210.

Hai, J., & Zhao, L. (2023). Research on the Theory of Data Value. Finance and Trade Economics, 44(6), 5–20.

Hao, X., Ji, Z., Li, X., et al. (2021). Construction and application of a knowledge graph. Remote Sensing, 13(13), 25–41.

Lai, Q. F., Li, H. F., Li, C. S., & Feng, X. (2023). Rural labor factor allocation, agricultural and rural modernization, and rural economic development: PVAR analysis based on inter provincial panel data. Journal of Agriculture and Forestry Economic Management, 22(2), 203–212.

Lee, D. (2020). The Role of R&D and Input Trade in Productivity Growth: Innovation and Technology Spillovers. The Journal of Technology Transfer, 45, 908–928.

Navas, A., Nocco, A., & Choi, E. K. (2021). Trade Liberalization, Selection, and Technology Adoption with Vertical Linkages. Review of International Economics, 29(2), 76–83.

New, B. (2023). Data Rights Allocation under the Structural Separation of Property Rights. Global Legal Review, 45(4), 5–20.

Ryan, P., Buciuni, G., Giblin, M., et al. (2020). Subsidiary Upgrading and Global Value Chain Governance in the Multinational Enterprise. Global Strategy Journal, 10(3), 496–519.

Shen, T., Zhang, F., & Cheng, J. (2022). A comprehensive overview of knowledge graph completion. Knowledge-Based Systems, 25(5), 109–597.

Soete, L. (2022). The Impact of Technological Innovation on International Trade Patterns: The Evidence Reconsidered. *Research Policy, 16*(2), 101–130.

Wang, X. D., Shi, Y. T., & Liu, D. (2024). Research on the Impact of Market-oriented Allocation of Data Elements on Digital-Physical Integration: A Quasi-natural Experiment Based on the Establishment of Data Trading Platforms. Journal of Guangdong University of Finance and Economics, 39(2), 44–58.

Wu, J. X., Min, S., Wang, X. B., & Cheng, G. Q. (2022). Internet use and household production factor allocation of rural households in remote areas -- based on panel data of rural households in southwest mountainous areas. China Rural Economy, (8), 93–113.

Wu, Y. L., Yang, R. D., & Wu, B. L. (2022). Evolution of Total Factor Productivity in Chinese Agriculture and Factor Mismatch: An Analysis Based on Data from Rural Fixed Observation Points from 2003 to 2020. China Rural Economy, (12), 35–53.

Xie, D. X., Wei, W. S., Li, Y., & Zhu, X. W. (2022). Data Element Configuration, Credit Market Competition, and Welfare Analysis. China Industrial Economy, (8), 25–43.

Xie, K., Hu, Y. S., Liu, Y., & Luo, T. Y. (2023). Data driven high-quality digital transformation of enterprises: A longitudinal case study of Sofia Intelligent Manufacturing. Management Review, 35(2), 328–339.

Xie, K., & Yi, F. M., & Gu, F. T. (2022). Big Data Driven Agricultural Digital Transformation and Innovation. Agricultural Economic Issues, (05), 37–48.

Yang, X. Y., & Han, Q. (2023). Can open public data improve the total factor productivity of enterprises? Securities Market Guide, (12), 18–30.

Yin, X. M., & Chen, J. (2024). Crown Scenario driven: A new mechanism for market-oriented allocation of data elements towards new quality productivity. Journal of Social Sciences, (3), 178–188.

Yu, Y., Chen, F., & Wang, E. D. (2024). Data element configuration, new quality productivity, and regional green innovation performance. Statistics and Decision making, 40(17), 5–11.

Zheng, W., & Chen, H. (2023). The impact of market-oriented allocation of data elements on urban economic resilience: promotion or inhibition—— Quasi natural experiment based on data trading platform. Modern Finance and Economics (Journal of Tianjin University of Finance and Economics), 43(12), 78–92.

Zhong, L., Wu, J., Li, Q., et al. (2023). A comprehensive survey on automatic knowledge graph construction. ACM Computing Surveys, 56(4), 1–62.