Search

A Study on the Measurement and Temporal Evolution of Allocation Efficiency of Science and Technology Innovation Resources: Empirical Evidence from Shandong Province

Author(s)

Haiyun Sun1, Hongliang Xin2,*

Affiliation(s)

1Party School of the CPC Shandong Provincial Committee (Shandong Academy of Governance), Jinan, Shandong, China 2Shandong Guokong Capital Investment Co. LTD, Jinan, Shandong, China * Corresponding Author

Abstract

Against the strategic backdrop of high-quality development and coordinated regional innovation, the allocation efficiency of science and technology (S&T) innovation resources has emerged as a critical performance metric for regional innovation systems. As one of China’s leading economic and manufacturing provinces, Shandong possesses a robust foundation in S&T infrastructure and innovation platform development. However, it is currently transitioning from a resource-intensive to an efficiency-oriented paradigm in the allocation of innovation resources. This study employs the Super-Efficiency Slack-Based Measure (Super-SBM) model to assess static allocation efficiency and applies the DEA-Malmquist productivity index to examine dynamic efficiency evolution over time. Empirical findings indicate a sustained upward trend in the overall allocation efficiency of S&T innovation resources across Shandong Province; nevertheless, pronounced inter-regional disparities persist. Several cities exhibit disproportionately high input levels without commensurate output gains, and the marginal returns to resource agglomeration demonstrate significant spatial heterogeneity. To address these challenges, this study proposes a systematic optimization framework anchored in the tripartite principle of “core-driven growth—diffusion-led spillover—regionally coordinated development.” Specifically, it advocates establishing a collaborative governance architecture characterized by resource integration, tiered functional specialization, and spatially balanced configuration, while enhancing the adaptability and responsiveness of resource allocation through a dynamic, feedback-informed institutional mechanism.

Keywords

Science and Technology Innovation Resources; Allocation Efficiency; Regional Coordination; Dynamic Governance

References

[1]Wei Xinghua, Hou Weimin. Choice of Economic Growth Styles in China and Transition from the Extensive Style to the Intensive One. Economic Research Journal, 2007, (7): 15-22. [2]Zhang F, Wang Y, Liu W. Science and Technology Resource Allocation, Spatial Association, and Regional Innovation. Sustainability 2020, 12: 694. [3]Wang J Q.A Rational Analysis of Regional Science and Technology Innovation Resources and Their Allocation. Science and Technology & Innovation, 2018, (23): 95-96. [4]Wang X Y, Wang H Q.A DEA Analysis of the Efficiency of Science and Technology Innovation Resource Allocation in China. Statistics & Decision, 2008, (08): 108-110. [5]Qin Y B, Zhang Q, Wu J. Research Progress，Theoretical Foundations，and Future Prospects for Improving the Efficiency of Resource Allocation in Technological Innovation. Scientific Management Research, 2025, (1): 10-18. [6]Fan J P，Zhao Y Y, Wu M Q. Chinese Science and Technology Innovation Resources Allocation Based on the Improved Cross-Efficiency Method. Forum on Science and Technology in China, 2017, (12): 12-40. [7]Ding H. The New Challenges on S&T Resource Allocation and Strategies Analysis. Studies in Science of Science, 2005, (04): 474-480. [8]Farrell M J. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society．Series A(General), 1957, (120): 253-290． [9]Ren W H. Research on Dynamic Comprehensive Evaluation of Allocation Efficiency of Green Science and Technology Resources in China’s Marine Industry. Marine Policy, 2021, 131: 2-7. [10]Shi A N, Xu Q L. An empirical analysis of allocation efficiency of science & technology resources in China based on super-efficiency DEA and Malmquist index model. Sci. Technol. Manag. Res, 2015, 35: 54-59. [11]Zamanian G R, Shahabinejad V, Yaghoubi M. Application of DEA and SFA on the Measurement of Agricultural Technical Efficiency in MENA Countries. International Journal of Applied Operational Research, 2013, 3(2): 43-51. [12]Hsieh C-T, Klenow PJ. Misallocation and manufacturing TFP in China and India. Quarterly Journal of Economics. 2009, 124(4): 1403-1448. [13]Leoncini R. The Nature of Long- run Technological Change：Innovation，Evolution and Technological Systems . Research Policy, 1998, 27(1): 75-93. [14]Ekboir J M. Research and Technology Policies in Innovation Systems：Zero Tillage in Brazil. Research Policy, 2003, 32(4): 573-586. [15]Tone K, Tsutsui M. An Epsilon-based Measure of Efficiency in DEA-A Third Pole of Technical Efficiency. European Journal of Operational Research, 2010, 207(3): 1554-1563. [16]Malmquist S. Index Numbers and Indifference Curves .Trabajos de E-statistica, 1953, 4(2): 209-242. [17]Fare Grosskopf S, Norris M, et al. Productivity Growth, Technical Progress and Efficiency Change in Industrialized Countries .American Economic Review, 1994, 84(1): 66- 83. [18]Cui W Y, Shadow and Spillover: The Influence of Neighboring Innovative Cities on Regional Innovation Growth. China Economic Review, Elsevier, 2025, 90(C). [19]Kerr W R, Robert-Nicoud F. Tech Clusters. Journal of Economic Perspectives, 2020, 34(3): 50-76. [20]Zang S W, Chen H H, Mei L. Research on the Relationship between Capability Evolution, Institutional Supply and Radical Innovation. Studies in Science of Science, 2021, 39(05): 930-939. [21]Hou J X. Theoretical Logic and Path Coupling of Regional United Front Cooperation Facilitating Coordinated Regional Development: From the Perspective of United Front Practice Based on the Coordinated Development of the Beijing-Tianjin-Hebei Region. Journal of Hebei Institute of Socialism, 2025(2): 72-80.