EXPLORING THE PERFORMANCE PRODUCTION OF SCI-TECH FINANCE POLICY: TYPICAL PATHS AND INNOVATIVE INSIGHTS FROM CHINA PROVINCES
Volume 3, Issue 2, Pp 6-15, 2025
DOI: https://doi.org/10.61784/wjebr3040
Author(s)
YangFan Lu*, Yuan Mei
Affiliation(s)
School of Public Administration, South China University of Technology, Guangzhou 510640, Guangdong, China.
Corresponding Author
YangFan Lu
ABSTRACT
This paper focuses on "how differentiated policy configurations lead to industrial development". It proposes the performance production as a novel perspective for policy management, utilizing China's provincial policies for sci-tech finance development as a case for operational observation. Qualitative data was converted into a quantifiable format using structured calibration procedures, and then three typical paths of system management were simulated via the fsQCA method. The 'limited adaptation' path involves selecting policy objectives and targeted investment of public resources through industrial basis analysis, corresponding to provinces with bright spots in the underdeveloped tier. The 'motivated action' path entails setting policy objectives to drive task arrangement and resource allocation, aligning with provinces demonstrating evident progress in the backward tier. The 'systematic improvement' path signifies that policies are derived through specific, successive steps, corresponding to leading provinces with developed industries. This study constructs a bridge between industrial policy analysis and performance evaluation, emphasizing the mutual guidance of multi-dimensional policy contents and their combined output mechanism. It offers practical and innovative implications for designing and implementing industrial policies across different regions.
KEYWORDS
Industrial policy; Performance production; Configuration path; Sci-tech finance development
CITE THIS PAPER
YangFan Lu, Yuan Mei. Exploring the performance production of Sci-tech finance policy: typical paths and innovative insights from China provinces. World Journal of Economics and Business Research. 2025, 3(2): 6-15. DOI: https://doi.org/10.61784/wjebr3040.
REFERENCES
[1] Tausif B, Philip S, Paul M. Industrial policy initiatives in manufacturing: Examining cross-country interventions through an evolutionary typology of technology systems. Science and Public Policy, 2024, 51(5): 823-825. DOI: 10.1093/scipol/scae026.
[2] Shen H, Xiong P, Yang L, et al. Quantitative evaluation of science and technology financial policies based on the PMC-AE index model: A case study of China’s science and technology financial policies since the 13th five-year plan. Pos One, 2024, 19(8): 1-28. DOI: 10.1371/journal.pone.0307529.
[3] Huang C, Yue X, Yang M. A quantitative study on the diffusion of public policy in China: evidence form the s&t finance sector. Journal of Chinese Governance, 2018, 3(2): 235-254. Doi: 10.1080/23812346.2017.1342381.
[4] Lin Junda. Analysis of the effect of financial subsidy on China’s new energy vehicle industry R & D. activities. Modern Economy, 2019, 10(1): 96-107. DOI: 10.4236/me.2019.101007.
[5] Su T. Investigating Science and Technology Finance and Its Implications on Real Economy Development: A Performance Evaluation in Chinese Provinces. Journal of the Knowledge Economy, 2023, 11: 1-28. DOI: 10.1007/s13132-023-01502-7.
[6] Brix J, Krogstrup H, Mortensen N. Evaluating the outcomes of co-production in local government. Local Government Studies, 2020, 46(2): 169-185. DOI: 10.1016/j.jclepro.2020.122381.
[7] Nahm, Jonas. Exploiting the Implementation Gap: Policy Divergence and Industrial Upgrading in China’s Wind and Solar Sectors. The China Quarterly, 2017: 1-23. DOI: 10.1017/s030574101700090x.
[8] Omrani H, Oveysi Z, Emrouznejad A. A mixed-integer network DEA with shared inputs and undesirable outputs for performance evaluation: Efficiency measurement of bank branches. The Journal of the Operational Research Society, 2023, 74(4):1150-1165. DOI: 10.1080/01605682.2022.2064783.
[9] Tian R, Xu B. China's Science and Technology Finance and Economic Corridor Development: A Coupling Relationship Analysis. International Journal of Advanced Computer Science and Applications, 2024, 15(2): 39-48.
[10] Basurto, X and Speer J. Structuring the Calibration of Qualitative Data as Sets for Qualitative Comparative Analysis (QCA). Field Methods, 2010, 24(2): 155-174. DOI: 10.2139/ssrn.1831606.
[11] Manuel Fernández-Esquinas, María Isabel Sánchez-Rodríguez, José Antonio Pedraza-Rodríguez, et al. The use of QCA in science, technology and innovation studies: a review of the literature and an empirical application to knowledge transfer. Scientometrics, 2021, 126: 6349-6382. DOI:10.1007/s11192-021-04012-y.
[12] Hans-Jütirgen Z. Fuzzy Set Theory and its Applications (4th edition), Kluwer Academic Publishers Group, Dordreeht. The Netherlands, 2001.
[13] Zhang M, Du Y. Qualitative Comparative Analysis(QCA) in management and organization research: position, tactics, and directions. Chinese Journal of Management, 2019, 16(9): 1312-1323.
[14] Cheng C, Yang Z, He Y, et. al. How configuration theory explains performance growth and decline after Chinese firms cross-border M&A: using the fsQCA approach. Asia Pacific Business Review, 2021, 555-578. DOI: 10.1080/13602381.2021.1910900.
[15] Schneider, C Q, Wagemann, C. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge. Cambridge University Press, 2012. DOI: 10.1017/CBO9781139004244.
[16] Du Y, Kim P. One size does not fit all: Strategy configurations, complex environments, and new venture performance in emerging economies. Journal of Business Research, 2021, 124: 272-285. DOI: 10.1016/j.jbusres.2020.11.059.
[17] Ragin C. The Comparative Method: Moving beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press, 2014.
[18] Rihoux B, Ragin C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques, Thousand Oaks CA. Sage Publications, 2009.
[19] Meckling J. Making Industrial Policy Work for Decarbonization. Global Environmental Politics, 2021, 21(4): 134–147. DOI: 10.1162/glep_a_00624.
[20] Maloney W, Nayyar G. Industrial policy, information, and government capacity. The World Bank Research Observer, 2018, 33(2): 189-217. DOI: 10.1596/1813-9450-8056.
[21] Pauliuk S, Arvesen A, Stadler K. Industrial ecology in integrated assessment models. Nature Climate Change, 2017, 7(1): 13-20. DOI: 10.1038/nclimate3148.
[22] Jovanovic J, Morschett D. Under which conditions do manufacturing companies choose FDI for service provision in foreign markets? An investigation using fsQCA. Industrial marketing management, 2022(Jul.): 104.
[23] Schneider M, Schulze-Bentrop C, Paunescu M. Mapping the Institutional Capital of High-Tech Firms: A Fuzzy-Set Analysis of Capitalist Variety and Export Performance. Journal of International Business Studies, 2010, 41(2): 246-266. DOI: 10.1057/jibs.2009.36.
[24] Vilmos F Misangyi, Abhijith G Acharya. Substitutes or Complements? A Configurational Examination of Corporate Governance Mechanisms. Academy of Management Journal, 2014, 57(6): 1681-1705. DOI: 10.5465/amj.2012.0728.