Science, Technology, Engineering and Mathematics.
Open Access

DATA-DRIVEN EVALUATION OF REGIONAL SCI-TECH FINANCE EFFICIENCY

Download as PDF

Volume 6, Issue 7, Pp 10-20, 2024

DOI: https://doi.org/10.61784/ejst3051

Author(s)

Yuan Wang1, YaLiu Yang1*, Cui Wang1, XiaoWei Zheng1XiaoXiao Si2

Affiliation(s)

1Business School, Suzhou University, Suzhou 234000, Jiangsu, China.

2Economics & Management School, Huaibei Institute of Technology, Huaibei 235000, Anhui, China.

Corresponding Author

YaLiu Yang

ABSTRACT

Sci-tech finance is the catalyst for the transformation of technological progress into real productivity. Hence, to improve sci-tech finance efficiency and promote high-quality regional economy development, a data-driven evaluation model of sci-tech finance efficiency is constructed. The collected data are processed using the DEA-Malmquist index. The efficiency level of sci-tech finance is determined according to the Total Factor Productivity index, and the main influencing factors of sci-tech finance efficiency are determined through the decomposition analysis of this index. This study calculates the efficiency level and dynamic change of sci-tech finance in the Yangtze River Delta. The results show that technological progress is an important factor affecting sci-tech finance efficiency. Surprisingly, the growth of R&D personnel has a negligible effect on sci-tech financial efficiency instead, and Shanghai ranked third in terms of sci-tech financial efficiency, which is no match for Zhejiang and Jiangsu provinces. Hence, we propose targeted suggestions to improve sci-tech finance efficiency in the Yangtze River Delta. This study not only provides theoretical and methodological support for the evaluation of regional sci-tech financial efficiency but also provides a reference for sci-tech finance policymakers and researchers. 

KEYWORDS

DEA-Malmquist index; Sci-tech finance; High-quality development

CITE THIS PAPER

Yuan Wang, YaLiu Yang, Cui Wang, XiaoWei ZhengXiaoXiao Si. Data-driven evaluation of regional sci-tech finance efficiency. Eurasia Journal of Science and Technology. 2024, 6(7): 10-20. DOI: https://doi.org/10.61784/ejst3051.

REFERENCES

[1] Rumyantseva A, Bichurina V. Special Aspects of Technological Entrepreneurship Financing at the Present Stage, International Science Conference SPBWOSCE-2017 Business Technologies for Sustainable Urban Development, 2018.

[2] Schumpeter JA. The Theory of Economic Development; Harvard University Press: Cambridge MA, 1912: 170.

[3] Ma LY, Li XM. Does science and technology finance policies promote regional innovation? Quasi – Natural experiment based on the pilot policy of combining science and technology with finance. China Soft Sci, 2019, 12: 30–42.

[4] Fombang MS, Adjasi CK. Access to finance and firm innovation. Journal of Financial Economic Policy, 2018, 10: 73–94. DOI: 10.1108/JFEP-10-2016-0070.

[5] Sabir S, Latif R, Qayyum U, et al. Financial development, technology and economic development: The role of institutions in developing countries. Annals of Financial Economics, 2019, 14. DOI: 10.1142/S201049521950012X.

[6] Wang MX, Gu R, Zhang JR. Research on the impact of finance on promoting technological innovation based on the state-space model. Green Finance 2021, 3: 119–137.

[7] Benfratello L, Schiantarelli F, Sembenelli A. Banks and innovation: Microeconometric evidence on Italian firms. Journal of Financial Economic Policy, 2008, 90: 197–217. DOI: 10.1016/j.jfineco.2008.01.001.

[8] Khan SU, Shah A, Rizwan MF. Do financing constraints matter for technological and non-technological innovation? A (re)examination of developing markets. Emerging Markets Finance and Trade, 2021, 57: 2739–2766. DOI: 10.1080/1540496X.2019.1695593.

[9] Lee J, Lee C, Kim J, et al. An empirical study on the effect of innovation financing on technology innovation competency: Business performance of SMEs in Korea. Journal of Electronic commerce in organizatlons, 2019, 17: 1–15. DOI: 10.4018/JECO.2019010101.

[10] Czajkowska A. The role of financing innovative technological investments with credit for technological innovations. Ekonomia I Prawo-Economics and Law 2019, 18: 413–423.

[11] Jun-won L. Comparative analysis of business performance between technology financing SMEs and general SMEs – Analysis based on technology financing SMEs that received technology credit loan. Innovation Studies, 2019, 14: 279–300.

[12] Davidenko N, Skrypnyk H, Titenko Z, et al. Modeling of the optimum level of financial provision of Ukrainian enterprises’ innovative activities. Global Journal of Environmental Science and Management, 2019, 5: 197–205.

[13] Zhang L. A research on the effect of equity and debt financing on technological innovation performance. Management Science, 2020, 41: 95–104.

[14] Adikari AMP, Liu HY, Marasinghe MMSA. Inward foreign direct investment-induced technological innovation in Sri Lanka? Empirical evidence using ARDL approach. Sustain, 2021, 13: 7334.

[15] Tsedilin LI. Funding of science: A comparison of approaches and outcomes in Russia and Germany. Voprosy Ekonomiki, 2021, 2: 147–160. DOI: 10.32609/0042-8736-2021-2-147-160.

[16] Spatareanu M, Manole V, Kabiri A. Do bank liquidity shocks hamper firms’ innovation? International Journal of Industrial Organization, 2019, 67. DOI: 10.1016/j.ijindorg.2019.06.002.

[17] Dang CL, Wang BQ, Hao WY. An optimal banking structure from the perspective of enterprise technological innovation—Empirical evidence from Chinese industrial enterprises. Applied Economics, 2020, 52: 6386–6399. DOI: 10.1080/00036846.2020.1795069.

[18] Zhao CW, Chen CF, Tang YK. Science Technology Finance. Science Press: Beijing, 2009, 4: 91–92.

[19] Wang RX, Yang M. Spatial agglomeration and dynamic evolution of the coupling efficiency of technology and finance system in China. Economic Geography, 2018, 38: 104–112.

[20] Hu JL, Yang CH, Chen CP. R&D efficiency and the national innovation system: An international comparison using the distance function approach. Bulletin of Economic Research, 2014, 66: 55–71. DOI: 10.1111/j.1467-8586.2011.00417.x.

[21] Li YN, Yang Y, Zhao X. Evaluating financial support efficiency for innovation: A comparative study of the coastal and non-coastal regions of China. Journal of Coastal Research, 2019, 94: 971–975. DOI: 10.2112/SI94-191.1.

[22] Li JX, Wen XN. Research on the relationship between the allocation efficiency and influencing factors of China’s sci-tech finance. China Soft Science, 2019, 1: 164–174.

[23] Li XM. Evaluating the input/output efficiency between science-technology and finance via analytic hierarchy process, Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015), 2015: 462–465.

[24] Hosseinzadeh Lotfi FHJahanshahloo GREbrahimnejad A, et al. Target setting in the general combined-oriented CCR model using an interactive MOLP method. Journal of Computational and Applied Mathematics, 2010, 234: 1–9. DOI: 10.1016/j.cam.2009.11.045.

[25] Adamovsky P, Gonda V. Differences in efficiency of national innovation systems of Slovakia and selected EU countries. Politicka Ekonomie, 2019, 67: 181–197. DOI: 10.18267/j.polek.1234.

[26] Liu LJ, Teng Y. Does the level of technological innovation depend on technical efficiency or scale effect?—Measurement research from China and OECD countries. Science of Science and Management 2020, 41, 50–61.

[27] Maddahi R, Jahanshahloo GR, Lotfi FH, et al. Optimising proportional weights as a secondary goal in DEA cross-efficiency evaluation. International Journal of Operational Research, 2014, 19: 234–245. DOI: 10.1504/IJOR.2014.058953.

[28] Ebrahimnejad A, Hosseinzadeh Lotfi FH. Equivalence relationship between the general combined-oriented CCR model and the weighted minimax MOLP formulation. Journal of King Saud University, 2012, 24: 47–54. DOI: 10.1016/j.jksus.2010.08.007.

[29] Ma XY, Dong JC, Li XT. Measuring the efficiency of science & technology combined with finance in Xinjiang based on DEA and Malmquist index method. Science and Technology Development Journal, 2017, 13: 988–993.

[30] Zhang CC. Factors influencing the allocation of regional sci-tech financial resources based on the multiple regression model. Mathematical Problems in Engineering, 2021, 2021: 1–9. DOI: 10.1155/2021/6688549.

[31] Yu LY, Li WS, Chen ZX, et al. Multi-stage collaborative efficiency measurement of sci-tech finance: Network-DEA analysis and spatial impact research. Economic Research-Ekonomska Istra?ivanja, 2021, 34: 2337–2353. DOI: 10.1080/1331677X.2020.1863827.

[32] Vysochan O, Boychuk A, Hyk V. Relationship between financing and efficiency of innovative activities of industrial enterprises: Evidence from Ukraine. Casopis za Ekonomiju I Trzisne Komunicacije, 2021, 11: 94–108.

[33] Wang WJ, Liu SL. Research on the industrialization efficiency of scientific and technological achievements. Research on Science and Technology Management, 2019, 3: 77–85.

[34] Liu C, Gao M, Zhu G, et al. Data driven eco-efficiency evaluation and optimization in industrial production. Energy, 2021, 224.

[35] National Bureau of Statistics. 2022. http://www.stats.gov.cn/tjsj/ndsj.

[36] Anhui Provincial Bureau of Statistics. 2022. http://tjj.ah.gov.cn/ssah/qwfbjd/tjnj/index.html.

[37] Jiangsu Provincial Bureau of Statistics. 2022. http://tj.jiangsu.gov.cn/col/col83749/index.html.

[38] Zhejiang Provincial Bureau of Statistics. 2022. http://tjj.zj.gov.cn/col/col1525563/index.html.

[39] Shanghai Bureau of Statistics. Available online: http://tjj.sh.gov.cn/tjnj/index.html (accessed on 10 Dec 2022).

[40] Peykani P, Seyed EFS. Malmquist productivity index under fuzzy environment. Fuzzy Optimization and Modelling, 2021, 2: 10–19.

[41] Zou L, Zhu YW. Universities’ scientific and technological transformation in China: Its efficiency and influencing factors in the Yangtze River economic belt. PLOS ONE, 2021, 16: e0261343. DOI: 10.1371/journal.pone.0261343.

[42] Liu Z, Zheng X, Li D, et al. A novel cooperative game-based method to coordinate a sustainable supply chain under psychological uncertainty in fairness concerns. Transportation Research Part E: Logistics and Transportation Review, 2021, 147. DOI: 10.1016/j.tre.2021.102237.

[43] Liu C, Cai W, Zhai M, et al. Decoupling of wastewater eco-environmental damage and China’s economic development. Science of The Total Environment, 2021, 789.

[44] Abad-Segura E, Infante-Moro A, González-Zamar MD, et al. Blockchain technology for secure accounting management: Research trends analysis. Mathematics, 2021, 9: 9141631. DOI: 10.3390/math9141631.

[45] Tavana M, Ebrahimnejad A, Santos-Arteaga FJ. Mansourza-deh S.M. and Matin R.K., A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia. Socio-Economic Planning Sciences, 2016, 61: 70–89.

All published work is licensed under a Creative Commons Attribution 4.0 International License. sitemap
Copyright © 2017 - 2024 Science, Technology, Engineering and Mathematics.   All Rights Reserved.