Abstract
The research aims to improve the competitiveness of Chinese banking enterprises and China's banking industry in an increasingly competitive economic globalization (EG) environment. This paper takes research and development (R&D) investment as a practical method to improve the competitiveness of banks. Firstly, it introduces the enterprise sustainable development (SD) theory and total factor productivity (TFP) theory and analyzes the importance of R&D innovation to the development of banks. Then, radial basis function (RBF) network is proposed to test the impact of bank R&D investment on enterprise performance. Therefore, a bank performance evaluation (PE) system based on the Internet of Things (IoT) is established. Secondly, block matrix (BM) and the incremental learning algorithm are used to optimize the RBF network. The RBF network model is further improved, and the RBF network model based on the IoT cloud platform (CC-RBF) is proposed, which improves model convergence speed and accuracy. The results show that (I) BM and incremental learning algorithm can greatly simplify the calculation and improve the efficiency of RBF network model. (II) Bank R&D investment will significantly improve TFP. (III) The proposed CC-RBF network model can improve prediction accuracy and reduce the model training time. The research content provides a reference for analyzing the impact of bank R&D investment on bank performance.
Similar content being viewed by others
References
Kumar A, Anand N, Batra V (2020) Trends in Indian Private sector bank efficiency: non-stochastic frontier DEA window analysis approach. J Asian Finance Econ Bus 7(10):729–740
Blanco-Oliver A (2021) Banking reforms and bank efficiency: Evidence for the collapse of Spanish savings banks. Int Rev Econ Financ 74:334–347
Sun H, Ji X (2020) The changes of bank efficiency and the reason of the changes. Int J Soc Sci Educ Res 2(11):1–9
Tu DL, Ho TH, Nguyen DT (2021) Fintech credit and bank efficiency: International evidence. Int J Financial Stud 9(3):1–16
Ma Y, Wei J, Li C et al (2020) Fuzzy comprehensive performance evaluation method of rolling linear guide based on improved analytic hierarchy process. J Mech Sci Technol 34(7):2923–2932
Jalal AA, Ali BH (2021) Text documents clustering using data mining techniques. Int J Electr Comput Eng 11(1):664–670
Christian J (2015) Spyridon, Provatas, Niklas, and Lavesson, “An Online Machine Learning Algorithm for District Heating Systems,.” Euroheat & power 12(4):16–19
Zheng X, Zhang Y, Zhang H, Xue Q (2019) An RBF neural network-based dynamic virtual network embedding algorithm. Concurr Pract Exp 31(23):e4516.1-e4516.12
Lu L, Zhang J, Yang F, Zhang Y (2020) Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model. Sustain Comput Informatics Syst 27:100397
Tang M, Liao H (2021) Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy. Technol Forecast Soc Change 167(6):120719
Na A, Yhk B, Js C, Sk C (2020) Elevated blood alcohol impacts hospital mortality following motorcycle injury: A National Trauma Data Bank analysis - ScienceDirect. Injury 51(1):91–96
Porter ED, Goldwag JL, Wilcox AR, Li Z, Briggs A (2021) Geriatric skiers: active but still at risk, a national trauma data bank study. J Surg Res 259(10):121–129
Hu J, Fei Y, Li WQ (2021) Predicting the mortality risk of acute respiratory distress syndrome: radial basis function artificial neural network model versus logistic regression model. J Clin Monit Comput no. Suppl 1, pp. 1-10
Deng Y, Zhou X, Shen J, Xiao G, Liao BQ (2021) New methods based on backpropagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. Sci Total Environ 772(6):145534
Li TJ, Sun TY, Zhang YM, Zhao CY (2021) Prediction of thermal error for feed system of machine tools based on random radial basis function neural network. Int J Adv Manufact Technol 114(5):1545–1553
Dong XJ, Shen JN, He GX, Ma ZF, He YJ (2021) A general radial basis function neural network-assisted hybrid modeling method for a photovoltaic cell operating temperature prediction. Energy 234(7):121212
Ayodele BV, Mustapa SI, Witoon T et al (2021) Radial basis function neural network model prediction of thermo-catalytic carbon dioxide oxidative coupling of methane to C2-hydrocarbon. Top Catal 64(5):328–337
Xu LL, Hong LI, Jin LI (2019) Research on population prediction based on grey prediction and radial basis function network. Comput Sci 4(2):14–22
Ma L, Zhang X, Ding X (2020) Enterprise social media usage and knowledge hiding: a motivation theory perspective. J Knowl Manag 24(9):2149–2169
Al S (2021) Conservative working capital policy: can it increase profitability and sustainable growth rate? Turkish J Comput Math Educ (TURCOMAT) 12(3):5630–5637
Wu G, Liu C, Liang Y (2020) Computational simulation and modeling of freak waves based on longuet-higgins model and its electromagnetic scattering calculation. Complexity 2020(16):1–14
Tofighi D, Hsiao YY, Kruger ES, Mackinnon DP, Horn MV, Witkiewitz K (2019) Sensitivity analysis of the no-omitted confounder assumption in latent growth curve mediation models. Struct Equ Model 26(1–2):94–109
Rahnama E, Bazrafshan O, Asadollahfardi G, Samadi SY (2021) Comparison of Box-Jenkin time series and radial basis function for sodium adsorption rate forecasting; a case study Aras, Sepidrud, Karun, and Mond Rivers. Desalin Water Treat 218:193–209
Wang M et al (2021) Towards sustainable development: How does technological innovation drive the increase in green total factor productivity? Sustain Dev 29(1):217–227
Axelsson O, Liang ZZ, Kruzik J, Horak D (2020) Inner product free iterative solution and elimination methods for linear systems of a three-by-three block matrix form. J Comput Appl Math 383(4):113–117
Baklacioglu T (2020) Predicting the fuel flow rate of commercial aircraft via multilayer perceptron, radial basis function and ANFIS artificial neural networks. Aeronaut J -New Ser- 125(1285):1–19
Sridevi S (2020) Classification of coronary heart artery disease using IVUS images by SVM Classifier with Modified Radial Basis Function Kernel (MRBFK). Int J Adv Trends Comput Sci and Eng 9(3):3877–3886
Acknowledgements
This work was supported by a Postdoctoral general project in Heilongjiang Province Research on the digital Transformation strategy of small and medium-sized banks (No. LBH—Z20027).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Du, E. Impact of bank research and development on total factor productivity and performance evaluation by RBF network. J Supercomput 78, 12070–12092 (2022). https://doi.org/10.1007/s11227-022-04358-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-022-04358-x