A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks
Abstract In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14610-y |
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| author | Dedai Wei Zimo Wang Hanfu Kang Xinye Sha Yiran Xie Anqi Dai Kaichen Ouyang |
| author_facet | Dedai Wei Zimo Wang Hanfu Kang Xinye Sha Yiran Xie Anqi Dai Kaichen Ouyang |
| author_sort | Dedai Wei |
| collection | DOAJ |
| description | Abstract In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments. This series of tests effectively revealed the theoretical linear relationships between economic variables. However, the double fixed-effects model has limitations in capturing nonlinear relationships and making predictions. Given the growing body of research on existing hybrid models, we acknowledge the importance of exploring and contributing to this evolving area. To address this issue, based on the results of traditional economic analysis, we introduced improved time series models. These advanced deep learning models allow us to better capture the complex nonlinear relationship between DIF and TFP. The experiment initially explored the preliminary structural relationship between DIF and TFP using double fixed-effects models combined with robustness and heterogeneity tests. Then, based on the results of these tests, we selected deep learning features and combined Kolmogorov–Arnold Neural Network (KAN), Graph Neural Network (GNN) models with classic time series deep learning models (Transformer, LSTM, BiLSTM, GRU) to capture the latent nonlinear features in the data for prediction. The results show that, compared to traditional time series forecasting methods, the improved deep learning models perform better in capturing the nonlinear relationships of economic variables, improving prediction accuracy, and reducing prediction errors. Finally, paired t-tests and Cohen’s d effect size tests were used to evaluate error metrics, and the results indicate that the introduction of KAN and GNN models significantly improved the performance of time series forecasting models. |
| format | Article |
| id | doaj-art-5ae558a8cd3f4e5b9e2868d65f20d977 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-5ae558a8cd3f4e5b9e2868d65f20d9772025-08-20T04:03:13ZengNature PortfolioScientific Reports2045-23222025-08-0115113310.1038/s41598-025-14610-yA comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworksDedai Wei0Zimo Wang1Hanfu Kang2Xinye Sha3Yiran Xie4Anqi Dai5Kaichen Ouyang6College of Economics, Shenyang UniversityDepartment of Philosophy, Nankai UniversityDepartment of Mathematics, Kings College LondonGraduate School of Arts and Sciences, Columbia UniversitySchool of Humanities and Social Sciences, University of Science and Technology of ChinaSchool of Finance, Dongbei University of Finance and EconomicsDepartment of Mathematics, University of Science and Technology of ChinaAbstract In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments. This series of tests effectively revealed the theoretical linear relationships between economic variables. However, the double fixed-effects model has limitations in capturing nonlinear relationships and making predictions. Given the growing body of research on existing hybrid models, we acknowledge the importance of exploring and contributing to this evolving area. To address this issue, based on the results of traditional economic analysis, we introduced improved time series models. These advanced deep learning models allow us to better capture the complex nonlinear relationship between DIF and TFP. The experiment initially explored the preliminary structural relationship between DIF and TFP using double fixed-effects models combined with robustness and heterogeneity tests. Then, based on the results of these tests, we selected deep learning features and combined Kolmogorov–Arnold Neural Network (KAN), Graph Neural Network (GNN) models with classic time series deep learning models (Transformer, LSTM, BiLSTM, GRU) to capture the latent nonlinear features in the data for prediction. The results show that, compared to traditional time series forecasting methods, the improved deep learning models perform better in capturing the nonlinear relationships of economic variables, improving prediction accuracy, and reducing prediction errors. Finally, paired t-tests and Cohen’s d effect size tests were used to evaluate error metrics, and the results indicate that the introduction of KAN and GNN models significantly improved the performance of time series forecasting models.https://doi.org/10.1038/s41598-025-14610-yDigital inclusive financeTotal factor productivityDeep learningKolmogorov–Arnold neural networkGraph neural networkHigh-quality enterprise development |
| spellingShingle | Dedai Wei Zimo Wang Hanfu Kang Xinye Sha Yiran Xie Anqi Dai Kaichen Ouyang A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks Scientific Reports Digital inclusive finance Total factor productivity Deep learning Kolmogorov–Arnold neural network Graph neural network High-quality enterprise development |
| title | A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| title_full | A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| title_fullStr | A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| title_full_unstemmed | A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| title_short | A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| title_sort | comprehensive analysis of digital inclusive finance s influence on high quality enterprise development through fixed effects and deep learning frameworks |
| topic | Digital inclusive finance Total factor productivity Deep learning Kolmogorov–Arnold neural network Graph neural network High-quality enterprise development |
| url | https://doi.org/10.1038/s41598-025-14610-y |
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