How to cite this paper
Dong, Y & Wang, D. (2023). China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model.Decision Science Letters , 12(4), 729-738.
Refrences
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Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data Envelopment Analysis Theory, Methodology and Applications. Journal of the Operational Research Society, 48(3), 332–333. https://doi.org/10.1057/palgrave.jors. 2600342
Chen, D. Y., & Tang, Y. G. (2021). Impact of Artificial Intelligence Industry on Regional Economic Development. Science and Technology Management Research, 41(2), 138–144.
Cui, Q., Ma, X. Y., & Zhang, S. S. (2022). Study on Evaluation and Influencing Factors of Green Total Factor Energy Efficiency Analysis: Based on Data from China's Eight Economic Regions. Journal of Technical Economics & Management, 3, 94–99.
Feng, R., & Yu, X. (2020). TFEE Measurement, Decompositionand Influencing Factors Analysis of EightEconomic Regions in China. Polish Journal of Environmental Studies, 29(6), 4291–4301. https://doi.org/10.15244/pjoes/120086.
Fiorentino, E., Karmann, A., & Koetter, M. (2006). The cost efficiency of German banks: A comparison of SFA and DEA. Banking and Financial Studies.
Geng, Z. H., & Wang, W. X. (2022). Research on Development Trend and lnfluencing Factors of China's Artificial Intelligence Industry. Enterprise Economy, 41(3), 36–46. https://doi.org/10.13529/j.cnki.enterprise.economy.2022.03.004
Heera, S. N., & Kumar, K. N. R. (2023). Economic analysis of groundnut farmers participating in Rythu Bharosa Kendras (RBKs) in Guntur district of Andhra Pradesh. The Pharma Innovation, 12(3), 5586–5590.
Hou, S. Y., & Song, L. R. (2021). The Impact of Intelligentization on the Quality of Regional Economic Growth and Its Internal Mechanism: Based on 2012 - 2018 Provincial Panel Data in China. Journal of Guangdong University of Finance and Economics, 36(4), 4–16.
Hou, Z. J., & Zhu, C. L. (2018). China's Artificial Intelligence Enterprise Total Factor Productivity and Its Influencing Factors. Enterprise Economy, 37(11), 55–62.
Huang, J., Yang, Z., Yin, L., Zhang, M., & Qin, Y. (2017). Analysis on R&D Efficiency of Domestic Robot Enterprises and Related Factors in China Based on DEA-Tobit Two-Stage Analysis Method. Science & Technology Progress and Policy, 34(18), 101–106.
Kong, X., Ai, B., Kong, Y., Su, L., Ning, Y., Howard, N., Gong, S., Li, C., Wang, J., Lee, W.-T., Wang, J., Kong, Y., Wang, J., & Fang, Y. (2019). Artificial intelligence: A key to relieve China’s insufficient and unequally-distributed medical resources. American Journal of Translational Research, 11(5), 2632–2640.
Li, L., Bao, Y., & Liu, J. (2020). Research on the influence of intelligentization on total factor productivity of China's manufacturing industry. Studies in Science of Science, 38(4), 609-618+722. https://doi.org/10.16192/j.cnki.1003-2053.2020.04.006
Ling, F., & Hu, D. (2020). Efficiency of Technology lnnovation in China's Artificial lntelligence lndustry: Based on DEAand Malmquist Index. Journal of Hubei University of Arts and Science, 41(8), 47–52.
Liu, C., Fu, R., Li, J., & Zhou, W. (2019). Research into Financing Efficiency of Artificial Intelligence Industry Based on DEA-Tobit Method. Operations Research And Management Science, 28(6), 144–152.
Liu, L., & Hu, G. (2020). Artificial Intelligence and Total Factor Productivity: Chinese Proof of "Productivity Dilemma' Falsification. Jianghai Academic Journal, 3, 118–123.
Nasierowski, W., & Arcelus, F. J. (2000). On the stability of countries’ national technological systems (pp. 97-111). Springer US.
Qiu, Z., & Zhou, Y. (2021). Development of Digital Economy and Regional Total Factor Productivity: An Analysis Basedon National Big Data Comprehensive Pilot Zone. Journal of Finance and Economics, 47(7), 4–17.
Shu, T., Nie, X., Guo, H., Li, X., & Bai, B. (2021). Analysis on the Efficiency of Scientific and Technological Resource Allocation in Beijing-Tianjin-Hebei Urban Agglomeration: Based on DEA-Malmquist Index Model. Science and Technology Management Research, 41(4), 89–96.
Svitalkova, Z. (2014). Comparison and Evaluation of Bank Efficiency in Selected Countries in EU. Procedia Economics and Finance, 12, 644–653. https://doi.org/10.1016/S2212-5671(14)00389-X
Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485–498. https://doi.org/10.1016/j.ribaf.2015.10.002
Xia, M., & Li, X. (2023). Research on technology innovation efficiency and influencing factors in Beijing-Tianjin-Hebei, Yangtze River Delta, West River Delta and Pearl River Delta —— Empirical analysis based on DEA-Malmquist and multiple regression model. Journal of Liaoning University of Technology(Social Science Edition), 25(2), 12-18+35.
Xu, D. (2008). An Theoretical Explanation and Validation of the Determination and Measurement of the Form of Industrial Structure Upgrading. Fiscal Research, 1, 46-49.
You, J., Xu, T., & Yu, A. (2018). Process for Companies in Intellectual Vehicle Industry Evaluation of Operational Efficiency with the Consideration of Corresponding innovation. Journal of Tongji University(Natural Science), 46(1), 133–140.
Zhang, C. (2020). Total Factor Productivity Analysis of Regional Differences in Economic Development in China ——Based on DEA Model of Malmquist Index Perspective. Journal of Technical Economics & Management, 9, 118–122.
Zhang, C., Xu, Y., & He, X. (2023). Research on the Development Path of China's Artificial Intelligence Industry -- Based on the Comparison of Chinese and American Artificial Intelligence. Studies in Science of Science, 1–19. https://doi.org/10.16192/j.cnki.1003-2053.20230221.001
Zhang, W., & Xuan, Y. (2022). How to Improve the Regional Energy Efficiency via Intelligence? Empirical Analysis Based on Provincial Panel Data in China. Business and Management Journal, 44(1), 27–46.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data Envelopment Analysis Theory, Methodology and Applications. Journal of the Operational Research Society, 48(3), 332–333. https://doi.org/10.1057/palgrave.jors. 2600342
Chen, D. Y., & Tang, Y. G. (2021). Impact of Artificial Intelligence Industry on Regional Economic Development. Science and Technology Management Research, 41(2), 138–144.
Cui, Q., Ma, X. Y., & Zhang, S. S. (2022). Study on Evaluation and Influencing Factors of Green Total Factor Energy Efficiency Analysis: Based on Data from China's Eight Economic Regions. Journal of Technical Economics & Management, 3, 94–99.
Feng, R., & Yu, X. (2020). TFEE Measurement, Decompositionand Influencing Factors Analysis of EightEconomic Regions in China. Polish Journal of Environmental Studies, 29(6), 4291–4301. https://doi.org/10.15244/pjoes/120086.
Fiorentino, E., Karmann, A., & Koetter, M. (2006). The cost efficiency of German banks: A comparison of SFA and DEA. Banking and Financial Studies.
Geng, Z. H., & Wang, W. X. (2022). Research on Development Trend and lnfluencing Factors of China's Artificial Intelligence Industry. Enterprise Economy, 41(3), 36–46. https://doi.org/10.13529/j.cnki.enterprise.economy.2022.03.004
Heera, S. N., & Kumar, K. N. R. (2023). Economic analysis of groundnut farmers participating in Rythu Bharosa Kendras (RBKs) in Guntur district of Andhra Pradesh. The Pharma Innovation, 12(3), 5586–5590.
Hou, S. Y., & Song, L. R. (2021). The Impact of Intelligentization on the Quality of Regional Economic Growth and Its Internal Mechanism: Based on 2012 - 2018 Provincial Panel Data in China. Journal of Guangdong University of Finance and Economics, 36(4), 4–16.
Hou, Z. J., & Zhu, C. L. (2018). China's Artificial Intelligence Enterprise Total Factor Productivity and Its Influencing Factors. Enterprise Economy, 37(11), 55–62.
Huang, J., Yang, Z., Yin, L., Zhang, M., & Qin, Y. (2017). Analysis on R&D Efficiency of Domestic Robot Enterprises and Related Factors in China Based on DEA-Tobit Two-Stage Analysis Method. Science & Technology Progress and Policy, 34(18), 101–106.
Kong, X., Ai, B., Kong, Y., Su, L., Ning, Y., Howard, N., Gong, S., Li, C., Wang, J., Lee, W.-T., Wang, J., Kong, Y., Wang, J., & Fang, Y. (2019). Artificial intelligence: A key to relieve China’s insufficient and unequally-distributed medical resources. American Journal of Translational Research, 11(5), 2632–2640.
Li, L., Bao, Y., & Liu, J. (2020). Research on the influence of intelligentization on total factor productivity of China's manufacturing industry. Studies in Science of Science, 38(4), 609-618+722. https://doi.org/10.16192/j.cnki.1003-2053.2020.04.006
Ling, F., & Hu, D. (2020). Efficiency of Technology lnnovation in China's Artificial lntelligence lndustry: Based on DEAand Malmquist Index. Journal of Hubei University of Arts and Science, 41(8), 47–52.
Liu, C., Fu, R., Li, J., & Zhou, W. (2019). Research into Financing Efficiency of Artificial Intelligence Industry Based on DEA-Tobit Method. Operations Research And Management Science, 28(6), 144–152.
Liu, L., & Hu, G. (2020). Artificial Intelligence and Total Factor Productivity: Chinese Proof of "Productivity Dilemma' Falsification. Jianghai Academic Journal, 3, 118–123.
Nasierowski, W., & Arcelus, F. J. (2000). On the stability of countries’ national technological systems (pp. 97-111). Springer US.
Qiu, Z., & Zhou, Y. (2021). Development of Digital Economy and Regional Total Factor Productivity: An Analysis Basedon National Big Data Comprehensive Pilot Zone. Journal of Finance and Economics, 47(7), 4–17.
Shu, T., Nie, X., Guo, H., Li, X., & Bai, B. (2021). Analysis on the Efficiency of Scientific and Technological Resource Allocation in Beijing-Tianjin-Hebei Urban Agglomeration: Based on DEA-Malmquist Index Model. Science and Technology Management Research, 41(4), 89–96.
Svitalkova, Z. (2014). Comparison and Evaluation of Bank Efficiency in Selected Countries in EU. Procedia Economics and Finance, 12, 644–653. https://doi.org/10.1016/S2212-5671(14)00389-X
Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485–498. https://doi.org/10.1016/j.ribaf.2015.10.002
Xia, M., & Li, X. (2023). Research on technology innovation efficiency and influencing factors in Beijing-Tianjin-Hebei, Yangtze River Delta, West River Delta and Pearl River Delta —— Empirical analysis based on DEA-Malmquist and multiple regression model. Journal of Liaoning University of Technology(Social Science Edition), 25(2), 12-18+35.
Xu, D. (2008). An Theoretical Explanation and Validation of the Determination and Measurement of the Form of Industrial Structure Upgrading. Fiscal Research, 1, 46-49.
You, J., Xu, T., & Yu, A. (2018). Process for Companies in Intellectual Vehicle Industry Evaluation of Operational Efficiency with the Consideration of Corresponding innovation. Journal of Tongji University(Natural Science), 46(1), 133–140.
Zhang, C. (2020). Total Factor Productivity Analysis of Regional Differences in Economic Development in China ——Based on DEA Model of Malmquist Index Perspective. Journal of Technical Economics & Management, 9, 118–122.
Zhang, C., Xu, Y., & He, X. (2023). Research on the Development Path of China's Artificial Intelligence Industry -- Based on the Comparison of Chinese and American Artificial Intelligence. Studies in Science of Science, 1–19. https://doi.org/10.16192/j.cnki.1003-2053.20230221.001
Zhang, W., & Xuan, Y. (2022). How to Improve the Regional Energy Efficiency via Intelligence? Empirical Analysis Based on Provincial Panel Data in China. Business and Management Journal, 44(1), 27–46.