Research on Industrial Economic Innovation Development Strategy Based on Ordered Logit Modeling

[1] Du, K., Cheng, Y., & Yao, X. (2021). Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Economics, 98, 105247. DuK.ChengY. & YaoX. (2021). Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Economics, 98, 105247.Search in Google Scholar

[2] Duan, W., Madasi, J. D., Khurshid, A., & Ma, D. (2022). Industrial structure conditions economic resilience. Technological Forecasting and Social Change, 183, 121944. DuanW.MadasiJ. D.KhurshidA. & MaD. (2022). Industrial structure conditions economic resilience. Technological Forecasting and Social Change, 183, 121944.Search in Google Scholar

[3] Zhang, J., Zeng, W., Wang, J., Yang, F., & Jiang, H. (2017). Regional low-carbon economy efficiency in China: Analysis based on the Super-SBM model with CO2 emissions. Journal of Cleaner Production, 163, 202-211. ZhangJ.ZengW.WangJ.YangF. & JiangH. (2017). Regional low-carbon economy efficiency in China: Analysis based on the Super-SBM model with CO2 emissions. Journal of Cleaner Production, 163, 202-211.Search in Google Scholar

[4] Zeqiraj, V., Sohag, K., & Soytas, U. (2020). Stock market development and low-carbon economy: The role of innovation and renewable energy. Energy Economics, 91, 104908. ZeqirajV.SohagK. & SoytasU. (2020). Stock market development and low-carbon economy: The role of innovation and renewable energy. Energy Economics, 91, 104908.Search in Google Scholar

[5] Zhang, Y., Wang, W., Liang, L., Wang, D., Cui, X., & Wei, W. (2020). Spatial-temporal pattern evolution and driving factors of China’s energy efficiency under low-carbon economy. Science of the Total Environment, 739, 140197. ZhangY.WangW.LiangL.WangD.CuiX. & WeiW. (2020). Spatial-temporal pattern evolution and driving factors of China’s energy efficiency under low-carbon economy. Science of the Total Environment, 739, 140197.Search in Google Scholar

[6] Feng, B., Sun, K., Chen, M., & Gao, T. (2020). The impact of core technological capabilities of high-tech industry on sustainable competitive advantage. Sustainability, 12(7), 2980. FengB.SunK.ChenM. & GaoT. (2020). The impact of core technological capabilities of high-tech industry on sustainable competitive advantage. Sustainability, 12(7), 2980.Search in Google Scholar

[7] Liu, C., Gao, X., Ma, W., & Chen, X. (2020). Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry. Journal of computational and applied mathematics, 369, 112597. LiuC.GaoX.MaW. & ChenX. (2020). Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry. Journal of computational and applied mathematics, 369, 112597.Search in Google Scholar

[8] Chen, X., Liu, Z., & Zhu, Q. (2020). Reprint of” Performance evaluation of China’s high-tech innovation process: Analysis based on the innovation value chain”. Technovation, 94, 102094. ChenX.LiuZ. & ZhuQ. (2020). Reprint of” Performance evaluation of China’s high-tech innovation process: Analysis based on the innovation value chain”. Technovation, 94, 102094.Search in Google Scholar

[9] Ravi, G., Nur, M. F., & Kiswara, A. (2023). Analyzing changes in traditional industries: Challenges and opportunities in the e-commerce era. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(1), 39-49. RaviG.NurM. F. & KiswaraA. (2023). Analyzing changes in traditional industries: Challenges and opportunities in the e-commerce era. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(1), 39-49.Search in Google Scholar

[10] Zhang, Y., Long, H., Ma, L., Tu, S., Li, Y., & Ge, D. (2022). Analysis of rural economic restructuring driven by e-commerce based on the space of flows: The case of **aying village in central China. Journal of Rural Studies, 93, 196-209. ZhangY.LongH.MaL.TuS.LiY. & GeD. (2022). Analysis of rural economic restructuring driven by e-commerce based on the space of flows: The case of **aying village in central China. Journal of Rural Studies, 93, 196-209.Search in Google Scholar

[11] Breen, R., Karlson, K. B., & Holm, A. (2018). Interpreting and understanding logits, probits, and other nonlinear probability models. annual review of sociology, 44(1), 39-54. BreenR.KarlsonK. B. & HolmA. (2018). Interpreting and understanding logits, probits, and other nonlinear probability models. annual review of sociology, 44(1), 39-54.Search in Google Scholar

[12] Norton, E. C., & Dowd, B. E. (2018). Log odds and the interpretation of logit models. Health services research, 53(2), 859-878. NortonE. C. & DowdB. E. (2018). Log odds and the interpretation of logit models. Health services research, 53(2), 859-878.Search in Google Scholar

[13] Sarrias, M., & Daziano, R. (2017). Multinomial logit models with continuous and discrete individual heterogeneity in R: the gmnl package. Journal of Statistical Software, 79, 1-46. SarriasM. & DazianoR. (2017). Multinomial logit models with continuous and discrete individual heterogeneity in R: the gmnl package. Journal of Statistical Software, 79, 1-46.Search in Google Scholar

[14] Liao, H., Chen, T., Tang, X., & Wu, J. (2019). Fuel choices for cooking in China: Analysis based on multinomial logit model. Journal of cleaner production, 225, 104-111. LiaoH.ChenT.TangX. & WuJ. (2019). Fuel choices for cooking in China: Analysis based on multinomial logit model. Journal of cleaner production, 225, 104-111.Search in Google Scholar

[15] Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel behaviour and society, 20, 22-35. ZhaoX.YanX.YuA. & Van HentenryckP. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel behaviour and society, 20, 22-35.Search in Google Scholar

[16] Lee, D., Derrible, S., & Pereira, F. C. (2018). Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Transportation Research Record, 2672(49), 101-112. LeeD.DerribleS. & PereiraF. C. (2018). Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Transportation Research Record, 2672(49), 101-112.Search in Google Scholar

[17] Fountas, G., Sarwar, M. T., Anastasopoulos, P. C., Blatt, A., & Majka, K. (2018). Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach. Accident Analysis & Prevention, 113, 330-340. FountasG.SarwarM. T.AnastasopoulosP. C.BlattA. & MajkaK. (2018). Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach. Accident Analysis & Prevention, 113, 330-340.Search in Google Scholar

[18] Azimi, G., Rahimi, A., Asgari, H., & **, X. (2020). Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accident Analysis & Prevention, 135, 105355. AzimiG.RahimiA.AsgariH. & **X. (2020). Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accident Analysis & Prevention, 135, 105355.Search in Google Scholar

[19] Muya Mwajuma,Ilembo Bahati & Anasel Mackfallen. (2024). Predictive accuracy of the logit model to determine factors affecting delivery and postnatal care services utilization in Tanzania. International Journal of Health Governance(4),412-421. MwajumaMuyaBahatiIlembo & MackfallenAnasel. (2024). Predictive accuracy of the logit model to determine factors affecting delivery and postnatal care services utilization in Tanzania. International Journal of Health Governance(4),412-421.Search in Google Scholar

[20] Stephan L Cleveland,Carol A Carman,Niti Vyas,Jose H Salazar & Juan U Rojo. (2024). Evidence-based approach for the generation of a multivariate logistic regression model that predicts instrument failure.. Laboratory medicine. ClevelandStephan LCarmanCarol AVyasNitiSalazarJose H & RojoJuan U. (2024). Evidence-based approach for the generation of a multivariate logistic regression model that predicts instrument failure.. Laboratory medicine.Search in Google Scholar

[21] Rys Dawid,Jaczewski Mariusz,Pszczola Marek,Kamedulska Agnieszka & Kamedulski Bartosz. (2023). Factors affecting low-temperature cracking of asphalt pavements: analysis of field observations using the ordered logistic model. International Journal of Pavement Engineering(2). DawidRysMariuszJaczewskiMarekPszczolaAgnieszkaKamedulska & BartoszKamedulski. (2023). Factors affecting low-temperature cracking of asphalt pavements: analysis of field observations using the ordered logistic model. International Journal of Pavement Engineering(2).Search in Google Scholar