Optimizing Local Government Decision Support Systems: Policy Innovations in Machine Learning and Business Intelligence
DOI:
https://doi.org/10.52152/3125Keywords:
use of ML and BI for local government DSS; e-governance; overcoming inefficiencies of local government through application of technology; urban planning and management using technology.Abstract
This research analysed the emerging technological tools like ML and BI for enhancing the efficiency of local government DSS in China. As per the multiple cases analysed in the study, it has been found that the use of ML models and BI analysis could significantly improve the decision-making process in the government institutions that could help such organisations in better serving local citizens and overcoming inefficiencies that such organisations were once witnessing. Although Chinese local governments, including Suzhou, Guangzhou, and Hangzhou, have adopted ML models and BI analytics, as per the analysis carried out in the study, the comparative analysis of the three local governments revealed that Guangzhou has been taking most of the advantages of ML and BI tools, while the local government of Suzhou has been witnessing most of the challenges and impediments in accessing the true synergies of the two technologies. It has been found that the government of Guangzhou has not only improved the efficiency of governance through the use of ML and BI tools, but also the government has been leveraging the data-driven policies for resource optimisation, enhanced urban planning and management, and improving transparency of government operations. Although there are some barriers and challenges that are encountered in the execution of ML and BI technologies, the local administration of Guangzhou has successfully overcome most of the challenges and barriers.
References
Chatterjee, S., Khorana, S., & Kizgin, H. (2022). Harnessing the potential of artificial intelligence to foster citizens’ satisfaction: an empirical study on India. Government Information Quarterly, 101621, 39(4).
Chen, Y., & Wang, Z. (2017). Apply Deep Learning Neural Network to Forecast Number of Tourists. In. Advanced Information Networking and Applications Workshops (WAINA), 2017 31st International Conference, 259-264.
Goodfellow, I., Bengio, Y., & Courville, A. (2022). Deep Learning. MIT Press, 453-480.
Guangzhou Municipal Government. (2023). Smart Traffic Management Initiative Report. Guangzhou: GMG Press.
Guenduez, A., Mettler, T., & Schedler, K. (2020). Technological Frames in Public Administration: What do public mangers think of Big Data. Government Information Quarterly, 37(1).
Hangzhou Smart City Office. (2023). Big Data in Public Service Delivery: Lessons and Outcomes. Hangzhou: HSC Press.
Huang, C. C., Laing, W. Y., Wen, D. W., Ting, P. H., & Shen, M. Y. (2022). Qualitative analysis of big data in the service section. The Service Industries Journal , 42(3), 206-224.
Kim, S., Anderson, K. N., & Lee, J. (2022). Platform government in the era of smart technology. Public Administration Review, 82(2), 362-368.
Li, D. Y., & Liu, J. (2014). Dynamic capabilities, environmental dynamism, and competitive advantage: Evidence from China. Journal of Business Research, 67(1), 2793-2799.
Li, Y., Chandara, Y., & Fan, Y. (2021). Unpacking government social media messaging strategies during the COVID-19 pandemic in China. Policy & Internet, 14(3), 651-672.
Li, Y., Meng, Q., & Rao, D. (2020). Corticosteroid therapy in critically ill patients with COVID-19: a multicentre, retrospective study. Critical Care, 24(6), 698-705.
Liang, C., & Qian, Y. (2024). Predictive Policy Modelling with Machine Learning: A Chinese Case Study. Technology and Public Policy Journal, 24(1), 42-58.
Liu, H. W., Lin, C. F., & Chen, Y. J. (2019). Beyond State v Loomis: artificial intelligence, government algorithmizing and accountability. International Journal of Law and Info Technology , 27(2), 122-141.
National Development and Reform Commission. (2023). Annual Report on Smart City Development in China. Beijing: NDRC.
National Development and Reform Commission. (2023). Annual Report on Smart City Development in China. . Beijing: NDRC.
Qian, S. T., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368-382.
Simon, H. A. (1977). The New Science of Management Decisions. Harlow: Prentice Hall.
Soe, R. M., & Dreschsler, W. (2018). Agile local governments: experimentation before implementation. Government Information Quarterly, 35(2), 323-335.
State Council of China. (2022). White Paper on Smart Governance and AI Integration. Beijing: State Council Press.
Sun, J., & Liu, X. (2024). Comparative Study of DSS Adoption in Chinese Cities. China Review of Technology and Society, 12(3), 47-69.
Suzhou Industrial Zone Authority. (2022). Resource Optimization through Business Intelligence: A Case Study. Suzhou: SIZA.
Tang, P., Koopman, J., McLean, S. T., & Chen, W. (2022). When conscientious employees meet intelligent machines: an integrative approach inspired by complementarity theory and role theory. Academy of Management Journal, 65(3), 1019-1054.
Wang, C., Teo, T. S., & Janssen, M. (2021). Public and private value creation using artificial intelligence: an empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management, 102401, 61.
Wang, G., Xie, S., & Li, X. (2022). Artificial intelligence, types of decisions, and street-level bureaucrats: evidence from a survey experiment. Public Management Review, 1-23.
Wang, R., & Gao, X. (2024). The Impact of BI and AI Integration in Smart City Governance in China. Journal of Smart Urban Planning, 19(4), 93-115.
Wang, T., & Liu, Z. (2024). The Making of Government-Business Relationships Through State Roles in Artificial Intelligence Development in China. Journal of Chinese Government, 19(2), 215-233.
Wang, Y., Zhang, N., & Zhao, X. (2022). Understanding the determinants in the different government AI adoption stages: evidence of local government chatbots in China. Social Science Computer Review, 40(2), 534-554.
Wu, M., & Zhao, X. (2023). Advancements in Machine Learning for Smart Governance in Chinese Local Administrations. Journal of Chinese Governance, 8(3), 356-372.
Wu, W., Chen, W., Yun, Y., Wang, F., & Gong, Z. (2022). Urban greenness, mixed land-use, and life satisfaction: Evidence from residential locations and workplace settings in Beijing. Landscape and Urban Planning, 104428, 224.
Xing, J., & Sieber, R. (2023). The challenges of integrating explainable artificial intelligence into GeoAI. Transaction in GIS, 27(3), 6265-645.
Xu, L., & Zhang, W. (2024). Business Intelligence Applications in Local Governance: A Case Study of Jiangsu Province. Chinese Journal of Public Management, 41(3), 89-112.
Xu, P., & Huang, L. (2022). The Role of AI in Public Administration: Lessons from China. Artificial Intelligence and Society, 37(2), 489-504.
Xu, X., Cugurullo, H., Zhang, A., Gaio, A., & Zhang, W. (2024). The emergence of artificial intelligence in anticipatory urban governance: Multi-scalar evidence of China’s transition to city brains. Journal of Urban Technology, 134-150.
Zhang, C., Wang, X., Cui, A. P., & Hans, S. (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483-494.
Zhang, X., Li, H., & Chen, Y. (2024). Unlocking Artificial Intelligence Adoption in Local Governments. Smart Cities, 7(4), 64-81.
Zhao, K., & Li, H. (2023). Optimizing Local Governance with Business Intelligence: Success Stories from China. International Review of Smart Governance,, 14(2), 167-181.
Zhen, Y., Yu, H., Cui, L., & Chen, C. (2018). SmartHS: an AI Platform for improving government service provision. In: Proceedings of the AAAI conference on artificial intelligence, New Orleans, LA, USA, February 2018, 7704-7711.
Zheng, J. (2021). China’s artificial intelligence innovation: a top-down national command approach? . Global Policy, 12(3), 399-409.
Zheng, Y., Rajasegarar, S., & Leckie, C. (2015). Parking availability prediction for sensor-enabled car parks in smart cities. IEE 10th International Conference on intelligent sensors, sensor networks and information processing, IEEE (2015), 1-6.
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