The Role of Artificial Intelligence in Enhancing Urban Managers’ Decision-Making Quality: A Systematic Review
Keywords:
Artificial intelligence, Urban management, Decision-making, Smart city, Data analysis, Smart governanceAbstract
This study aims to systematically examine the role of artificial intelligence in improving the quality of decision-making among urban managers. This research employed a qualitative systematic literature review approach. Comprehensive searches were conducted in Scopus, Web of Science, IEEE Xplore, and Google Scholar to identify studies related to AI applications in urban management. After screening titles, abstracts, and full texts, a total of 12 eligible articles were selected, and sampling continued until theoretical saturation was achieved. All articles were imported into NVivo 14 and analyzed using qualitative content analysis with open, axial, and selective coding to identify core themes regarding AI’s contribution to urban decision-making. Findings indicated that AI enhances urban managerial decision-making through improved data accuracy, greater predictive power, rapid information processing, reduced human error, advanced scenario simulation, integrated data systems, increased decision transparency, and strengthened capabilities in crisis management, traffic control, public safety, and service delivery. The review also revealed challenges associated with AI adoption, including data infrastructure deficiencies, ethical and legal concerns, privacy issues, limited digital skills among managers, and the absence of standardized data governance frameworks. This systematic review demonstrates that artificial intelligence is a critical driver of urban management transformation and a key enabler of higher-quality decision-making, fostering more efficient, resilient, and sustainable cities; however, effective implementation requires attention to infrastructure development, ethical standards, policymaking, and human capacity-building.
Downloads
References
Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6.
Bryson, J. (2019). The past decade and future of AI’s impact on society. IEEE Intelligent Systems, 34(6), 18–31.
Chang, I., & Wei, S. (2021). Environmental monitoring using AI in urban ecosystems. Sustainable Cities and Society, 69, 102838.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42.
Eren, T., & Uzun, A. (2020). Smart waste management using machine learning. Waste Management, 101, 252–265.
Hashem, I. et al. (2016). Big data in smart cities: A survey. International Journal of Information Management, 36(5), 748–758.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics. Nature Machine Intelligence, 1, 389–399.
Khan, S., et al. (2018). Intelligent video surveillance. ACM Computing Surveys, 50(1), 1–32.
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.
Li, Y., Chen, Z., & Wang, H. (2021). AI-based early warning systems for natural disasters. Natural Hazards, 108, 2205–2227.
Sun, Y., & Zhang, L. (2020). Predictive analytics in smart city governance. Cities, 97, 102523.
Zhao, X., Feng, H., & Liu, J. (2019). Reinforcement learning in urban planning. Computers, Environment and Urban Systems, 76, 163–173.
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 1–55.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
