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Rapid urbanization can result in challenges, such as overcrowding, congestion, and a lack of urban services. To address these challenges, an increasing number of communities are exploring the concept of a smart city (SC). Although rapid urbanization is a problem for cities around the world, its consequences can be severe for those located in developing nations. While previous studies have focused on SCs that were built from the ground up, there is a critical need for studies that focus on how to advance SC initiatives in developing regions faced with limited land and resources. This study identified two proposed SCs in India-Kakinada and Kanpur-which are currently implementing SC projects to explore their SC transformation. This case study aims to explore how "smartness" is understood in these cities and examines the local conditions shaping SC objectives by studying the existing issues in the cities, the proposed projects, and the perception of SC experts on a) what they understand by "smartness"; b) why cities want to become smart; and c) how they will become smart. The study findings indicate that although the high-level goals of the proposed SCs in India are similar to those of existing SCs in developed nations, the underlying objectives and strategies vary and are shaped by the urbanization challenges facing the Indian cities. This research also highlights the key questions a SC planning effort should address, especially in a developing nation context.

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With an increasing number of smart cities initiatives in developed as well as developing nations, smart cities are seen as a catalyst for improving the quality of life for city residents. However, current understanding of the risks that may hamper successful implementation of smart city projects remains limited due to inadequate data, especially in developing nations. The recent Smart Cities Mission launched in India provides a unique opportunity to examine the type of risks, their likelihood, and impacts on smart city project implementation by providing risk description data for area-based (small-scale) development and pan-city (large-scale) development projects in the submitted smart city proposals. We used topic modeling and semantic analysis for risk classification, followed by risk likelihood–impact analysis for priority evaluation, and the keyword co-occurrence network method for risk association analysis. The risk classification results identify eight risk categories for both the area-based and pan-city projects, including (a) Financial, (b) Partnership and Resources, (c) Social, (d) Technology, (e) Scheduling and Execution, (f) Institutional, (g) Environmental, and (h) Political. Further, results show risks identified for area-based and pan-city projects differ in terms of risk priority distribution and co-occurrence associations. As a result, different risk mitigation measures need to be adopted to manage smart city projects across scales. Finally, the paper discusses the similarities and differences in risks found in developed and developing nations, resulting in potential mitigation measures for smart city projects in developing nations.