Development of a dynamic quantitative digital model for the measurement of smart city maturity level in the city of Amman
Amman is making serious efforts to improve its performance as a Smart City, yet, no evidence-based approach is found to measure their indicators, which hinders decision-makers from making strategic decisions regarding the city’s development. The Holistic Key Performance Indicators (H-KPI) framework...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19397038.2024.2449379 |
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| Summary: | Amman is making serious efforts to improve its performance as a Smart City, yet, no evidence-based approach is found to measure their indicators, which hinders decision-makers from making strategic decisions regarding the city’s development. The Holistic Key Performance Indicators (H-KPI) framework enhances conventional KPIs by incorporating metrics aligned with community priorities and addressing urban disparities. This study focuses on Amman’s urban divide found in literature discussions, with East Amman characterised by older, denser infrastructure and West Amman by modernised, higher-quality neighbourhoods. H-KPI provides a robust method for assessing urban smartness across sectors, infrastructures, and population needs, ensuring evaluations reflect varied priorities. The research evaluated six districts, generating statistical indicators on a scale from 0 to 1. The findings showed that community alignment scored 0.54, and infrastructure quality scored 0.45, closely matching global benchmarks. These results underscore the need for digital indicators to clarify the concept of ‘Smart Cities’, particularly in developing countries. The study concludes that adopting H-KPI can aid Amman in improving its Smart City Index score (previously standing at 135 out of 142 countries in the IMD Smart City Index 2023, and moving up to 128 in the 2024 version), providing a dynamic and infrastructure-oriented adaptive evaluation tool. |
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| ISSN: | 1939-7038 1939-7046 |