Data-Driven Decision-Making for Smart City Investments: A Multi-Criteria Framework for Strategic Digital Governance

Abstract

The strategic alignment of smart city investments with public governance priorities has become a critical issue in the digital transformation of urban management, especially in developing and non-Western contexts. This study develops a hybrid Analytic Hierarchy Process (AHP)–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate smart city initiatives in Konya Metropolitan Municipality in Türkiye. By integrating semi-structured interviews with decision-makers and a multi-criteria decision-making (MCDM) framework, we assess eight smart city dimensions—ranging from Smart People to Smart Environment—across five criteria: technical adequacy, cost-efficiency, integration, sustainability, and citizen impact. The findings reveal a strong prioritization of human capital and economic development, while environmental and infrastructural dimensions remain underemphasized. The analysis highlights persistent gaps in artificial intelligence (AI) adoption, interdepartmental data governance, and participatory citizen engagement, limiting governance maturity and long-term sustainability. Policy recommendations include embedding AI-supported decision-making, institutionalizing open data and interoperability standards, aligning investments with Sustainable Development Goals (SDGs), and designing inclusive cocreation platforms to enhance citizen-centric innovation.