Building a Smart City with the Desire of the Citizenry

Press/Media: research_highlightHumanities and Social Science


The concept of a smart city, based on advanced information and communications technology (ICT), emerged to mitigate the impact of rapid urbanization and was considered feasible. However, the selection of technology and policy for providing better services to citizens and ensuring sustainable development not only involves financial capabilities and outlay on the ICT adoption but also includes conflicting views from different stakeholders. Therefore, the study proposes a structural method which integrates a modified Delphi method, an analytic hierarchy process, and zero-one goal programming, to select a feasible portfolio based on the political goal and the annual budget. In addition, an empirical study was conducted for demonstration and validation. The results of the study can facilitate policy-making in the public sector and advance the research field of multiple-criteria decision making and information management strategies.


Rapid urbanization presents a crucial challenge around the world; thus, initiatives and technologies are being developed and continuously modified to provide the public with better services. However, migration from rural areas to cities is inevitable, so cities continue to face challenges from the accompanying increase in demand for space, resources, and facilities. Indeed, rapid population growth in urban areas has created many problems in modern cities, such as greater pollution, more traffic, and the need to satisfy higher demand for energy and sanitation services. In this context, problems associated with urbanization need smart solutions that involve human capital, creativity, and collaboration with involved stakeholders. The smart city projects based on advanced information and communications technology (ICT) offer feasible solutions to diverse problems in urban areas.

At present, however, many cities have focused on infrastructure-centric solutions, and the majority of smart city projects rely on predictive maintenance, asset tracking, and smart buildings. The results of these projects might overlook systemic problems related to the basic needs of the public, such as alleviation of poverty, inequality, and discrimination. However, the public, forming the heart of a city, expect that city government will use limited resources in citizen-centric solutions to promote citizen satisfaction, economic competitiveness, and sustainability in the city. Moreover, public agencies may not have adequate resources to perform all the desired projects simultaneously to address urban problems, so a rational process is needed to select feasible projects and achieve the organizational goals with the agencies’ available resources. 

To that end, the study aims to propose a structural method for policy selection, which consists of three phases (see Figure 1). First, the modified Delphi method (MDM) is used to determine the elements of the decision by surveying panel members for their opinions. Next, an analytic hierarchy process (AHP) is used to ascertain the priority of each alternative according to the goal of the decision. Finally, zero-one goal programming (ZOGP) models are developed to select a feasible portfolio based on the political goal and the annual budget. 

To demonstrate the applications and advantages of the proposed method, we examined an empirical case (Taipei City), the capital and the hub for commerce, politics, education, mass media, and pop culture in Taiwan. Like many major Asian cities, Taipei has confronted challenges due to rapid urbanization and adopted the smart city concept to provide a better quality of life and improve its competitiveness. In the empirical case, the proposed method can induce the municipality to consider citizens’ requirements, identify the strengths and weaknesses of proposed policies, and select a feasible project portfolio in response to public expectations. In addition, the study found that a feasible portfolio, including consideration of citizens, business, and the environment, enables the public perceptions of government performance within the resource constraints of the organization. The major contribution of the study can be summarized as follows: 

It provides a structured method to assist decision makers at public agencies in evaluating smart city projects. The successful implementation of smart city projects not only depends on municipal capability in finance, planning, and operation but also needs stakeholder involvement and engagement in the decision-making process. The proposed method can cope with this multi-criteria decision-making problem. The method first uses MDM to clarify decision elements, determines the relative priority of each project with the AHP technique, then applies the ZOGP model to attain the organizational goals of optimizing the project portfolio utilizing limited resources.
It offers insights into the decision process for selecting a feasible portfolio of projects within the municipal administration. The trust in government is a measure of quality to appraise the performance of the municipal administration. To promote public trust, tangible factors, e.g., budget requirements and economic benefits, and intangible factors, e.g., sustainability and citizen satisfaction, should be considered decision elements in the project selection process.
Furthermore, the case study reveals that the implementation of a smart city project relies on public expectations and a municipality’s capacity. In addition, smart city projects including citizens, businesses, and the environment are highly related to public trust in government and the promotion of public satisfaction. The proposed method successfully demonstrates the need for the municipality to consider citizens’ requirements, identify the strengths and weaknesses of proposed policies, and select feasible project portfolios in response to public expectations. The results of the proposed method expand on the existing research on selection problems for a smart city. 


Figure 1. Overview of the proposed model
Figure 1. Overview of the proposed model




Period2021 Nov 9

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