Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model

Abstract

The concept of smart cities has gained significant attention due to the potential of smart cities to optimize city services and enhance citizens’ quality of life. Cities are investing in digital transformation to become smarter, sustainable, and resilient. Therefore, there is a need to build a comprehensive and holistic model to assess smart city initiatives. This paper aims to develop a model that can capture the maturity of smart city adoption across various functional domains. These domains are divided into focus areas that capture different dimensions of a smart city and grouped into seven groups: ICT, economy, environment, social, resources, services, and governance. Each focus area has a set of maturity levels that describe the capabilities and outcomes of the city at different stages of development. To develop the model, the focus areas were extracted from the literature based on 16 models that have been reviewed. Assessing these models helped in identifying gaps and building the foundation of the model. Using the information extracted from the literature, a focus area model was designed and developed. The model development included seven main phases, which were: scope, design, populate, test, deploy, and maintain. The current paper validates the proposed model using the Delphi method, which involves the participation of a panel of sixty field experts. The experts evaluated the model’s correctness and completeness based on their experience and provided feedback. This feedback was used to revise and finalize the model. The smart city maturity model provides a framework for benchmarking, planning, and improving smart city initiatives. Cities can use the model to measure their performance and evaluate their weaknesses and strengths. The model is also the most comprehensive in terms of the scope of the focus areas included, and the results show that the model has a high level of accuracy and consistency and can effectively assess smart city adoption.

1. Introduction

Cities worldwide face population growth, rapid urbanization, improper resource management, and inadequate infrastructure. These challenges pose environmental threats, drain resources, weaken infrastructure and the economy, and trigger social problems such as high crime rates and inequality. To manage and mitigate these problems, cities are racing to digital transformations and to become smarter. According to the World Cities Report 2020 [1], the global demand for smart cities grew from USD 622 billion in 2017 to USD 1 trillion in 2019 and is forecasted to reach USD 3.48 trillion by 2026. The United Nations’ 2030 Agenda for Sustainable Development emphasizes the need for smart and sustainable cities globally, particularly the sustainable development goal (SDG) 11: “make cities and human settlements inclusive, safe, resilient and sustainable” [2] (p. 26). Successful smart city implementation will contribute to achieving the SDGs [3,4].

Smart city implementation not only helps governments provide more efficient services but also promotes innovation, encourages private–government partnerships, enhances decision-making processes, improves project financing, and promotes sustainability [5,6]. However, many technical, social, economic, and strategic challenges must be overcome to realize the benefits of the smart city concept. Contemporary cites are characterized by complexity, diversity, and intelligence [7], which can be barriers to smart transformation. Therefore, it is crucial to ensure the availability of reliable governance systems that can plan, manage, and measure smart transformation.

Academic studies and practitioners have provided a variety of conceptualizations of smart cities. The definition in [8] (p. 11) focuses on the main characteristics of a smart city: “a city well performing in a forward-looking way in the six characteristics (economy, people, governance, mobility, environment, living), built on the smart combination of endowments and activities of self-decisive, independent and aware citizens”. Other definitions emphasize the role of technology. For example, the International Data Corporation defines smart cities as cities that use ubiquitous networks, wireless sensors, and intelligent management systems to solve current and future challenges and create new services [9].

Standards bodies such as the British Standards Institution (BSI) adopt a holistic view of smart cities and the utilization of best practices when defining a smart city as “a city where there is effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens” [10] (p. 18). The International Telecommunication Union Telecommunication Focus Group on Smart Sustainable Cities (ITU-T FGSSC) analyzed 120 definitions to develop a comprehensive definition of a smart city: “an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operations and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects” [11] (p. 13).

On the basis of the definitions in the literature, this paper defines a smart city as a city that innovatively utilizes ICT enablers to enhance the quality of life for citizens in a sustainable and balanced environment while considering the economic and social aspects of society. This definition highlights the vital role of ICT in a smart city while emphasizing that the smart city concept is about more than just technology. To achieve the desired outcome of smart city adoption, namely improving citizens’ quality of life, smart city projects should also consider sustainability and a balanced lifestyle.

The literature acknowledges the need for proper governance tools to facilitate smart city adoption and connect forces in different domains [12]. However, previous attempts to build models and frameworks have not considered all city domains [13], failed to engage stakeholders in model building or implementation, missed differences between different domains [14], or ignored the level of granularity [15]. Several smart city metrics and performance indicators have been proposed, but most cannot provide comprehensive evaluations of complex systems such as smart cities. One reason that cities lack standards to measure their progress [16,17,18] is the absence of a globally accepted definition of the smart city concept [19].

The objective of this study is to is to develop a model that can capture the maturity of smart city adoption based on different focus areas. Cities can use the model to measure their performance and evaluate their weaknesses and strengths.

This paper builds on the past efforts of various practitioners and scholars to develop a tool that addresses these gaps. Specifically, a maturity model for assessing smart city implementation is developed and empirically examined. Maturity models can help entities to achieve continuous improvements. Cities can use maturity models to assess their situation and determine the capabilities required to build a smart city [9]. Smart city maturity models are important for the proper evaluation of smart city implementation to prioritize funding and improve city performance. This model will provide significant input to all smart city stakeholders and empower cities to successfully undergo smart transformation to face urban challenges.

The smart city maturity model was developed through an extensive literature review and analysis. This article is structured as follows: Section 2 gives background information about the smart city concept and smart city assessment. Section 3 describes the methods used to develop the model. Section 4 presents and discusses the results. Finally, the paper is concluded in Section 5.

2. Related Work

Studies such as those reported in [12,14] have highlighted the importance of tools for assessing and evaluating smart city implementation. Frameworks and practices for smart city assessment belong to five main categories: best practices, ranking frameworks, index-based frameworks, initiative-based evaluation, and maturity models. Some background information about the five categories is outlined in Section 2.1, Section 2.2, Section 2.3, Section 2.4 and Section 2.5.

2.1. Best Practices

International, regional, and local standards bodies issue best practices to ensure the quality of services and enable cities to perform benchmarking. These bodies include the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), the International Telecommunication Union (ITU), the European Committee for Standardization (CEN), the British Standards Institution (BSI), and the National Institute of Standards and Technology (NIST). These organizations have published standards that define a smart city and specify relevant indicators for assessment, such as ISO 37120:2018 (Sustainable cities and communities—Indicators for city services and quality of life), ISO 37153:2017 (Smart community infrastructures—Maturity model for assessment and improvement), ISO/IEC 30146:2019 (Information technology—Smart city ICT indicators), PAS 181:2014 (Smart city framework—Guide to establishing strategies for smart cities and communities), and ITU-T L.1603 (Key performance indicators for smart sustainable cities to assess the achievement of sustainable development goals). However, applying best practices in real-life projects can be challenging.

2.2. Ranking Frameworks

Ranking frameworks compare cities by using specific criteria, which allows cities to act based on their relative positions. The European smart cities ranking (ESCR) [8,20] is a very well known ranking framework. Giffinger and colleagues studied 58 medium-sized cities to define a ranking mechanism for European cities based on six main characteristics: society, environment, economy, governance, mobility, and living. They also identified 31 factors and 74 indicators that can be used to measure cities’ performance. A limitation of ranking systems is that they focus on the final results and do not capture enough detail about cities’ strengths and weaknesses. Moreover, these frameworks do not consider cities as complex and unique systems that require comprehensive yet specific measurements based on local context.

2.3. Index-Based Frameworks

Index-based frameworks use key performance indicators. The CITYKeys project, which is funded by the European Commission, has developed a indices-based performance measurement framework for monitoring smart city implementation [21]. A limitation of these frameworks is that they are subject to data availability.

2.4. Initiative-Based Evaluation

Initiative-based evaluation is similar to the framework developed by [22] to examine smart city initiatives based on a set of identified critical success factors. This framework includes management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and the natural environment and provides a comprehensive conceptualization of the smart city. However, initiative-based evaluation frameworks may encourage isolation and barriers between different sectors of smart city projects.

2.5. Maturity Models

A maturity model is an assessment tool for establishing the current situation and identifying necessary improvements to progress in maturity. Maturity models have been used as assessment tools in many sectors to ensure continuous improvement such as process management, project management, knowledge management, sustainability management, risk management, supply chains, education, government, construction, and healthcare [23]. Maturity models can be applied to measure the success of smart city adoption [24].

Maturity models comprise several components [25]:

• The domain is the model’s first layer and provides a high-level view of the scope;
• The domain components, sometimes referred to as focus areas or factors, are the significant aspects of the domain, such as critical success factors;
• The domain subcomponents, also called capabilities or processes, provide further details. Achieving the capabilities will determine the level of maturity;
• The levels can be present in any number, depending on the model scope and application. Maturity can range from the minimum value, i.e., the quality of the elements underlying the processes is in the lowest required state, to the maximum value, i.e., no further improvements are needed [26];
• The assessment tool defines how the capabilities will be measured against the maturity scale using qualitative (descriptions) or quantitative (numerical scales) approaches. Assessment can either be self-assessment or performed by a third party.

Maturity models can be classified into two types based on stages, fixed levels, and focus areas. The fixed levels have a specific number of levels of maturity for all defined focus areas. A well-known fixed-level model is the CMM. It is popular and simple to implement but is not recommended for complex environments [23]. The focus area maturity model divides the functional domain into a number of focus areas that need to be developed to achieve maturity in the functional domain. Related focus areas can be grouped to facilitate the assessment. Each focus area includes (1) different capabilities that represent the steps of development of the focus area and (2) different numbers of maturity levels. The final maturity is a combination of the maturity levels of the focus areas.

The focus area maturity model has been used to develop models in disciplines such as enterprise architecture [27], information security [28], software governance [29], master data management, disaster risk management, and social media [30]. However, the focus area maturity model has not been used to develop a smart city maturity model. This research adopts the focus area approach for three key reasons:

• The focus area maturity model facilitates the measurement of different dimensions within a smart city, each of which has different maturity levels. This is not possible in fixed-level models;
• A smart city is a complex system that depends on multiple interconnected processes that must be measured. The focus area maturity model illustrates the interdependencies between processes and enables the measurement of incremental improvements;
• The focus area maturity model can include any number of levels. Including a greater number of incremental levels allows more detailed guidance for capability improvements.

3. Research Approach

3.1. The Model Development Method

This section presents the development of the smart city maturity model based on the focus area maturity model method. It outlines the method used to guide the development of the maturity model, discusses each step of the model development and then provides information about the assessment tool created to perform the assessment. Finally, the Delphi method is presented, which is used to validate the model components.

Table 1. Maturity model development methodologies.

Methodology	Phase	Description
Generic development framework	Scope	Decide on the model focus (general or domain specific) and development stakeholders (academia, practitioners, government)
	Design	The architecture basis: Why the model is required? How it will be applied to different organizations? Who should be involved? What can be achieved?
	Populate	Specify what needs to be measured and how it can be measured?
	Test	Test the model validity, reliability, and generalizability
	Deploy	Making the model available to its intended users
Development framework for focus area maturity models	Identify the scope and functional domain	Decide on what to include and exclude
	Determine focus areas	Based on the literature review, then exploratory methods
	Determine capabilities	Capabilities define the incremental path for maturity levels’ progress
	Determine dependencies	Specify the order of the capabilities within and between focus areas
	Position capabilities in matrix	Based on the specified dependencies
	Develop assessment instrument	Specify assessment questions for the capabilities based on the description
	Define improvement actions	General suggestions
	Implement maturity model	The first implementation is for model evaluation
	Improve matrix iteratively	Enough assessment data to be collected for the model evaluation
	Communicate results	To practitioners and academia

provides an overview of the two methods used in this study to create the smart city maturity model: the generic development framework [25] and the development framework for focus area maturity models [31]. The different phases of each method are listed in sequence under the second column (Phase), along with a description of each phase in the last column (Description).

These two methods have been combined previously to develop focus area maturity models such as the software ecosystem governance maturity model [29] and the focus area maturity model for API management [32]. Figure 1 illustrates the steps followed to develop the smart city maturity model.

3.1.1. Scope

The first phase of the maturity model development was to establish scope, as shown in Figure 1. Defining the aim of the model, the gaps it will fill, the problems it will address, and the potential opportunities is fundamental. The scope must be clearly formulated, and domain(s) must be identified.

To define the scope clearly and collect information about existing maturity models for smart city assessment, a literature review was conducted following the guidelines proposed by [33]. The first step in this methodology is definition, which includes defining the inclusion and exclusion criteria, specifying the search sources, and determining the search string. The inclusion criteria were publications in English that were related to the research questions and included basic information about the model components and levels. Publications that did not meet these criteria were excluded. Several platforms were searched: Scopus, ScienceDirect, SpringerLink, Taylor & Francis Online, ACM, and ResearchGate. The search string was (“smart” AND (“city” OR “cities”) AND (“maturity” OR “assessment”) AND (“model” OR “method” OR “framework”)). The query string was adjusted according to the syntax of each search engine.

The next step in [33]’s review methodology is search, which includes executing the search, extracting the results, and performing a selection step to refine the results. As shown in

Table 2. Count of publications.

Database	No. of Papers in the Initial Results	No. of Papers after 1st Round of Exclusion	No. of Papers after 2nd Round of Exclusion
Scopus	43	37	5
Springerlink	779	32	3
Taylor & Francis	890	18	1
ScienceDirect	17	5	0
ACM	6	6	5
ResearchGate	24	16	2
Total	1759	114	16

, the initial search on 29 May 2020 yielded 1759 publications.

After removing duplicates and publications that addressed smart cities from a limited angle (i.e., a specific domain such as smart transport or smart tourism), 114 studies were considered for a second round of quality checks to ensure relevance and reliability by reading the abstract and parts of the study. Finally, 16 papers were retained for inclusion in the literature review (Appendix A). The various conceptualizations of smart city assessment models in the literature included diverse domains. Some domains were used in multiple models, whereas others were mentioned in only a single publication. The domains were extracted from the models, studied, and classified into seven logical categories: ICT, economy, environment, social, resources, services, and governance. These categories provided a clearer idea of the areas that have received attention in the literature on maturity models.

Governments are initiating multidimensional smart city projects to satisfy citizens’ needs and improve city competitiveness. However, cities have struggled to chart improvement paths and effectively measure success. Therefore, this study aims to introduce a smart city maturity model that covers all dimensions and assists governments in their smart city journeys. None of the studied maturity models included comprehensive measures encompassing all of the domains. Moreover, all of the models were based on fixed-stage models that did not consider differences between domains. Each domain may have unique specifications and components that must be considered separately. By contrast, a focus area model considers differences and can provide a reasonable improvement path that can be applied globally. The gaps identified in the literature review will be addressed by introducing a comprehensive focus area maturity model that can be used to measure the success of smart city implementation globally while considering the local differences between cities. Accordingly, the model’s scope is domain specific in assessing smart city maturity. In addition, the model can be applied to any city and is not region specific.

Another critical decision in the scope phase is the model’s target audience and stakeholders. The smart city maturity model developed in this research is intended for both researchers and practitioners. It will support government representatives, decision makers, all city actors, and, indirectly, citizens.

3.1.2. Design

The second phase of model development is design. In this phase, the model’s foundation is constructed by answering the following questions:

• Why is the model required?

The primary goal of the model is to assist cities in measuring the maturity of smart city implementation.

• How will it be applied to different entities?

Cities can utilize the model to assess their level of maturity and then focus on the practices that need to be implemented to achieve the intended improvement.

• Who should be involved?

City officials and decision makers will be the main users of the model. However, since the model measures the city’s activities, everyone in the city can be considered as a potential stakeholder.

• What can be achieved?

Cities can use the model to identify a path for improving capabilities and practices to progress and mature.

3.1.3. Populate

Once the model design is ready, the next phase is to populate. According to [25], the model’s components should be defined along with what will be measured and how. The steps from the focus area development framework were followed to identify the components. First, the focus area groups and the focus areas were defined. Each group was divided into several focus areas, defined as groups of related activities, events, deliverables, or products that support the domain [34]. Grouping the focus areas into labeled categories can increase model accessibility [31]. The seven categories that were identified in the literature review were considered the focus area groups. These groups were divided into eighteen focus areas: ICT infrastructure, digital transformation, data, labor market, entrepreneurship, pollution, environmental management, education, social equity, citizen active lifestyle, water resources management, sustainable and efficient energy, urban planning and management, healthcare system, transport system, safety and security, political structure, and strategic planning. All focus areas were clearly defined to eliminate confusion in scope and precisely identify each area’s capabilities.

To describe the ability to achieve the focus areas, the relevant capabilities and practices within the capabilities were defined [35,36]. According to [37] (p. 8), capabilities are “the ability to achieve a predefined goal”. Capabilities can be considered the building blocks of the focus area maturity model [38]. Each focus area was studied to determine the main capabilities that should be satisfied to achieve success in that particular focus area. In general, capabilities should satisfy the following criteria: have a theoretical foundation; be measurable, clear, and precise; and have available related data. For the smart city maturity model, the capabilities and practices were extracted from the academic literature, international standards, and best practices.

3.1.4. Test and Deployment

In the test phase, experts were interviewed and consulted to verify the content of the model using the Delphi method, which will be discussed in Section 3.3. The Delphi method has been used previously to verify frameworks [39] and, more specifically, to validate maturity models [40,41].

3.2. Building the Assessment Tool

After validating the model and finalizing the components, the assessment tool was built and used to evaluate the city’s level of maturity [42]. Each level within a focus area had three checkpoints: questions that can be answered by “yes” or “no” to assess the current situation [28,38]. The maturity of the focus area level was achieved only if all three questions were answered “yes”. Appendix D lists all the checkpoints that are under ICT focus area as an example and below in

Table 3. Checkpoints to determine level 1 of ICT infrastructure/connectivity.

ICT
Focus Area: ICT Infrastructure/Connectivity
Broadband consistency	1	Does the broadband network cover all parts of the city?
	2	Is the broadband network accessible to all relevant stakeholders?
	3	Is the connectivity affordable and reliable?

are the questions for measuring level one of the ICT infrastructure/connectivity focus area.

The checkpoints were derived from the definitions of the focus areas, capabilities, and practices. The questions were also based on standards and frameworks from the literature that were related to the focus areas and capabilities. The questions were formulated considering five desirable properties [43]: unambiguous, comprehensive, direct, operational, and understandable. In addition to the mentioned properties, within each focus area there are three categories of questions according to what they measure: input, output, or outcome [44]. First, the input questions measure the resources allocated to the focus area, such as budget and human resources. However, the input questions will measure the efforts dedicated to that focus area but do not measure whether the resources are spent efficiently or the objectives achieved. Second, the output questions measure the actions produced to accomplish the focus area’s goals but do not measure the effectiveness of the actions. A typical output was the presence of a plan or strategy. Finally, the outcome questions measure the effectiveness of the actions in reaching the focus area objectives, such as questions about citizens’ satisfaction. Within each focus area, the questions covered strategy, governance, people, communication, and reporting.

3.3. The Delphi Method

The goal of the test phase is to validate the components of the maturity model. The focus areas, capabilities, and practices were extracted from the literature. Testing and validation of the data were essential to ensure the model’s completeness and correctness. Specifically, the objectives of the test phase were as follows:

• Agree on the definitions of focus areas and capabilities, which is essential to establish a solid foundation for the model;
• Identify any missing capabilities to ensure model completeness;
• Identify any potential overlap between the proposed focus areas/capabilities to avoid confusion between the different components;
• Identify any potential dependencies between focus areas/capabilities to build the model matrix based on any dependencies;
• Agree on all focus areas and capabilities to ensure model validity;
• Identify the importance of each focus area/capability to the smart city.

The Delphi method is an iterative process of gathering experts’ opinions and assumes that the opinion of a group of experts is more accurate than the opinion of a single individual [45]. The process ends when it reaches the predefined level of consensus or completes the predefined number of rounds [46]. The Delphi method has been successfully used to evaluate the identification and development of concepts within theoretical frameworks such as the maturity model [39,40,41,47]. Figure 2 shows the steps of the Delphi method.

3.3.1. Clustering of Questions

To avoid confusing the panelists, the questions were formulated clearly and concisely and fitted the study’s purpose. As shown in

Table 4. Focus area clusters for interviews.

Cluster No.	Focus Area Group	Focus Area
1	ICT	ICT infrastructure, Digital Transformation, Data
2	Economy	Labor Market, Entrepreneurship
3	Environment	Pollution, Environmental Management
4	Social	Education
5	Social	Social Equity, Citizen Active Lifestyle
6	Resources	Water Resources Management, Energy Sustainability
7	Services	Urban Planning and Management
8	Services	Healthcare System
9	Services	Transport System
10	Services	Safety and Security
11	Governance	Political Structure
12	Governance	Strategic Planning

, the focus areas were grouped into twelve clusters, and an online tool was used to create and publish each cluster’s interview questions online through a secure link shared with the panelists during the interview for better accessibility.

3.3.2. Panel Selection

Panel selection is essential to data credibility; an improperly selected panel may provide misleading information. In addition, choosing unmotivated panelists can result in a low response rate. The panelists were chosen based on their knowledge and extensive experience, which ranged from 8 to 41 years. In addition, to ensure a range of perspectives, panelists with diverse backgrounds and professional experiences were selected. The experts worked in government agencies, the private sector, universities, UN agencies, and NGOs and held jobs such as managers, professionals, CEOs, researchers, and lecturers. The snowball approach was used [49], asking panelists to recommend other potential participants.

3.3.3. Interview Process

The data were collected in two rounds [39]. Five panelists were assigned to each cluster (Table 4) to answer interview questions related to their area of expertise. Each interview began by explaining the study and the questions. The panelist was asked to either answer the questions during the interview or return responses (based on the panelist’s preference) within a week using the link provided. If the panelist chose to send answers later, a supplemental document describing the model components was sent to the panelist along with the link.

Out of the 86 panelists who were approached to participate in the interviews, 60 participated and actively contributed to the interviews by giving their opinions and feedback in two rounds. The data were extracted from the online tool into Microsoft Excel and then downloaded into SPSS for analysis.

In the first round, the panelists were presented with four questions about their agreement with the definitions of the focus areas and capabilities. They were asked to suggest modifications when applicable, add any missing capabilities to the model, and identify any overlap or dependencies between the focus areas/capabilities.

After finalizing the focus areas and capabilities, a second round was conducted in which the panelists were asked to agree on all capabilities and practices and rate the importance of different focus area groups, focus areas, and capabilities.

3.3.4. Reliability and Validity

The following aspects of the Delphi method ensured the reliability and validity of the Delphi method:

• The selected experts were knowledgeable, experienced, and representative of the related domain. In addition, they were willing and able to participate in the Delphi process. The number and diversity of experts were sufficient to ensure a balanced and comprehensive perspective on the topic;
• The design of the questionnaire avoided leading, ambiguous, or biased questions. The experts had space to comment and give open-ended responses and comments. The questionnaire was pilot tested and refined before being administered to the panel;
• The data were analyzed using appropriate statistical methods with criteria to measure the level of agreement among the experts. The results were presented in a transparent and systematic matter that highlighted the main findings and areas of disagreement.

4. The Final Smart City Maturity Model

After the expert review and validation, the final proposed model included eighteen focus areas belonging to seven groups: ICT, economy, environment, social, resources, services, and governance. Each focus area was classified into two to four capabilities, depending on the nature of the focus area. These focus areas and capabilities were compiled to form the smart city maturity model. Cities can use this model to obtain a clear picture of the sectors they should focus on to improve smart city adoption. Figure 3 provides an overview of the model’s architecture.

The smart city maturity matrix is available in Appendix B, which describes the five levels of maturity of each model component. The matrix also lists all practices. However, more details are included in the full model about each capability. To illustrate the concept, a list of the practices and related capabilities that are under the ICT focus area group are provided in Appendix C. Below, in Figure 4, is the capability connectivity in the focus area ICT infrastructure in:

• Focus Area Name;
• Capability Name;
• Practice Code: A unique identifier that comprises three parts: the first part is the focus area number, the second part is the capability number, and the last part is the number of the practice within the capability;
• Practice Name;
• Practice Description;

5. Discussion

The findings of this study indicate that the smart city maturity model involves various components. Accurately defining the model components provides a structured framework for the maturity model and ensures its ability to provide an effective assessment of smart city maturity. The components were initially extracted from the literature on smart city assessment frameworks and validated by experts using the Delphi method, as explained in Section 3 and Section 4. The final key components, or focus areas, of the smart city maturity model are ICT infrastructure, digital transformation, data, labor market, entrepreneurship, pollution control, environmental management, social equity, citizen active lifestyle, water resources management, sustainable and efficient energy, urban planning and management, healthcare system, transport system, safety and security, political structure, and strategic planning. Measuring these focus areas using the developed maturity model will ensure the effectiveness of the assessment.

The model was built and evaluated using techniques and guidelines from the literature. The use of the Delphi method during model development strengthened the model’s foundation and grounded the model in theory and practice.

The developed model is based on the concept of dividing the functional domains into a number of focus areas, which provides a structural approach that can guide the city in adopting a phased development approach, focusing on incremental improvements and prioritizing investments based on assessment. Breaking down the process based on the maturity level of each capability will improve resource allocation and allow the city to focus on priorities. Over time, cities will be able to measure their progress and demonstrate their achievements.

Furthermore, the model facilitates knowledge transfer by enabling benchmarking and sharing best practices based on the assessment results. Additionally, the model can support capacity building by identifying gaps and areas for improvements.

The findings of this study have several implications for future research since there is no globally accepted definition of a smart city. The developed smart city maturity model contributes to a better understanding of what constitutes a smart city and how it can be measured. It shows how to build an effective maturity model that can effectively contribute to achieving successful digital transformation. The developed model opens new research opportunities and can be used as a basis to investigate the impact of smart city initiatives on different areas related to city development, such as sustainability, economic development, and culture. In addition, the developed model is the most comprehensive with respect to the inclusion of focus areas and has been empirically validated. The model bridges gaps in the literature and contributes to the smart city field.

Similarly, the smart city maturity model has implications for practice; governments around the world can use the proposed model to influence smart city implementation. The decision makers and planners in government can benefit from the model to set priorities and adopt and evaluate digital transformation strategies. The model not only identifies challenges but also highlights strengths and best practices. Consequently, it can be replicated in other areas or shared with other cities. Additionally, the success of smart city adoption relies on the engagement of stakeholders and the collaboration between them, and the model can be used to explore the roles of different stakeholders, such as officials, decision makers, and citizens. Establishing priorities will clarify the roles of stakeholders. Finally, the proposed model can be used to accelerate the progress of cities in sustainable development and achieving the SDGs. The practices in the model align with all of the SDGs.

Overall, the smart city maturity model offers a structured and holistic approach to the development of smart cities. Utilizing the model’s recommendations can navigate the complexities of smart city development and provide a roadmap to successful smart initiatives.

6. Conclusions

This research aimed to develop a maturity model to support cities in assessing smart city adoption. The study started with a review of models in the literature to extract the main focus areas. These areas were then used to develop the model using the focus area maturity model development method. This method allowed interconnected domains within a smart city to be measured. In addition, the focus areas technique facilitated the fragmentation of domains into capabilities, thereby improving the accessibility of the model and enabling cities to concentrate on areas of improvement. To validate the model’s correctness and completeness, the Delphi method was used to solicit feedback from sixty expert panelists from multiple sectors. The model was then amended based on quantitative analysis of the feedback.

This study faced three main challenges. The first was the stakeholder engagement, since developing this model required input from a wide range of stakeholders. Ensuring the involvement of all stakeholders was challenging. The second was the data availability challenge; building a such model demands access to data about the city from different angles. Some data were difficult to obtain either due to unavailability or sensitivity. The third was the heterogeneity of cities; differences in the sizes, populations, social lives, economies, and priorities of cities made it challenging to develop one model that could be applied to all cities. These challenges notwithstanding, the developed model is a significant contribution to the assessment of smart city maturity.

This research is the first attempt to develop a comprehensive maturity model for assessing smart city implementation using focus areas. The topic of smart cities is vast and constantly changing, thus any model of smart city maturity must be able to evolve. Potential avenues for future research include the deployment of the model in multiple cities; then, the data can be analyzed and used to improve the model. Additionally, during the interviews, the majority of the experts expressed interest in using the output of the maturity model assessment to improve their internal processes. Continued collaboration with government and private entities would allow the model’s findings to be put into action.