Urban Stakeholders for Sustainable and Smart Cities: An Innovative Identification and Management Methodology

Highlights

What are the main findings?

• A new methodology for developing and monitoring stakeholder identification processes under the sustainable and smart cities model, including an index that reflects the degree of coverage and homogeneity of the process.
• The methodology considers the holistic nature of the smart city, taking into account the linkages between the different urban stakeholders and city dimensions in order to define the involvement of the groups identified and apply multi-criteria analysis for management aspects.

What is the implication of the main finding?

• The significance of urban stakeholder participation and engagement in smart city projects is emphasized in leading research. The methodology proposed in this paper addresses the existing gap by providing practical and concrete guidelines to ensure effective participation.
• The proposed methodology offers a new framework for the development of urban stakeholder identification processes in their early stages or the monitoring and evaluation of ongoing or completed processes as a tool for urban planners and smart city policymakers.

Abstract

The global challenges that cities must face regarding sustainability, efficiency, integration, and resilience have found in the smart city concept a guideline of action as a model for urban development and transformation. The multidimensional nature of the smart city, along with the importance of identifying key urban stakeholders and ensuring their engagement, are two widely recognized characteristics within the scientific community. However, proposals for the identification, classification, and management of urban stakeholders are very scarce and almost non-existent when considered in conjunction with the holistic nature of smart cities. Thus, the significant importance attributed to stakeholder engagement contrasts with the lack of clear guidelines to develop it properly. Based on an iterative analysis of the scientific literature combined with the cross-referencing of smart city dimensions, statistical analysis tools, and multi-criteria analysis methods, this paper proposes a new methodology for the identification and management of urban stakeholders. The proposal includes a comprehensive classification and a new framework for developing urban stakeholder identification processes at their early stages or the monitoring and assessment of ongoing or completed processes, including tools for analyzing the extent and homogeneity achieved. The practical application of the methodology to a specific case study is also discussed.

1. Introduction

The smart city concept is now widely accepted as a tool for urban transformation to address the global challenges of sustainability, quality of life, and efficiency [1]. This model has undergone an evolution over the last few years, from the initial conception mainly based on the use of technology [2] to a holistic concept focused on urban demand (citizen) considering the different dimensions of the city [3].

In recent years, there has been a significant proliferation of assessment models based on the Smart City concept, covering different areas and with different scopes and evaluation philosophies: quantitative, qualitative, based on systems theory [4], oriented to the development of rankings [5], maturity models [6], and aimed at the evaluation of specific smart city projects and initiatives [7].

Since the work of Giffinger et al. in 2007, “Ranking of European Medium-sized Cities”, in which a qualitative model for evaluation of medium-sized European cities was set to rank them [8,9,10,11], the six dimensions of the smart city have been established, and in the vast majority of subsequent conceptual models, are generally accepted by the scientific community as the basis for holistic Smart City models. These are Economy, Human Capital, Governance, Mobility, Environment, and Quality of Life. They are also adopted by the European Commission in the report “Mapping Smart Cities in the E.U.” as the basis for holistic city dimensioning [12]. Multidimensionality is, therefore, a widely recognized characteristic within the scientific community in the approach to strategies and the evaluation of transformation processes inherent in the smart city model.

Additionally, the essential character of the involvement, collaboration, and commitment to the decision making of the agents involved in the urban environment, as a critical part of the transformation towards a smart city [13], is also widely available in the scientific literature [14,15,16,17]. The above-mentioned report of the European Commission, “Mapping Smart Cities in the E.U.” actually incorporates the term stakeholder even in the definition of Smart City, conceiving this as a multi-stakeholder municipality-based partnership and considering the involvement of citizens, representatives, and local businesses as a fundamental factor in the development and success of initiatives in smart cities [12]. Other authors highlight the relevance of stakeholders, even incorporating them as an integral part of their conceptual models [1,4,18,19] and granting a central role to citizens [3,7,20,21,22]. In any case, it is deemed essential that smart city initiatives adopt a combination of top-down and bottom-up approaches [23], as participatory approaches increase the sense of ownership within the community [24].

The direct participation of key urban stakeholders has been considered a fundamental step developed throughout the process within the methodology of modeling the urban environment and strategic planning, bringing together technologists, urban planners, and key urban stakeholders to reflect the diversity of the city [4]. The involvement of urban stakeholders should occur at various degrees and stages of the smart city development [25], but the identification processes of these stakeholders correspond to the initial phases of the transformation strategies for cities. They serve as a starting point for achieving the necessary engagement in developing smart city initiatives. These identification processes should primarily be carried out by the city’s governance sector [26], and complex frameworks may pose an additional challenge [21] in a process that should be straightforward and will have a significant impact on the outcome of smart city initiatives. In fact, engagement of stakeholders is one of the aspects specifically analyzed in recent critical reviews of smart city models, identifying that it deserves particularly further attention [23], as urban stakeholders are not perceived as objects of observation but as active participants in the processes of urban transformation [27]. To ensure effective stakeholder engagement in smart city projects, it is essential to develop a clear engagement plan that incorporates co-design and co-production strategies [28].

The prevailing notion in the current scientific literature emphasizes the holistic nature of cities and highlights the significance of engaging urban stakeholders in the context of smart cities. These two characteristics are commonly acknowledged and are prevalent in most approaches. However, methods for the identification, classification, and management of urban stakeholders for smart cities are very scarce and almost non-existent when considering both aspects together; that is, in conjunction with the holistic nature of smart cities. Consequently, a discernible gap is detected within the existing literature pertaining to the methodology of stakeholder identification processes.

The relevance of this work lies in addressing this gap: the need to establish a methodology for the identification and management process of key urban stakeholders, integrating the holistic nature of smart cities for its use in strategies and assessment models for sustainable and smart cities. The developed framework offers practical and actionable guidelines for implementing urban stakeholder identification processes, along with tools for monitoring and evaluating their progress at intermediate stages or upon completion, including an index to measure the degree of coverage and uniformity in these processes. The application of the Analytical Hierarchy Process (AHP) method is proposed to effectively manage and process data collected from the various stakeholder categories. The aim is to assist city administrators, urban planning officials, and urban planners in avoiding the initial mistake of insufficient consideration of urban stakeholders, which could undermine and constrain the proper subsequent development of smart city initiatives.

The article includes an initial section describing the methods followed across its different phases: a preliminary stage and three subsequent stages. The next section details the results and discussion derived from each stage. Based on the review of the scientific literature selected during the preliminary stage, an iterative analysis with various objectives, combined with the cross-referencing of smart city dimensions, results in a detailed classification of urban stakeholders organized into three levels: groups, subgroups, and typologies. This classification provides the identification framework, which, together with statistical analysis tools and data processing methods, constitutes the proposed methodology. The last two sections include the application of the methodology to a specific case study (the city of Alcoy in the Valencian Community, Spain) and conclusions and aspects to consider in future research.

2. Methods

This research has been conducted in four stages: a preliminary phase followed by three iterative phases based on the findings of the preliminary phase, concluding with a final application to a case study (Figure 1):

Preliminary stage: A systematic literature review is conducted to identify theories and conceptual models related to stakeholders in an urban environment utilizing the following scientific databases:

• Web of Science (WoS), https://apps.webofknowledge.com/. Accessed on 23 February 2023 and 4 November 2024.
• Scopus, https://www.scopus.com/. Accessed on 23 February 2023 and 4 November 2024.

This study employed a Systematic Literature Review (SLR) to ensure a rigorous, transparent, and replicable approach to identifying and analyzing relevant scientific literature. The SLR methodology was chosen due to its ability to synthesize existing research, minimize bias, and provide a comprehensive overview of the state of knowledge in each field [29]. By systematically collecting and analyzing scholarly publications, this approach facilitates the identification of key themes, trends, and research gaps related to urban stakeholders in smart cities. WoS and Scopus databases were selected for data retrieval due to their extensive coverage of peer-reviewed scientific literature and their frequent use in bibliometric and systematic reviews. The combined use of Scopus and WoS in a systematic review ensures broad and high-quality coverage of the academic literature.

The search terms were designed to capture key concepts related to urban stakeholders while maintaining a balance between comprehensiveness and specificity. All documents have been included: journals, conference proceedings, books, and reports. The selected keywords are as follows:

• Urban stakeholders.
• Smart city stakeholders.
• Stakeholders identification.
• Stakeholders classification.
• Stakeholders management.
• Smart cities conceptual models
• Smart cities assessment models.

Results have been analyzed: the goal of this preliminary stage is to identify research that includes urban stakeholders under the smart city paradigm and to determine if a defined description, identification, or classification process is present. The selection criteria consider the relevance of stakeholders in these models, whether they are included or not, and if so, how they have been integrated.

A total of 31 reports, articles, and research papers related to models and development of smart city strategies and initiatives have been selected (

Table 1. Works selected in the preliminary stage. Own elaboration from the literature review.

Authors	Year	Title	Ref	Summary
Fernandez-Anez, V., Fernandez-Güell J.M., Giffinger R.	2018	Smart City implementation and discourses: An integrated conceptual model. The case of Vienna	[1]	Conceptual model for smart cities.
Marrone, M., Hammerle, M.	2018	A Review and Analysis of Stakeholders’ Literature	[3]	Review and analysis of stakeholders in smart cities.
Fernández-Güell, J. M., Collado-Lara, M., Guzmán-Araña, S., & Fernández-Añez, V.	2016	Incorporating a Systemic and Foresight Approach into Smart City Initiatives: The Case of Spanish Cities.	[4]	Conceptual model for smart city initiatives with a systemic approach.
Aljowder T, Ali M, Kurnia	2023	Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model.	[6]	Smart cities maturity model.
Monzon, A.	2015	Smart Cities Concept and Challenges: Bases for the Assessment of Smart City Projects.	[7]	First-year outcomes of the ASCIMER project.
Manville, C.; Cochrane, G., Cave, J., Millard, J., Pederson, J.K., Thaarup, R.K.; Liebe, A.; Wissner, M; Massik, R.; Kotterink, B.	2014	Mapping Smart Cities in the EU.	[12]	Report of the European Commission on smart cities.
Nam, T., & Pardo, T. A.	2011	Conceptualizing smart city with dimensions of technology, people, and institutions.	[13]	Analysis of strategic principles related to smart cities, including a conceptual model.
Fernandez-Anez, V.	2016	Stakeholders Approach to Smart Cities: A Survey on Smart City Definitions.	[14]	Research on stakeholders in smart cities.
Gracias, J.S.; Parnell, G.S.; Specking, E.; Pohl, E.A.; Buchanan, R.	2023	Smart Cities A Structured Literature Review.	[15]	Literature review of smart cities.
Lacson JJ, Lidasan HS, Spay Putri Ayuningtyas V, Feliscuzo L, Malongo JH, Lactuan NJ, Bokingkito P Jr., Velasco LC.	2023	Smart City Assessment in Developing Economies: A Scoping Review	[16]	Literature review of smart cities in developing economies.
Duc Le-Nguyen, C.	2020	Stakeholder Engagement in Smart Cities.	[17]	Maturity model for stakeholder engagement in smart cities.
Castelnovo, W., Misuraca, G., & Savoldelli, A.	2016	Smart Cities Governance: The Need for a Holistic Approach to Assessing Urban Participatory Policy Making.	[18]	Holistic assessment framework for smart cities
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T., Hans. H.J.	2012	Understanding Smart Cities: An Integrative Framework.	[19]	Conceptual model for smart cities.
Fernández Áñez, M. V.	2019	Smart Cities: Implementation vs. Discourses.	[20]	Doctoral thesis based on a conceptual model for smart cities.
ASCIMER	2014–2017	Assessing Smart City Initiatives for the Mediterranean Region	[21,30,31,32]	Assessment model for smart city initiatives in the Mediterranean Region, developed for EIB.
Sharifi, A.	2019	Smart Cities: Implementation vs. Discourses.	[23]	Review and critical analysis of smart city assessment tolls.
Khan HH, Malik MN, Zafar R, et al.	2020	Challenges for sustainable smart city development: A conceptual framework	[25]	Study on challenges associated with sustainable smart cities.
Ruhlandt, R.	2018	The governance of smart cities: A systematic literature review.	[26]	Study on smart city governance.
Leydesdorff, L., & Deakin, M.	2010	The Triple Helix Model and the Meta-Stabilization of Urban Technologies in Smart Cities.	[33]	Model specifically related to stakeholders.
Lombardi, P., Giordano, S., Caragliu, A., Del Bo, C., Deakin, M., Nijkamp, P., & Kourtit, K.	2011	An advanced triple-helix network model for smart cities performance.	[34]	Model for smart city performance based on the triple helix network model.
Lombardi, P., Giordano, S., Farouh, H., & Yousef, W.	2012	Modelling the smart city performance.	[35]	Model for smart city performance specifically related to stakeholders and indicators.
Fernandez-Anez, V., Velazquez, G., Perez-Prada, F., & Monzón, A.	2018	Smart City Projects Assessment Matrix: Connecting Challenges and Actions in the Mediterranean Region.	[36]	Paper related to ASCIMER’s project assessment method.
Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F.	2014	Current trends in smart city initiatives: Some stylized facts.	[37]	Model applied to the analysis of smart city initiatives.
Jayasena N.S., Mallawaarachchi, H., Waidyasekara, K.G.A.S.	2018	Stakeholder Analysis for Smart City Development Project: An Extensive Literature Review.	[38]	Analysis developed specifically for stakeholders in smart cities.
Moreno-Alonso, C.	2016	Development of a model for evaluating cities based on the smart city concept.	[39]	Doctoral thesis based on assessment of a model for smart cities.
Mayangsari, L., Novani, S.	2015	Multi-stakeholders co-creation analysis in smart city management: an experience from Bandung, Indonesia.	[40]	Management of stakeholders in smart cities.
Berrone P, Ricart JE, Duch AI, Bernardo V, Salvador J, Piedra Peña J, Rodríguez Planas M.	2019	EASIER: An Evaluation Model for Public–Private Partnerships Contributing to the Sustainable Development Goals.	[41]	Assessment model for public–private partnership in sustainable development goals.
Castellani, P., Rossato, C., Giaretta, E., Vargas-Sánchez, A.	2024	Partner selection strategies of SMEs for reaching the Sustainable Development Goals.	[42]	Strategies for SMEs and sustainable development goals.
City keys	2017	CITYkeys indicators for smart city projects and smart cities.	[43]	Indicator for smart city projects and smart cities.
Albrechts, L.	2004	L. Strategic (spatial) planning reexamined.	[44]	General approach to urban stakeholders.
Scottish cities alliance.	2014	Smart Cities Maturity Model and Self-Assessment Tool	[45]	Smart cities maturity model.

).

Stage 1: Based on the results from the preliminary stage, the methodologies used for stakeholder identification are analyzed. A thorough examination of each theory and methodology identified is conducted. This analysis includes an examination of the definitions of urban stakeholders and the theoretical basis for their description and classification. The inclusion and influence of stakeholders in urban models and the basic principles of classification are studied. In addition, the role and influence of stakeholders in urban models, as well as the fundamental principles guiding their classification, are explored.

As a result of this first stage, an initial general classification of urban stakeholders was obtained, divided into broad groups, and a basic structure was formed.

Stage 2: Based on the groups of the basic structure identified in Stage 1, two approaches are implemented:

• A new and detailed analysis of the classifications from the selected works from the preliminary stage, and
• A cross-referencing process with the dimensions of the smart city.

The objective is to enhance the level of detail in the classification obtained after Stage 1 while also considering the holistic nature of the smart city in classifying urban stakeholders. Additionally, we aim to identify urban stakeholders within each of the main groups related to the smart city dimensions. The result is a comprehensive classification of stakeholders with a breakdown of the identified groups of the basic structure into more detailed subgroups.

Stage 3: A third analysis of the scientific literature selected at the preliminary stage is conducted. This time, the focus is on the management of urban stakeholders. The analysis identifies the relationships and involvement of various agents, their interests and objectives, their contributions, their degree of participation in the process, and how their involvement in transformation processes is perceived.

This analysis compares the results with the classification obtained in the previous stage to ensure that the typologies of stakeholders are properly represented in relation to the thematic areas related to the smart city dimensions. We examine the relationships between stakeholder subgroups and each dimension, identifying stakeholders that have a direct relationship with specific dimensions and evaluating the level of expertise of each stakeholder subgroup in relation to each smart city dimension. As a result, we obtain a stakeholder identification framework based on subgroup and dimension.

The statistical analysis of data on the presence of different stakeholder typologies within the identification framework provides tools for evaluating identification processes. Although the methods used to develop the identification framework are qualitative, this analysis will allow us to obtain quantitative results for the identification processes to be examined. This analysis leads to the definition of an index that captures the degree of stakeholder typology coverage and the level of homogeneity achieved throughout the process.

At this point, management aspects related to the treatment of data collected from urban stakeholders are also addressed, considering the nature of the stakeholder group. Multi-criteria decision tools, specifically the use of the Analytic Hierarchy Process (AHP) method [46], are presented as a method for analyzing these aspects.

In this case, the application scheme has the following elements:

• Goal: Establishing the weight to be assigned to each stakeholder category identified in the processing of data collected from consultations.
• Criteria: The different thematic areas where stakeholder opinions will be taken into account.
• Alternatives: The different categories of stakeholders to be considered.

Once the scheme is defined, the first step is the pairwise comparison of the criteria in order to establish the weight of each criterion. The comparison between pairs of criteria is conducted using the Saaty scale [47], giving a value between 1 and 9 according to the relative importance of each criterion compared to another.

The results are used to construct the comparison matrix (A) between the criteria:

$$A=\left[\begin{array}{ccc}1& \cdots & a_{1\mathrm{n}}\\ ⋮& \ddots & ⋮\\ {\displaystyle \frac{1}{a_{1\mathrm{n}}}}& \cdots & 1\end{array}\right]$$

(1)

where $a_{ij}$are the values of the comparison between the pairs of criteria i and j (value 1 in the case of a criterion against itself and $a_{ij}$ = 1/$a_{ji}$ in case of reciprocal comparisons), and n is the number of criteria.

This matrix must be checked for consistency by calculating CI (consistency index) and CR (consistency ratio) according to the following expressions:

$$CI={\displaystyle \frac{{\lambda }_{max}-\mathrm{n}}{\mathrm{n}-1}}$$

(2)

$$CR={\displaystyle \frac{CI}{RI}}$$

(3)

where ${\lambda }_{max}$ is the maximum eigenvalue, n is the size of the matrix and RI is the consistency index of a random matrix of the same size. CR must be less than 0.1 for an adequate degree of consistency in the pairwise comparison process.

Once the consistency is verified, the eigenvector or preference vector is obtained according to the following expression:

$$A w_c={\lambda }_{ max}{ w}_c$$

(4)

where A represents the comparison matrix (1) and $w_c$ is its eigenvector or preference criterion vector. The vector $w_c$ contains the weight of each criterion.

In the same way, the different alternatives are compared according to each of the criteria. In this case, the groups or typologies of stakeholders considered as alternatives are compared pairwise according to each of the criteria. In our case, this process is carried out with the support of consultations with an expert panel. A comparison matrix between alternatives is obtained for each criterion (matrixes A1, A2, … as many matrixes as criteria) and proceeding in the same way in the calculation of eigenvalues, eigenvectors, and consistency, vector of weights of the alternative by criterion ($w_{a1}$, $w_{a2}, \dots$) is obtained.

With all these vectors of weights by columns, a matrix of alternatives by criteria ($W_a$) is constructed, which will have as many rows as alternatives and as many columns as criteria. This matrix provides us with the weight of each stakeholder category to be considered for each criterion and, therefore, an important input for data processing.

The multiplication of this matrix by the vector of criteria will give us the result of the weight of each alternative, considering all the criteria ($w$).

$$W_{a } w_c=w$$

(5)

These three elements—the framework for the identification processes, the analytical tools for these processes based on that framework, and the application of multi-criteria methods for processing data collected from urban stakeholders—make up the outcome of this stage.

Case study: As an example of the practical application, the classification developed in this study is applied to a case study of Alcoy, a city in the Valencian Region of Spain. This process begins with an initial analysis of the database used for its Smart City Master Plan, which is then reclassified according to the methodology established in this research. Once analyzed according to the framework developed, it is completed with the guidelines of the developed methodology in order to try to improve it. To address the gaps identified in the case study, we conduct a search for urban stakeholders within the city according to the typologies identified in the framework. Finally, a comparative study is performed to evaluate the effectiveness of the stakeholder identification process both before and after applying the developed methodology.

3. Results and Discussion

3.1. Basic Structure for Identification of Urban Stakeholders

The most common definition of “stakeholder” is an interested party. Stakeholders are any group or individuals who can affect or are affected by achieving an organization’s objectives [48]. In the case of the transformation processes of an urban environment, the very definition of stakeholders leads us to an enormous range of possible urban agents to be considered in the modeling of the city, as Freeman’s work shows, especially if the holistic conception of the smart city itself is established as a starting point. Identification processes are, therefore, complex and, in practice, tend to be somehow iterative processes in which additional stakeholders are added as the process itself develops [49]. In any case, the processes of strategic urban planning and city modeling are sufficiently complex [44] and have different characteristics from those considered in business theories, so their identification processes are also different.

It is a common practice to understand the city as an “organic whole”, a network with numerous interconnected links [13], or from a more technical point of view, as an organic system with multiple interlinked subsystems [19]. The need to understand local factors and the self-identity of the urban community is related to the integration of urban actors in the transformation processes [34,37]. Additionally, the holistic approach required in the transformation under the smart city model can only be obtained by connecting the different agents involved in the urban environment and including all possible points of view in the planning processes in which policymakers, ultimately governance, have a fundamental role and is a necessary premise to achieve citizen involvement [14,20].

The importance of the relationships between urban stakeholders in the urban sphere and their involvement in decision-making processes and even in governance reaches the point of being taken as an essential part of some models, even as a dimension on its own. Castelnovo emphasizes the involvement of stakeholders and the quality of their relationships [18]: this aspect is central to the city’s model, which establishes connections with developing strategies, creation of public value, financial and economic sustainability, and resource and knowledge management.

Leydesdorff and Deakin [33] examine the interplay between government, university, and business in driving knowledge and innovation within the framework of the triple helix model. They explore the creation of value through innovation in urban settings using this model. The researchers emphasize the crucial role of strong city leadership in fostering collaboration among urban stakeholders. They also assert that cultural development is not spontaneous but rather a result of local government policies, academic leadership, and business strategies that must be coordinated and leveraged to foster urban regeneration [33].

The triple helix model, with the three agents involved, is conducive to the study of knowledge-based innovation systems. Lombardi et al. [34] completed it by introducing a fourth line, the urban market, i.e., urban demand: since the three triple helix actors determine and create knowledge and innovation, their use and accumulation are fostered by the interaction with the local market potential and contour conditions, so a strong social and intellectual base is necessary [34]. Therefore, adding a fourth line to the urban actors is essential (Figure 2).

Recent models emphasize governance, a central view of citizenship, and the essential involvement of urban stakeholders within the framework itself. Fernández Añez [20] identifies the main urban stakeholders in four groups, following the modified triple helix model:

• Knowledge stakeholders.
• Social stakeholders.
• Economic stakeholders.
• Political stakeholders.

In the implementation of the model in the cities of Vienna, Milan, and Barcelona, the selection method consisted of covering all groups of stakeholders involved, related to the six dimensions of the city and to the transversal subsystems of planning and technology (at least one representative in each dimension and subsystem) [20].

Ligorio et al. [50], in an analysis of the sustainable city concept using the institutional work theory, state that sustainable cities, conceptualized as smart cities, are realized through the interaction of the three main institutional works: political, cultural, and technical. In this case, local governments set standards and monitor sustainable practices, citizen involvement drives change and technical work, and, through integrated knowledge of the actors and professionals in the city, enables transformation. In this way, the development of a sustainable city requires the collaboration of different stakeholders in a complex system considering the political, technical, and cultural dimensions [50]. This is a different approach but certainly not contradictory to the extended triple helix model.

The evaluation model for smart city initiatives and projects developed under the ASCIMER project [30,31,32] also positions the main stakeholders at the core of the model, similar to previous models and authors [1,8,18,19]. In this case, two primary groups of stakeholders are considered: internal and external. For internal stakeholders, while the model does not explicitly reference the extended triple helix model, its stakeholder classification aligns with its guidelines, considering groups such as Municipal government, Local Economic Agents, Local Social Agents, and Local R&D&I [21].

Several studies that adhere to the nomenclature of the extended triple helix model include those conducted by Marrone and Hammerle [3], Duc Le-Nguyen [17], and Mayangsari and Novani [40]. Additionally, the analysis identifies models that, although they do not fit neatly into the four main categories outlined, contain stakeholders who can be reclassified into these groups. An example of such a study is by Fernández-Güell et al. [4]. Other models and studies do not adhere to the extended triple helix distribution either. However, all stakeholders identified in these studies can be reclassified into the four main groups, though one or more of the groups may be omitted.

As a conclusion of the analysis aimed at establishing an initial basic structure, the relevance of incorporating stakeholders into the conceptual models of smart cities in the implemented research, considering their influence, opinions, and involvement in smart city policies in urban centers, has been shown to be critical. Following the systematic literature review conducted, the extended triple helix model emerges as a suitable overarching framework for the classification of stakeholders according to smart city models. To enhance the clarity of application to modern urban environments under the model of a smart city and following the trend of some of the models analyzed, simple nomenclature modifications are made to Lombardi’s model in the classification of the major stakeholder groups (see Figure 2). Thus, the University group is more generally referred to as Knowledge and Innovation, the Government group is referred to as Political and Public Administration, the Civil Society group is simply referred to as Social, and the Industry group is referred to as Economic and Financial (Figure 3).

3.2. Detailed Classification of Stakeholders Reflecting the Holistic Character of the Smart City

After establishing the basic structure for classifying urban stakeholders, a practical methodology needs to be developed. This methodology should provide clear guidelines for the identification processes of stakeholders in smart cities and define a classification with a higher grade of detail.

3.2.1. Analysis of Detailed Classification of Urban Stakeholders

Several models analyzed in the previous stage provide more detailed classifications of stakeholders. ASCIMER project [21], in addition to the mentioned internal stakeholders, identifies six groups as external stakeholders:

• Financial institutions.
• Involved companies and clusters.
• Innovation and research institutions.
• Political institutions.
• Networking institutions.
• Social organizations.

In the case of the model developed by Fernandez-Guell et al., a survey is conducted with a series of interviews on attitudes towards the proposed model. The following stakeholders are considered [4]:

• City councils.
• Municipal departments in charge of smart city initiatives.
• Urban services managers.
• Autonomous administration in the field of transport.
• State administration in charge of urban policies.
• University research centers related to smart cities.
• Telecommunication operators.
• Consultants related to telecommunication infrastructure in smart cities.
• Internet service companies related to smart cities.

The identified stakeholders can be subscribed within the categories established by the basic structure, covering three of the four, with the civil society group remaining unrepresented in this case.

Other models also consider the participation of stakeholders, including some classification to identify them and their involvement processes. In Moreno Alonso’s model [39], a smart city is considered an innovative environment that requires the participation of multiple stakeholders and arouses the interest of the business world. The following stakeholders are considered:

• Local administration, politicians, and city managers.
• Citizens and local businesses.
• Public and private municipal service providers.
• Investors: private banking, venture capital, funds, etc.
• Providers of technological and financial solutions.

In this case, the unrepresented aspect of the extended triple helix model concerns knowledge agents.

Other studies also highlight the importance of including the private sector in the achievement of smart and sustainability objectives [34] due to its experience and knowledge, management capacity, and financial resources [41], and more specifically, the consideration of SMEs due to their high specific weight in the urban economic structure [42]. Jayasena, Mallawaarachchi, and Waidayasekara’s research [38] focused on identifying stakeholders in the smart city field, including a more detailed classification of several categories of urban stakeholders with a wider perspective, considering specific agents related to the fields of energy, finance, real estate, and telecommunications, for instance. The following are described:

• Knowledge and research institutions.
• Local and regional administrations.
• Investors and financial institutions.
• Energy supply companies.
• Representatives of the telecommunications sector.
• Citizens.
• Government.
• Real estate developers.
• Non-profit organizations.
• Urban planners.
• Politicians.
• Experts and scientists.
• Political institutions.
• Media.

In this case, the four types of agents involved are covered, as political, economic, societal, and knowledge stakeholders are each represented by at least two groups. This classification is more detailed (as it is a study focused on identification), with a high representation of the political agents’ part and a very specific one in business.

Mayangsari and Novani, with a simpler classification, also identify some key stakeholders in each group of the basic structure [40]:

• Universities.
• Research institutions.
• City mayor.
• Strategic committees.
• Citizens.
• NGOs.
• Information and communication technology (ICT) enterprises.
• Consulting companies.
• Businesses.

As a preliminary subclassification, the result of this first analysis after reclassifying the stakeholder subgroups identified according to the basic structure defined in Figure 3 is shown in

Table 2. Results of a preliminary subclassification of urban stakeholders with subgroups detected after the first analysis. Own elaboration.

Knowledge and Innovation	Political and Public Administration
Research centers related to smart cities	Municipal government
University researchers	Political parties
Research centers not directly related to smart cities	Municipal SC department (technicians)
Consultants related to telecommunications	Municipal technicians, urban services management
Consulting companies.	Public entities at a supra-municipal level
	Urban planners (strategic committees).
Social	Economic and Financial
Citizen groups	Private companies managing urban services
NGOs	Telecommunications operators
Media	Telecommunications services companies related to smart cities.
Social organizations	Local businesses
	National businesses
	Investors and financial entities
	Energy companies
	Real estate development companies
	Networking institutions.
	Clusters

.

3.2.2. Holistic Character: Cross-Referencing with the Dimensions of the Smart City

A preliminary classification has been established. However, to effectively develop the smart city model, we must consider two key concepts: the holistic view of the city as a multidimensional entity and the importance of stakeholders in shaping smart city policies. Therefore, identifying stakeholders should involve cross-referencing them with the city’s holistic nature. This approach entails examining stakeholders within each group to ensure that all defined dimensions are thoroughly addressed.

As previously mentioned, the six dimensions of the smart city established in the work of Giffinger et al. [8,9,10,11] are practically the standard for classifying smart city dimensions in terms of evaluation models. For this study, we will use this classification as a foundation, with nuances established in a previous work of the authors related specifically to an assessment model structure [51]. The considered dimensions with its thematic areas are as follows:

• Economy and Competitiveness: Business and labor innovation, Entrepreneurship, Productivity, and Local–Global interconnectedness.
• Human and Intellectual Capital: Academic and digital training, Creativity, Management and promotion of urban life, and Work flexibility and work–life balance.
• Governance: Transparency and citizen communication channels, E-government and online services, Participation in decision making, and Innovation and efficiency in municipal management.
• Infrastructure and Mobility: Public transport and multimodal network, ICT infrastructures, Urban logistics, and Sustainable mobility.
• Environment and Energy: Energy efficiency, Resource and waste management, Environmental monitoring, and Renewable energy and social awareness.
• Social Well-being, Services, and Tourism: Public social and security services, Tourism, culture and leisure, Social cohesion and inclusion, and Health and welfare.

Building on Table 2 and cross-referencing the identified stakeholder groups with the six dimensions of the smart city, any gaps detected in this intersection are addressed. The aim is to ensure that stakeholders related to all dimensions can be identified within each major stakeholder group, thereby embedding the holistic nature of the classification not only as a whole but also within its individual components. As a consequence, new subgroups or modifications with respect to those detected in the first analysis in Table 2 are added.

In the Knowledge and Innovation group, the stakeholders identified in the analysis of the existing detected subgroups adequately cover the spectrum of city dimensions, so no gap is detected. For example, the university researcher subgroup allows the identification of specialists in each thematic area represented by the dimensions. The only modification to this subgroup compared to Table 2 is the generalization of consultants instead of specifying Telecommunications consultants and Consulting companies as initially indicated. This adjustment ensures a broader perspective of all dimensions within this group.

In the Political and Public Administration group, a clear trend emerges in the subgroups identified so far, with stakeholders clearly linked to governance. However, the holistic nature of subgroups such as Municipal technicians and urban services management allows specialists in each dimension to be identified. Additionally, a gap is detected related to the Social Well-being dimension. The Urban Security and Health Services subgroup is included to strengthen this dimension.

In the Social group, the Unions subgroup is added to provide a broader perspective on aspects of the Economy and Competitiveness and Human and Intellectual Capital dimensions. The Neighborhood and Citizen Associations subgroup is also included to enhance the classification’s representation of citizenship in general aspects of all dimensions, instead of the more generic Social institutions when identifying stakeholders.

In the Economic and Financial group, the subgroups detected in the first analysis cover most of the thematic areas when crossing with the dimensions; however, several gaps are detected, so the following subgroups are added:

• Transportation companies, due to their influence on the Infrastructure and Mobility dimension, particularly concerning urban transport and logistics.
• Professional associations of various types, as their diverse memberships can offer a general perspective across multiple thematic areas, so they cover several thematic areas related to all the dimensions.
• Business associations and Associations of self-employed workers, to provide a more comprehensive vision of urban aspects compared to the previous business representation. These ones and the previous ones are included instead of Networking institutions and Clusters to gain more clarity on the identification framework.
• The previously specific categorization of Telecommunications services companies related to smart cities is broadened and included as general (not only related to smart cities).

With these adjustments, the proposed classification for stakeholder identification processes is reflected in

Table 3. Subgroups of stakeholders identified after cross-referencing with dimensions. Own elaboration.

Stakeholders’ Groups and Subgroups	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism
Knowledge and Innovation
Research centers related to smart cities	Representatives for each thematic area of the dimensions to be identified
University researchers	Representatives for each thematic area of the dimensions to be identified
Research centers not directly related to smart cities	Representatives for each thematic area of the dimensions to be identified
Consultants	Representatives for each thematic area of the dimensions to be identified
Political and Public Administration
Municipal government	Representatives for each thematic area of the dimensions to be identified
Political parties	Representatives for each thematic area of the dimensions to be identified
Municipal SC department (technicians)	Representatives for each thematic area of the dimensions to be identified
Municipal technicians, urban services management	Representatives for each thematic area of the dimensions to be identified
Urban security or health services	Linked to one or several specific dimensions
Public entities at a supra-municipal level	Representatives for each thematic area of the dimensions to be identified
Urban planners	Representatives for each thematic area of the dimensions to be identified
Social
Citizen groups	Impossible to establish specialists due to the nature of the group.
NGOs (Non-Governmental Organizations)	Linked to one or several specific dimensions
Unions	Representatives for each thematic area of the dimensions to be identified
Neighborhood and citizen associations	Impossible to establish specialists due to the nature of the group.
Media	Representatives for each thematic area of the dimensions to be identified
Economic and Financial
Private companies managing urban services	Linked to one or several specific dimensions
Telecommunications operators	Linked to one or several specific dimensions
Telecommunications services companies	Linked to one or several specific dimensions
Local businesses	Representatives for each thematic area of the dimensions to be identified
Transportation companies	Linked to one or several specific dimensions
National businesses	Representatives for each thematic area of the dimensions to be identified
Investors and financial entities	Linked to one or several specific dimensions
Energy companies	Linked to one or several specific dimensions
Real estate development companies	Linked to one or several specific dimensions
Professional associations	Representatives for each thematic area of the dimensions to be identified
Business associations	Linked to one or several specific dimensions
Associations of self-employed workers	Linked to one or several specific dimensions

.

The result of the cross-referencing between stakeholder groups and smart city dimensions, as shown in Table 3, provides a stakeholder identification tool that delivers a highly comprehensive spectrum of the city’s main actors. At this point, for a better understanding, we have to define the nomenclature of each element of the identification framework: Groups are the components of the basic structure, Subgroups are the subdivisions of each group, and Typologies are the parts of the subgroups related to a specific dimension.

Once the subgroups are established, stakeholders related to each of the six dimensions of the model must be identified within them (typologies). If all subgroups had influence in all dimensions, there would be 168 stakeholder typologies as a result of multiplying the number of dimensions (6) by the number of subgroups (28). For example, municipal technicians related to the dimension of economy and competitiveness would be one of these 168 typologies.

Furthermore, additional aspects are detected as there are three additional categories of urban stakeholders: the ones who, by their nature, can be part of multidisciplinary teams and can be identified as specialists in each of the dimensions, and those who do not have that possibility, either because they are focused on specific dimensions or because they are not specialist linked to any dimension. For example, it is possible to associate specialists within Municipal technicians for each dimension based on their respective departments. However, the same cannot be done with Non-governmental organizations or Telecommunications operators, which, by their nature, may be more related to the dimensions of Social well-being and Infrastructure, respectively. Subgroups related to Citizens cannot be linked to any dimension by their own nature, but they deserve special treatment and are analyzed with management aspects.

3.3. Classification and Management of Urban Stakeholders: Identification Framework and SH Index

Managing the stakeholders involved in the process is considered a fundamental activity to achieve the success of urban transformation processes [6,12,38,45]. It is a matter of identifying the planning and development phases of projects, initiatives, and strategies where each stakeholder should intervene more directly. Stakeholders’ engagement in assessment tools is also considered part of smart city planning processes [23]. Therefore, a framework for urban stakeholder identification processes should inherently consider stakeholder management aspects related to smart city dimensions.

3.3.1. Analysis of Aspects Related to the Management of Urban Stakeholders

The involvement of citizens in all stages, especially in those projects based on innovation, is considered fundamental by several authors [22,34,37], specifically to reflect the needs and preferences of citizens and to ensure their sustainability and scalability [15]. The involvement of the largest number of stakeholders in strategic planning processes is also widespread [4,18], especially community participatory and multidisciplinary experts [16]. However, achieving this involvement is related to the interests of the different stakeholders [17]. Additionally, a correct selection of urban stakeholders must consider heterogeneity and representativeness regarding sectoral origin, targets, and interests [38].

Some studies analyze stakeholder management from the perspective of the maturity level of engagement achieved [45]. For example, Duc Le-Nguyen [17] proposes a maturity model for levels of engagement across five stages, focusing on aspects such as leadership, integration, and communication. However, the focus of this analysis is on stakeholder management in relation to the thematic areas of smart cities. In other studies, such as the City Keys collection of indicators for smart city and smart city projects [43], stakeholder engagement is simply measured using a series of indicators for the project itself or for the city as a whole, such as “The extent to which professional stakeholders outside the project team have been involved in planning and execution” or “The extent to which the project team included all relevant experts and stakeholders from the start”. Lombardi et al. [35] include a set of indicators that relate to the extended triple helix model, cross-referencing city dimensions (referred to as clusters in this case and not considering infrastructure and mobility) with the components of the model. However, these components are understood more as elements of the smart city itself rather than as stakeholder types, serving as indicators of performance within the dimensions of each city component.

Fernández-Añez [14] studies the interests of stakeholders in the urban environment based on the definitions of smart cities provided by each entity, considering political, knowledge, and economic stakeholders (excluding social ones due to the lack of available definitions of smart city). This research highlights that human capital is the primary focus in terms of interest for knowledge stakeholders. In contrast, political stakeholders’ main interests are governance and environmental issues at regional and state levels and governance and human capital at a local level. For economic stakeholders, governance, economy, and environment are also significant areas of interest. In terms of objectives, knowledge agents emphasize that improving the quality of life is the main goal to be achieved. Sustainability is the objective for political stakeholders at regional and national levels, and efficiency, sustainability, and quality of life at a local level are the objectives for economic stakeholders [14].

Regarding the expected contributions in theoretical studies of each group of stakeholders involved, according to Jayasena, Mallawaarachchi, and Waidayasekara [38], some aspects must be considered (see

Table 4. Aspects related to stakeholder’s contributions. Own elaboration based on [38].

Stakeholder	Main Aspects to Consider in Contribution
Knowledge agents	Planning and strategy development processes.
Local and regional administrations	Management of administrative, technical, and economic–financial resources.
Investors and financial entities	Financial resources.
Energy supply companies	Support development with a focus on sustainability.
Representatives of the telecommunications sector	Operational aspects of project and initiative deployment.
Citizens	Main agents involved in all areas and the most important phases of the processes possible.
Government	The starting point of the transformation process.
Real estate developers	Detection of conflicts of interest.
Non-profit organizations	Monitoring and results of projects and initiatives.
Urban planners	Planning and strategy development.
Politicians	Transparency and governance.
Experts and scientists	Involvement in the planning process.
Political institutions	Governance
Media	Impact of projects and initiatives, monitoring and results of projects and initiatives.

).

In the work of Fernandez-Guell et al. [4], the approach of the identified stakeholders to smart city initiatives is included. These approaches are mainly focused on technological aspects, but the main thematic areas can be taken as a contribution:

• City councils: sectoral initiatives and best practices among cities.
• Municipal departments in charge of smart city initiatives: technological platforms for integrating smart city public initiatives.
• Urban services managers: technological platforms for integrating service providers and specific initiatives for the provision of urban services.
• Autonomous administration in the field of transport: an integrated approach to all issues related to transport planning and management.
• State administration in charge of urban policies: smart city initiatives that develop integrated urban information systems.
• University research centers related to smart cities: sectoral smart city initiatives and studies related to transport and energy-efficiency issues.
• Telecommunication operators: transversal and open tech platform for integrating various applications.
• Consultants related to telecommunications infrastructure in smart cities. Advanced software and tech platforms for smart city initiatives.
• Internet service companies related to smart cities: internet applications focused on the needs of urban users.

3.3.2. Identification Framework

The analysis carried out allows us to determine the special relationships between the different urban stakeholder subgroups and the dimensions. The detailed classification in Table 3 identified the urban stakeholder subgroups as a general view, indicating whether specialists in each dimension could be detected in each group. This classification can now be completed in terms of the thematic areas to which the respective subgroups are most closely related. The main contributions and interests of stakeholders are included in this more detailed classification, along with their connections to the dimensions of the model. Stakeholder engagement is also facilitated by ensuring that their contributions are directly related to their areas of interest. Therefore, we can build a final identification framework where management aspects are also considered.

This identification framework is shown in

Table 5. Framework for identification processes of urban stakeholders. Own elaboration.

Stakeholders’ Groups and Subgroups	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism	Sum of Coverage	% of Coverage (Ci)
Knowledge and Innovation							24	100%
Research centers related to SC	X	X	X	X	X	X	6	100%
University researchers	X	X	X	X	X	X	6	100%
Research centers not related to SC	X	X	X	X	X	X	6	100%
Consultants	X	X	X	X	X	X	6	100%
Political and Public Administration							35	100%
Municipal government	X	X	X	X	X	X	6	100%
Political parties	X	X	X	X	X	X	6	100%
Municipal SC department	X	X	X	X	X	X	6	100%
Municipal technicians	X	X	X	X	X	X	6	100%
Urban security or health services						X	1	100%
Public entities (supra-municipal level)	X	X	X	X	X	X	6	100%
Urban planners	X		X	X	X		4	100%
Social							22	100%
Citizen groups	X						6
NGOs						X	1	100%
Unions	X	X				X	3	100%
Neighborhood and citizen assoc.	X						6
Media	X	X	X	X	X	X	6	100%
Economic and Financial							34	100%
Private companies: urban services	X			X	X		3	100%
Telecommunications operators	X			X			2	100%
Telecommunications services comp.	X			X			2	100%
Local businesses	X	X	X	X	X	X	6	100%
Transportation companies	X			X			2	100%
National businesses	X	X	X	X	X	X	6	100%
Investors and financial entities	X						1	100%
Energy companies	X				X		2	100%
Real estate development companies	X			X			2	100%
Professional associations	X	X	X	X	X	X	6	100%
Business associations	X						1	100%
Associations self-employed workers	X						1	100%
Sum of coverage	26	16	16	21	18	18	115	100%
% of coverage (Cj)	100%	100%	100%	100%	100%	100%	100%	100%

. In each subgroup, the typologies of stakeholders to be identified for each dimension in a process of identification within a smart city strategy are listed, defining typology as each cross subgroup–dimension identified. An “X” in grey means that this typology of stakeholder is necessary. There are subgroups of urban stakeholders where up to six typologies should be identified, i.e., one for each dimension, and other subgroups where only one typology of stakeholder (related to a single dimension) should be identified. The column ‘Sum’ shows the number of typologies to be identified for each subgroup, and the column ‘% of coverage (Ci)’ is used to monitor the coverage that has been achieved in the process. In the scope of this work, the identification process is considered completed if all needed typologies in Table 5 are covered. Therefore, the analysis proposed by this identification framework is entirely qualitative, focusing on covering specific typologies to ensure that the perspectives of stakeholders related to the dimensions are adequately represented.

However, with the analysis from a management perspective, another singularity is detected related to the engagement of urban stakeholders: it must be defined whether the subgroup is more directly linked to a dimension due to its stakeholder–dimension relationship (for example, Energy companies—Environment and Energy dimension) or if they have a more generalist character, and therefore, no specific dimension is assigned. If a subgroup is directly linked to a main dimension, this is marked with an “X” in a darker color. Subgroups of stakeholders directly linked to specific dimensions will receive special attention in the identification processes.

Special attention is required for the subgroup called “Citizen groups” (and “neighborhood and citizen associations”), which are considered stakeholders within the Social group. Some models consider this group the model’s core [18,20,36], thus playing a key role [22]. Due to its nature, this subgroup cannot be directly linked to any specific dimension, and there will be no specialist in each dimension. However, given its special significance, it is proposed to carry out a sampling that adjusts to the characteristics of the city’s population to ensure representativeness; members should be selected in the same proportions as the demographic data available at the urban level, such as income levels, education levels, age, employment status, neighborhood of residence, etc.

3.3.3. SH Index

From the analysis of an urban stakeholder identification process using Table 5, we can obtain the coverage percentages of each subgroup ${\mathrm{C}}_{\mathrm{i}}$ and each dimension ${\mathrm{C}}_{\mathrm{j}}$ separately. In this table, for each typology (subgroup dimension) in which at least one representative has been identified, a value of 1 is assigned and a value of 0 otherwise. The coverage percentage for each subgroup ${\mathrm{C}}_{\mathrm{i}}$ is calculated based on the value obtained during the identification process as the sum of the typologies within the subgroup relative to the maximum indicated in Table 5 when all needed typologies are identified. For instance, if, in the process, we identify three typologies out of the four required in the subgroup urban planners, its ${\mathrm{C}}_{\mathrm{i}}$ would have a value of 75%. With a simple statistical treatment of these data, we can develop an index that summarises the degree of compliance of an identification process in two aspects: degree of total coverage of the process and homogeneity in the subgroups considered. This index, called SH index (SHI), can be used both for monitoring identification processes and for evaluating a process carried out.

We can calculate the average coverage (6) and the standard deviation of coverage (7) for a given set of subgroups as follows:

$$\overline{\mathrm{C}}={\displaystyle \frac{{\mathsf{\Sigma }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{C}}_{\mathrm{i}}}{\mathrm{n}}}$$

(6)

$${\mathsf{\sigma }}_{\mathrm{C}}=\sqrt{{\displaystyle \frac{{\mathsf{\Sigma }}_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{C}}_{\mathrm{i}}-\overline{\mathrm{C}}\right)}{\mathrm{n}}}}$$

(7)

where ${\mathrm{C}}_{\mathrm{i}}$ is the percentage of coverage for each subgroup and n is the number of considered subgroups (it could be calculated for any set of subgroups).

This defines the SH Index generically as follows:

$$\mathrm{S}\mathrm{H}\mathrm{I}=\mathsf{\alpha }\overline{\mathrm{C}}-\mathsf{\beta }{\mathsf{\sigma }}_{\mathrm{C}}$$

(8)

where α and β are two coefficients that will have values depending on the importance to be assigned to total coverage (value of α) or to homogeneity (value of β). In our case, we take a value of α = 1 and β = 0.5, so we are giving more value to the total coverage.

In order to calculate the SHI of an identification process, the SHI of each stakeholder group must first be calculated separately, i.e., the Knowledge and Innovation group (${\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{K}\mathrm{I}}$), the Political and Public Administration group (${\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{P}\mathrm{P}}$), the Social group (${\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{S}}$), and the Economic and Financial group (${\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{E}\mathrm{F}}$). Once the individual SHI of each of the groups has been obtained, the average of the SHI of each group is the SHI of the identification process (9).

$$\overline{\mathrm{S}\mathrm{H}\mathrm{I}}={\displaystyle \frac{{\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{K}\mathrm{I}}+{\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{P}\mathrm{P}}+{\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{S}}+{\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{E}\mathrm{F}}}{4}}$$

(9)

This ensures that each of the major groups has the same weight regardless of the number of subgroups they have. In the scope of this work and with the assigned α and β values, SHI values close to 1 mean high coverage with good homogeneity (or low dispersion). In the case study, examples of the SHI calculation are developed for two samples of identification processes.

Finally, an SHI based on dimensions can be calculated (${\mathrm{S}\mathrm{H}\mathrm{I}}_{\mathrm{D}\mathrm{I}\mathrm{M}}$). in the same way that SHI is obtained for each group on the basis of ${\mathrm{C}}_{\mathrm{i}}$ values in Table 5, the SHI for dimensions would be calculated considering the data for ${\mathrm{C}}_{\mathrm{j}}$ in (6)–(8) for the six dimensions. This value provides information about the coverage and homogeneity of stakeholders regarding the dimensions of the model.

The framework for analysis of the stakeholder identification processes that Table 5 and the defined SHI imply focus on the coverage of stakeholder subgroups and typologies according to their relationship with the thematic areas of the smart city dimensions. These typologies are defined for the identification process to be considered as fulfilled; thus, this is a qualitative analysis, not a quantitative approach. However, the quantitative analysis, i.e., the number of stakeholders identified in each group and subgroup, is also important. From a quantitative point of view, it is understood that the number of stakeholders identified would vary according to the size of the city and the stakeholders present in the city. A sample resulting from a stakeholder identification process must have a specific size to be a compromise solution between representability in terms of number and feasibility of managing the data obtained from its queries.

3.3.4. Urban Stakeholder Management and Data Processing Using Multi-Criteria Decision Tools

It is essential to keep in mind that the ultimate goal of stakeholder identification processes is to collect information from these stakeholders to support decision making in the development of smart city policies. Therefore, stakeholder management aspects are not only linked to their identification processes but, more importantly, to the processing of data gathered from the consultations conducted. In this regard, it is important to ensure that the activities of the different stakeholders are aligned with the fundamental objectives of the intended smart city plans and projects [38]. In addition to the clear ethical implications and potential conflicts of interest regarding the community’s well-being, an overwhelming presence of a specific sector, such as technology companies, can distort the conversation. This shift may divert the focus away from comprehensive planning that embraces a holistic approach and instead prioritizes narrow strategies and initiatives [38]. Recent analyses of the evaluation of smart cities have found that consideration of the conflicting views of different stakeholders deserves more attention [52]. Achieving an optimal degree of involvement and assessing the state of stakeholder involvement are tasks for which local governments must have maximum responsibility [17]. The analysis conducted and the results outlined in Table 5 indicate that the major stakeholder groups, i.e., Knowledge and Innovation, Political and Public Administration, Social, and Economic and Financial, are predominantly linked to specific dimensions due to their interests and targets.

The question, therefore, arises as to how to process data from stakeholders according to their link to dimensions. Multi-criteria decision-making tools, particularly the Analytic Hierarchy Process (AHP) method [46], are introduced as an approach to analyze this aspect. The application scheme would be as detailed in Figure 4, where the goal is to establish the relative significance of the conclusions drawn from data collected during a consultation process with a specific stakeholder category (group, subgroup, or typology), depending on the thematic area (dimension); in other words, the importance of each stakeholder category’s opinion on aspects related to each dimension. The criteria are the different dimensions of the smart city, and the alternatives are the different stakeholder categories to be considered.

Applying the analysis described in Section 2: Methods, the first step would be the comparison of the dimensions with each other. The criteria matrix (1) would have a size of 6 × 6 when comparing the importance of the dimensions relative to each other, and the criteria vector $w_c$ obtained according to Equation (4) would contain the weights assigned to each of the six dimensions. However, a logical initial approach would be to assign equal importance to all dimensions, resulting in the vector $w_c$ containing six equal values of 1/6.

Regarding the stakeholder categories to be analyzed as alternatives and following the logic of the classification structure defined in this study, the first analysis would focus on determining the relative importance of the four main groups within the basic structure. This involves comparing the importance of the four groups with each other, pairwise, based on each criterion or dimension separately using Saaty’s scale. To carry out these comparisons, it is necessary to first appoint a committee of experts who possess the required qualifications to ensure that these comparisons are conducted objectively and fairly.

The comparison of the four main stakeholder groups across the six dimensions yields six 4 × 4 comparison matrices. After verifying the consistency of the comparisons using Equations (2) and (3), the six weight vectors for the stakeholder groups by dimension $w_{a1}$, $w_{a2}$, $w_{a3}$, $w_{a4}$, $w_{a5}$, and $w_{a6}$would be obtained. The matrix of alternatives $W_a$ built with them would have a size of 4 × 6 (with the weights of each stakeholder group in each of the dimensions). An example of a hypothetical result is provided in

Table 6. Example of a matrix of alternatives with the weight of each stakeholder group for each dimension.

Stakeholder Groups	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism
Knowledge and Innovation	20%	35%	20%	30%	25%	20%
Political and Public Administration	25%	20%	35%	20%	25%	25%
Social	20%	25%	25%	20%	25%	35%
Economical and Financial	35%	20%	20%	30%	25%	20%

, presented for illustrative purposes. This result is significant and represents the most compelling aspect of the analysis, as the results from consultations with the various stakeholder groups (opinions) would have these weights for each dimension.

Following the example of the data in Table 6, for instance, the opinions of the Economical and financial group would carry a weight of 35% in the Economy dimension, but 20% in the Social well-being dimension.

Applying Equation (5) to this matrix of alternatives and the criteria vector containing the weights for each dimension would yield the total average weights across all dimensions for each stakeholder group. This result would allow us to analyze potential inequalities in the influence of stakeholder groups on decision-making processes in a general sense. The example represents an ideal scenario where all groups achieve a result of 25%, indicating identical total influence among them.

3.3.5. Management and Data Processing of Stakeholders Based on Their Status as Specialized or Non-Specialized

The defined typologies set out in Table 5 are characterized as “specialized”, as they are subgroups directly related to dimensions. The rest of the typologies will be considered “non-specialized”, as they also refer to the subgroups of citizens as mentioned above since the subgroup in question does not have a direct relationship with the thematic area of the dimension. Interventions by non-specialist stakeholders should not be understood as unimportant; on the contrary, their involvement is essential in smart city policies and initiatives, as all subgroups need to be included. What is necessary is to strike a balance between the opinions of specialized and non-specialized stakeholders in each area of assessment. The AHP method is applied to obtain the weights of specialized and non-specialized stakeholders by dimension (a very simple case of two alternatives and six criteria).

In this case, for the assessment of the alternatives in each dimension, a panel of experts from the International Project Management Association (IPMA) was surveyed. The chosen panel of experts for weighting results may also vary; choosing an international project management association, as in this study, seems to be a valid choice when using the stakeholder classification in evaluation models for smart city initiatives and projects. However, for other purposes, the choice of experts used to establish the importance of stakeholders through dimensions could be different. In this case, the experts were asked to evaluate the weight that should be assigned to specialized and non-specialized opinions for each dimension.

The results of the matrix of alternatives by criteria $W_a$ after obtaining the six comparison matrices as per Equation (1) and calculating the eigenvector of each one as per Equation (4) are shown in

Table 7. Matrix of alternatives based on the AHP criteria for specialized and non-specialized stakeholders.

Stakeholders	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism
Specialized	0.71	0.68	0.64	0.72	0.72	0.66
Non-Specialized	0.29	0.32	0.36	0.28	0.28	0.34

. These values are the weights that should be applied to the data collected from the stakeholders depending on whether they belong to the specialized or non-specialized category of each dimension.

With these results, it can be concluded that specialized opinions have a higher weight, with a proportion of approximately 70-30 over non-specialized opinions as an average of the different thematic areas that the dimensions represent. Actually, if we multiply this matrix by the vector of criteria $w_c$ according to Equation (5), we would obtain an overall or average value for all dimensions of 0.69 for specialized stakeholders and 0.31 for non-specialized stakeholders. This confirms that the opinions of non-specialized stakeholders across dimensions hold significant importance. Even though the identification process is conceptually based on detecting specialized stakeholders, the relevance of non-specialized stakeholders in opinion gathering during consultations is by no means negligible.

By dimension, infrastructures and environment have the highest weight of specialized opinions, coinciding with those of a more technical component, followed closely by economy. The dimensions of governance, social welfare, and human capital have lower percentages of specialized opinions (even though they are higher than 60-40), coinciding with the thematic areas less related to technical aspects and more related to the democratic and social life of the city.

In a stakeholder identification process, the final number of urban actors identified may vary depending on the development of the process itself and the actual presence of the different typologies. However, if this methodology is followed, regardless of the number of stakeholders identified in each category, the final weight of the opinions of stakeholders directly related to the dimension in each category must be adjusted to the percentages obtained. In general, from a quantitative perspective, the raw number of data collected from stakeholders within a specific category (whether a group, subgroup, or typology) must be analyzed for that category separately, considering for the decision-making process all categories with their respective weights.

4. Case Study: Stakeholders Considered in a Small–Medium Smart City

This case study serves as a practical example of the framework for the identification process of urban stakeholders, focusing on the city of Alcoy in the Valencian Region of Spain. Alcoy is a municipality located in the interior of the province of Alicante, with a population slightly exceeding 60,000 residents. As a result, it is classified as a small city according to the European Commission’s definition [53]. The city has a smart cities master plan and a strategic plan for its development. The initial identification process has been analyzed and assessed according to the framework defined and it has been completed following the methodology developed.

As a first step, the stakeholder database of the smart cities department of the city used for the development of the smart cities master plan [54] was analyzed. In addition, interviews were carried out with department managers to find out how the data in the stakeholder database had been collected. The identification process used by the municipal smart city department did not follow a formal framework or procedure with a predetermined set of steps; rather, it was an informal process. After a first analysis of the database, an uneven presence of stakeholders, with an excessive weight of certain groups, was detected. At the same time, other parts of the classification defined in this work were under-represented or not represented at all. The first striking aspect is the over-representation of stakeholders related to technology and innovation (Figure 5). These data reflect a possible technological conception of the smart city.

4.1. Analysis of the Existing Stakeholder Database Using the Proposed Identification Framework

To analyze the existing database, we reclassified the stakeholders identified initially according to the model in Table 5. The results are provided in

Table 8. Analysis of the stakeholder database of the case study before applying the proposed methodology. Own elaboration.

Stakeholders’ Groups and Subgroups	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism	Sum of Coverage	% of Coverage (Ci)
	(Number of stakeholders identified in each typology)
Knowledge and Innovation							12	50%
Research centers related to smart cities	-	1	-	1	-	-	2	33%
University researchers	1	6	-	1	1	-	4	67%
Research centers not directly related to smart cities	3	-	-	-	1	1	3	50%
Consultants	8	-	-	1	-	1	3	50%
Political and Public Administration							4	11%
Municipal government	-	-	-	-	-	-	0	0%
Political parties	-	-	-	-	-	-	0	0%
Municipal SC department (technicians)	-	-	1	1	-	-	2	33%
Municipal technicians, urban services management	-	-	-	-	-	-	0	0%
Urban security or health services						-	0	0%
Public entities at a supra-municipal level	-	-	1	-	-	-	1	17%
Urban planners	-		-	1	-		1	25%
Social							0	0%
Citizen groups	-						-	-
NGOs (Non-Governmental Organizations)						-	0	0%
Unions	-					-	0	0%
Neighborhood and citizen associations	-						-	-
Media	-	-	-	-	-	-	0	0%
Economic and Financial							15	44%
Private companies managing urban services	5			1	4		3	100%
Telecommunications operators	1			1			2	100%
Telecommunications services companies	-			-			0	0%
Local businesses	13	-	-	4	2	1	4	67%
Transportation companies	2			2			2	100%
National businesses	9	-	-	1	2	-	3	50%
Investors and financial entities	-						0	0%
Energy companies	-				-		0	0%
Real estate development companies	-			-			0	0%
Professional associations	-	-	-	-	-	-	0	0%
Business associations	8						1	100%
Associations of self-employed workers	-						0	0%
Sum of coverage	9	2	2	10	5	3	31	27%
% of coverage (Cj)	35%	13%	13%	48%	28%	17%

and show the stakeholders in each category and dimension, as well as the percentage covered in each group and subgroup.

A quantitative analysis shows that the identification results in an unequal presence of stakeholders in the groups: the Knowledge and innovation group (26 stakeholders identified) and the economic and financial group (56 stakeholders identified) are well represented, but the Social and Political groups are practically absent. However, from the point of view of this paper, the qualitative analysis of stakeholder groups and subgroups covered by dimension and category in terms of links with dimensions is of greater interest: the percentage of gaps covered in the last column reflects an omission of all subgroups in the Social group and a minimal presence in the Political and public administration group. In this line, the omission of the Citizen groups as part of the process of identifying the main stakeholders is noticeable. In addition, only the Knowledge and innovation group reaches 50%, with the Economic and financial group below that figure; both are reasonably represented but with significant omissions, especially in the latter (56%).

As for the main interests of stakeholders, only 9 out of 20 priority aspects are covered, which reflects that a large number of stakeholders are overlooked even in their fundamental interests.

The SHI calculated with this process of identification gives a final value of 0.165, far from the ideal 1.0, and only the SHI of the Knowledge and Innovation group is close to 0.5, with a value of 0.432, which means low coverage and homogeneity.

As pointed out, the process of stakeholder identification conditions the subsequent development of the transformation process, and the clear choices, therefore, influence the final results. Possible problems related to these biases in the identification processes of urban stakeholders are the rejection of some initiatives by urban sectors (detected in the case study) or the increase of the digital divide (in case of over-representation of technology sectors, as in this case).

4.2. Application of the Proposed Methodology to Improve the Identification Process

Within the scope of this study, the next step is the application of the methodology to identify possible improvements in the identification process of urban stakeholders in the case study. Additional stakeholders should be detected with a general search of entities for each part of the classification. The original database is supplemented with a search of companies and entities with a presence in the city, following the steps in the defined methodology, covering subgroups of stakeholders related to the dimensions of the city with the ultimate goal of achieving a comprehensive representation of urban stakeholders.

Regarding data for characterizing citizen groups, we refer to the demographic overview of the city in the latest developed strategic plan [55], providing comprehensive data on population characteristics. For this case, age range, economic level, and education level data were considered as they were available and updated in the aforementioned strategic plan. Citizen samples are considered representative by adapting them to the percentages of these parameters from the strategic plan, supplemented with a representation of the population quantity from the various neighborhoods of the city.

Once the search process is completed, the results are entered into the framework template (Table 5) to be compared with the data from the original identification process before the application of the methodology. In this way, a comparison of stakeholder categories can be obtained, and the advantages of applying the proposed identification method can be assessed.

The results of the new identification process by stakeholder groups and subgroups and dimensions are presented in

Table 9. Results after applying the identification methodology to the case study. Own elaboration.

Stakeholders’ Groups and Subgroups	Economy and Competitiveness	Human and Intellectual Capital	Governance	Infrastructure and Mobility	Environment and Energy	Social Well-Being, Services, and Tourism	Sum of Coverage	% of Coverage (Ci)
	(Number of stakeholders identified in each typology)
Knowledge and Innovation							19	79%
Research centers related to smart cities	-	3	-	3	3	-	3	50%
University researchers	1	1	1	1	1	1	6	100%
Research centers not directly related to smart cities	3	2	-	-	1	1	4	67%
Consultants	8	2	1	5	3	2	6	100%
Political and Public Administration							30	86%
Municipal government	1	1	2	1	1	4	6	100%
Political parties	1	1	6	1	1	1	6	100%
Municipal SC department (technicians)	-	-	1	1	-	-	2	33%
Municipal technicians, urban services management	1	1	1	1	1	1	6	100%
Urban security or health services						4	1	100%
Public entities at a supra-municipal level	1	2	1	3	2	1	6	100%
Urban planners	2		-	1	1		3	75%
Social							18	82%
Citizen groups	68						6	100%
NGOs (Non-Governmental Organizations)						6	1	100%
Unions	2	2				-	2	67%
Neighborhood and citizen associations	7						6	100%
Media	1	1	1	-	-	-	3	50%
Economic and Financial							31	91%
Private companies managing urban services	9			8	9		3	100%
Telecommunications operators	4			4			2	100%
Telecommunications services companies	3			3			2	100%
Local businesses	39	2	-	2	1	2	5	83%
Transportation companies	2			2			2	100%
National businesses	12	2	-	5	3	2	5	83%
Investors and financial entities	4						1	100%
Energy companies	4				4		2	100%
Real estate development companies	3			3			2	100%
Professional associations	1	-	1	2	1	1	5	83%
Business associations	8						1	100%
Associations of self-employed workers	3						1	100%
Sum of coverage	24	14	11	19	16	14	98	85%
% of coverage (Cj)	92%	88%	69%	90%	89%	78%

. A detailed list of the stakeholders identified as specialized in each category and dimension can be found in Appendix B of this paper.

4.3. Results of Application of the Methodology

From a quantitative point of view, the number of stakeholders considered has been increased significantly, especially due to the presence of previously omitted citizen groups. In any case, the number of stakeholders identified is not considered excessive for the population size of the case study in terms of feasibility for consultations and data management.

As for the qualitative analysis of the categories covered, representation is obtained in all the subgroups of stakeholders, although not 100% is reached in all the categories, mainly due to the limitations of finding stakeholders in the type of city studied and the obvious budgetary and means limitations that a study of this type suffers from in comparison with other initiatives. In any case, all the major groups exceed 75%, and no subgroup is below 50% of consideration (except for the municipal department of smart cities of Alcoi, which is limited because of the number of its members).

In addition, all the priority aspects of the subgroups are represented, and a homogeneous sample is achieved. The improvement obtained by following the proposed methodology concerning the initial identification of stakeholders is notable in all categories.

Table 10. SHI of both processes of identification (before and after applying the methodology). Own elaboration according to Table 5 and Equations (3) and (4).

Stakeholder Groups	SHI Before	SHI After
Knowledge and Innovation	0.432	0.667
Political and Public Administration	0.037	0.742
Social	0.000	0.715
Economical and Financial	0.194	0.921
Total	0.165	0.761

shows the value of the SH indexes calculated in the original process and in the one carried out following the methodology described in this work (the SHI of each group and the final SHI).

The values of the SH indexes for dimensions (SHI_DIM) before and after applying the methodology are 0.182 and 0.797, respectively.

5. Conclusions

The essential character of involvement, collaboration, commitment, and participation in decision making by urban stakeholders in the processes of transformation of urban centers under the smart city model and the multidimensional nature of the city are generally accepted concepts in the scientific literature related to models of representation of the urban environment. Starting at this point, the research develops a new methodology for processes of identification, engagement, and management of stakeholders in sustainable and smart cities.

The extended triple helix model, considering economic, knowledge, political, and social stakeholders without losing sight of the location of citizens as the core of the model, is a simple classification and identification tool that is broad and complete enough to be taken as a basis for a general point of view and as a first step. The literature review shows that stakeholders considered in the models studied can be adjusted to this general classification. Additionally, the triple helix model is based on the development of innovative environments, so it can also be applied to smart city initiatives.

The first classification based on the extended triple helix model must be completed with an identification of stakeholders in a more detailed sub-classification. As the comprehensive concept of the smart city is embraced as a foundation, the resulting classification of urban stakeholders must be aligned with the dimensions of the smart city (Economy, Mobility, Environment, Human and Intellectual Capital, Quality of Life, and Governance as commonly recognized in the scientific literature). A representative heterogeneity of the urban environment must be achieved, considering the interests and targets of each sector so as not to condition the transformation processes, ensuring that these are aligned with the fundamental objectives of the plans and initiatives.

The methodology described in this work, therefore, provides a first basic structure of classification of stakeholders into four broad groups, a further classification of stakeholders into subgroups within this basic structure, and a more detailed breakdown of stakeholder typologies based on the relationship of the subgroups to the city dimensions. The guidelines and framework for the development, monitoring, and analysis of stakeholder identification processes are based on a qualitative analysis of the degree of coverage of these typologies that are considered fundamental. For the analysis of the processes carried out, an index (SH Index or SHI) is defined that includes two fundamental aspects: the degree of achievement of coverage of the stakeholder typologies and the homogeneity of the identification process carried out. This index allows a quick comparison of identification process stages and a final assessment of the process carried out. Aspects related to the influence of stakeholder opinions on the thematic areas represented by the dimensions are addressed through the application of multi-criteria tools, specifically the AHP method. In this approach, dimensions are treated as criteria, and stakeholder types as alternatives. These three elements (the identification framework, statistical analysis tools, and data processing methods) constitute the proposed methodology to ensure the effective and adequate management and involvement of urban stakeholders in strategies for transforming cities into smart and sustainable environments.

The identification framework serves as a tool and a criterion for the detailed classification of urban stakeholders, providing guidelines for the development of stakeholder identification processes during the early stages of smart city projects. Proper development of these processes represents the first and fundamental step toward achieving effective engagement of urban stakeholders, aligning initiatives with urban demand, and enhancing citizens’ sense of ownership, thereby increasing the ultimate success of the smart city plans.

The guidelines provided by the identification framework are complemented by practical analysis tools (SH Index) for the assessment and monitoring of identification processes. From the perspective of stakeholder management, the methodology serves a dual purpose: on the one hand, the typologies outlined in the framework consider the relationship between urban actors and the thematic areas represented by the dimensions; on the other hand, it includes methods for managing data on different stakeholders based on their nature. This management improves the final commitment of urban actors and enhances the quality and utility of the data collected.

The proposed guidelines have been applied to the case study of the city of Alcoy in the Valencian Region of Spain to check its practical application. In this process, it has been detected that the original database used in the smart cities master plan had clear biases of over-representation and under-representation of some sectors and entities. Applying the methodology results in a much more complete representation of urban stakeholders, in line with the holistic nature of the city. In this way, the smart city plans and strategies to be developed are less likely to fall into the problems detected, such as the rejection of some initiatives by urban sectors or the increase of the digital divide (due to the over-representation of the technological sector, as detected in the case study). Nevertheless, the application of the developed methodology to a single case as an illustration of its practical use represents a limitation of the research, as it cannot be considered a comprehensive empirical validation.

The preliminary stage of the study revealed a limited number of existing models that consider stakeholders, either explicitly or implicitly. Additionally, when it comes to assessment models that provide a detailed classification of urban stakeholders while considering the holistic nature of a smart city, the gap is particularly evident. This reflects a significant weakness in this area and a clear limitation of the research. The working philosophy of the proposed methodology is eminently qualitative, so the quantitative aspects, regarding the number of stakeholders to be identified according to the size of the city or feasibility and representativity aspects, represent another limitation of the methodology that must be analyzed in subsequent research. Aspects related to the weighting of stakeholder data have also been addressed, considering their classification, their status as specialized or non-specialized, and their relationship with the thematic areas representing the dimensions. Nevertheless, for future research, a deeper analysis of the varying levels of involvement of different stakeholder groups, considering their characteristics and expertise in each specific area, should be implemented. The most appropriate stages for their inclusion based on their interests and objectives should also be determined. Moreover, it is important to ensure that the identification of stakeholders is unbiased and objective, depending on the executing entity or organization; thus, tools for monitoring these processes should be applied. In fact, the proposed methodology is understood to be a tool for use in the initial stages of more complex decision-making processes; thus, political implications have not been considered. How to integrate it into more comprehensive processes is also an aspect to be addressed in future research. Furthermore, a more detailed classification of citizen groups, including homogeneous subgroups with common interests and characteristics, should be considered in future research to ensure their representation and participation.