Introduction

The rise of information technology (IT) has impacted the needs of digital cities. As a natural progression, digital cities develop in the same evolutionary context, and smart cities have been implemented in the genuine sense in different countries, with several pilot projects being developed worldwide (Lam & Yang, 2020). Information resources lead to a convergence between humans and their surroundings through the regular use of technological devices to store and process the volume of data produced (Antwi-Afari et al., 2021; Hanafizadeh & Harati Nik, 2020; Johnson et al., 2021).

Technology can drive a city’s dynamics by extracting valuable information from data, thereby providing long-term sustainability and creating or optimizing citizen services. The demand for digital public services is influenced by the ability of IT resources to integrate, process, and analyze the city’s data. This process requires the strategic development of digital cities (Arvidsson et al., 2014; Engin et al., 2020). The adoption of a multidimensional, intelligent information management system that interacts and connects with the technological needs of citizens stands as an option to ensure the dynamism of social relations in digital cities, including smart people (citizens), places and city planning (Engin et al., 2020; Flores & Rezende, 2018; Fumagalli et al., 2021; Hasija et al., 2020; Longo, 2011; Pan et al., 2016).

The data and information networks that impact cities, organizations, and citizens grow as the internet and technologies for managing different systems and levels of decision-making mature. The trait of multidimensionality, in turn, is not limited to the internal environment of public management. It pervades strategic information management, permeates external environments, and interacts with different actors within the municipality. A city’s information dispersion becomes integrated into an information flow model through orderly movements and systems. The system’s accessibility and agility are based on specific technologies and their alignment with public policies (Chakraborty et al., 2015; Fumagalli et al., 2021).

Research problems emerge as the city evolves from a physical to a digital entity, with information taking on an increasingly important role; consequently, cities are no longer merely physical as their buildings and streets transform into digital cities (Ahvenniemi et al., 2017; Lam & Yang, 2020). Additionally, as the city evolves from a physical to a digital entity, information becomes increasingly important; consequently, cities are no longer just physical, with their buildings and streets becoming digital cities. This problem becomes increasingly relevant as public policies do not match citizens’ digital needs, thus breaking the chain and impeding efforts to create a sustainable digital city (Antwi-Afari et al., 2021).

In addition, information flow should be considered strategically once the process of connecting different urban systems ventures beyond the common-sense measure of creating an official city website to the level of creating a digital and smart city. Nevertheless, data and information are not always customized to respond to citizens' needs or integrated into different virtual management networks (Katapally & Chu, 2019; Longo, 2011; Mainka et al., 2018; Pan et al., 2016).

Challenges continue to emerge in the context of urban information systems with a simple bidirectional structure once the city's management information transcends the rudimentary functionality of a repository. In addition, the concept of a digital city has transcended its initial perception to focus on solving public information problems through the use of technology (Fumagalli et al., 2021; Lam & Yang, 2020). Bidirectional Information Systems does not necessarily manage the dynamic and multidimensional information generated by different urban policies, information systems, and the city's strategic planning (Barth et al., 2017; Hasija et al., 2020). Thus, our research question is: Is urban information multidimensional regarding digital public services?

The research objective of this study is to conduct a multidimensional information analysis in two cities to develop a multidimensional framework, considering the different realities in two cities from different countries. The research justification is based on the need to develop a multidimensional information model to improve the digital city concept (Barth et al., 2017; Bouskela et al., 2016; Rezende, 2016; Rezende & Procopiuck, 2018). Furthermore, connecting public policies, digital services, and information systems can reinforce the city's strategic planning.

This research was based on the notion of the strategic digital city (SDC), which is the application of information technology resources to a municipality's management. This approach also provides information and public services to citizens and city inhabitants (Rezende, 2012). The literature review of the notion of a digital city includes technology as a strategic element that integrates different public policies (Flores & Rezende, 2018). In addition to information access, a strategic digital city (SDC) should integrate public policies into the higher operational capacity of the public sector, ensuring that it is strategically aligned and customized (Almeida & Rezende, 2021; Fumagalli et al., 2021; Rezende et al., 2015).

However, information management models cannot maintain the speed at which data circulate in management systems, and bidirectional systems cannot manage multidimensional information generated from various information systems (IS).

This article is structured following the research process. Section 2 presents the theoretical review that supports research with a special focus on the central concepts studied to determine which variables can be used to determine the framework constructs. The methodology is described in Sect. 3 and includes where the data were obtained and the technique used to converge it in a multidimensional information model. All used methods are presented in this section as well. In Sect. 4, results are presented and discussed, and the earned results emanated from that are shown in Sect. 5. At the end of the article, in Sect. 6, conclusions are made around a flexible, multidimensional information framework for digital cities. Section 7 brings the research limitations and further research recommendations.

Literature Review

The tremendous amount of information managed by a city’s IS represents an essential resource for the sustainable development of a democratic society. Information volume accelerates the integration between IT and public services strategies (Barth et al., 2017; Johnson et al., 2021). Information management and interactions between citizens and institutions are increasingly customized in collaboration with the concepts of digital and smart cities (Ahvenniemi et al., 2017; Longo, 2011; Pan et al., 2016).

The approaches modeling smart cities or understanding the constructs that constitute a smart city has been proposed and developed using different frameworks (Kar & Dwivedi, 2020; Katapally & Chu, 2019).

Information Systems

Information has gained prominence due to the increased use of technologies, highlighting its strategic value for decision-making processes; it has thereby become an integral part of city activities and public services (Ahvenniemi et al., 2017; Rezende & Procopiuck, 2018).

In this context, IS can be defined as an interrelated set of elements that collect, process, and distribute information to support decision-making. The main characteristics of an IS are the interdependent relationship between the variables that constitute the system and the hierarchical levels of the data (Antwi-Afari et al., 2021; Almeida et al., 2022). The IS variables establish a relationship between efficiency and coherence in all directions, thus promoting the multidimensionality of a strategic IS (Alaimo, 2016; Awwad et al., 2022). The dimensions of an IS are also recognized as an organization’s structure to ensure accessible and efficient use. Therefore, the IS acquires a strategic character, using digital services to support inclusive elements.

The construction of an information system begins with dimensional bases for operation, thus highlighting the database’s structure within the system (Kar & Dwivedi, 2020). The design of information concepts uses elements of dimensional configuration at different layers and structural levels, thus establishing interrelations and articulation among the elements of the information system (Barth et al., 2017; Patašienė & Patašius, 2014; Sorokine et al., 2016).

The articulations provided by the IS via the triangulation of informational levels connect and assist strategic management, thus allowing the IS to develop into customized information architecture, making it relevant to users in different urban information contexts. The concept of the evolution of systems differs from the growth of physical goods in that it deals with changes in the same system (a focus on improvement). The evolution of IS establishes relations among the various stages of conception: (1) initiation; (2) contagion; (3) control; (4) integration; (5) data administration; and (6) maturity.

The evolution of the information system due to changes in requirements or the development of new information architecture arises from a process of restructuring that considers the dimensions of the IS variables and the citizen's needs (Chatterjee et al., 2018; Kar et al., 2021). Digital cities provide digital services to citizens. The public manager must collect and process the data to develop and share an informational context with users. However, most cities cannot create sufficient space for the emergence of the urban information system (UIS) since they cannot provide the required services due to a lack of resources, the deficiency of modern administrative techniques and standards of care, or the inability to establish local-level cooperation (Alaimo, 2016; Barth et al., 2017; Pan et al., 2016; Sorokine et al., 2016).

The UIS, an advanced IS, provides services to citizens based on IT infrastructure, facilitating interactions among information, citizens, public services, and managers (Barth et al., 2017; Leem et al., 2014). In this context, urban information extrapolation plays a complex operational role and establishes complex relationships with health, education, science, technology, and culture. Therefore, the UIS provides contextualized information for each reality and plays an essential role in developing a digital city (Barth et al., 2017; Pan et al., 2016).

A Smart and Strategic Digital City

The Primary concepts thus extend to the idea of an online platform aimed at fostering community networks to integrate and create UIS in various cities. One of the concepts of a smart city includes creativity and knowledge (Almeida & Rezende, 2021; Flores & Rezende, 2018; Fumagalli et al., 2021).

Unlike conventional concepts of a smart city, the strategic digital city can be understood as the application of information technology resources to the city’s management and public policies. The Strategic Digital City (SDC) concept created by Rezende (2012) is a comprehensive project that offers more than the internet to citizens via conventional telecommunication resources; this concept is based on the city's strategies for addressing different thematic and public policies in the municipality (Almeida & Rezende, 2021; Flores & Rezende, 2018; Fumagalli et al., 2021; Rezende & Procopiuck, 2018; Rezende et al., 2015; Ribeiro et al., 2019). One of the most frequently quoted definitions of this concept was created over a decade ago by IBM, which presented a smart city as a city that uses all available technologies to understand better, control and optimize the use of limited resources. In the same technological context, CISCO defined smart cities as scalable solutions that utilize ICT to increase efficiencies, reduce costs and enhance the quality of life (Chatterjee et al., 2018; Kar et al., 2021).

These features are supported and updated via networks and digital applications. Information technology promotes open and active functions within smart cities (Ahvenniemi et al., 2017; Lam & Yang, 2020).

Innovation, information, and knowledge are essential components of smart cities. The innovation system guides the development of experience and technologies by various entities, including companies, universities, technology centers, incubators of activities, and digital applications of information and knowledge (Ribeiro et al., 2019). The system concentrates on the diffusion of information, communication, decision-making, the transfer and use of technologies, and collaboration for innovation.

Smart cities are conceptualized in terms of the collection and organization of the city’s digital information, which provides space to facilitate interactions among inhabitants and visitors to the city (Fietkiewicz & Stock, 2014; Fumagalli et al., 2021; Hasija et al., 2020).

Interactions between managers and citizens must also exist in the smart city to enhance project plans, information, and services. Strategies for implementing a digital city refer to the levels mentioned in the natural, institutional, and digital space of a contemporary city: (1) people; (2) collaborative institutions; and (3) digital tools for knowledge management and innovation. The term “smart city” refers to territories at different scales and includes the ability to foster education, technological development, innovation procedures, digital spaces, information processing, knowledge transfer, and technical instruments (Kar et al., 2021; Pan et al., 2016; Patašienė & Patašius, 2014; Ribeiro et al., 2019; Teixeira, 2018).

Despite some technological similarities between a digital city and a smart city, a strategic digital city can create more flexibility via technological enhancement as well as attitudinal citizen changes once the city's information and public policies become more closely associated with and customized to the citizen's needs, thereby connecting different municipal themes or goals to specific strategies to create flexible, customized information responses and digital services (Almeida & Rezende, 2021; Chatterjee et al., 2018; Flores & Rezende, 2018). Therefore, implementing the SDC concept requires collaboration among several projects, such as Strategic Planning of the Municipality (SPM), which includes the municipality’s objectives and strategies in terms of municipal functions and themes. Similarly, Planning of Municipal Information (PMI) and Planning of IT (PIT) involve city halls and public organizations. Information is the main product of the PMI project, which is a prerequisite for the planning of municipal IS and knowledge systems (KS) as well as the human resource (HR) profiles (whether associated with local managers or municipal officials) that are required for this project. The PIT facilitates the provision of resources to city services via IT structures (Rezende, 2012; 2018).

Strategically, municipal themes (macro-activities) are present in all cities. These themes are necessary for the integrated and effective functioning of such cities. Several themes can be found, including agriculture, science and technology, education, sports, health, safety, services, and transportation. These functions can be divided into modules or municipal issues (Ribeiro et al., 2019). The integration and execution of the city’s strategies challenge public managers. This information is an essential tool for combining policies in the context of digital cities.

The term “strategy” focuses on the organization and its environment. Its essence is sophisticated, and strategy affects the organization’s functioning. The strategy involves questions and processes related to the determined path; however, organizations exhibit superior performance when prioritizing information management. Strategy is compelling when viewed from three perspectives: (1) information and the definition of strategy; (2) information and the execution of strategy; and (3) information and integration. Therefore, the strategic element of these cities is related to their capacity for information management and related connections to other contexts.

Information regarding the multidimensional nature of the organization results from its dynamism and ability to align with different systems levels, as noted by Arvidsson et al. (2014) and Barth et al. (2017).

Research Methodology

The research methodology emphasized a circumstantial theoretical reality based on the model theory (Hodges & Wilfrid, 1993; Marker, 2006). It also focused on the deductive method, non-participatory observation of the city’s informational systems, and a quasi-mixed approach due to the qualitative data associated with infometric mapping (Collis & Hussey, 2013; Yin, 2017).

The data retrieved were combined and compared using the infomapping technique, whose outcome matches the deductive method, in accordance with the quasi-mixed approach. The open data policies of Canadian and Brazilian cities are similar, and relevant information is regularly disclosed by the surveyed cities. In addition, the capital of Saskatchewan—the city of Regina—is an active member of Municipal Benchmarking Networking Canada (MBNCanada). MBNCanada is a partnership among Canadian municipalities focused on information measurement to facilitate the improvement of public services, policies, and communities, which is fully aligned with the methodologies used in this research.

The research was designed to feature three phases: theoretical foundations, model building, and multidimensional experiencing. The framework thus developed was examined in two cities, i.e., Regina, Saskatchewan, Canada, and Rio de Janeiro, Brazil. The research mechanisms were developed hierarchically, connecting all phases and their subprocesses as shown in Fig. 1.

Fig. 1
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Research phases and mechanism

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The procedures were organized and interconnected, considering each research phase's specificities and scientificity (Collis & Hussey, 2013; Yin, 2017).

To identify the scientific achievements made regarding keywords related to the concepts used, a comprehensive search of the Web of Science database was conducted in 2021 using the terms related to smart cities, digital cities, and urban information cities listed in Table 1.

Table 1 Boolean hierarchical search results in the Web of Science database
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The investigation followed the assumptions suggested by Yin (2017). It employed a quasi-mixed approach supported by social network analysis and comparative configurational methods (Collis & Hussey, 2013), non-participatory exploratory observation of information management systems, and corresponding analysis.

Multidimensional Constructs

The multidimensional information model of an SDC took into account correlated models that have been identified and investigated in the context of digital and smart cities, concentrating on the information elements of these systems. In this context, the research took into consideration the models of Yountaik Leem, developed at National Hanbat University, South Korea (2014), Yunhe Pan, developed at Beijing University, China (2016), Alexander Sorokine, developed at the University of Tennessee, USA (2016), and Julia Barth, developed at the University of Duesseldorf, Germany (2017). The construct experimentation was conducted in an IS-related context, based on each city’s official website considering different contexts, organizational patterns, and multidimensional information dynamics. Comparatively, both cities presented information that was convergent with the framework developed. The data crossing among the three constructs and variables allows us to take a strategic view (see Table 2).

Table 2 Comparative analysis structure of constructs and variables
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The framework was established by reference to three constructs: (1) multidimensional information, (2) public services, and (3) SDC. Each construct was composed of different variables.

Multidimensional Information Construct—availability of information, type of information management, type of information architecture. Public Services Construct—number of public services, name of public services, type of public services. Strategic Digital City Construct – theme name, number of topics, strategy name, number of strategies.

The model’s conceptual basis was a survey of bibliographical references, an analysis of related models, and new information technologies. Considering the topic of originality, the model merges the notion of the SDC with a combination of information conceived of as multidimensional. SCD information management was composed of constructs and variables that, when articulated, converge into multidimensional information systems to consolidate their dynamic character. The information is conceived of in its architectural state as a pattern of matter and energy to which meaning has been attributed. The information’s multidimensional nature offers several possibilities for connections to the city’s information systems.

The dynamic character of the information assigned to a particular construct is represented by cubes and the potential rotation around its axis. The alignment or disconnections of the cube’s faces highlight the corresponding spatiality, which creates the information’s multidimensional nature, a consequence of its dynamic character and the different assumed contextualization.

The Data retrieval and consolidation in the context of multidimensional information are performed using database searches, which are subject to the specific characteristics of the user’s research. The strategic character of this process is evidenced by the ability to align the variables in the IS to match different users’ specific characteristics. Therefore, the customized information is parameterized via the SDC construct, considering the conceptual connections among different systems and city policies. Three variables formed the multidimensional information constructs. Information Availability: this variable pertains to the structure of public information. It also represents that information’s specific, customized connections with the city’s citizens. Therefore, informational multidimensionality determines the need for the search, the management system's specific characteristics, and the customization parameters. Management Category: this variable involves the capacity of systems to connect pieces of information to specific contexts. The management type variable relates to systems used by the SDC to handle information at various levels, such as enterprise resource planning (ERP), management IS (MIS), administrative systems (AS), database management systems (DBMS), data warehouses (DW), artificial intelligence (AI), specialist systems (SE), data mining (DM), transaction processing systems (TPS), and decision support systems (DSS).

The multidimensional capacity of the variable in question requires the orderly arrangement of each system of origin that composes the city’s complex system without losing the context of the customization of meaning to the citizen. Information Architecture Type: this variable pertains to the types of informational architecture associated with digital cities. It focuses on intelligent systems, which include but are not limited to functional layer architecture, reference architecture, and domain architecture. The information architecture type variable was structured in accordance with a spatial perspective to analyze the informational interconnections in a specific SDC context. The management layer interacts with the features; the security layer assigns reliability to the procedures. Both layers provide articulation to the other layers and their elements of composition.

Nevertheless, regarding the open data used, the model thus developed expresses a two-dimensional directional action (not multidimensional) once the system has retrieved the same information in a different context and is unable to customize variables.

Consequently, the IS articulations developed in a manner analogous to the faces of a cube and in terms of combinations of customization and multidimensional shapes. The public services construct adheres to CDE, is designed in a virtual infrastructure-based environment and has a technical nature. However, the model fulfills the needs of its citizens by reference to their needs and demands.

The cube as shown in Fig. 2 represents the digital city’s multidimensional context; its faces demonstrate its dynamic ability to orient the compositional variables and information flow to improve the connections within the system.

Fig. 2
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Hierarchical and dynamic information flow for digital cities

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The model developed through these constructs and articulated variables represent the spatial structure (which is characteristic of multidimensionality). The research variables promote informational ordering in the context of the city’s IS. In this context, data connect the information managers, ranging across various orders and hierarchical levels, which constitute the city’s complex multidimensional management systems. On the other hand, data customization in the form of information ranges beyond the ordinary barriers and hierarchical levels associated with IS. The dynamic character and rearranged composition of information offer different meanings to citizens when requested by identical UIS. These meanings converge to provide customized information for each consult. The cube’s multidimensional overlap (Fig. 3) was designed to flow through city management systems in accordance with themes related to public policy.

Fig. 3
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Dynamic and multidimensional nature of the information

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Nevertheless, the natural evolution of the research advanced to the point of an articulate composition, including research elements such as constructs and variables. In accordance with the user’s profile, the multidimensional informational composition variables and their data follow customized paths within management systems against different backdrops, as shown in Fig. 3. When integrated with the IS, these strategies create a multifaceted system that feeds the user’s preferences or needs to the management system.

Multidimensional IS Analysis

Initially, strategic information management, UIS, and public policies converge on a common point: multidimensional informational management in smart and strategic digital cities (Kar et al., 2021; Lam & Yang, 2020; Teixeira, 2018). This convergence indicates that strategic management elements are disconnected, and strategic blindness is implied (amplitude surpasses the research objective). The experimentation associated with the information construct variables converged, thus confirming the framework developed here. Other noteworthy points include specific data management systems on an individual basis, a strategy employed in the two cities included in the experiment. This strategy focuses on providing information provision systems that are connected to digital public services. The information highlighted by this experiment exhibited its multidimensional character and was inseparable from its dynamic nature regarding the structure of digital information availability. The traditional bi-dimensional framework research features layers; it does not reflect the information’s contextualization capabilities and dynamic character.

On comparison, the city’s digital public services exhibited linearity, offering services related to tax collection. In addition to the number of services, the experience highlighted their connection with the municipal themes. The information flow was in line with the informational multidimensionality; one issue indexes different strategies and public services.

Regarding public services, the cities exhibited distinct behaviors and focus. Regina concentrated on services aimed at social development. Rio de Janeiro focused on services in the area of public administration. In this scenario, when combined with the digital information provided by the surveyed cities, the public services displayed ruptures or disconnections at different IS levels as shown in Fig. 4.

Fig. 4
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Extrapolated arrangement of the multidimensional nature of the information

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Even when disconnected from informational availability, the architecture variable maintained the order of the IS, respecting its hierarchical nature. Both cities presented three or more SI codes in their architectures. However, this approach does not ensure the capability of multidimensional connections, a behavior found in both cities surveyed. Considering the combinations of the variables, i.e., the name of public services, information availability, and informational architecture, the conformation of the triad resulted in disconnection with respect to at least one system variable. The isolation of the green cube indicates disruptions among public service variables and municipal thematics. This aspect did not lead to customized information or address the specific needs of citizens.

Preliminarily, public services were identified and mapped on the cities’ websites, indicating twenty-nine (29) public services for the city of Regina and thirty-seven (37) for Rio de Janeiro (distributed across different municipal themes). After evaluating the public services mentioned and discarding duplicates, twenty-six (26) services were available and recovered for Regina and twenty-four (24) for Rio de Janeiro. However, the existence variable does not relate to the multidimensional attributes of an SDC. Conversely, different pages of the same site were browsed to obtain the same service variation.

Variables were crossed in both cities. Website navigation was required from the user to obtain specific information, leading to a disconnection from the IS.

The SDC constructs, via its variables, exhibited information-related connections with the public services offered. Nevertheless, these services were identified in linear or two-dimensional forms and layers, separated by different systems and disconnected. The IS of both cities exhibited bi-dimensional typification, and the disconnected constructs converged into nonconformity and multidimensional disorder.

The experimentation with the SDC constructs considered the four-variable structure, the assumption of strategic ordering capacity in municipal themes, and the possibilities of spatial arrangement between the construct and model composition variables. The partial connections among the variables and ordering abilities in different municipal themes were identified in the cities. Multidimensionality emerged as one of the strategies for digital cities; nevertheless, the research found disruptions and disconnections in one or more variables when crossed.

The variables researched exhibited information-related connections with the public services offered; however, these connections remained linear and bidirectional. The experiments pertaining to both cities exhibited a two-dimensional typification in their urban information systems. Therefore, no multidimensional informational connection elements were identified between the researched constructs, thus limiting their ability to customize and adhere to the digital city’s strategies.

The expected multidimensional model for strategic digital cities, whose constructs and variables are designed to be connected at distinct levels, was developed by predicting all alignments; this model is shown in Fig. 5.

Fig. 5
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Multidimensional model developed with distinct levels IS variables

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The SDC model represents the way in which multidimensionality and information should be connected and alignment. This experimentation scenario illustrates the flow of alignment and the arrangement of the variables. It facilitates strategic multidimensional customization, such that a proper data connection to the user’s context allows for the retrieval of information from different systems.

Discussion

The smart and strategic digital cities research exposed a critical discussion involving the multidimensional information character used on the city IS, highlighting digital service's strategic links.

The literature reviews identified the city's information management as an essential strategy for connecting customized digital services and public policies with citizens. The research also considered correlated models of smart cities from different countries, such as the models of Yountaik Leem, developed at National Hanbat University, South Korea (2014), Yunhe Pan, developed at Beijing University, China (2016), Alexander Sorokine, developed at the University of Tennessee, USA (2016), and Julia Barth, developed at the University of Duesseldorf, Germany (2017); the research concentrated on the information elements of these systems.

Regardless of smart city experiences and efforts to connect technologies to municipal management, the correlated models researched have confirmed a unidirectional use of information whose responses remain binary-based and unable to be customized to citizens’ needs.

The multidimensional information framework study was conducted in two cities in different countries: Regina, Saskatchewan, Canada, and Rio de Janeiro, Brazil. Both cities were chosen for strategic reasons since they have been recognized for using new technologies to benefit their citizens and businesses. The city of Regina is the capital of Saskatchewan and exhibits substantial dedication to innovation and social inclusion. Rio de Janeiro, the capital of the homonymous state, is recognized as one of the world's most beautiful cities, as well as for its use of technologies to provide better public services, including smart people (citizens), places, and city planning. The Canadian city surveyed showed a historical improvement in terms of open information and technology use, as noted in the Municipal Benchmarking Network Canada report—Information Technology & Performance Measurement subsection (MBNCanada, 2020).

The research objective was achieved, and the framework thus developed connected three constructs and ten multidimensional variables, thereby relating the conceptual theories to the developed and applied model. The developed model’s relevance lies in its characterization of information as a multidimensional element of an SDC via its capacity for spatial arrangement, thereby avoiding technological blindness and enhancing urban management. However, both cities exhibited partial multidimensionality in the context of IS, and architectural limitations were the central points experienced.

Whether the research variables and constructs are connected, the information flows in only two directions, i.e., bi-dimensional. The identified constructs and variables were experienced; the outcomes were partially associated with the other IS researched, thus reinforcing the dimensionality of information.

This framework highlighted a different perspective. Public digital services and city strategies converged into a multidimensional structure. These factors were configured as one of the contributions to both cities. The dynamic data flow from each variable facilitated the partial integration of the IS and the customization of public services previously mentioned. The experienced variables were interconnected in terms of their dimensional structure, considering the different websites and IS of the cities surveyed. Both towns presented virtualized information; the infomapping indicated distinct levels of information architecture.

Nevertheless, the infomapping procedure identified public services whose data were not indexed to the website subsection. This identification led to digital disconnection or partial connection with public services. The indexation of the experimental variables led to discontinuities between the information available and the public services offered. However, the descriptions identified among variables did not converge into the context of multidimensional informational management, connecting different IS.

The construct SDC was surveyed and recognized as one of the strategic elements for Digital Cities, considering information alignment and hierarchy functionality. The public digital services indexation and website information resulted in partial multidimensionality in both surveyed cities. However, these factors reinforced the tactical character of SDC, public services and information. Public policies can interact dynamically, thereby offering intelligent and personalized IS. Besides, such policies adhere to citizens' needs and improve the quality of life in a circumstantial technological context.

The multidimensional information framework revealed a new strategy for public management. The information is integrated as part of the process by which public policies connect citizens with customized digital services. In addition, this model responded to a scientific concern related to the strategic digital cities regarding digital information, public services, and bi-dimensional IS once information architecture played an essential role in facilitating access and SDC development, connecting public policies to citizen needs to improve flexible public management.

Conclusion

The urbanization of our planet calls for intelligent and flexible information responses from policy-makers and the research community. This study shows that a prosperous digital city requires a multidimensional alignment of information connecting public policies, technologies, digital services, and citizens.

This successful alignment is necessary for increasing information flexibility and customization to improve digital services and public policies. The multidimensional information was identified and mapped to connect at least two different information systems and their dynamic rearrangements.

The multidimensional information framework created a new architecture based on the city's information systems and public policies. As multidimensional information connects different contexts and IS dimensions, it assumes a customized meaning in accordance with citizens’ needs, thereby creating customized digital services for the city.

Unlike smart cities, the strategic digital city (SDC) concept uses technology to improve the connections between city planning, services, and citizens via comprehensive information flexibility customization.

The new framework thus developed exceeded the capacities of the traditional use of information, whose queries are unidirectional and limited by each city’s data repository responses. Additionally, this framework has systematization and data customization capabilities, providing different meanings to different users and facilitating the customized use of information.

Academically, the framework thus developed emphasizes the nature of multidimensional information and helps prevent strategic blindness, thereby improving UIS performance and prompting the democratization of information. The framework that has been presented shows information in its dynamic context, including customization and multidimensional connection with distinct levels of the city information system.

In addition, easy access to the city’s public information and customized services improves the relationships between citizens' needs and city policies, whose goals focus on establishing flexible relationships focusing on customized and meaningful digital services.

Public management requires the understanding and implementation of solutions aligned with the potential of a multidimensional informational framework, and this research presented a model according to which such information can be aligned for customization, regardless of the IS level used to retrieve it, thereby preventing technological blindness.

Therefore, the notion of a strategic digital city (SDC) goes beyond the concept of a smart city by using technology as an instrument for effective collaboration in the context of urban management with the aim of expanding the public space, strengthening the digital city, disseminating access to information to address the needs of cities and citizens, thereby multidimensionally connecting city information with digital services.

Limitations and Future Research

Regarding the respondents to the survey, the complexity of the concept of multidimensional information highlighted a limitation with respect to retrieving data, i.e., the surveyed respondents often assumed homogeneous information use and ignored the dynamic nature of such information.

The study compared four correlated cases, which can eventually be understood, albeit not comprehensively, by considering the expansive effects of innovative technologies, which can be extended in future studies. However, these limitations do not influence the study’s results since both surveyed cities exhibited a hierarchical information system.

Future research might extrapolate variables or add different cities and realities to reinforce the information's multidimensional concepts linked to the strategic digital city's nature. Finally, the discussions described are relevant regarding a multidimensional perspective of information and its strategic use to enhance the SDC, providing customized services and flexibility management.