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Conceptualizing Smart Government: Interrelations and Reciprocities with Smart City

Published: 06 January 2022 Publication History

Abstract

Smart government (SG) is an emerging topic, which increasingly attracts attention from scholars who work in public administration, political, and information sciences. Smart city (SC) on the other hand, is an emerging and multidisciplinary domain of study. It is not clear whether the two terms SG and SC co-exist or concern different domains that interrelate and interact. The aim of this paper is to investigate the term SG; to conceptualize it with components; to define the importance of these components to the SG with their relative strengths; and to clarify its relationship with the SC term. In this respect, this paper follows a multi-method approach: a comprehensive literature review to define and conceptualize the SG, and determine its components, and a Delphi study for validating the literature findings and for calculating the relative components’ strengths. The SG definitions that are in literature have several weaknesses and the authors proposed a definition to the SG that overcomes them, while a model with three rings, three dimensions and 13 components conceptualizes it. The Delphi study showed that all the SG conceptual entities are useful, and highlighted that Citizens Engagement, Economic Growth, and Accountability are more important compared to the others, but it is hard to decide about the less important component. Third, the ICT Innovation entity appears to be the most important compared to emerging technologies and data. Finally, SC and SG are indeed related but, SC is proved to be a complimentary part of the broader SG term.

1 Introduction

The terms smart governance and smart government are emerging, and they evolve with the contribution of political and information sciences. Numerous scientific works have attempted to define smart government (SG), while other terms like digital (or e-) or open government still appear in literature studies and generate questions about the clear meaning of SG. Existing definitions give different meanings to SG, which range from organizational efforts that optimize public sector's performance to the extensive use of technology and innovation by governments, in their attempts to enhance their efficiency, openness and transparency (Hope, 2001). Moreover, smart governance is -among other meanings- one of the six dimensions that conceptualize the smart city (SC) and concerns again the transformation of the local government into a transparent, efficient, and open administration to its citizens with the use of the information and communications technologies (ICT) [23], as well as the formulation of the appropriate SC policies. Governments and city governments play a major role in the SC growth by encouraging, funding, and authorizing urban innovation development and by generating policies for SC. In this regard, a close -but still unclear- connection between the SG and the SC appears, which cannot be located between SC and the other adjectives to government (digital-, open-, online- etc.).
Defining the smart city (SC) has also been a long and extremely complex process, which involved scientists and practitioners from various disciplines: social and political sciences; urban technology; information and communications technologies (ICT); education and training; health; transportation; energy, water, and other utility sectors; and tourism, are only some of the involved domains. Nevertheless, the generation of this much lively discussion and academic debate is a normal phenomenon that occurs during the appearance of a new area of study and practice [5]. More specifically, the development process of a new industry consists of the following steps [69]: Innovation development; Imitation: firms develop their competitive approaches; Technological competition: research and development (R&D) improves the innovation; and Standardization: an ideal product has been determined and R&D aims to improve the production process. A similar process can be observed that has been followed during the evolution of the SC industry [5, 7]:
1.
The SC term initially appeared in literature in 1997 and various SC approaches have appeared since then with the form of various types of urban innovation that are promising to deal with numerous challenges [6].
2.
Many schools of thought were engaged to define the SC and multiple models have been composed to conceptualize it [8].
3.
Many industrial vendors struggle to provide with alternative products almost all the SC dimensions: people, governance, economy, environment, mobility, and living.
4.
Many standardization bodies (e.g., the International Telecommunications Union (ITU), the International Standards Organization (ISO), the US National Institute for Standards and Technology (NIST), the European Committee for Standardization (CEN), the British Institute for Standards (BSI), etc.) develop standards to define and specify the SC components or the SC as an entire system [5].
The SC industry is growing rapidly, and it is expected to dominate against the other important economic sectors, with an estimated size of $1 U.S. trillion by 2025 [10]. This growth is based on the involvement of almost all the industrial domains, which struggle to develop products that deal with the emerging SC challenges (e.g., climate change; energy efficiency and emissions control; and livability) as they are defined in strategic documents -like the United Nations (UN) 2030 Agenda for sustainable development [82] - and to the international scope, which involves all the cities across the globe where more than 80% of the international Gross Domestic Product (GDP) is being produced [89].
On the other hand, the SG evolution does not follow the same maturation process as the SC, since it could be more likely labeled as “a phenomenon” or “trend”, while it does not seem to concern a product or a set of products that must be technically specified and homogenized by standards, and it emerges mainly according to scholars’ and schools of thought's contribution. Moreover, although smart city government has been conceptualized quite recently [68] via literature analysis with the inclusion of stakeholders; organizations; processes; roles; technology and data; policies; and arrangements as its components, such a conceptualization process cannot be located about SG. Due to the missing standardization of the SG term, to the plethora of the SG approaches that can be seen in literature -many of which relate it with the SC-, as well as to the use of several adjectives to “government” (e.g., digital-, open-, internet-, online-, etc.) -which someone could claim that they could be seen as “fast fashion” or “buzzwords” [92]- this paper explores SG and concludes on its concept, while it tries to clarify it against the SC. In this regard, it grounds and provides with answers the following three research questions:
RQ1:
Which are the conceptual entities that define the SG?
RQ2:
What are the relative strengths between the SG conceptual entities?
RQ3:
What is the relation between SC and SG?
These questions are very important to be answered: there is no broadly accepted SG definition but several discussed corresponding concepts and approaches, which synthesize the SG with several components that play different roles in the SG mission and evolution (RQ1). Thus, the clarification of these components and the estimation of their comparative importance can be useful for the SG understanding, providing scholars with a clearer view of its future evolution (RQ2). Additionally, researchers from both the SC and SG domains, ground relatively common research problems, while they publish corresponding scientific works, calls for scientific conferences, workshops, and journal special issues. Nevertheless, it is still not clear whether smart government and smart governance are synonymous with each other or complimentary and how they address the SC (RQ3).
To answer the previously defined RQs, a multi-method research methodology has been followed: a comprehensive and incremental literature review is performed about SG and important findings are generated, aggregated, and discussed. The outcomes are used to define the SG conceptual structure, which consists of several components. This structure, together with the comparative importance that could measure the impact of these components to the SG are validated with the contribution of a group of experts who come both from the SC and the SG domains. This validation is performed with a two-round Delphi method, which asked the opinion of the contributors with a structured questionnaire and the Analytical Hierarchical Process (AHP).
The research process was a complex one: participants of the questionnaires were in several countries and participated remotely, which took time to collect their inputs since their availability was different. Also, the overall study lasted almost three years, a period when the SG and the SC terms evolved in the literature. The findings of this screening concluded that the discussion on SG is new, while many combine SG and SC. Efforts to define a multi-tier model for the SG which will be later discussed in the literature review, became complicated for several reasons, especially when there is no clear prioritization or direct connection between components of these layers in the conceptual model.
The remainder of this article is structured as follows: Section 2 concerns the background of this paper. The background is brief because a lot of corresponding information is collected under the literature review, later in Section 3, which contains the multi-method research methodology and findings. Finally, Section 4 summarizes conclusions and presents some future thoughts.

2 Background

The term SG does not have a widely accepted definition, but with the use of technology (i.e., data, artificial intelligence (AI) and other emerging ICT) and innovation, it appears to be the next step for digital (or e-) government. However, it is not clear how its components interrelate and interact with the concept of smart city. SG can be considered as a basis for developing smart governance through information and communication technologies (ICT). The lack of a clear definition of SG in connection to smart governance and SC, emphasize the need for conceptual clarity in the quest of the finding elements that define SG.
Although the term “smart” has become fashionable, there is not a common consensus for it, while it is also broadly used as a synonym of almost anything considered to be modern and intelligent: A servant surrounded by servants, which may be a configuration of both humans and devices, from both public and private sectors (Cellary, 2013). While the word “servant” evokes images from aristocracy to slavery, in the evolving smart ecosystems, a person or system will be surrounded by -or embedded with- other “servant systems”, which are the smart systems, and they are normally technology-based.
A “city” on the other hand, is considered as an urban area, which according to the United Nations [81] it typically begins with a population density of 1,500 people per square mile, but it varies across countries. Another definition for city, claims that “city” is an urban community falling under a specific administrative boundary (ISO, 2014) where “community” is a group of people with an arrangement of responsibilities, activities, and relationships [38]. These definitions highlight a close relation between “city” and “government” at a specific geographic place.
According to the previously given definitions to “smart” and “city”, the smart city (SC) relegates to technology embeddedness in a city, but it has been quite a “fuzzy” topic until recently, when various scholars and the international standards managed to define it more precisely [10]: the International Telecommunications Union (ITU) (ITU, 2014) names “a smart sustainable city as an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects”.
Similarly, the International Standards Organization (ISO) ([37] views SC “as a new concept and a new model, which applies the new generation of information technologies, such as the Internet of Things, cloud computing, big data, and space/geographical information integration, to facilitate the planning, construction, management and smart services of cities”.
Finally, the British Standards (BSI, 2014) concerns the SC “as the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens”.
These seemingly competitive SC definitions combine cutting-edge technology and sustainability at the local level, and they could be converged to something like that the SC concerns innovation -not necessarily but mainly based on the ICT- that enhances urban living in terms of people, governance, economy, mobility, environment and living [7].
Smart government (SG) on the other hand is still quite a fuzzy term, which lacks a clear definition. Although the prior definition for “smart” leads to a public administration that utilizes servant systems, such a definition comes too close to past electronic or digital or internet-based (or e-) or even open government definitions like the utilization of the ICT by governments to become more effective, efficient, transparent, and accountable [9]. In this respect, what is the difference that the adjective “smart” brings to government compared to the past adjectives “digital” or “electronic”? The following literature review section provides an answer to this question, it provides with conceptualization the term, with a goal of more precisely clarifying how SC and SG are synonymous or complementary to one another.

3 Research Methodology

This paper uses a multi-method research methodology. It starts in the following Section 3.1 with a literature review to collect scholars’ approaches to the concept and definition of SG. This review was performed periodically for three years starting from 2016 and ended in the end of 2018, collecting, and comparing the updates. The outcomes from the first literature review in 2016 structured the conceptual model for SG consisting of three layers and 16 entities, whose synthesis did not change from the literature updates of the followed reviews. Then Section 3.2 presents the model testing with a two-round Delphi methodology by a group of 14 experts who were in different countries and participated remotely via filling-in a structured questionnaire. The questionnaire was quite complex since it aimed to define the comparative strengths between the identified SG components, with the AHP, which affected the duration of the two Delphi rounds and made them end in mid-2019. It is extremely important that the literature evidence did not affect the SG structure, a fact that makes the outcomes of this study valid for the SG definition. Each of the Sections 3.1 and 3.2 ends with a discussion of the results of the corresponding method that return evidence to answer the RQs.

3.1 Literature Review

For the purposes of this article, the authors queried three widely used scientific databases (SCOPUS®, Google SCHOLAR® and Science Direct®) progressively in three phases: April 2016, September 2017, and September 2018, with the use of the keywords “smart” AND “government” including all the articles that had been published until the day that the searches took place. The combination of these key words was preferred compared to others (i.e., “smart government” or “smart governance” directly) that returned only a few results, while no clear or broad use of “smart government” could be located at the beginning of this study (in the early 2016). Moreover, the authors wanted to uncover all the potential use of “smartness” in government and these keywords addressed this objective. As such, the authors had to keep this keyword combination during the entire study duration, otherwise the results could be different.
This progressive review was performed due to the following reasons: first, because a concurrent Delphi study was performed, where the contributors participated distantly and asynchronously during the two rounds of the methodology. The overall Delphi study was long, because the appropriate experts had to be located and reserve their time for contribution. The authors respected their availability but it took time to collect their inputs. In parallel, the literature review results were updated, in case for some important changes to the concept of the SG to be collected too. The collected outcomes (Table 1) were not as many as were expected, while many of the articles that the queries returned appeared in all the pools of results.
Table 1.
 SourceResultsArticles After Screening 
  201620172018201620172018Citation
1SCOPUS®637948915242[15, 2428, 30, 33, 34, 36, 39, 43, 46, 51, 53, 5557, 59, 62, 65, 66, 73, 75, 7779, 85]
2Google SCHOLAR®1,3601,8703,6409122[1, 5, 7, 8, 11, 23, 29, 32, 43, 47, 63, 67, 87]
3Science Direct®5567163121613[2, 12, 13, 14, 1620, 22, 30, 31, 35, 41, 42, 45, 50, 52, 54, 58, 61, 64, 68, 71, 74, 76, 80, 83, 88, 91]
Table 1. Literature Review Findings
After the literature review search, the authors then proceeded with screening the results, leaving out irrelevant articles and articles about SC alone or government alone as well as editorial articles. In 2016, out of 1,477 results from the three scientific databases used, only 35 articles were chosen after the screening. In 2017, 52 were chosen from 2,016 results, and in 2018, 13 articles were selected out of 4,290 results. The remaining articles returned useful outcomes. First, this evidence shows that the concept of SG is quite new, since it is being discussed from the early 2000’s as the expected form of government, which will be based on intelligent and well-trained human resources. Moreover, this evolving pool of articles demonstrate that scholars’ view on SG changes: they have recently initiated discussions about SG as quite a “whole-of-government” approach, many of them hardly separate SG from digital (or e-) government, while others combine smart government and smart city (Table 2).
Table 2.
TermDefinitionSourceConceptual clarity challenge(s)
Smart GovernmentSmart Government not only stands for the digitization of current processes and services, but also for the development and establishment of completely new processes and public services in a participatory manner.[11, 73]Comparative definition (“digitization of current processes and services” – “completely new processes and public services in a participatory manner”).
Lack of parsimony.
The extensive use of smart technology to perform governmental tasks.[15, 55]Unclear term “smart technology”.
Interoperability / The implementation of a set of business processes and underlying ICT capabilities that enable seamless information flow across government agencies and programs, to become intuitive in providing high quality citizen services across all government programs and activity domains.[59, 67]Conflation between the concept and its impacts.
Lack of parsimony.
Government's strategic role in society and the development of managerial capacities that enhances effectiveness (i.e., intra-governmental coordination, decentralization, increased participation, and renewal of organizational structures).[47]Conflation between the concept and its impacts.
Unclear term: “managerial capacities” defined using examples.
Lack of parsimony.
Smart ICT government operations (i.e., cross-agency working groups for every ICT field; infrastructure for educational training; and instituting procurement strategies).[24]Unclear term: “smart ICT operations” defined using examples.
Lack of parsimony.
The evolution of the term ‘smart government’ to the term ‘smart governance’ in an attempt of governments to cope with complex and uncertain environments and to achieve resilience.
Set of smart government elements:
openness and decision making, open information sharing and use, stakeholder participation and collaboration, and improving government operations and services, all through the use of intelligent technologies as they act as a facilitator of innovation, sustainability, competitiveness, and livability.
[77]Conflation between the concept and its impacts.
Circularity “smart”.
Unclear term “smart government” defined using examples.
Lack of parsimony.
A creative mix of emerging technologies and innovation in the public sector.[14, 16, 18, 20, 27, 61, 63, 64, 88]Unclear term “emerging technologies”.
Smart government is the next step for e-government.[11, 35, 40, 50, 56, 71, 73]Comparative definition (“e-government”).
Smart government is the next step for open government.[4345, 65, 83]Comparative definition (“open government”).
Smart GovernancePrinciples, factors, and capacities that constitute a form of governance able to cope with the conditions and exigencies of the knowledge society.[87]Conflation between the concept and its impacts.
Unclear term “knowledge society”.
Lack of parsimony.
A dimension of smart city, which measures local smart government performance with the following indexes: participation in decision making; public and social services; transparent governance; and political strategies and perspectives.[22, 23, 58, 66, 74, 91]Comparative definition (“smart city”).
Unclear term (“local smart government performance”) defined using examples.
Lack of parsimony.
Smart governance describes the smart and connected communities (SCC)-enabled suite of policy interventions that can respond immediately, or long term, to observable trends in the city.[90]Circularity (“smart”).
The facilitator for local economy via the effort of local governments to adjust local regulatory frameworks for new business attraction and creation.[25]Circularity (“local”).
Conflation between the concept and its impacts.
Lack of parsimony.
Better governance to manage smart city initiatives.[4, 39]Conflation between the concept and its impacts.
Unclear term (“smart city initiatives”).
Smart City GovernmentTransform a geographical area in significant and fundamental ways with the ICT.[85]Conflation between the concept and its impacts.
Data and smart city efforts enable local governments’ transformation to deliver enhanced smart services and optimize city processes and to enhance transparency and co-production for urban solution.[1, 2, 13, 32, 46, 49, 54]Circularity (“smart”).
Conflation between the concept and its impacts.
Lack of parsimony.
Smart city is an area of practice for smart government.[57, 58]Circularity (“smart”).
Comparative definition (“smart city”).
Smart government is the source of smart public service delivery within a smart city, of city administration and of public engagement.[28, 29, 48, 52]Comparative definition (“smart city”).
Circularity (“smart”).
Unclear term (“smart public service”).
Lack of parsimony.
Smart city is an area for smart government development.[7, 34, 36]Comparative definition (“smart city”).
Circularity (“smart”).
Smart government deals with smart city government, which manages and implements policies by leveraging ICTs and institutions and by actively involving and collaborating with stakeholders.[7678]Comparative definition (“smart city government”).
Circularity (“smart”).
Conflation between the concept and its impacts.
Lack of parsimony.
Table 2. Definitions Collected to SG
Another interesting finding is that only a couple of journals participate in this SG debate (i.e., GIQ and Information Polity, etc.), which however was not surprising for the authors, since these leaflets lead the government innovation research agenda. Moreover, results from other journals were excluded from this study since they were focusing on SC alone. Even in early 2021, when Google Scholar® for instance returns more than 2 million records with the same keywords, only a few additional journals host articles with the SG subject (i.e., IJEGR, International Journal on Emerging Technologies, International Journal of Management and Human Science (IJMHS) etc.), while others are still discussing mainly about government and SC.
Moreover, there is a lack of conceptual clarity in the identified definitions and in this regard the approach from Vial [84] was followed to document corresponding challenges according to conceptual definition rules [84; 86]: use of primitive and derived terms; uniqueness; unambiguous and clear terminology; consistency with the field; narrowness; new hypothesis avoidance; and statistical testing of the terms.
More specifically, scholars provided with alternative meanings the SG term, which can initially be located in 2001 (Hope, 2001) as the type of government that focuses on its strategic roles with an organizational design and managerial setup that permit it to perform its roles in an optimally effective and efficient manner. On the one hand, Mellouli et al. [55], Cellary [15] and Puron-Cid [64] name SG as the extensive use of technology by governments to perform governmental tasks, while Taylor [79] and Gil-Garcia et al. [26, 27] relate the terms “smart city” and “government” demonstrating innovation and intelligence for local or national governments as the means to increase their efficiency and effectiveness, a definition that adopt Eom et al. [20], Scholl and Scholl [77], and Scholl and Dwivedi [76] too, who interrelate this innovation-mix with an enhanced government ability to deal with problem solving and livability at a local level.
Works like from [33, 46] claim that smart governments utilize the power of “data” in their attempt to improve public services; to enable an integrated, seamless service experience; to engage with citizens; to co-develop policies; and to implement solutions for well-being of the community. They adopt Rubel's [67] definition for smart government according to which, government smart transformation engages citizen participation, information transparency and service improvement. This definition seems to be followed by Ruhlandt [68], Scholl and Al Awadhi [75] and by Gil-Garcia [24], who see a “whole-of-government” result behind this information integration, while Maheshwari and Janssen [53] recognize the need for public organizations’ interconnection too and discuss corresponding interoperability issues. The power of data for the development of a smarter government is labeled “data-smart government” by Janowski [41], it is adopted by Cornadie and Choenni [17] too, while Turle [80] sees shared service provision to be the outcome of this transformation.
Other SG definitions are given by Gil Garcia et al. [27] and Gil-Garcia and Sayogo [31], who conclude that SG is a creative mix of emerging technologies and innovation in the public sector, which can handle complexity and uncertainty with coordination, continued engagement, access to open data, and shared information. More specifically, they claim that smart government is a continuous effort and not a specific goal, which is supported by a set of emerging technologies (i.e., big data, open government data, social networking, blogs, Really Simple Syndication (RSS) feeds, web design and programs, mobile government, smartphone applications, cloud computing, and sensors). Similarly, Criado et al. [18] determine this creative mix to be the combination of social media, open government and big data or in other terms social technologies, while Cazares [14] views SG as the result of the technological progress in Artificial Intelligence (AI). Chatfield and Reddick [16] and Wirtz et al. [88] examine the Internet-of-Things (IoT) – based smart government. Ojo and Mellouli [61] compare alternative approaches of networks as a means for governments to deal with social challenges and view SG through a collaborative, cross-boundary governance network.
SG is also defined as the next step for e-government, with the use of innovation and updated services [71, 73] or as the next step of open government [83, 65] with citizen engagement, interoperability and accountability. Innovation can lead to the production of new public value, that is “value created by government through services, law regulations and other actions” and in this respect a triangle controls the migration from e-government to smart government, which consists of politics, values, and evidence. Similarly, Jimenez et al. [44] introduce an intelligent model for public organizations, entitled “Smart Government Ecosystem Matrix”. This model is a two-dimensional matrix that combines open government features (transparency, collaboration, participation, and interoperability) and smart city context (organizational and management, technology and infrastructure, governance and policy, social, economy, and natural environment) and defines SG -again- as the next step of open government. Jun and Chung [45] agree with this approach and introduce a platform (so-called Government 3.0 platform), which shares open data with citizens, government and enterprises and enables public process co-design. Moreover, Jetzek [42] uncovers smart government's foundation to be interoperability split in three layers (technical, conceptual, and organizational).
From a quite similar viewpoint, Linders et al. [50] consider this open government's next step as the transformation of typical public services to pro-active ones, which will drive smart development, meaning that innovations in ICT can serve as an enabler for national development. Additionally, Jansen and Olnes [40] adopt the European Commission's approach [21], which understands SG as the availability of specific e-government key enablers and more specifically of the smart applications eID, eDocuments, eSafe, Single Sign On (SSO) and authentic sources.
Finally, various scholars also see SG within the SC nexus and a corresponding research-practice consortium has been structured to investigate this relationship. This discussion started quite early in Telecities (Dai, 2003), where SG was one of the primary aspects and continued with the definition of smart governance dimension for smart city [22, 23, 58, 74, 90, 91]. Under these lens, Gil Garcia et al. [27] locate a shift of government innovation from a value-based concept into a concrete goal with specific targets, which is used to measure smartness. In this respect, governments utilize innovation to gain a good understanding of the communities (being percipient); to accurately assess situations or people (being astute); to judge sharply (being shrewd), and to decide and respond quickly or effectively (being quick). According to their approach, SC is only a subset of smart government, where local governments understand the term “being smart” as their attempts to enhance their efficiency, effectiveness, transparency and collaboration with emerging technologies and innovation.
Moreover, Nam and Pardo [57] see SC as an area of practice for government innovation, which enhances government effectiveness and efficiency, service delivery, process transparency and collaboration; quite similarly, Anthopoulos and Reddick [7] see SC as a means for smart government deployment, as well as utilities for recent government challenges’ management and new policy development (i.e., climate change management); Gil Garcia et al. [28] identify SG as the source of smart public service delivery within a SC, of city administration and of public engagement; Scholl and Scholl [77] view SG as SC government, where the local government implements policies for smart local development and stakeholders’ engagement; Lee and Lee [48] view smartness in city's ability to deliver smart services classified in several typologies; Gil-Garcia and Aldama-Nalda [25] document smart governance as the facilitator for local economy via the efforts of local governments to adjust local regulatory frameworks for new business attraction and creation, while Gil-Garcia et al. [29] claim that smart government in its attempt to develop a SC uses and integrates ICT and innovation in the activities of governing, including internal operations, but also public services and citizen engagement; Alawadhi et al. [4] compare alternative definitions to justify the need for better governance to manage SC initiatives, while from a quite similar point of view, Iwan and Poon [39] consider SG as government practices that deal with SC challenges (e.g, climate change); Lv et al. [52] view SG via the dissemination of typical e-government services via SC platforms; Aguilera et al. [2; and Barns [13] view locally produced and collected data as a key-enabler for a variety of purposes, including transparency, performance monitoring, reporting, planning, and policy-making by local governments in SC, and Matheus et al. [54] emphasize on corresponding dashboards that are fueled with this data. All these definitions are depicted and compared in Table 2.
Since both SG and smart governance terms are used in literature, a distinction must be given: “Government occurs when those with legally and formally derived authority and policing power execute and implement activities” and “Governance refers to the creation, execution, and implementation of activities backed by the shared goals of citizens and organizations, who may or may not have formal authority or policing power” and in this regard smart governance is “an abbreviation for the ensemble of principles, factors and capacities that constitute a form of governance able to cope with the conditions and exigencies of the knowledge society” [87]. Therefore, it is concluded that smart governments implement smart governance initiatives [27].
The above definitions are aggregated and presented in Table 2 and some important findings can be summarized (Figure 1). More specifically, the figure depicts the number of scholars that mention each of the definitions (directly or ‘closely’) with the size of bubble and the position on the axis-Y; and the year (axis-X) when most of the collected references mention each definition. Results show that the SG was introduced as the next step of open government, which aims to transform governments to more open, effective, and attractive to their citizens with the use of smart technology and these approaches are mostly discussed in 2014. Later in 2015, scholars identified both a high relation between the SG and the digital (e-) government (orange bubble with 7 citations) and the SG and the SC (green bubble with 13 citations). This shift shows either that “smart” is the evolution of “digital” government or that SG is obtained at a local level (alternatively, that SG affects the local communities). Finally, later in 2018, scholars interrelate the SG with innovation and technology in the public sector, where data plays an important role.
Fig. 1.
Fig. 1. The broadness of SG definitions’ appearance.
All the above definitions could be combined to the following one: SG concerns the transformation of government to an open, more effective, efficient, and transparent one, with the mix of emerging technologies and innovation, which goes beyond typical digital and open government approaches. Moreover, SG is seen under the lens of local government, where SC government is performed at a specific geographic location and community. This definition partially provides with answer the RQ1, without however avoiding conceptual clarity mistakes. In this regard and following the process of Vial [84] for digital transformation's definition, the above SG definition could be revised to SG concerns the process that aims to improve the government's performance and operations with the combination of digital technologies (data, computing, and networking) and innovation, which is more likely to take place at a local level. This definition does not conceptualize the SG, something that is being performed in the following sub-section.

3.2 The SG Conceptual Structure

The literature evidence that was presented above return significant findings about the SG conceptualization too, but only a few SG conceptual models could be in the beginning of this study (early 2016). The authors were inspired by [10] and [30] because they both included more components for the SG conceptualization: Anthopoulos [10] attempted to recognize the conceptual entities that define the SG in general and to propose a multi-tier model, with components that interrelate and interact; Gil Garcia et al. [30] define SG conceptual entities, but their definition and analysis is performed with a focus on SC. As such, the combination of these two conceptual models can cover both a generic and a local government approach. However, the model had to be more complicated for the following reasons: first, the SG was met in the literature with the key-terms smart government, smart governance, and smart city government, which are being approached with the following labeling: digital (or e-) government; open government; and smart city (even in the form of smart community). These labels can be considered as the terms or more likely the dimensions that synthesize the first layer of the SG conceptual analysis (outer ring), since they explain better the initial key-terms that were directly excluded from the literature review (Table 2).
Moreover, these three dimensions are explained with quite the same terminology: they deal with technologies (more likely ICT-based) that address economic growth (at local or regional or national levels); government effectiveness and efficiency; citizen engagement in political issues; new forms of policy making; the delivery of modern smart services; innovation development (in or supported by) government; accountability and transparency; the technology itself and more specifically the necessary key enablers for government digital transformation; and smart city government.
These second-level terms can be considered as the entities that comprise the second layer of the SG conceptual analysis (medium ring), since they explain what the terms aim to achieve. Finally, these nine entities are being implemented with the combination of ICT innovation, emerging technologies (e.g., artificial intelligence (AI); autonomous objects; Internet-of-Things (IoT); 5G networks etc.) and data, which concern the elements for all the SG entities’ (and in this regard, even the SG itself) implementation and comprise the third layer of the SG conceptual analysis (inner ring). Some contents of this conceptual model appear to be duplicate (e.g., smart city (outer ring) and smart city government (medium ring); innovation (medium ring) and ICT innovation (inner ring). However, they concern different aspects in the SG definition, they were identified by literature review and they could be interpreted with the following text (with a flow from the outer to the inner ring of the model): (a) SG concerns government performance enhancement within a smart city as well as smart city government, which is enabled with data, ICT innovation and emerging technologies; On the other hand: (b) SG concerns the process of improving government performance with innovation activities, which are enabled by data, emerging technologies and ICT innovation in general. After all, this model came out from literature evidence and it must be clarified and tested with regard to its validity and contents’ mutual strengths.
These two models have similarities and differences, while some associations can be extracted from their contents (Table 3) that can be validated: (1) Government accountability concerns a topic of openness in government. (2) SG considers smart public service design around citizen needs and, in this regard, smart services can be associated with citizen centricity. (4) SG enhances decision and policy making with the use of -even real time collected data and in this regard, data is associated with evidence-based decision making. (5) SG prioritizes and supports economic growth at the local level, with the support of a startup launch and with the simplification of the business-oriented services. As such, economic growth can be associated with entrepreneurialism. (7–8) Emerging technologies is quite an ambiguous term and its context changes in time. For instance, during this article's development emerging technologies evolved mainly to AI, cloud, IoT and blockchain, while during its completion new ones appeared (e.g., edge computing). Key-enablers on the other hand are the technological artefacts that make Nevertheless, it is a term that describes new and evolving ICT and aligns to technology savviness context, which deals with corresponding knowledge and competencies in government to utilize emerging technologies. (8–9, 10) Innovation and ICT Innovation in government is the process for defining or updating services and internal processes with the use of the ICT, while creativity deals with human characteristics that enable innovation (managerial leadership, support, and commitment). (9–11) Interoperability deals with the ability of different -either in technological or organizational terms- government systems to interact and exchange information, while integration addresses inter-organizational information sharing. Finally, (10–12, 13, 14) smart city government concerns the governance of a smart city in terms of successful project and program development, as well as of deliverables’ operations management that meet the predefined local challenges; while, sustainability is a critical challenge that refers to the environmental, economic and social viability; equality is another challenge that deals with social inclusion; and resiliency is the ability of government to respond against emergent and disastrous effects. The rest of the elements are the same (3–3: citizen engagement; 6–6, 7 effectiveness and efficiency).
Table 3.
Conceptual model 1 [10]Conceptual model 2 [30]
AccountabilityOpenness
Smart servicesCitizen centricity
Citizen EngagementCitizen engagement
Data – Policy MakingEvidence-based decision making
Economic GrowthEntrepreneurialism
Effectiveness and EfficiencyEffectiveness
Efficiency
Emerging Technologies – Key EnablersTechnology savviness
Innovation – ICT InnovationInnovation
Creativity
InteroperabilityIntegration
Smart City GovernmentSustainability
1. Equality
2. Resiliency
Table 3. Comparing the Selected Conceptual Models
The above outcomes show that the model introduced by [10] appears to be more generic, since it discusses SG in general and not with a strong focus on SC, while it avoids components’ hierarchical adjustment with the use of 3-rings and in this regard, it is considered appropriate to define the SG (Figure 2). Rings’ contents are “loosely” interrelated and their position in the figure is indicative, which means that any of them influence the upper level's contents, while any of them is also affected by the lower level's contents. For the purposes of this study, the authors preferred to adopt and test an existing conceptualization model, rather than introducing an alternative (e.g., a Venn Diagram with two intersecting circles for “smartness” and “government” that could also be applicable).
Fig. 2.
Fig. 2. The validated SG conceptual model [10].
However, the authors wanted to test the selected model about its efficiency to discuss the fundamentals of the SG and SC terms, which have been found in literature that some scholars interrelate. As such, the authors defined a three-tier framework (Figure 3) with associated items between the selected model and others that were in literature and associate SG SC. More specifically, the selected model is located on layer 1 (the SG framework); an SG - SC framework that was introduced by Jiménez et al. [44] and presents the synthesis of the SG and SC is placed on layer 2; and a unified development framework for the SC that was introduced by [9] is located on layer 3 (the SC framework). Connections between the three models are depicted on Figure 3, which come from the interrelations of Table 4.
Fig. 3.
Fig. 3. Validation of the selected model's capacity to discuss the SG theoretical foundations.
Table 4.
Layer 1: SG Framework model [10]Layer 2: SG & SC Framework model [44]Layer 3: SC Framework [8]
Policy making
Economic Growth
Smart Services
1. Open & Smart EcosystemFacilities
Services
People
Accountability
Citizen Engagement
Effectiveness and Efficiency
Smart City Government
Emerging Technologies
Key-enablers
Innovation – ICT Innovation
2. Open Gov & SCGovernance
Interoperability3. InteroperabilityPlanning & management
Architecture
Data4. Ontologies, big data & open data
5. Datasets
Data
Table 4. Validating the Selected Model's Capacity to Discuss the SG Theoretical Foundations
Jiménez et al. [43] consider SG as the highest modernization phase for the public sector in a smart city, which is evolved from open government and in this regard, they introduced the SG-SC framework (layer 2), which they name as Smart Government Matrix. This framework is analyzed in layers that from top to bottom are as follows: they consider SG as open and smart ecosystem (layer 1) as a system of systems within an urban space, where actors and institutions, systems, environments, energy, citizens, infrastructures, information, policy and technology co-exist and interact. This ecosystem is the outcome of an open government -in terms of transparency, collaboration, and participation- and smart city -in terms of the existence ICT networks, intelligence, sensors and software at a local area- (layer 2); that followed an interoperability strategy, documented on an interoperability framework, which addressed the organizational dimension of the local government (layer 3); which defined the appropriate ontologies for big and open data collected from the urban area (layer 4); as pieced of data received from sensors (layer 5).
On the other hand, Anthopoulos et al. [8] introduced a unified SC model (USCM), which synthesizes several SC conceptual models and it is analyzed in the following layers: (layer 1) physical facilities located in a city (e.g. for transportation, energy, water, buildings, etc.); (layer 2) services that are being offered in the city and utilize the above facilities (e.g. health, education, tourism, etc.); (layer 3) SC governance, which is performed with planning & management and follows a specific SC architecture that enables monitoring and benchmarking, with data that are being collected from the city.
According to the above explanations the open and smart ecosystem in the middle is connected on the left with polices and smart services, whose outcome is -among others- economic growth, while on the right with facilities, services, and people. Similarly, open gov is connected on the right with governance and on the left with accountability, citizen engagement, and effectiveness and efficiency and SC relates to SC government, emerging technologies, key-enablers, and innovation and ICT innovation. Additionally, interoperability of the SG & SC framework has a clear connection with interoperability on the left and with planning and management on the right, which are performed on a specific architecture. Finally, the connection between data and ontologies is clear between all the tested frameworks.
These connections validate that the selected model is efficient to discuss the fundamental theories of SG and can be considered appropriate to lead to a combined SG-SC development, which in turn can lead to SC development. The total schema can be also considered an SG-SC development framework, where SG and SC interact.
Additionally, the selected model must be tested for its efficiency to support the future research evolution of SG and SC. In this respect, the model was checked for interrelations with the common research agenda that was identified by [7]. More specifically, this two-tier framework consists of the selected model (layer 1) and SC & SG evolution framework (layer 2). The meta level of layer 2 represents the SG & SC future challenges that are being or expected to be discussed and, in this respect, key-enablers, data and emerging technologies appear in the identified literature evidence and they are directly associated. About the role of government and SC, the government interventions for livability, sustainability and resilience, city management and city competition are all associated with the following elements on the left: economic growth, smart city government, effectiveness and efficiency, smart services, and data. Finally, the topics that deal with e-government and SC can be associated with the model's elements on the left: citizen engagement, service co-design and digital neighborhood relate to citizen engagement, accountability, policy making and interoperability; standardization is associated with emerging technologies, key-enablers, innovation, and ICT innovation.
The outcomes (Figure 4) (Table 5) return the existence of close connections between the entities of the model and the SG-SC evolution framework, which validate the appropriateness of the selected model, while they introduce an SG-SC research development framework.
Fig. 4.
Fig. 4. Validation of the selected model's capacity to lead SC-SG research development.
Table 5.
Layer 1: SG Framework model [10]Layer 2: SG & SC Evolution Framework Model [7]
Economic Growth
Smart services
Key-enablers
Emerging Technologies
Data
Meta level: challenges for SC studies
Economic Growth
Smart services
Effectiveness and Efficiency
Smart City Government
Data
Government and Smart City
Livability
Urban Sustainability
Resilient City
City Management
City Competition
Accountability
Citizen Engagement
Policy making
Interoperability
Emerging Technologies
Key-enablers
Innovation – ICT Innovation
e-Government and Smart City
Citizen participation and engagement
Service co-design
Digital neighborhood
Standardization
 Foundation: SC in depth understanding
Table 5. Validation of the Selected Model's Capacity to Lead SC-SG Research Development
The above outcomes validate that the selected model of (Figure 2) is efficient to describe the SG and could be considered unified, since it has “loosely-coupled” connections between the terms, the entities and the elements that the literature review returned; it is capable to discuss the fundamentals of the SG and SC terms (Figure 3) (Table 4); it contains the theoretical foundations to describe the SG and to lead to SC-SG evolution, while it has the capacity to support SC-SG research evolution (Figure 4) (Table 5). The selected conceptual model, together with the previously given SG definition, provides with answer the RQ1.
Summarizing the current knowledge on the terms, “smart government”, “smart governance”, and “smart city government” to answer research questions. The conceptual entities that define SG from the list of definitions from Table 2 and Figure 1 are: the combination of emerging technologies, data, and innovation which are the elements of the first layer of SG in the conceptual model [10]. This framework builds upon a relationship that emerged through our analysis across overarching three rings of the SG conceptual model.
A view to the selected and tested conceptual model returns useful findings: the first, outer ring shows that SG utilizes the outcomes from both digital and open government, while it deals with SC due to its easier applicability at a local level. The intermediate, second ring with the SG entities, due to the rapid technological emergence, government must take into consideration the complexity of SG and SC framework when implementing smart governance. In connection with the relative strengths between the SG conceptual entities is that these interrelated entities can be employed by the government that forces the growth and development of a digitally transformed and open government or of a SC. Based on the research conducted, SC is a subset of SG and relies on emerging technologies, data and ICT innovation. The three rings of SG can support the relationship between SG and SC, and multiple elements must cooperate to achieve better, sustainable outcomes with the use of emerging technologies, ICT innovation and data.

3.3 The Delphi Methodology

The previously presented conceptual model consists of three rings that contain several interrelated elements and entities, it defines the SG, and it has the capacity to lead an SG-SC development. Nevertheless, it is important to measure the relevant strengths of the model's components since the collected values can depict the relative importance of each of the component to the SG definition and evolution. In this regard, the model was decided to be tested with the contribution of experts, who would express their opinion on the relative importance of the model's components, via a two-round Delphi method [60]. The research methods used in this paper have been successfully employed in past research [60, 72]. For instance, the literature review was broad and systematic, it lasted a long time, and it explored a diverse number of sources. Moreover, the sample of the experts on both the SG and the SC domains was not large (a fact that makes Delphi an optimal choice when having a small sample size) due to the complexity in locating experts with the appropriate combination of skills and availability to participate.
The underlying principle of the Delphi method is that group-based forecasts are more accurate compared to individual forecasts [60, 72]. The experts were asked to validate the conceptual SG model (Figure 2(b)) with a pair-comparison of the elements of the intermediate ring from the perspective of the outer ring (e.g., how important is “Economic Growth” compared to “Policy Making” from the “Open Government” perspective?); and a pair-comparison of the elements of the inner ring, from an element-perspective (e.g., how important is “Emerging Technologies” compared to “ICT Innovation” from the “Economic Growth” perspective?).
\begin{equation} \left( {\begin{array}{@{}*{1}{c}@{}} n\\ k \end{array}} \right) = {\rm{\ }}\frac{{n!}}{{k!{\rm{\ }} \times \left( {n - k} \right)!}},{\rm{\ }}n \ge k \end{equation}
(1)
\begin{equation} \left( {\begin{array}{@{}*{1}{c}@{}} {10}\\ 2 \end{array}} \right) = \ \frac{{10!}}{{2!\ \times \left( {10 - 2} \right)!}} = \ \frac{{3.628.800}}{{2\ \times \ 40.320}} = 45 \end{equation}
(2)
\begin{equation} \left( {\begin{array}{@{}*{1}{c}@{}} 3\\ 2 \end{array}} \right) = \ \frac{{3!}}{{2!\ \times \left( {3 - 2} \right)!}} = \ \frac{6}{{2\ \times \ 1}} = 3 \end{equation}
(3)
Numerous combinations are generated (1) for the requested comparison and more specifically, 45 combinations for each of the three perspectives of the outer ring's dimensions (2) that makes 135 comparisons for the intermediate ring; and three combinations for each of the 10 perspectives (3) and for each of the three dimensions, for the inner ring's entities that makes 90 comparisons for the inner ring. For this reason, and in an attempt to simplify the contribution of the experts in terms of time and concentration, the Analytic Hierarchy Process (AHP) was selected for the definition of the questions. The AHP [70] is a Multi Criteria Decision Making (MCDM) Technique that can structure any complex, multi-criterion, multi-person problem hierarchically, and identifies the strength with which one alternative dominates another with respect to a given criterion. Moreover, since the AHP can also handle qualitative inputs and subjective judgements of individuals, it appears to be a suitable process for this kind of mutual comparisons and its application reduced the number of comparisons too (from 225 to 135).
The AHP was used to structure an online questionnaire that was filled-in during the first round of the Delphi process. The choices that were used to the AHP concerned the model's components, which had to be compared in pairs from different perspectives, as it was explained earlier. The Expert Choice® software was used to validate the collected inputs from the participants.
A group of experts was assembled late in 2016, after the end of a relative scientific workshop, where the initial concept of this study was discussed. All the experts were approached during the first Delphi round with an e-mail invitation and a brief explanation of the interview (a process that lasted until the end of 2017) and they were asked to fill in an online structured questionnaire. The expert's names and affiliations were kept confidential in order to get more candidate responses to the questions. This group consisted of 24 SC experts, coming from universities from all around the world. The identification process used the following criteria:
(a)
Being involved in a publication, research or case study that is relative to either the SG or the SC or both.
(b)
Coming from different countries and continents. In this regard, five (5) experts came from the USA, one (1) from Latin America, ten (10) from Europe, five (5) from Asia, two (2) from Australia and one (1) from Africa, while they were in 15 different countries.
(c)
Not all the initially invited participants accepted the invitation from the beginning. In some cases, because of a lack in their availability, it took more than six months for the interview to be performed and validated. As such, the first round took almost one year to be completed, a period where literature was enriched with corresponding articles, which were checked progressively for potential updates to study.
(d)
To secure experts’ relevance with the survey, the model was communicated and explained to the experts before each interview.
(e)
Questions were codified on the questionnaire (Qi: i = 1 to 135 for the questions; Ej: j = 1 to 16 for all the model's components), to collect as much information as possible against each topic and issue. The answers were decoded after the completion of the interview. The first interview contained a traditional Delphi round. When all interviews were completed and the results were processed, the second Delphi round was initiated (early February 2018), and the same experts were invited to confirm the first round's outcomes, as the Delphi methodology suggests (Figure 5). The second round was also long and ended in February 2019, a period during which -again- literature was checked for updates.
Fig. 5.
Fig. 5. Summary of the followed Delphi process.

3.3.1 Round 1.

To identify the components’ relative strengths, they were compared in pairs with the use of an online questionnaire1 that was provided to the interviewed experts, who were asked to present their opinions for their relative importance. The model's components concerned the AHP choices, which the experts had to compare in pairs, from different perspectives as it was explained earlier.
Participants used Likert scale values (from 1 to 9) (Table 6), which expressed their preference between two alternative components (I and J), according to a followed perspective each time. The registered preference represented the relative importance between the compared components. When all the experts’ completed their contribution, data were consolidated and were statistically analyzed with the use of SPSS® software and several statistical methods. Two limitations were recognized during the statistical analysis of the collected data sample. The first concerns the small sample size, while the second refers to the following observation: Table 7 illustrates the percentage of the collected value one (1) that the experts gave to the comparisons either to each one of the entities (E1, E2, E3) and the Q27 (inner ring) section or in general. This outcome demonstrates that one third of the experts consider all the compared variables (model components) to be equally important.
Table 6.
ValueExpression according to the Expert Choice®Explanation
1EquallyComponent I is equally important to the component J
3ModeratelyComponent I is slightly more important to the component J
5StronglyComponent I is more important to the component J
7Very StronglyComponent I is much more important to the component J
9ExtremelyComponent I is completely more important to the component J
2, 4, 6, 8Intermediate values
Table 6. Values that were used to Compare the Model's Components
Table 7.
E1E2E3Q27TOTAL
0,43
0,32
0,000,000,000,000,00
0,000,000,000,000,00
0,46
0,000,000,000,000,00
0,000,000,000,000,00
0,220,160,410,32
0,030,000,000,000,01
0,000,000,000,000,00
0,190,490,410,41
0,310,310,380,460,38
Table 7. Percentages of Equally Important Considerations in the Comparisons
For the authors to decide the type of the statistical analysis that they had to follow, they defined the primary assumptions for using parametric statistics and tests: first, data are considered independent amongst each other. Second, each of the experts answered without being aware what the other experts answered to the same question. Third, since all the variables were ordinal, one of the normality assumptions does not comply. As such, the non-parametric methods appeared to be more suitable for the analysis.
Two data analysis methods were performed: after all the mutual entities’ test, the authors checked the rest 27 comparisons (for the inner ring). Each value represents the rate for Emerging Technologies, ICT Innovation and Data in the context of the nine main variables (Economic Growth, Policy Making, Citizens Engagement, etc.). The lowest p value was equal to 0,095 for the comparisons of the nine variables in the context of the E1 dimension (Smart City Government). As such, there is not sufficient information to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ rate for E1 expressed a complete disagreement.
About the dimension E2 (Open Government) the lowest p value was equal to 0,180 for all the comparisons, except from the case where the economic growth versus all other variables was compared (p = 0,004). So, there was not sufficient information to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ rate for E2 presented no agreement, except from the comparison of the economic growth entity, which is translated as follows: Economic Growth entity was more important than the others, from the Open Government perspective.
Regarding the dimension E3 (Digital Government) the lowest p value was equal to 0,108 for all the comparisons. So, there was not sufficient information to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ rate for E3 expressed no agreement. The above analysis returns that Economic Growth entity was more important than the other components from the Digital Government perspective. The investigation for agreements about the model's inner ring returned important findings: Table 8 shows that the experts expressed agreement that ICT Innovation versus Data comparison is more powerful from the perspectives Citizens Engagement, Accountability, Key Enablers and Smart City Government. This part of the analysis returns that ICT Innovation is the most important element compared to the others (Emerging Technologies and Data) from the perspectives of Citizens Engagement, Accountability, Key Enablers, and the Smart City Government dimension.
Table 8.
 Emerging Technologies VSICT InnovationEmerging Technologies VS DataICT Innovation VS DataWp
Economic Growth1,881,652,460,220,058
Policy Making1,731,922,350,1610,123
Citizen Engagement1,581,772,650,5050,001*
Accountability1,51,852,650,5520,001*
Interoperability2,041,732,230,1070,250
key enablers1,522,50,50,002*
Smart Services1,6922,310,1410,161
Gov. Effectiveness & Efficiency1,731,922,350,1720,107
Smart City Government1,691,882,420,2980,021*
Table 8. Inner Ring's Inputs Analysis
Finally, an examination of the comparisons between all the entities of the intermediate ring was performed, with the use of the Kendall's W test for the level of the respondents’ agreement to be extracted. Disagreement is assumed when W = 0 and agreement is considered when W = 1. Results are depicted (Table 9), where no values for the variable Government Effectiveness & Efficiency exist due to the structure of the AHP questionnaire that contained only one comparison for this entity. So, the Kendall's W test could not be applied.
Table 9.
 Smart CityOpen GovE Gov
Economic Growth0,0740,2280,125
 0,4550,0040,123
Policy Making0,1190,0940,054
 0,1580,3450,646
Citizen Engagement0,1370,0680,014
 0,1130,4900,971
Accountability0,0190,1190,030
 0,9120,1850,812
Interoperability0,1140,0530,037
 0,2180,5570,692
Key-enablers0,1630,0290,156
 0,0950,7710,108
Smart Services0,0000,1380,026
 1,0000,1800,564
Table 9. Kendall's W and p values for Entities and Dimensions
To interpret the evaluation that the experts gave to each the entity, the authors decided to use the mean rank: this method compares the ranking that each respondent gave to a component, with the rest of the values that this entity collected during the survey. Practically, a low mean rank value denotes variables that are close to 1 (equally important). As such and with the use of Friedman's ANOVA process, figures like Figure 6 are extracted, which “classify” the entities. The extracted figures contain diagrams with two axes (each one is a model's dimension) and bubbles, the size of which represents the mean rank of the entities. For example, in Figure 6 the horizontal axis depicts the mean rank of each question in the Smart City dimension (E1) and the vertical axis the mean rank of the same question in the Open Government dimension (E2). As such, the yellow bubble that represents the comparison Economic growth VS Key Enablers belongs to the quartile I, as it received values (25.15, 24.75). The size of this bubble corresponds to the mean rank of the question in the Digital (e-) Government (E3) which is equal to 21.54. Bubbles that are big and located in the quartile I express a strong importance of the first variable compared to the second variable, and this importance holds for all the entities. Similarly, bubbles of small size in the quartile III expressed same importance of the compared variables in all the entities.
Fig. 6.
Fig. 6. Mean ranks’ representation in a quadruple.
This method generated Figure 7; Figure 8, which return the following outcomes: citizen engagement appears to be the most important entity from the digital government perspective, while accountability and government efficiency follow. The Economic Growth and Key-enablers entities on the other hand, appear to be the less important from the digital government perspective. Similarly, citizen engagement is the most important entity from the open government perspective, while economic growth follows. On the other hand, key enablers and policy making are the less important entities from the open government perspective. Finally, from the smart city perspective, economic growth is the most important entity with citizen engagement to follow, while key enablers and interoperability appear to be the least important entities.
Fig. 7.
Fig. 7. Mean ranks’ representation from the Digital Government Perspective.
Fig. 8.
Fig. 8. Mean ranks’ representation from the Open Government perspective.

3.3.2 Round 2.

After the completion of the above analysis about the results from round one, a second structured online questionnaire2 was given to the same experts. The questionnaire asked the experts to express their agreement (or not) with the use of the Likert scale (1: absolute disagreement to 5: total agreement) regarding the collected results from the first round. The online form contained 18 questions (six per each of the three model's dimensions: E1-Smart City; E2: Open Government; E3: Digital Government). This was the final round of interviews and the results were consolidated and statistically processed (Table 10; Table 11).
Table 10.
QUESTIONMEANMEDIANp-value
E1: The lowest p value was equal to 0,095 for the comparisons of the 9 variables in the context of E1 (Smart City). So, there is no sufficient information from the sample to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ ranking in E1 presented disagreement.3,644,000,239
E2: The lowest p value was equal to 0,180 for all comparisons except the case where we compared the economic growth versus all other variables (p = 0,004). So, there is no sufficient information from the sample to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ ranking in E2 presented no agreement, except the comparison of economic growth.3,644,000,136
E3: The lowest p value was equal to 0,108 for all comparisons. So, we have not sufficient information to reject the null hypothesis (that W = 0, disagreement). The conclusion is that the experts’ ranking in E3 presented no agreement.3,503,500,068
Table 10. Processed Results of the First Question of Each of the Dimensions
Table 11.
QUESTIONMEANMEDIANp-value
Economic growth is the most powerful factor for the smart city dimension3,434,000,071
Citizen engagement is the second most powerful factor for the smart city dimension3,574,000,165
Key enablers and interoperability are the less important factors for the smart city dimension2,863,000,001*
Economic Growth was ranked to be the most important entity compared to the remainders in the Open Government dimension.3,363,500,057
Figure 6: every point that lies in the quartile I seems to have significant importance for all the dimensions. Express your agreement with the findings.3,643,500,174
Figure 6: points that lie in the quartile III are of limited importance for all the dimensions. Express your agreement with the findings.3,363,000,022*
Citizen engagement is the most important entity for the Open Government dimension4,294,000,165
Economic growth is the second more important entity for the Open Government dimension3,934,000,775
Key enablers and policy making are the less important entities for the Open Government dimension2,792,500,001*
Figure 7: every point that lies in the quartile I seems to have significant importance for all the dimensions. Express your agreement with the findings.3,794,000,336
Figure 7: points that lie in the quartile III are of limited importance for all the entities. Express your agreement with the findings.3,643,500,096
Citizen engagement is the most important entity for the e-government dimension3,864,000,612
Accountability is the second most important entity for the e-government dimension3,714,000,302
Economic growth and key enablers are the less important entities for the e-government entity3,143,500,012*
ICT Innovation versus Data comparison is more powerful for the entities Citizens Engagement, Accountability, Key Enablers and Smart City Government.3,864,000,500
Table 11. Processed Results of the Rest of the Questions
The authors assumed that collected data follow a normal distribution in the evaluations (the Kolomogorov – Smirnov test was applied) and they checked the mean values that were equal to 4 (agreement on the results of the 1st Delphi round). The corresponding p-values showed that the null hypothesis (μ = 4) could not be rejected. Moreover, the outcomes showed that (i) for the Smart City (E1) dimension the mean value (3.64) expresses that most of the experts agree with this outcome, while 50% of them (Median) ranked with a value up to 4 this question; (ii) for the Open Government (E2) dimension the mean value (3.64) shows that most of the experts agree with the collected outcome and 50% of them ranked with a value up to 4; and (iii) for the Digital Government (E3) dimension the mean value (3.5) demonstrates again that most of the experts agree with the first round's outcome and 50% of them ranked with up to 3.5 this question.
Regarding the rest of the questions, Table 11 shows interesting results too: Citizen Engagement was considered more important than the other entities, in all the dimensions. The Economic Growth was also evaluated significantly important in all the dimensions, except from the Digital Government one, where Accountability was ranked more important. Moreover, the questions that referred to the less importance factors (for each dimension), were not supported by the participants. So, the question “Key enablers and interoperability are the less important factors for the smart city dimension” was ranked with 2.86, since 50% of the respondents answered up to 3.00. The same observation stands for the question “Key enablers and policy making are the less important factors for the open-government dimension”, where the experts ranked it with 2.79 and 50% ranked it with up to 2.50; and the question “Economic growth and key-enablers are the less important factors for the digital government dimension” the average score was 3.14 and mode equals to 3.50. The previous statements were supported by the experts’ answer on the question “Figure 6: points that lie in the quartile III are of limited importance for all entities. Express your agreement with the findings”. They did not agree as they were evaluated with the average score 3.36 and with mode equal to 3.00. The authors concluded that this disagreement is related with the role of Key Enablers, which was evaluated of low importance during the first round. The same finding was extracted from the corresponding question for the Figure 7, “Figure 7: points that lie in the quartile III are of limited importance for all entities. Express your agreement with the findings”. The experts evaluated the question with an average score 3.64 and on mode 3.50.

4 Conclusions

This paper had an ambitious mission: to investigate and conceptualize the term SG with components; to estimate the comparative importance of these components and express it with their relative strengths; and to clarify the relation between the SG and the SC term. In this regard, it grounded three research questions (RQ1, RQ2 and RQ3) and followed a multi-method study to provide them with answers.
Regarding RQ1 and the definition and conceptualization of the SG, a comprehensive literature review was followed, and numerous articles were collected and studied. Literature evidence about SG initially appeared in the early 2000s and they still emerge. Several alternative definitions were in the collected articles that combine government transformation, innovation, and technology in the public sector, while the SG is seen under the lens of the SC by many scholars. The collected definitions had several weaknesses that were documented in Table 2 and the authors proposed a definition to the SG that can overcome them.
Moreover, the adjective “smart” to government (following up the previous ones for “digital”, “open”, “Internet”, etc.) can generate skepticism whether it is an additional “buzzword” or a “fast fashion” trend [92] and in this regard, SG had to be conceptualized from the collected evidence and validated. Several conceptualization models were located and compared and the one introduced by [10] was selected as the more appropriate for this study and validated. The selected model provides an answer to RQ1, it consists of three rings, it has “loose” connections between the identified components, and it has the capacity to define the SG, and the capacity to lead the SG-SC research evolution. This conceptualization and the long-run use of the term SG, is considered that it can deal with this potential skepticism.
Regarding RQ2, the authors aimed to define the importance of each of the model's components for the SG definition and evolution. As such, it followed a two-round Delphi methodology with a sample of experts. Each of the selected model's entities had to be compared with the others, under the identified three dimensions (Smart City, Open Government and Digital Government) and the AHP method that was used, which led to the definition of a structured questionnaire that was administered in the first Delphi round. Collected data were consolidated and statistically analyzed and mean values can be shown in figures that depicted the entities’ relative strengths. The processed outcomes were validated by the same experts under the Delphi's second round. The outcomes from both the rounds provide with alternative meanings this study, which is the answer to RQ2: first, Figures 7 and 8 depict the relative importance of the SG model's components. Second, it is hard to claim that any of the model's entity is not important. Among the study's outcomes. Citizens Engagement, Economic Growth and Accountability are more important for SG, compared to the other components in all the three dimensions. However, it is hard to decide about the less important component (or components). Third, the role of ICT Innovation appears to be the most important compared to emerging technologies and data.
Finally, regarding the RQ3, this study provides evidence that seems to support that a relation between the SG and the SC exists and affect their evolution. However, SG cannot to be considered synonymous to SC but a broader term that describes the next step for government transformation, while the SC is considered to be an area within the overarching term SG and it is controlled by a significant role of “smart local government”. Therefore, the authors believe that SC is a complimentary part of the broader SG term. These outcomes provide with answer the RQ3, while Figures 3 and 4 depict specific connections between the SG and the SC conceptual components.
Nevertheless, this paper has some limitations that must be recognized: first, the duration of the study was long, which was caused mainly due to the study's complexity, in combination with the experts’ availability. The overall study lasted almost three years, a period when the SG and the SC terms evolved in the literature. However, the authors continually checked for literature updates and found no important changes on the SG and the SC terms that should affect the study. Another limitation comes from the small sample of experts that contributed to the study. As such, future thoughts concern the testing of the selected SG model and in parallel, the scientific collection of changes in the context of the examined terms. For instance, emerging trends (like AI and mixed reality, etc.) may affect the way the SG, the SC or both evolve, and in this respect, the model could be revised accordingly. Finally, findings themselves highlight areas for future studies, like how “economic growth” is associated with “smartness in government” and how it can be measured, and how “citizen engagement” can affect “smartness” in government, etc.

Acknowledgements

Parts of this paper were presented at the Web Applications and Smart Cities (AW4City workshop 2016), in conjunction with the 25th World Wide Web International Conference (WWW2016), Montreal, Canada, April 11-15, 2016. The authors would like to express their special thanks to all the experts that contributed to the Delphi methodology.

Footnotes

References

[1]
I. Abaker, T. Hashem, et al. 2016. The role of big data in smart city. International Journal of Information Management 36, 5 (2016), 748–758.
[2]
U. Aguilera, O. Peña, O. Belmonte, and D. López-de-Ipiña. 2017. Citizen-centric data services for smarter cities. Future Generation Computer Systems 76, 234–247.
[3]
L. Alcaide–Muñoz, M. P. Rodríguez–Bolívar, M. J. Cobo, and E. Herrera–Viedmac. 2017. Analysing the scientific evolution of e-government using a science mapping approach. Government Information Quarterly 34, 545–555.
[4]
S. Alawadhi, A. Aldama-Nalda, H. Chourabi, J. R. Gil-Garcia, S. Leung, S. Mellouli, T. Nam, T. A. Pardo, H. J. Scholl, and S. Walker. 2012. Building understanding of smart city initiatives. In Electronic Government, LNCS 7443, Scholl, H.J., Janssen, M., Wimmer, M.A., Moe, C.E. and Flak L.S. (Eds). Springer: London, New York, 40–53.
[5]
L. Anthopoulos. 2015. Defining smart city architecture for sustainability. In Proceedings of 14th Electronic Government and 7th Electronic Participation Conference (IFIP2015) (Thessaloniki, Greece, August 30-September 2, 2015), Tampouris, E. et al. (Eds). IOS Press, Amsterdam, 140–147. DOI:
[6]
L. Anthopoulos and P. Fitsilis. 2014. Exploring architectural and organizational features in smart cities. In Proceedings of the 16th International Conference on Advanced Communications Technology (ICACT2014), Seoul, February 16–19, 2014.
[7]
L. Anthopoulos and Ch. Reddick. 2015. Understanding electronic government research and smart city. Information Polity, Special Issue on “Smartness in Governance, Government, Urban Spaces, and the Internet of Things” 1, 1–19. DOI:
[8]
L. Anthopoulos, M. Janssen, and V. Weerakkody. 2015. Comparing smart cities with different modeling approaches. In Companion Volume of the Web Applications and Smart Cities (AW4City 2015) Workshop, in Conjunction with the WWW2015 ACM 24th World Wide Web International Conference (Florence, Italy, 2015).
[9]
L. Anthopoulos, Ch. Reddick, N. Mavridis, and I. Giannakidou. 2016. Why e-government projects fail? An analysis of the healthcare.gov website. Government Information Quarterly. DOI:
[10]
L. Anthopoulos. 2017. Understanding smart cities - a tool for smart government or an industrial trick? Public Administration and Information Technology, 22, Springer Science+Business Media, New York.
[11]
K. C. Andermatt and R. A. Göldi. 2018. Introducing an electronic identity: The co-design approach in the canton of Schaffhausen. Swiss Yearbook of Administrative Sciences 9, 1 (2018), 41–50. DOI:
[12]
O. M. Awoleye, B. Ojuloge, and M. O. Ilori. 2014. Web application vulnerability assessment and policy direction towards a secure smart government. Government Information Quarterly 31(S1), (2014), S118–S125.
[13]
S. Barns. 2018. Smart cities and urban data platforms: Designing interfaces for smart governance. City, Culture and Society 12, 5–12.
[14]
A. P. Cazares. 2018. The brain of the future and the viability of democratic governance: The role of artificial intelligence, cognitive machines, and viable systems. Futures 103, 5–16.
[15]
W. Cellary. 2013. Smart governance for smart industries. In Proceedings of the 7th International Conference on Theory and Practice of Electronic Governance (ICEGOV'13) (October 22-25, 2013, Seoul, Republic of Korea), 91–93.
[16]
A. T. Chatfield and Ch. Reddick. 2018. A framework for Internet of Things-enabled smart government: A case of IoT cybersecurity policies and use cases in U.S. federal government. Government Information Quarterly.
[17]
P. Cornadie and S. Choenni. 2014. On the barriers for local government releasing open data. Government Information Quarterly 31, S10–S17.
[18]
J. I. Criado, R. Sandoval-Almazan, and J. R. Gil-Garcia. 2013. Government innovation through social media. Government Information Quarterly 30 (2013), 319–326.
[19]
X. Dai. 2003. A new mode of governance? Transnationalisation of European regions and cities in the information age. Telematics and Informatics 20 (2003), 193–213.
[20]
S.-J. Eom, N. Choi, and W. Sung. 2016. The use of smart work in government: Empirical analysis of Korean experiences. Government Information Quarterly. DOI:
[21]
European Commission. 2012. eGovernment benchmark framework 2012–2015. Brussels: European Commission DG Communications Networks, Content and Technology Method paper No. SMART 2012/0034-1. Retrieved, Sept. 2016 from https://ec.europa.eu/futurium/en/system/files/ged/egovernment_benchmarking_method_paper_published_version_0.pdf.
[22]
V. Fernandez-Anez, J. M. Fernández-Güell, and R. Giffinger. 2018. Smart city implementation and discourses: An integrated conceptual model. The case of Vienna. Cities, 78, 4–16.
[23]
R. Giffinger and H. Gudrun. 2010. Smart cities ranking: An effective instrument for the positioning of cities? ACE: Architecture, City and Environment 4, 12 (2010), 7–25.
[24]
J. R. Gil-Garcia. 2013. Towards a smart State? Inter-agency collaboration, information integration, and beyond. In ICT, Public Administration and Democracy in the Coming Decade, Innovation and the Public Sector Series Meijer, A.J., Bannister, F. and Thaens, M. (Eds), Vol. 20. IOS Press BV: Amsterdam, 59–70.
[25]
J. R. Gil-Garcia and A. Aldama-Nalda. 2013. Smart city initiatives and the policy context: The case of the rapid business opening office in Mexico City. In Proceedings of the 14th Annual International Conference on Digital Government Research (dg.o), 234–237.
[26]
J. R. Gil-Garcia, T. A. Pardo, and A. Aldama-Nalda. 2013. Smart cities and smart governments: Using information technologies to address urban challenges. In Proceedings of the 14th Annual International Conference on Digital Government Research (dg.o), 296–297.
[27]
J. R. Gil-Garcia, N. Helbig, and A. Ojo. 2014. Being smart: Emerging technologies and innovation in the public sector. Government Information Quarterly 31, (S1) (2014), I1–I8.
[28]
J. R. Gil-Garcia, T. A. Pardo, and T. Nam. 2015. What makes a city smart? Identifying core components and proposing an integrative and comprehensive conceptualization. Information Polity 20, 1 (2015), 61–87.
[29]
J. R. Gil-Garcia, T. A. Pardo, and T. Nam. 2015b. Comprehensive view of the 21st century city: Smartness as technologies and innovation in urban contexts. In Smarter as the New Urban Agenda, Public Administration and Information Technology Series Gil-Garcia et al. (Eds) (11), 1–19, Springer International Publishing, Switzerland. DOI:
[30]
J. R. Gil-Garcia, J. Zhang, and G. Puron-Cid. 2016. Conceptualizing smartness in government: An integrative and multi-dimensional view. Government Information Quarterly 33, 3 (2016), 524–534. DOI:
[31]
J. R. Gil-Garcia and D. S. Sayogo. 2016. Government inter-organizational information sharing initiatives: Understanding the main determinants of success. Government Information Quarterly. DOI:
[32]
S. Goldsmith and S. Crawford. 2014. The Responsive City: Engaging Communities through Data-Smart Governance. San Francisco: John Wiley & Sons.
[33]
A. Harsh and N. Ichalkaranje. 2015. Transforming e-government to smart government: A South Australian perspective. Advances in Intelligent Systems and Computing 1, 9–16.
[34]
I. A. T. Hashem, V. Chang, N. B. Anuar, K. Adewole, I. Yaqoob, A. Gani, E. Ahmed, and H. Chiroma. 2016. The role of big data in smart city. International Journal of Information Management 36, 5 (2016), 748–758.
[35]
K. R. Hope. 2001. The new public management: Context and practice in Africa. International Public Management Journal 4, 119–134.
[36]
Y. Hu and J. Wang. 2016. Building smart government or developing industry? Study on the designs of local smart city pilot projects in China. In the Proceedings of the 17th International Digital Government Research Conference on Digital Government Research (dg.o’16), Shanghai, China.
[37]
International Standards Organization (ISO). 2014. ISO 37120:2014: Sustainable Development of Communities – Indicators for City Services and Quality of Life. Retrieved, Aug. 2018 from https://share.ansi.org/ANSI%20Network%20on%20Smart%20and%20Sustainable%20Cities/ISO%2B37120-2014_preview_final_v2.pdf.
[38]
International Standards Organization (ISO). 2016. Sustainable Development in Communities. Retrieved, Aug. 2018 from http://www.iso.org/iso/iso_37101_sustainable_development_in_communities.pdf.
[39]
A. Iwan and K. K. Y. Poon. 2018. The role of governments and green building councils in cities’ transformation to become sustainable: Case studies of Hong Kong (east) and Vancouver (west). Int. J. Sus. Dev. Planning 13, 4 (2018), 556–570.
[40]
A. Jansen and S. Olnes. 2016. The nature of public e-services and their quality dimensions. Government Information Quarterly. DOI:
[41]
T. Janowski. 2015. Digital government evolution: From transformation to contextualization. Government Information Quarterly 32, 221–236.
[42]
T. Jetzek. 2016. Managing complexity across multiple dimensions of liquid open data: The case of the Danish basic data program. Government Information Quarterly 33, 89–104.
[43]
C. E. Jiménez, A. Solanas, and F. Falcone. 2014. E-government interoperability: Linking open and smart government. Computer 47, 10 (2014), 22–24.
[44]
C. E. Jiménez, F. Falcone, A. Solanas, H. Puyosa, S. Zoughbi, and F. González. 2015. Smart government: Opportunities and challenges in smart cities development. In Handbook of Research on Democratic Strategies and Citizen-Centered E Government Services, Dolićanin, Ć., Kajan, E., Randjelović, D. and Stojanović, B. (Eds). Hershey, PA: IGI Global, 1–19.
[45]
C. N. Jun and C. J. Chung. 2016. Big data analysis of local government 3.0: Focusing on Gyeongsangbuk-do in Korea. Technological Forecasting & Social Change 110, 3–12.
[46]
Z. Khan, J. Dambruch, J. Peters-Anders, A. Sackl, A. Strasser, P. Fröhlich, S. Templer, and K. Soomro. 2017. Developing knowledge-based citizen participation platform to support smart city decision making: The smarticipate case study. Information, 8, 47 (2017). DOI:
[47]
B. Kliksberg. 2000. Rebuilding the state for social development: Towards “smart government”. International Review of Administrative Sciences 66, 2 (2000), 241–257.
[48]
J. Lee and H. Lee. 2014. Developing and validating a citizen-centric typology for smart city services. Government Information Quarterly 31, S93–S105.
[49]
C. Lim, K.-J. Kim, and P. P. Maglio. 2018. Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86–99.
[50]
D. Linders, C. Z-P Liao, and C-M Wang. 2015. Proactive e-Governance: Flipping the service delivery model from pull to push in Taiwan. Government Information Quarterly. DOI:
[51]
L. F. Luna-Reyes and T. A. Pardo. 2015. The smart cities and smart government research-practice (SCSGRP) consortium. In the Proceedings of the 16th Annual International Conference on Digital Government Research (dg.o’15), Phoenix, USA.
[52]
Z. Lv, X. Li, W. Wangb, B. Zhang, J. Hud, and S. Feng. 2018. Government affairs service platform for smart city. Future Generation Computer Systems 81, 443–451.
[53]
D. Maheshwari and M. Janssen. 2014. Reconceptualizing measuring, benchmarking for improving interoperability in smart ecosystems: The effect of ubiquitous data and crowdsourcing. Government Information Quarterly 31, S84–S92.
[54]
R. Matheus, M. Janssen, and D. Maheshwari. 2018. Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly.
[55]
S. Mellouli, L. F. Luna-Reyes, and J. Zhang. 2014. Smart government, citizen participation and open data. Information Polity 19, 1–4.
[56]
R. I. Muhamedyev, A. Ishmanov, A. V. Andreev, I. Alikhodzhayev, and J. Muhamedijeva. 2015. Technological preconditions of monitoring of renewable energy sources of the Republic of Kazakhstan. In the Proceedings of the 2015 12th International Conference on Electronics Computer and Computation (ICECCO), Almaty, Kazakhstan.
[57]
T. Nam and T. A. Pardo. 2014. The changing face of a city government: A case study of Philly311. Government Information Quarterly, 31, S1–S9.
[58]
P. Neirotti, A. De Marco, A. C. Cagliano, G. Mangano, and F. Scorrano. 2014. Current trends in smart city initiatives: Some stylised facts. Cities 38, 25–36.
[59]
N. Netten, M. S. Bargh, S. van den Braak, S. Choenni, and F. Leeuw. 2016. On enabling smart government: A legal logistics framework for future criminal justice systems. In the Proceedings of the 17th International Digital Government Research Conference on Digital Government Research (dg.o’16), Shanghai, China.
[60]
B. Niehaves. 2011. Iceberg ahead: On electronic government research and societal aging. Government Information Quarterly, 28, 310–319.
[61]
A. Ojo and S. Mellouli. 2018. Deploying governance networks for societal challenges. Government Information Quarterly 35, S106–S112.
[62]
A. Paulin, L. Anthopoulos, and A. Adewale. 2016. Beyond bureaucracy VS smart government (BBSG 2016): Towards the ecosystem. In the Proceedings of the 17th International Digital Government Research Conference on Digital Government Research (dg.o’16), Shanghai, China.
[63]
A. Pradana, G. O. Sing, Y. J. Kumar, and A. A. Mohammed. 2018. Blockchain traffic offence demerit points smart contracts: Proof of work. International Journal of Advanced Computer Science and Applications 9(11).
[64]
G. Puron-Cid. 2014. Factors for a successful adoption of budgetary transparency innovations: A questionnaire report of an open government initiative in Mexico. Government Information Quarterly 31, S49–S62.
[65]
A. A. Pourezzat, M. H. Moghadam, M. S. Ejlal, and G. Taheriattar. 2018. The future of governance in Iran. Foresight 20, 2 (2018), 175–189.
[66]
R. Recupero, M. Castronovo, S. Consoli, T. Costanzo, A. Gangemi, L. Grasso, G. Lodi, G. Merendino, M. Mogiovi, V. Pressutti, R. Davide, S. Rosa, and E. Spampinato. 2016. An innovative, open, interoperable citizen engagement cloud platform for smart government and users’ interaction. Journal of Knowledge Economy 7, 2 (2016), 388–412.
[67]
T. Rubel. 2014. Smart government: Creating more effective information and services. Retrieved, 5 December 2015, from http://www.govdelivery.com/pdfs/IDC_govt_insights_Thom_Rubel.pdf.
[68]
R. W. S. Ruhlandt. 2018. The governance of smart cities: A systematic literature review. Cities 81, (2018) 1–23.
[69]
E. M. Rogers. 1996. Diffusion of Innovations. The Free Press, New York.
[70]
T. Saaty. 1987. The analytic hierarchy process - what it is and how it is used. Mathematical Modelling 9 (3–5), 161–176.
[71]
J. Sangki. 2018. Vision of future e-government via new e-government maturity model: Based on Korea's e-government practices. Telecommunications Policy 42, 860–871.
[72]
M. N. Saunders, A. Thornhill, and P. Lewis. 2009. Research Methods for Business Students (5th Edition). Pearson Education Limited, Essex: England.
[73]
A. Savoldelli, C. Codagnone, and G. Misuraca. 2014. Understanding the e-government paradox: Learning from literature and practice on barriers to adoption. Government Information Quarterly 31, S63–S71.
[74]
P. Silveira and T. P. Dentinho. 2018. A spatial interaction model with land use and land value. Cities, 78, 60–66.
[75]
H. J. Scholl and S. AlAwadhi. 2015. Pooling and leveraging scarce resources: The smart eCity gov alliance. In Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2355–2365.
[76]
H. J. Scholl and Y. K. Dwivedi. 2014. Forums for electronic government scholars: Insights from a 2012/2013 study. Government Information Quarterly 31, 229–242.
[77]
H. J. Scholl and M. C. Scholl. 2014. Smart governance: A roadmap for research and practice. In Proceedings of the iConference 2014, 163–176.
[78]
H. J. Scholl and A. Suha. 2016. Creating smart governance: The key to radical ICT overhaul at the city of Munich. Information Polity 21, 1 (2016), 21–42.
[79]
J. A. Taylor. 2015. The art of the possible: Innovation, smart government and the enduring braking-power of traditional public administration. Information Polity 20, 1–2.
[80]
M. Turle. 2010. Shared services: An outline of key contractual issues. Computer Law & Security Review 26, 178–184.
[81]
United Nations. 2005. Demographic Yearbook 2005 [online]. Retrieved, May 2019 from http://unstats.un.org/unsd/demographic/sconcerns/densurb/Defintion_of%20Urban.pdf.
[82]
United Nations. 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. Retrieved, July 2019 from https://www.un.org/pga/wp-content/uploads/sites/3/2015/08/120815_outcome-document-of-Summit-for-adoption-of-the-post-2015-development-agenda.pdf.
[83]
N. Veljković, S. Bogdanović-Dinić, and L. Stoimenov. 2014. Benchmarking open government: An open data perspective. Government Information Quarterly 31, 278–290.
[84]
G. Vial. 2019. Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems 28, 118–144.
[85]
D. J. Vieira and A. Alvaro. 2017. A centralized platform of open government data as support to applications in the smart cities context. International Journal of Web Information Systems 14, 1 (2017), 2–28.
[86]
J. G. Wacker. 2004. A theory of formal conceptual definitions: Developing theory-building measurement instruments. Journal of Operations Management 22, 6 (2004), 629–650.
[87]
H. Willke. 2007. Smart Governance: Governing the Global Knowledge Society. Campus Verlag.
[88]
B. W. Wirtz, J. C. Weyerer, and F. T. Schichtel. 2018. An integrative public IoT framework for smart government. Government Information Quarterly.
[89]
World Bank. 2019. Urban Development [online]. Retrieved, July 2019 from https://www.worldbank.org/en/topic/urbandevelopment/overview.
[90]
A. Wray, D. L. Olstad, and L. M. Minaker. 2018. Smart prevention: A new approach to primary and secondary cancer prevention in smart and connected communities. Cities 79, 53–69.
[91]
T. Yigitcanlar, Md. Kamruzzaman, L. Buys, G. Ioppolo, J. Sabatini-Marques, E. M. da Costa, and J. J. Yun. 2018. Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework. Cities 81, 145–160.
[92]
A. Paulin. 2019. Smart City Governance. Elsevier: New York.

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cover image Digital Government: Research and Practice
Digital Government: Research and Practice  Volume 2, Issue 4
October 2021
99 pages
EISSN:2639-0175
DOI:10.1145/3505190
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Published: 06 January 2022
Online AM: 07 May 2021
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Revised: 01 March 2021
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Published in DGOV Volume 2, Issue 4

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