Understanding Smart Cities:
Innovation Ecosystems, technological advancements, and societal challenges
Francesco Paolo Appioa, Marcos Limab, Sotirios Paroutisc
aLéonard de Vinci Pôle Universitaire, Research Center, 92916 Paris La Défense, France
bSkema Business School/Université Côte d'Azur, France
cWarwick Business School, Warwick University, United Kingdom
Appio, F.C., Lima, M., Paroutis, S. (2018). Understanding Smart Cities: Innovation
Ecosystems, Technological Advancements, and Societal Challenges. In press at
Technological Forecasting and Social Change. Editorial for the special issue
Understanding Smart Cities: Innovation Ecosystems, Technological Advancements,
and Societal Challenges. https://doi.org/10.1016/j.techfore.2018.12.018
Smart Cities initiatives are spreading all around the globe at a phenomenal pace. Their bold ambition
is to increase the competitiveness of local communities through innovation while increasing the
quality of life for its citizens through better public services and a cleaner environment. Prior research
has shown contrasting views and a multitude of dimensions and approaches to look at this
phenomenon. In spite of the fact that this can stimulate the debate, it lacks a systematic assessment and
an integrative view. The papers in the special issue on “Understanding Smart Cities: Innovation
Ecosystems, Technological Advancements, and Societal Challenges” take stock of past work and
provide new insights through the lenses of a hybrid framework. Moving from these premises, we offer
an overview of the topic by featuring possible linkages and thematic clusters. Then, we sketch a novel
research agenda for scholars, practitioners, and policy makers who wish to engage in – and build – a
critical, constructive, and conducive discourse on Smart Cities.
Keywords: Smart Cities, hybrid framework, phyisical infrastructure, quality of life, innovation, review
1. Introduction and motivation
There are hundreds of smart city projects currently being developed around the world (Lee et al.,
2014). Smart Cities initiatives aim to “provide more efficient services to citizens, to monitor and
optimize existing infrastructure, to increase collaboration amongst different economic actors and to
encourage innovative business models in both private and public sectors” (Marsal-Llacuna et al., 2015,
p. 618). Ultimately, smart cities strive to increase the competitiveness of local communities through
innovation while increasing the quality of life for its citizens through better public services and a
cleaner environment. In order to achieve these goals, smart cities rely on state-of-the-art information
technology (e.g., fiber optic networks, sensors and connected devices, open data analytics, internet of
things, ICT-enabled participatory planning frameworks) on the one hand (Albino et al., 2015; Stratigea
et al., 2015), and on human capital (e.g., research universities, knowledge-intensive companies and
public institutions) on the other hand (Ahvenniemi et al., 2017; Neirotti et al., 2014). Angelidou
(2014) calls the former “hard” smart cities strategies (smart buildings, smart energy grids, smart water
management, smart mobility) and the latter “soft” strategies (developing human and social capital
through education, culture, social inclusion, social innovation). It is widely assumed that the digital
infrastructure of modern cities offers a unique opportunity to facilitate entrepreneurship, creativity,
and innovation in order to drive local economic growth (Kraus et al., 2015; Grimaldi and Fernandez,
2015). The city of London, for instance, has based its smart city initiative on four dimensions: a)
technology innovation; b) open data and transparency; c) collaboration and engagement; d) efficiency
and resource management (Angelidou, 2015). These dimensions echo Lee and co-authors' (2014) six
enablers of smart city development: urban openness, service innovation, partnership formation, urban
proactiveness, infrastructure integration, and smart city governance. Chourabi et al. (2012) propose an
“integrative framework” involving the dimensions of organization, policy, and technology as the
pillars of smart city initiatives, surrounded by secondary factors such as governance,
people/communities, economy, infrastructure, and natural environment. Alternative frameworks
highlight the “transboundary” nature of smart city projects. Thus Angelidou (2014) suggests the
necessity to go beyond the “hard versus soft” infrastructure dichotomy and to also consider the
national versus local implications for smart city projects; the new (green field) versus the existing
(brownfields) approaches to urban development; and the economic versus geographic approaches.
Similarly, Ramaswami and co-authors (2016) suggest thinking about the local infrastructure provision
(the smart management of energy, buildings, public spaces, waste and sanitation, food supply, water
supply and transportation) as subject to a larger flow of national and global actors and institutions. The
performance of these initiatives must be measured in terms of their environmental, economic, and
social benefits (Ahvenniemi et al., 2017). These initiatives can also be studied from a strategic
perspective, as they can spark the emergence of new value chains in the firms and stakeholders
involved in designing and executing smart city projects (Paroutis et al., 2014). According to the neo-
evolutionary perspective of the Triple Helix framework, smart city projects represent a unique
innovation platform for companies, government agencies, and researchers (Leydesdorff and Deakin,
2011). In this perspective, smart cities are perceived above all as “Intelligent Communities”,
collaborative ecosystems that facilitate innovation, by creating linkages among citizens, government,
businesses, and educational institutions. These innovative clusters foster the development of high
added value activities of the “knowledge economy.” To capture most of these elements, Bill Hutchison
(Hutchison et al., 2011) created a 5-level pyramid framework called “Intelligent Community Open
Architecture – i-COA®.” The first two levels correspond to the “hard” smart city strategies (places
and infrastructure). The top three levels (collaboration ecosystems, applications, and life) correspond
to “soft” strategies. This framework has the merits of being synthetic, easy to visualize, and suggests
that the ultimate goal of smart cities is not merely to connect hardware and infrastructure, but to create
collaborative environments where innovation and quality of life can thrive. All of these models are
indebted to Giffinger et al.'s (2007) seminal classification of smart city characteristics around six key
dimensions: quality of life (Smart Living), competitiveness (Smart Economy), social and human
capital (Smart People), public and social services and citizen participation (Smart Governance),
transport and communication infrastructure (Smart Mobility), and natural resources (Smart
Environment). For the purposes of this discussion, therefore, we propose to merge Hutchison's and
Giffinger's frameworks as a background to understand how smart cities may foster collaboration
ecosystems that may improve both the standards of living and the competitiveness of urban spaces
(Fig. 1). Urban strategist Boyd Cohen (2013) developed a “Smart City Wheel” that suggests how to
measure the six dimensions of Giffinger's model. At the risk of oversimplifying the problem, this
model has the merit of reducing the metrics of each dimension to three indicators only. It is a good
synthesis of an introductory discussion about smart cities limits and possibilities. However, it lacks the
structural perspective of Hutchinson's i-COA® framework to create a hierarchy of smart city elements.
Indeed, according to Hutchinson's model, every smart city project must start with the physical
infrastructure (Smart Environment and Smart Mobility). This is the basis for creating innovation
ecosystems based on human and social capital (Smart People and Smart Economy). Such de-
centralized initiatives require articulation and coordination by public entities or public-private
partnerships (Smart Governance). The raison d'être for these governance structures is to provide better
quality of life solutions to smart city citizens (Smart Living). Thus, by combining Giffinger's classic
categories and organizing them according to Hutchinson's pyramid, we suggest a visual diagram of
how to design, implement and measure smart city programs (see Fig. 1).
Fig. 1. An adaptation of Hutchison’s i-COA® framework highlighting Giffinger’s smart city elements
According to Dustdar et al. (2017), most definitions of smart cities are infrastructure-centric, focusing
on installation and subsequent management of connected devices and analytics of data. Table 1
corroborates this perception. As seen above, few definitions emphasize the three dimensions
Table 1. We summarize a number of smart cities definitions from the literature, classifying its primary focus in
terms of the three components described above: physical infrastructure (PI), Quality of Life (QL), and
Innovation Ecosystems (IE)
Definition of Smart City
A city that monitors and integrates conditions of all of its critical infrastructure
including roads, bridges, tunnels, rails, subways, airports, seaports,
communications, water, power, even major buildings can better optimize its
resources, plan its preventive maintenance activities, and monitor security aspects
while maximizing services to its citizens
Hall et al. (2000)
A city well performing in a forward-looking way in economy, people governance,
mobility, environment, and living built on the smart combination of endowments
and activities of self-decisive, independent, and aware citizens
Giffinger et al.
PI / IE / QL
The use of smart computing technologies to make the critical infrastructure
components and service of a city—which include city administration, education,
health care, public safety, real estate, transportation, and utilities—more
intelligent, interconnected, and efficient
Washburn et al.
Instrumented, interconnected and intelligent. Instrumented refers to sources of
near-real-time real-world data from both physical and virtual sensors.
Interconnected means the integration of those data into an enterprise computing
platform and the communication of such information among the various city
services. Intelligent refers to the inclusion of complex analytics, modeling,
optimization, and visualization in the operational business processes to make
better operational decisions.
Harrison et al.
Smart cities are those that are combining ICT and Web 2.0 technology with other
organizational, design and planning efforts to de-materialize and speed up
bureaucratic processes and help to identify new, innovative solutions to city
management complexity, in order to improve sustainability and “liveability”.
IE / QL
Systems of people interacting with and using flows of energy, materials, services
and financing to catalyse sustainable economic development, resilience, and high
quality of life
PI / IE / QL
A coherent urban development strategy developed and managed by city
governments seeking to plan and align in the long term the management of the
various city’s infrastructural assets and municipal services with the sole objective
of proving the quality of life for the citizens.
Dustdar et al.
PI / QL
Provide better services for citizens; provide a better life environment where smart
policies, practices and technology are put to the service of citizens; achieve their
sustainability and environmental goals in a more innovative way; Identify the need
for smart infrastructure; facilitate innovation and growth; and build a dynamic
and innovative economy ready for the challenges of tomorrow.
PI / IE / QL
The hybrid framework proposed here attempts to avoid this bias by emphasizing the role of
infrastructure in smart city projects simply as a means to achieving more collaborative innovation
ecosystems and ultimately leading to a higher quality of citizens' life. In the following session, we
conduct a literature review based on these three elements of the proposed hybrid model.
2. The physical infrastructure of smart cities
According to certain estimates (Suzuki, 2017), 180,000 people migrate to cities across the globe every
single day, which represents over 65 million new urban dwellers a year. The challenges created by this
massive urban migration in terms of housing, electricity, heating, and schooling (not to mention job
creation) are overwhelming. In order to develop intelligent solutions, a combination of smart networks
(Internet of Data, Internet of Things, Internet of Services and Internet of People) can be used to
minimize environmental impact while maximizing social well-being and promoting collaborative eco-
systems (Ijaz et al., 2016). The Internet of Data has been among us since the inception of the Arpanet
project in the 1960s. However, the advent of widespread broadband communication infrastructure in
offices and homes in the 21st century dramatically increased the velocity, volume, variety, veracity
and value of data transfers (commonly called Big Data networks). These massive data streams are
derived not just from humancreated content (blogs, social networks, video conferencing, etc.), but also
from machines exchanging data among themselves (Internet of Things). Coupled with sophisticated
statistical algorithms to gather, visualize and analyze this flow, Big Data has created opportunities to
learn in real-time about how to improve traffic, save energy, regulate public transit, reduce waste and
pollution and improve safety in large urban centers across the world (Kitchin, 2014; Lim et al., 2018).
This Internet of Data in smart cities is increasingly dominated by the growing Internet of Things
ecosystem. In the last two decades, there has been a dramatic acceleration of hardware performance at
lower costs (based on Moore's Law) coupled with drastic miniaturization of components, leading to
the ubiquity of smart objects. In 2003, there were an estimated 500 million connected devices
worldwide or 0.08 object per person. This proportion increased to 1.84 in 2010 (12.5 billion connected
devices to 6.8 billion humans) and reached 3.47 in 2015 (25 billion Internet of Things components to
7.2 billion humans). This ratio is expected to reach 6.58 by 2020 (Suzuki, 2017). The convergence of
Big Data, Internet of Things and Artificial Intelligence promises to create better places (parks,
buildings, homes) by providing smarter infrastructure (transportation, energy, waste management).
These correspond to Giffinger's Smart Environment and Smart Mobility elements (Fig. 1). The
following paragraphs discuss each of these domains.
2.1. Smart Environment
Smart Environment initiatives involve the use of technology to improve crucial aspects of city living
such as waste disposal, food growth, pollution control, smart electric grids, housing quality, and
facility management. This session presents a few state-of-the-art examples of how the Internet of Data
and the Internet of Things can help reduce the ecological footprint of smart cities. According to Perera
et al. (2014), the widespread use of IoT sensors (such as Radio Frequency Identification chips,
proximity detectors, pressure sensors, optical sensors) can drastically change the way we manage the
smart city environment. City councils may optimize garbage collection, sorting and recycling by
deploying low-cost smart sensors in garbage cans, trucks and recycling plants that share real-time data
about the quantity and the quality of urban waste in each neighborhood. This intelligence may not only
facilitate decision making in terms of logistics and urban strategy but can also inform educational
campaigns to improve recycling behavior. In agriculture, sensors can monitor plant growth under
different conditions, pest control and soil conditions, allowing bio-scientists and microbiologists to
develop customized treatments to minimize the use of toxic pesticides and fertilizers. Pollution control
is another major field of IoT application. Sensors can help detect and prevent wildfires, automatically
alert against the level of microparticles and other air pollutants, improve prediction, visualization and
simulation of city pollution. Wireless Sensor Networks can be deployed in buses, bus stations, metro
wagons and private vehicles to monitor emissions while also learning about how to make them more
energy efficient (Jamil et al., 2015). Concerning energy distribution opportunities, the so-called “smart
grid” architecture allows the deployment of systems that optimize the use of renewable energy sources
based on real-time statistics about usage. These grids are capable of self-healing (or at least self-
diagnosis) in severe weather conditions, reducing outages and improving the quality of service.
Thanks to connected solar panels, connected meters, virtual power plants and microgrids, consumers
can become net-positive energy providers to the grid (“prosumers”). This can be done by storing extra
capacity in connected battery packs that can redistribute energy in peak hours (Koutitas, 2018).
Finally, better infrastructure can be created through the development of smart homes, smart buildings,
and connected facility management initiatives (Al-Hader and Rodzi, 2009). In the consumer space,
Artificial Intelligence algorithms can learn about the habits of home dwellers and optimize heating
through connected thermostats; security can be increased through connected cameras, the ubiquity of
intelligent fridges can help individuals, supermarkets and food producers to better regulate their
stocks, possibly reducing food waste. Concerning business environments, advanced facility
management applications are being developed to monitor and improve electricity, communication,
water, sewer, gas, and air conditioning systems. These may rely on internal, private data monitoring
systems coupled with open data Geographic Information Systems (GIS) provided by government
agencies to create better facility management and production processes, increasing productivity and
2.2. Smart Mobility
One of the key motivations of smart city projects is to improve the current state of congestion in most
urban areas. Solutions range from autonomous vehicles that reduce the need for car ownership to
deploying sensors in critical urban infrastructure such as roads, rails, subways, bridges, tunnels,
seaports and airports. These sensors can provide valuable data on how to fluidify traffic, reduce
accidents, improve public transport and make parking faster and easier. Out of 42 smart city projects
studied by Dameri and Ricciardi (2017), almost half (18) were focused on these types of solutions.
Long before self-driving cars become the norm, Vehicular Social Networks (VSNs) are emerging as
one of the main short-term smart mobility trends (Ning et al., 2017). VSNs (such as the community
around Google's Waze app) can integrate GPS data from thousands of real-time drivers and their
smartphones with anomaly detection mechanisms (both human and algorithmic). In a near future,
vehicle-tovehicle and vehicle-to-infrastructure communication frameworks will complete this
ecosystem to enable not only more accurate traffic information but also better cooperative navigation
solutions, car sharing, theft control, safety warnings and cruise control. Mobility should not only
concern vehicles and infrastructure but above all quality of life of citizens. One of the less
technological yet essential ingredients of mobility in smart cities is “walkability” (Kumar and Dahiya,
2017). Cities like Paris and Nice are decreasing the number of car lanes in key transit corridors to
make way for pedestrians and bicycles. This effort to disincentivize motorized vehicles cannot be done
without the careful study of traffic data and how to compensate with alternative routes as well as
increased public transportation quality and availability. Barcelona, for instance, offers an augmented
reality service to facilitate commuter's decisions such as finding the closest bus stops, metro stations,
trams, and trains. The city is integrating data generated by different smart services into a unified urban
mobility platform in partnership with Cisco (Zygiaris, 2013). Furthermore, walkability initiatives can
be complemented by other ecological short-range mobility solutions such as electric bikes, scooters
and mini-scooters shared through a free-floating, pay-per-use business model.
3. Innovation ecosystems in smart cities
As previously mentioned, the infrastructure of smart cities can create a unique collaborative ecosystem
in which citizens, prosumers, industries, universities and research centers may develop innovative
products, services, and solutions. Contrary to traditional double-sided marketplaces in which only two
types of stakeholders participate (supply and demand), a smart city ecosystem involves a multitude of
actors engaged in public and private consumption, production, education, research, entertainment and
professional activities. This collaboration demands high levels of both human and social capital, as the
innovation process is based on knowledge and learning (Smart People). In places where these Triple
Helix dynamics is found (knowledge creation and knowledge application articulated by local
government), creativity and innovation lead to more competitive and attractive local environments
(Smart Economy). Both dimensions are discussed below.
3.1. Smart People
Smart cities can foster both human capital and social capital development (Toppeta, 2010). Human
capital can be defined as the skills and competencies embedded in an individual or a group, whereas
social capital is the quality and the number of links connecting social institutions. The interdependent
nature of these two concepts is essential for understanding how smart cities increase productivity and
innovation in local ecosystems. According to Goldin (2016), the concept of human capital can be
traced back to Adam Smith's Wealth of Nations. The pioneering work of Robert Solow in the 1950s
demonstrated that the majority of productivity growth in society derived not as much from technology
(capital) as from human knowledge and creativity, which are the two essential components of
innovation. In smart cities, the presence of universities and other higher education institutions are
essential to developing human capital, with clear impacts on economic growth as a result. Indeed,
according to Shapiro (2006), growth in a metropolitan area's concentration of college-educated
residents is directly correlated with employment growth. The same is not true of high school educated
citizens, however; this result emphasizes the knowledge intensity required to increase employability.
As Florida (2014) warns, though, it is not sufficient to develop human capital, cities must retain and
attract talent by making living there fun and engaging. Pittsburgh, for instance, has excellent
universities but fails to create an innovative environment as dynamic as Boston's or San Francisco's,
partly because it has a less exciting city life for young, talented graduates. Open minded, tolerant
communities attract a diverse pool of creative workers, which are the basis for developing social
capital in innovation ecosystems. Social capital must be reinforced by carefully targeted public
policies. By attracting talent and investments and providing high standards of living in terms of
security, health and leisure infrastructure, cities become a natural environment for creative minds to
gather, share and learn. Indeed, individual talent would not have as much economic impact without the
institutional relations surrounding and binding them. Thus, according to Coleman (1988), whereas
physical capital is embedded in material resources and human capital is embodied in the skills and
knowledge acquired by an individual, social capital exists in the relationships of trust among persons
and institutions. He argues that social capital is necessary to create human capital and vice versa. They
are mutually reinforcing, as is exemplified in the case of “knowledge economy” initiatives discussed
3.2. Smart Economy
Thanks to the hardware infrastructure, on the one hand, and the social and human capital abundancy,
on the other, smart cities can develop more competitive business environments. Thus, Smart
Environment, Mobility and People are the basis for the innovative business models of the Smart
Economy. Smart cities often create technology hubs to facilitate the sharing of knowledge in the forms
of research centers, start-up incubators, and accelerators, as well as innovation parks. According to the
Triple Helix perspective (Leydesdorff and Deakin, 2011), the physical proximity of talented
individuals, innovative companies and government agencies can lead to a knowledge economy
environment based on social networks of trust, sharing and learning. A notorious example of the
virtues of such a knowledge economy hub is The Research Triangle Park (RTP), implemented near the
city of Raleigh in the 1960s. The RTP is credited as having been the main source of territorial
economic growth in North Carolina in the last 60 years. According to Weddle (2009), before the RTP
this region was one of the poorest in the Southeast, mostly a backwater tobacco farmland. Today,
thanks largely to the successful attraction of companies like IBM, Cisco, Glaxo Smith Kline, and
BASF and the resulting virtuous relationships (hiring, cooperative research) with the Universities of
Duke, UNC and NCS, the region is one of the wealthiest, most creative hotspots for technology in the
US. Such a success has inspired several Smart Cities to create knowledge economy initiatives to
increase territorial attractiveness and thus create better quality jobs with all the positive externalities
that entail (Luger and Goldstein, 1991). Innovative cities and technology parks are natural magnets for
open innovation projects. Schaffers et al. (2011) argue that when advanced IT infrastructures are
developed locally by public-private partnerships, communities of lead users emerge both in companies
and university labs. They cite the example of Nice in France, where a “living lab” was created around
a green mobility project. This initiative involved the regional institution for air measurement quality,
the local research institute dealing with the Internet of Things solutions (INRIA), the Internet
Foundation for the New Generation, which facilitated workshops among local users, as well as a small
company which provided access to electric cars, environmental data, and sensors. Citizens could
participate in the project through the internet, developing Arduino-assembled kits to conduct
experiments and by building their own sensors. In this co-creation process, users become “prosumers”
and contribute directly to the development of the project. Such an initiative would not have been
possible without the social and human capital surrounding the Technopole of Sophia Antipolis near
Nice, where several of the participants were physically located. This kind of open innovation is
facilitated by the synergy and creativity that emerge from open collaboration in the knowledge
economy. The ICT infrastructure of smart cities can also facilitate the emergence of innovative, cloud-
based business models. Perera et al. (2014), for instance, mention the innovation possibilities created
by Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS)
business models. These new services (often called XaaS for “Anything as a Service”) can use the
flexibility of cloud computing to create turn-key solutions to businesses and start-ups, reducing the
entry barriers to develop new ideas and test new solutions for citizen well-being. Sensing-as-a-Service
business models can, for example, use Open Data protocols from shared sensors infrastructure to
gather real-time information about traffic, weather conditions, pollution, and logistics, making them
available to companies or government agencies wishing to create smart services solutions. These
business solutions can be provided by regional, national or multinational partnerships, which
emphasizes the transboundary, hard plus soft nature of smart cities as previously discussed
4. Quality of life in smart cities
As seen in the cases of Sophia Antipolis (France) and the Research Triangle Park (USA) briefly
described above, the collaboration among knowledge workers (Smart People) to create an innovation
ecosystem (Smart Economy) requires a great deal of local articulation among stakeholders (Smart
Governance), often led by government agents or Public-Private Partnerships (PPPs). The decentralized
nature of smart cities imposes effective coordination among hundreds of actors using an information
and communication system that allows stakeholders to be aware of each other's movements and to
facilitate active involvement and mutual support. In order to improve quality of life of a community
through better services in the domains of health, public entertainment, and social bonding, real or
virtual communities must be created and managed using state-of-the-art technology.
4.1. Smart Governance
According to the European Innovation Partnership on Smart Cities and Communities (EIP, 2013, p.
101), the role of Governance Entities is to “manage information flows among stakeholders,
collecting/aggregating/processing data related to value-added processes in smart cities”. GEs also may
certify data quality and integrity, enable financial mechanisms, coordinate stakeholders (including
citizens) throughout the value chains and generate both internal and external awareness about smart
city initiatives. Typical roles in such Governance Entities include promoting, executing, financing,
warrantying and certifying projects. Chourabi et al. (2012) also emphasize the role of these bodies in
assuring transparency, accountability, communication, and participation among all organizations
involved. “Smart” Governance presupposes the innovative use of ICT infrastructure to achieve those
goals, providing all stakeholders with a simplified, one-stop experience based on service application
integration (Tokoro, 2015). Dustdar et al. (2017) argue that such a solution should involve the
following tools: a) Data analytics and real-time process diagnosis: b) Activity coordination and social
orchestration of smart city initiatives; c) Citizen communication; d) Infrastructure management and e)
Services management; f) Incentives management. Far from a trivial integration effort, this
convergence could be essential to create a central dashboard for governance and intelligence.
Incentives management is, to these authors, an essential ingredient of smart city governance. Indeed,
based on evidence from the longstanding tradition of Self-Determination Theory (Ryan and Deci,
2000), they argue that government bodies should build both intrinsic (valuedriven) motivation
schemes as well as extrinsic ones (external rewards to compensate for the lack of intrinsic motivation)
into their projects. Whereas intrinsic motivation (such as curiosity, altruism, competitiveness) is
stronger and longer-lasting, it is harder to manage. It is more adapted to the left side of our hybrid
model (live/play). Extrinsic motivation mechanisms (financial incentives, public sanctions) are more
controllable, but also more volatile. They are more adapted to the right side of our model (work,
4.2. Smart Living
The culmination of all the preceding layers is the well-being of citizens. The OECD (2017) defines
well-being as a result of local material conditions, quality of life, and sustainability. This final section
analyzes how Smart Environment, Mobility, People, Economy, and Governance may lead to Smart
Living in modern cities. According to the OECD Better-Life Initiative framework (2017), smart living
must include initiatives to improve health, education and social services and empower citizen
participation (e-Government projects). It must have positive environmental impacts, reduce
vulnerability and improve safety. Quality of life also should rely on better jobs, housing, and
infrastructure (material conditions) as much on the preservation and development of natural,
economic, human and social capitals. All of these elements can be positively influenced by the use of
information and communication technologies in smart cities. Indeed, we've seen how real-time
information coupled with sophisticated algorithms can improve the energy infrastructure, monitor
environmental threats and fluidify public transit (Smart Environment and Mobility); how it can help
create value through better collaboration and innovation tools for learning and working (Smart People
and Economy) and how all of these efforts can be coordinated through centralized Smart Governance
tools. Dameri and Ricciardi (2017) cite two examples of Smart Living solutions (out of 24 projects
identified in this category in their survey). In Spain, several cities have adopted a centralized digital
solution to deliver real-time information about beach quality, mobility, touristic infrastructures, and
public services. In the case of Rome, a platform was created to facilitate the relationship between
citizens and government agencies, supporting entrepreneurship, events management, city security, and
tourism. The other examples cited in this text complete the picture of how infrastructure and
innovation through better information systems can lead to a better quality of life in smart cities.
5. Overview of the special issue contributions
5.1. Descriptive statistics
Following an extensive blind peer-review process a total of thirtyone papers were accepted for
inclusion to the special issue, on the basis of established selection criteria: novelty and originality of
the discussed topics, methods, and/or approaches; overall consistency with the aims of the call for
papers; relevance both for the academic and practitioner debates. As editors of the special issue, we
would like to take this opportunity to thank all the reviewers involved in the process for their
constructive feedback during the multiple review rounds. Table 2 provides an overview of the
universities/research centers/companies, departments, countries, and number of co-authors for each
special issue paper.
Table 2. Descriptive statistics of accepted contributions
Business models for
developing smart cities. A
fuzzy set qualitative
comparative analysis of an
Sciences, and Earth
annealing for alleviating
vehicular congestion in
Automatic Control and
College of Information
enhanced urban scenarios:
A design research model
towards human centered
School of Design
The role of universities in
management of smart city
Graduate School of
Economics and Local
Light the way for smart
cities: Lessons from Philips
Alliance for Internet of Things
Innovation & InnoAdds
of Data Science
Driving elements to make
cities smarter: Evidences
from European projects
NITEC — Innovation
Smart innovative cities:
The impact of Smart City
policies on urban
Engineering and Built
degli Studi di
Economic and policy
uncertainty in climate
change mitigation: The
London Smart City case
Research, imaginaries and
practices on people'
contribute to energy
systems in the smart city
Center for Sustainable
Development – CDO
Faculty of Science and
Understanding smart cities
as a glocal strategy: A
comparison between Italy
School of Finance and
representations, a new
development path for
supporting Smart City
policy: Evaluation of the
electric car use in Lorraine
Excluding citizens from the
European smart city: The
discourse practices of
pursuing and granting
Erasmus School of
History, Culture and
Centre for BOLD
Cities and the
Department of Public
Knowledge Lab of
Urban Big Data and
School of Social
Sciences and the
approach for smart city
School of Computing
Science; The Energy
Heuristic for the
localization of new shops
based on business and
La Salle -
The relationship between
regional compactness and
capacity (RIC): Empirical
evidence from a national
and Public Affairs
working’ as a mechanism
for alleviating traffic
congestion in smart cities
A cross-disciplinary path to
healthy and energy
Center of Technique
knowledge transfer in
integrated energy planning
of urban districts
clustering and text-based
analysis to reveal the main
development paths of smart
The Business School;
School of Engineering
and the Built
Strategic principles for
smart city development: A
multiple case study
analysis of European best
The Business School;
School of Engineering
and the Built
networking in smart cities:
Privacy and security
Centre for Research &
To the smart city and
beyond? Developing a
typology of smart urban
Faculty of Social
centric technology in
developing smart cities: A
model for predicting the
acceptance of urban
Smart Cities Research
Faculty of Architecture
and Urban Planning
Faculty of Architecture
Towards the smart city 2.0:
Empirical evidence of
using smartness as a tool
for tackling social
Graduate School of
An exploration of smart
city approaches by
international ICT firms
Measuring the extent to
which Londoners are
willing to pay for public art
in their city
Centre for Advanced
Business Model Innovation
for Urban Smartization
understanding of the Smart
City concept: an
exploratory analysis in
Smart City Institute;
Management School of
the University of Liége
Department of Spatial
Identifying and supporting
exploitative models of
innovation in municipal
urban planning; Key
challenges from seven
Towards a service-
dominant platform for
public value co-creation in
a smart city: Evidence from
two metropolitan cities in
Institutes of Science
Chinese Academy of
We received contributions from twenty-one countries; approximately 45% of the researchers work in
institutions located in Italy, United Kingdom, and The Netherlands. A total number of twenty-nine
among scholars and practitioners wrote for this special issue, with an average of 3.4 authors per paper;
60% of the contributions have been co-authored by no more than three co-authors, whereas 7% by a
single author, and 14% by six authors. In terms of institutions and departments, two
universities/companies and two departments on average worked together to build up the study, with a
maximum of six. It is worthy of notice the level of interdisciplinarity in undertaking such an endeavor:
among the many disciplines, we highlight management, economics, mathematics, computer science,
art, engineering, innovation, architecture, design, sustainability, history, energy, and anthropology.
Under these respects, the spirit of the call for papers was successfully accomplished since its aim was
to stimulate interdisciplinary collaboration and build up a community to enact a constructive discourse
around Smart Cities.
5.2. Reviewing the content
The thirty-one papers comprising this special issue advance our understanding of the underlying
technological and societal challenges smart cities initiatives pose to academics, practitioners, and
policy makers. It is worth noting that this collection of papers is heterogeneous in terms of theoretical
approaches, empirical methodologies, and focus of the investigation, spanning a wide range of
conceptual approaches and research designs. In so doing, it exposes the reader to diverse ideas and
methods, thus having the potential to stimulate creative scholarly conversations on the topic (Table 3).
Table 3. Overview of the contributions in the light of our hybrid framework (Fig. 1)
Business models for developing smart cities. A fuzzy set qualitative comparative analysis of an IoT
PI / IE / QL
Centralized simulated annealing for alleviating vehicular congestion in smart cities
Reframing technologically enhanced urban scenarios: A design research model towards human centered
PI / IE / QL
The role of universities in the knowledge management of smart city projects
IE / QL
Light the way for smart cities: Lessons from Philips Lighting
IE / QL
Driving elements to make cities smarter: Evidences from European projects
PI / IE / QL
Smart innovative cities: The impact of Smart City policies on urban innovation
PI / IE / QL
Economic and policy uncertainty in climate change mitigation: The London Smart City case scenario
PI / QL
Participatory energy: Research, imaginaries and practices on people' contribute to energy systems in the
PI / IE
Understanding smart cities as a glocal strategy: A comparison between Italy and China
PI / IE / QL
Understanding user representations, a new development path for supporting Smart City policy: Evaluation
of the electric car use in Lorraine Region
PI / QL
Excluding citizens from the European smart city: The discourse practices of pursuing and granting
IE / QL
A Multiple-Attribute Decision Making-based approach for smart city rankings design
PI / IE / QL
Heuristic for the localization of new shops based on business and social criteria
IE / QL
The relationship between regional compactness and regional innovation capacity (RIC): Empirical
evidence from a national study
Investigating ‘anywhere working’ as a mechanism for alleviating traffic congestion in smart cities
PI / QL
A cross-disciplinary path to healthy and energy efficient buildings
PI / QL
Intermediaries for knowledge transfer in integrated energy planning of urban districts
PI / QL
Combining co-citation clustering and text-based analysis to reveal the main development paths of smart
PI / IE / QL
Strategic principles for smart city development: A multiple case study analysis of European best practices
PI / IE / QL
Εnhancing social networking in smart cities: Privacy and security borderlines
IE / QL
To the smart city and beyond? Developing a typology of smart urban innovation
PI / IE / QL
Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance
of urban technologies
PI / IE / QL
Towards the smart city 2.0: Empirical evidence of using smartness as a tool for tackling social challenges
PI / IE / QL
An exploration of smart city approaches by international ICT firms
IE / QL
Navigating platform urbanism
PI / QL
Measuring the extent to which Londoners are willing to pay for public art in their city
IE / QL
Business Model Innovation for Urban Smartization
Municipalities’ understanding of the Smart City concept: an exploratory analysis in Belgium
PI / IE / QL
Identifying and supporting exploratory and exploitative models of innovation in municipal urban planning;
Key challenges from seven Norwegian energy ambitious neighborhood pilots
PI / IE / QL
Towards a service-dominant platform for public value co-creation in a smart city: Evidence from two
metropolitan cities in China
PI / IE / QL
In detail, more than one-third of the sample (i.e. twelve papers) provides the reader with some
conceptualizations, approaches, and typologies to read and interpret the smart cities phenomenon
through more critical lenses. Andreani et al. (2018) argue about how to move the locus of inquiry from
a technocentric and universalist approach on smart cities – mainly predictable, overplanned, top-down,
efficient, and quantitative – towards a design-driven and human-centric approach – which is more
unintentional, temporary, democratic, creative, and qualitative. Drawing from research pursued within
the ‘Real Cities/ Bergamo 2035’ joint initiative between the University of Bergamo and the Graduate
School of Design at Harvard University, authors focused on mid-sized European cities; three scenarios
were investigated: the adaptive street environments, the responsive urban safety, and the dynamic
retail spaces. The proposed model is articulated into three interwoven components: a grounded vision,
addressing the ideation of alternative futures that stem from specific needs or local opportunities; an
embraced technology, elaborating on the role played by urban technologies in augmenting the inner
intelligence of places; and an urban co-evolution, fostering a mutually-constructive interaction
between the urban players (i.e. citizens, researchers and designers, and stakeholders) for collaborative
innovation. Dameri et al. (2018) conceptualize smart cities as a glocal strategy. A smart city is global
since it is a phenomenon spreading all over the world, with some shared features and
interdependencies: they attract investments, talents, and innovative firms; however, it is a local
phenomenon as each city shows unique characteristics and problems policy makers can only deal with
by means of specific solutions: it suffices to think about the geographical and territorial specificities,
the cultural milieu, the needs and traditions of the communities. By comparing Italian (Bologna,
Milan, Turin, Florence, and Genoa) and Chinese (Shanghai, Beijing, Tianjin, Guangzhou, and
Chengdu) cities, authors develop a theoretical framework based on four dimensions: people (smart
citizens; smart city actors such as firms, universities, private bodies; people involvement), government
(political institutions; powers distribution; smart city governance processes; priorities), infrastructure
(better use of energy; renewal energy source; buildings efficiency; efficient services like transport),
and land (environmental and geographical aspects; cultural history and heritage; logistics). It results
that the Italian and Chinese smart city implementation path differ since the former exhibits a bottom-
up approach as a result of the following local drivers (existing infrastructures, lack of a national smart
city strategy, decentralized governance, lack of funding to support smart cities initiatives), whereas the
latter follow a top-down approach deriving from a national smart city strategy. Escolar et al. (2018)
review the existing ranking for smart cities highlighting their major weaknesses in the overlook of
technological criteria. To fill this gap, they advance a methodological approach for developing smart
cities rankings based on technological and smartness criteria; they do it by applying a multi-attribute
decision making-based approach (MADM). The smartness dimension authors propose considers
thirtyeight ICT indicators related to the main enabling technologies for smart cities realization: sensors
and actuators, networking, platforms and services deployed, applications, standardization level, and
metrics to determine their impact on the city. By testing their method on three case studies (Seoul,
Santander, and New York), authors highlight its strengths (i.e. coherence with the most commonly
accepted vision of the IoT and smart cities, set of new ICT and smartness indicators, and easy
extension with new indicators) and weaknesses (i.e. subjectivity of the MADM method, limited
number of cities involved in the ranking). Mora et al. (2018a) rely on two hybrid techniques to unveil
the main development paths of smart cities; precisely, they combine co-citation clustering and text-
based analysis to perform their bibliometric study (Appio et al., 2014, 2016; Glanzel and Thijs, 2011).
They show that research on smart cities is diverging into five development paths: experimental,
ubiquitous, corporate, European, and holistic. Importantly, four main dichotomies emerge which are
mainly rooted into the cognitive-epistemological structure of the smart city research and challenge the
scientific community: techno-led or holistic, top-down or bottom-up, double or triple/quadruple helix,
mono-dimensional or integrated. The ambiguity generated by these dichotomies challenges policy
makers in setting a proper smart cities development agenda. Moving from the need deal with these
dichotomies, Mora et al. (2018b) investigate the validity of the strategic principles for smart city
development by comparing four cities considered to be leading examples of European smart cities:
Amsterdam, Barcelona, Helsinki, Vienna. Through a best practice analysis, the authors identify six
strategic principles to support the decision-making process and speed up the effective deployment of
smart technologies in European urban environments: look beyond technology; move towards a
quadruple-helix collaborative model; combine top-down (government-led) and bottomup (community-
driven); build a strategic framework; boost the digital transformation by establishing a smart city
accelerator; adopt an integrated intervention logic. In reviewing what constitutes the smart in smart
cities, Nilssen (2018) concluded that the concept of smartness should be understood as a collection of
developmental features; smart cities initiatives have to be able to effectively connect the wide range of
existing activities, adopting a holistic approach. The latter should be based on a typology of smart
urban innovations based on new technological practices, products, and services; organizational
project-based levers internal to the municipal organization; public-private networks and triple helix
collaborative models; and a rhetoric dimension inspiring the vision of an innovative urbanism. She
discusses her typology in the light of the smart cities initiatives in the city of BodØ (Norway).
Sepasgozar et al. (2018) advance a new Urban Services Technology Acceptance Model (USTAM),
which is aimed at assisting governments and business to develop appropriate ‘urban service’
technologies for local contexts and emerging economies. Major emphasis is posed on the relevance of
local knowledge as a source of innovative potential for smart cities. Their model is able to assess to
what extent the behavior intention to use UST is influenced by factors such as service quality, self-
efficacy, a number of TAM factors (i.e. perceived security, relative advantages, perceived of use,
perceived usefulness, compatibility, reliability), as well as factors stemming from the social cognitive
theory (i.e. work facilitating, cost reduction, energy saving, and time saving). Trencher (2018) argues
about the need to move from a smart city 1.0 approach towards a smart city 2.0. A smart city 1.0
revolves around a centralized approach with exogenous development has at its focus the diffusion of
smart technologies for corporate and economic interests; the role of citizens is rather passive; the
objective of the technology and experimentations is to optimize infrastructures and services, serve the
demand side interests and spur new business opportunities, and address the universal technical
agendas (energy, transport, economy). On the contrary, a smart city 2.0 approach is focused on people,
governance, and policy; citizens have an active role as co-creators of innovations, problem solvers,
and planners; the objective of technology and experimentation is to mitigate or solve social problems,
enhance citizens' wellbeing and public services, and address specific endogenous problems and
citizen's needs. Smart city 2.0 is clearly a decentralized approach in which diverse actors are involved
and the development is endogenous to the system. Then, the author explains how the concept of Smart
City 2.0 works by looking at the case of a Japanese city – Aizuwakamatsu – where explicit attention to
tackle social issues and address citizens' needs is articulated and formalized in project documents.
Desdemoustier et al. (2018) investigate how – and to what extent – 113 Belgian municipalities
understand the concept of Smart Cities. Findings suggested the creation of a typology of
understandings comprising four dimensions: technological (a technology implementation), societal (a
human, sustainable and institutional positioning), comprehensive (an integration of technology,
human-centricity, sustainability, and institutional factors) and non-existent (an absence of
understanding). Interestingly, municipalities engaged in comprehensive understanding find setting up
smart city projects highly difficult; those with non-existent knowledge do not adhere to the
phenomenon. Nielsen et al. (2018) read the smart cities phenomenon through the lenses of the
ambidextrous organizations. Through a multiple cases analysis, they study seven pilot projects in
Norwegian municipalities, developers, and universities. They find that developing an ambidextrous
capability alongside leveraging upon a bottom-up capacity building could be the right way to adapt
recent technological advancements to emerging smart cities programs. Camboim et al. (2018) come up
with an integrated framework to make a city smarter on the basis of extant literature, interviews with
experts, and insights from four smart cities projects (Amsterdam, Barcelona, Lisbon, Vienna). Their
framework identified three steps: smart strategies, where governance takes the lead of the
transformation process from a traditional city into a smart city; smart projects, in which socio-
institutional, techno-economic and environmental and urban factors are the main drivers; and smart
performance inflected in terms of sustainable socioeconomic development. Finally, Yu et al. (2018)
argue about the possibility to adopt the concept of service dominant platform (SDP) to help the city
stakeholders to cocreate smart cities. By combining the foundational elements of the service-dominant
logic (SDL) with platform theory, they propose three dynamic conceptual pillars play a role: value
proposition, value in exchange, and value in use, consisting of ten sub-elements articulated on four
dimensions namely, openness, services innovation, governance, and resources. Findings from a
business-oriented platform in Guangzhou (i.e. WeChat) and a government-oriented platform in
Shanghai (i.e. Citizen Cloud) show that smart city initiatives subsume the multi-parties formulation of
a co-creation sustainable strategy. The remaining papers (i.e. nineteen) can be grouped into four
clusters labeled as follows: business models for smart cities (Abbate et al., 2018; Brock et al., 2018;
Schiavone et al., 2018; Van den Buuse and Kolk, 2018); applications to tackle specific smart cities
challenges (Amer et al., 2018; Grimaldi et al., 2018; Hopkins and McKay, 2018; Lex et al., 2018;
Moustaka et al., 2018; Tanguy and Kumar, 2018); actions and roles of stakeholders of the smart cities
triple/quadruple helix (Ardito et al., 2018; Corsini et al., 2018; Dupont et al., 2018; Engelbert et al.,
2018; Lindkvist et al., 2018; Van der Graaf and Ballon, 2018); policies for smart cities (Caragliu and
Del Bo, 2018; Contreras and Platania, 2018; Hamidi et al., 2018). Concerning the first cluster –
business models for smart cities – Abbate et al. (2018) explore the activities and strategic goals of
twentyone small and medium enterprises (SMEs) operating in eight different European countries that
took part to FrontierCities, one of the nine FIWARE (Future Internet-ware) Accelerators focused on
smart cities. The aim is understanding what type of business models they can adopt when exploiting
the technological potential of an IoT platform. Authors reveal that only key resources can be
considered as the core element in the customized products and service business model, while key
activities and key partners stand as complementary variable; then, when firms aim at developing smart
cities projects have to consider the cooperation with customer capabilities as the main key resources;
customers become an important part of the puzzle in order for firms to deploy proper business models.
By carrying out an in-depth case study at Philips Lighting, Brock et al. (2018) show what type of
business models are relevant for the smart city market. Philips is searching for new ways to create and
capture value within different smart city ecosystems; four of them – Amsterdam, Eindhoven,
Stratumseind, and Veghel – are instrumental to unveil the main business models: marbles business
model, in which there is no integration of value creation or value capture activities between the
different parties, and everything is developed inhouse and sold as a one-off sale; Tetris business
model, where value is created individually, while an extended set of revenue models are introduced
that build on each other and can be shared across the ecosystem; Jenga business model, characterized
by an extended value creation, where different ecosystem actors learn from each other, though with
limited revenue potential for the individual parties; finally, the Jigsaw Puzzle business model, in which
we have an extended value creation and value capture, by leveraging synergies within an ecosystem to
jointly create the most value for customers and the ecosystem. Schiavone et al. (2018) apply the
business model canvas to the smart cities literature. They identify the revenue stream, cost structure,
key resources, key activities, key partners, the value creation, customer relationships, market
segments, and channels identifying the basic building blocks of the smart city business model canvas.
Finally, Van den Buuse and Kolk (2018) investigate the strategic approaches three multinational
enterprises (MNEs) from the ICT industry (IBM, Cisco, Accenture) adopt as suppliers of smart city
technologies. Evidence from firm-specific programs like IBM's Smarter Cities, Cisco's
Smart+Connected Communities, and Accenture's Intelligent Cities, shows that both non-location-
bound firms specific advantages (e.g., building resources and capabilities in management from
heterogeneous urban contexts, building a position as international smart city technology supplier in a
potential growth market, exploring complementarities between existing resources and capabilities in
ICT and urban domains, among others) and location-bound firms specific advantages (e.g., building
relationships with city governments in prime cities for the spread of smart cities technologies, building
expert knowledge of specific urban system and infrastructures in a local context, gaining access to
local knowledge clusters and urban stakeholders in a local context) are relevant components of the
three MNEs' business models. In the second cluster – applications to tackle specific smart cities
challenges – Amer et al. (2018) introduce a new method in order to alleviate vehicular traffic
congestion in smart cities. This method is a centralized dynamic multi-objective optimization
algorithm based on vehicular ad hoc networks (VANETs); it integrates a centralized simulated
annealing (CSA) algorithm with the VIKOR method as a cost function. The aim of the CSA-VIKOR
method is to provide the drivers with the optimal paths according to multiple criteria in order to meet
the diverse navigation requirements of the drivers. The optimization algorithms, tested into the city
centers of Turin and Birmingham, results in journeys improvements concerning the minimum travel
time, the minimum travel distance, the minimum fuel consumption, the minimum amount of
emissions, or a combination of the four. Still on traffic congestion, this time in Melbourne, Hopkins
and McKay (2018) explore the role of environmental factors (climate change, global warming,
greenhouse gas emissions, atmospheric issues), economic factors (service economy, information-based
work activities, decoupling work task from place, skill, and performance-based work), technological
factors (widespread access to Internet, dematerialization, employee flexibility, bring-your-own-device
practices, distributed teams) on the ‘anywhere working’ practices (worker attitudes towards adoption,
and current commuter behavior). In turn, they also assess the benefits and constraints of the ‘anywhere
working.’ The heuristic proposed by Grimaldi et al. (2018) deals with the desertification of urban
areas due to the massive close of local shops in contexts hit by the financial crisis. Their heuristic
entails business and social criteria and results coming from the computational experiment run in the
Sant Andreu district (Barcelona) show that an effective smart city policy faces urban degeneration by
decreasing the risks of uniformity, monobusiness activity, and gentrification of the neighborhood.
Another contribution comes from a cross-disciplinary collaboration framed within four sub-themes:
local energy systems, indoor climate in buildings, social and organizational conditions, and political
circumstances (Lex et al., 2018). Authors propose a digital platform to deal with the indoor climate in
public buildings in Copenhagen and argue about the importance to enacting micro-social cross-
boundary collaborations among all the involved stakeholders as a way to create concrete scientific and
practical insights on smart city development initiatives. By analyzing the case of Trikala (Greece),
Moustaka et al. (2018) pay attention to the publicly available data generated by people and shared on
online social networks (OSN), providing measures to improve their privacy and security, smoothening
the risks, and boost community's engagement in smart cities. OSN is conceptualized as sensors of
urban dynamics with unquestionable advantages but not negligible threats and vulnerabilities. The
interactions between smart people/smart living and privacy/security concerns are discussed and a
multi-stage behavioral pattern model is advanced. Participation on OSN, education, and training for
secure behavior, tools and software for privacy and security protection, data privacy legislative
framework lead to better key performance indicators in smart cities. Finally, Tanguy and Kumar
(2018) give to art projects the role extant literature on smart cities neglected. They explore the impact
of public art projects on the life and demand of citizens in London. By collecting data from two public
art initiatives organized by the MTArt Agency they show that Londoners are willing to pay for more
public art in local areas; furthermore, these projects call for a transversal involvement of art experts,
urban planners, economists, sociologists, political scientists, and citizens. The third cluster discusses
the actions and roles of stakeholders of the smart cities triple/quadruple helix. Ardito et al. (2018)
argue that knowledge-based urban development (KBUD) frameworks are increasingly permeating the
smart cities debate. They outline a 2 × 2 matrix on the basis of two dimensions: knowledge
management (KM) issues, where project partners may address KM governance in different ways and
KM processes can change according to the knowledge domain; knowledge management domains, by
considering whether knowledge stems from similar or distant fields. A first quadrant captures KM
governance when knowledge of project partners is used; the second quadrant analyzes KM governance
when external knowledge is used; the third quadrant focuses on KM processes in cases of knowledge
coming from project partners; a final quadrant presents cases of KM processes when external
knowledge is used. By selecting cases of smart cities initiatives from Italian, English, American,
Spanish, and Belgian cities, they investigate the role of universities (i.e. knowledge intermediary,
provider, evaluator, gatekeeper in the triple or quadruple helix configurations). Engelbert et al. (2018)
focus their study on the role of citizens in the smart cities discourse. Moving from a characterization of
a smart city as an assemblage of ‘peripheral’ smart city network practices and ‘central’ smart city
project practices, they critically examine the political-economic ambitions of those cities able to grant
the recognition ‘smart’ (i.e. European Research and Innovation Schemes) and those needing to
‘pursue’ it (i.e. post-crisis municipalities). Through such a differentiation, they figure out why the
majority of the smart cities initiatives tend to exclude the needs and interests of citizens. Still focusing
on the role of citizens, Dupont et al. (2018) investigate the user representations French citizens in the
Lorrain Region have when they confront with specific technologies like electric cars. Four
complementary aspects characterize the social representation: the possibility of action of the subject on
the system, the stimuli caused by the system, how the user will identify with the system, as well as the
overall attraction. Authors were also able to qualify the pragmatic and hedonic attributes of the
relationship between potential users and the image and attractiveness of the electric cars. Corsini et al.
(2018) still focus on the role of citizens in contributing to the energy systems in a smart city initiative.
Through a bibliometric analysis and visual representation (Appio et al., 2017, 2017; Van Eck and
Waltman, 2010) of an overall set of 74,932 academic papers, they show that city dwellers are rarely at
the core of energy transition agendas. Instead, research overemphasizes the role of technological
advancements for energy production and consumption. Authors argue about those socio-technical
imaginaries that put citizens at the core of a participatory smart city revolution. Still concerning energy
planning practices in urban districts, Lindkvist et al. (2018) examine the role of intermediaries for
knowledge transfer in early, progressed, and implemented project stages. Findings from ongoing
projects based in Norway, Spain, France, Sweden, and Austria, show that intermediaries are absent in
the fuzzy front end of the project while showing up later as problem solvers. Authors call for more
integrated planning practices in which intermediaries become part of the helix of stakeholders since
the very early stages of the process. Finally, Van der Graaf and Ballon (2018) investigate the role of a
social traffic and navigation application, Waze, operationalizing the concept of platform urbanism in
which citizens, private and public organizations interact. By exploring the manifestations of dynamics
in mobility practices occurring between commerce and community in the city, they found out a
(complex and new) socio-spatial construct is emerging. Important questions arise concerning the role
of urban and transportation management and planning in the public space of the city. In the fourth
cluster, which emphasizes the policies for smart cities, Contreras and Platania (2018) investigate the
role of policies in climate change in the London Environment Strategy (LES) within the London smart
city initiative. By using a zero mean-reverting model for greenhouse gas emissions, the quantitatively
analyze the consistency of the LSE framework with the 2020 Zero Carbon objectives. Different policy
scenarios are considered by focusing on the domestic, industrial and commercial, and transport
sectors. Their simulation study shows that considering the 2000–2014 greenhouse emission trend, the
industrial and commercial sector and the domestic sector present levels far from the 2050 zero level
objective; only the transport sector improves the historical trend. This is the result of the smart
mobility and smart environment policies proposed within the LSE framework. Caragliu and Del Bo
(2018) assess the impact of smart cities policies on urban innovation. They collect data from 309
European metropolitan areas on the basis of six axes: human capital, social capital, transport
infrastructure, ICT infrastructure, natural resources, and e-government. Results from the propensity
score matching estimates show that smart city policies do have a non-negligible positive impact on
urban innovation measured through patenting activity, especially in high-tech classes. They also show
that these policies stimulate innovation, which in turn increases the city's stock of knowledge. Finally,
Hamidi et al. (2018) explore the link between regional compactness and the regional innovation
capacity (RIC). Compact urban forms are characterized by walkability, higher street connectivity, and
greater accessibility to urban amenities, jobs-housing balance, and mixed land use in addition to
density. They measure regional compactness by borrowing the recently released Metropolitan
Compactness Index (MIC), which includes 21 built environmental features and captures several
dimensions of sprawl. Their study finds that all the three RIC indicators – the average number of
patents, firm innovations, and number of innovative small firms – are positively associated with
regional compactness. Their findings have an impact on the physical and social landscapes of cites,
call for investments in increasing accessibility and improving public transit as factors contributing to
agglomeration economies and innovation production.
6. Towards a research agenda
Despite the value of the thirty-one articles presented, this special issue leaves space for scientists,
practitioners and policy makers to further explore the subject of smart cities. One research avenue
could deal with the risks and benefits of implementing smart cities initiatives. In fact, in this paper we
have discussed mostly the potential benefits of smart cities. However, one must keep in mind the
dangers and threats posed by this explosion in data and algorithms. Among the risks cited in the
literature are ideological manipulation (Morozov and Bria, 2018), the corporatization of city
governance (Paroutis et al., 2014; Söderström et al., 2014), hackable networks vulnerable to cyber-
attacks and a tendency to normalize a surveillance state (Bauman and Lyon, 2013; Ellul, 2012;
Kitchin, 2014). Surveillance come from the need of security and the means to reach it are the new
available techniques and technologies (Bauman and Lyon, 2013); and if we consider that we moved
from an era in which threats came from outside the city to a world in which threats come from within
the city, the risk to build a Panopticon society (Lyon, 2006) is seducing. According to Lehr (2018), a
city cannot be called ‘smart’ unless it has solved the complex issues associated with privacy in a world
of ubiquitous data, social interactions, and artificial intelligence. Perera et al. (2014) list a series of
challenges facing smart cities in the domains of technology (lack of integration across government
systems, interoperability, standardization, availability and compatibility of software, systems and
applications); security and privacy (threats from hackers and intruders, threats from viruses, worms
and trojans; breach of privacy, theft of personal data) and socio-cultural barriers (trust, social
acceptance, resistance to change, usability, digital illiteracy). These barriers cannot be overcome
simply with technological solutions. They must be followed by legal frameworks such as Europe's
General Data Protection Regulation (EU, 2016), active policies for developing human and social
capitals through training programs, civic awareness campaigns and curriculum reforms in schools and
universities. They have to articulate and engage all stakeholders in the public and private sectors,
develop standards and protocols, facilitate bottom-up as well as propose top-down guidelines.
Therefore, Smart Governance (e.g., Ruhlandt, 2018) emerges probably as the key factor mediating the
other dimensions of the model in order to assure that projects remain within ethical boundaries, that
stakeholders constantly communicate and learn from each other and that the resulting products and
services ultimately have a positive impact on the well-being of smart city dwellers. International
standards for smart cities (such as ISO 37122) could provide basic guidelines to all stakeholders
involved. Future studies could focus on examining the ways actors, groups, organizations and
stakeholders develop strategies (Paroutis et al., 2014) to deal with the risks and benefits associated
with smart cities. The contradictory but interrelated nature of smart city objectives means that studying
them could benefit from recent advancements found in ambidexterity and paradox studies (Knight and
Paroutis, 2017; Lewis, 2000; Papachroni et al., 2016; Smith, 2014), for example by studying the
rhetorical practices groups and organizations develop over time to deal with the tensions associated
with smart city initiatives (Bednarek et al., 2017). Another area of research could conceptualize and
study smart cities as business ecosystems and platforms (Jacobides et al., 2018; Kretschmer and
Claussen, 2016) where multiple actors and organizations act and interact over time to implement
innovative solutions (Kumaraswamy et al., 2018). For such future studies, the hybrid model we
proposed earlier (see Fig. 1) can assist scholars and policy makers visualize and appreciate the
interdependent nature of physical infrastructure, innovation ecosystems and quality of life in smart
cities. Overall, understanding the processes and practices related with the social challenges and impact
of smart cities represents a fundamental area for future research (Burgelman et al., 2018). Such
research endeavors will be impactful in providing stakeholders, policy makers and social actors with
the means, processes and technological solutions to measure and then improve the social impact of
smart city initiatives. Another risk class to be considered deals with the transformation of the urban
landscape. Contributions and intellectual leaps are necessary to introduce and contrast utopian and
dystopian representations of the intelligent city in the XXII century. Essays like the one written by
Wells in 1897 can be of inspiration to see how – and to what extent – urban transformations which
tend to pose too much emphasis on the technocracy show huge social inequalities, overcrowded
skyscrapers in few megalopolis crossed by congested air traffic, with countryside completely
abandoned, few big corporations managing the world economy, and citizens living and working under
an uncontrollable mental hypnosis. Driving the debate outwards to present the emergence of Orwellian
scenarios can help policy makers to focus their actions on more utopian models of intelligent cities.
Relatedly, and asking how we should live in the city, Sennett (2012, 2018) looks at the city as an
‘open city’ that embeds complexity, ambiguity, and uncertainty. By distinguishing between two
aspects of the city, namely the ville, which refers to the built environment, and the cité, which refers to
the modes of life and place attachments to which urbanity leads, Richard Sennett explains why long-
term and large-scale urban planning is difficult. This frustration is rooted into the huge divide between
the ville and the cité, which traces back to the nineteenth century when Baron Hausmann's boulevards,
Ildefons Cerdà's Eixample in Barcelona, and Frederick Olmsted's Central Park were mainly aimed at
refashioning neglecting the way people behave in the city. This divorce went on in the twentieth
century with the Chicago School, Le Corbusier, Jacobs, and Mumford's visions. For Sennett, the core
ethical problem in any city is dealing with others; moving from this ethical issue, and with the aim to
facilitate a city that is porous, incomplete and multiple, he suggests ways to remake the cité by
focusing on urban design, emphasizing the presence of permeable open spaces and variegated type-
forms, creating co-development practices by experts and public. Shape and size of the city matter
(Batty, 2014; West, 2017). Current statistics1 show that by 2030 the world's population is projected to
be 8.5 billion, increasing to 9.7 billion by 2050 and 11.2 billion by 2100; moreover, if 1950 only 30%
of the population lived in cities, in 2050 this percentage is expected to grow to 70% (already nowadays
more than half of the world lives in cities). New cities will necessarily emerge and become centers of
the new civilizations, life, and knowledge for centuries. These trends will challenge cites' services and
infrastructures in terms of scalability, environmental impact, security as they are supposed to adapt in
order to support this population growth. New research can be carried out by conceptualizing cities as
complex adaptive systems (West, 2017); but differently from companies and human beings, they
(almost) never die and are remarkably resilient; their urban metabolism – which is the sum total of the
technical and socio-economic processes that occur in cities, resulting in growth, production of energy
and elimination of waste (Kennedy et al., 2007, 2011) – is what needs to be investigated since it
provides with the basis to develop laws and indicators aimed to disentangle the dynamics of the visible
city (material and tangible components like roads, buildings, etc.) and the invisible city (immaterial
and intangible components like social networks and information). Central to this debate become the
suburbs-city centers' dynamics. If it is true that the majority of people will live in cities, it seems (from
current trends) that many of them populate the suburbs. Smart cities can be the way to rethink the role
of suburbs as a bridge to connect the city with the others and become the center of interconnections
with new communities. This can give a new role to small cities, rural areas and villages in that they
can potentially benefit from the Internet revolution and repopulate: in fact, smart working practices
take place to rethink one's lifestyle and promote factors such as sociability and well-being, which are
increasingly difficult to maintain in large cities (or megalopolises). Jean-Jacques Rousseau had already
thought of this when he stressed the need for people to distance from the cities and return to the
In this introduction to the special issue of the Technological Forecasting and Social Change
“Understanding Smart Cities: Innovation Ecosystems, Technological Advancements, and Societal
Challenges” we discussed the broader theme of Smart Cities and attempted to reframe associated
topics and practices in prior work. Next, we introduced the papers in the special issue and linked them
to the proposed hybrid framework. Finally, we offered a research agenda which points out the urgent
need to develop a science of smart cities, in which criticalities and tensions (Almirall et al., 2016),
contrasting views (Greenfield, 2013), strategic planning and wise urban policies (Sennett, 2018),
through a balanced adoption of qualitative and quantitative approaches, coexist and further stimulate a
constructive and critical debate. Thus, we hope this special issue will inspire future work on the nature
and challenges of current and future smart cities initiatives.
Abbate, T., Cesaroni, F., Cinici, M.C., Villari, M., 2018. Business models for developing smart cities.
A fuzzy set qualitative comparative analysis of an IoT platform. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.031 (in press).
Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., Airaksinen, M., 2017. What are the differences between
sustainable and smart cities? Cities 60 (A), 234–245.
Albino, V., Berardi, U., Dangelico, R.M., 2015. Smart cities: definitions, dimensions, performance,
and initiatives. J. Urban Technol. 22 (1), 1–19.
Al-Hader, M., Rodzi, A., 2009. The smart city infrastructure development & monitoring. Theoretical
Empirica. 2(11), 87–94.
Almirall, E., Wareham, J., Ratti, C., Conesa, P., Bria, F., Gaviria, A., Edmondson, A., 2016. Smart
cities at the crossroads: new tensions in city transformation. Calif. Manag. Rev. 59, 141–152.
Amer, H., Al-Kashoash, H., Hawes, M., Chaqfeh, M., Kemp, A., Mihaylova, L., 2018. Centralized
simulated annealing for alleviating vehicular congestion in smart cities. Technol. Forecast. Soc.
Chang. https://doi.org/10.1016/j.techfore.2018.09.013 (in press).
Andreani, S., Kalchschmidt, M., Pinto, R., Sayegh, A., 2018. Reframing technologically enhanced
urban scenarios: a design research model towards human centered smart cities. Technol. Forecast. Soc.
Chang. https://doi.org/10.1016/j.techfore.2018.09.028 (in press).
Angelidou, M., 2014. Smart city policies: a spatial approach. Cities 41 (S1), S3–S11.
Angelidou, M., 2015. Smart cities: a conjuncture of four forces. Cities 47, 95–106.
Appio, F.P., Cesaroni, F., Di Minin, A., 2014. Visualizing the structure and bridges of the intellectual
property management and strategy literature: a document co-citation analysis. Scientometrics 101,
Appio, F.P., Martini, A., Massa, S., Testa, S., 2016. Unveiling the intellectual origins of Social Media-
based innovation: insights from a bibliometric approach. Scientometrics 108, 355–388.
Appio, F.P., Martini, A., Massa, S., Testa, S., 2017. Collaborative network of firms: antecedents and
state-of-the-art properties. Int. J. Prod. Res. 55, 211–2134.
Appio, F.P., Martini, A., Messeni Petruzzelli, A., Neirotti, P., Van Looy, B., 2017. Search mechanisms
and innovation: an analysis across multiple perspectives. Technol. Forecast. Soc. Chang. 120, 103–
Ardito, L., Ferraris, A., Messeni Petruzzelli, A., Bresciani, S., Del Giudice, M., 2018. The role of
universities in the knowledge Management of Smart City Projects. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.030 (in press).
Batty, M., 2014. The New Science of Cities. MIT Press, Cambridge, MA.
Bauman, Z., Lyon, D., 2013. Liquid surveillance. A conversation. Polity Press, Cambridge, UK.
Bednarek, R., Paroutis, S., Sillince, J., 2017. Transcendence through rhetorical practices: responding
to paradox in the science sector. Organ. Stud. 38 (1), 77–101.
Brock, K., Den Ouden, E., van der Klauw, K., Podoynitsyna, K., Langerak, F., 2018. Light the way for
smart cities: lessons from Philips Lighting. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.021 (in press).
Burgelman, R.A., Floyd, S., Laamanen, T., Mantere, S., Vaara, E., Whittington, R., 2018. Strategy
processes and practices: dialogues and intersections. Strateg. Manag. J. 39 (3), 531–558.
Camboim, G.F., Zawislak, P.A., Pufal, N., 2018. Driving elements to make cities smarter: evidences
from European projects. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.09.014
Caragliu, A., Del Bo, C., 2018. Smart innovative cities: the impact of Smart City policies on urban
innovation. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.07.022 (in press).
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J.,
2012. Understanding smart city initiatives: an integrative framework. In: The 45th Hawaii
International Conference on System Sciences, 4–7 January Maui, HI, pp. 2289–2297.
Cohen, B., 2013. The smart city wheel. Available at: https://www.smart-circle.org/
Coleman, J.S., 1988. Social capital in the creation of human capital. Am. J. Sociol. 94, S95–S120.
Contreras, G., Platania, F., 2018. Economic and policy uncertainty in climate change mitigation: the
London Smart City case scenario. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.018 (in press).
Corsini, F., Certomà, C., Dyer, M., Frey, M., 2018. Participatory energy: research, imaginaries and
practices on people' contribute to energy systems in the smart city. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.028 (in press).
Dameri, R., Benevolo, C., Veglianti, E., Li, Y., 2018. Understanding smart cities as a glocal strategy: a
comparison between Italy and China. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.025 (in press).
Dameri, R.P., Ricciardi, F., 2017. Leveraging smart city projects for benefitting citizens: the role of
ICTs. In: Rassia, S., Pardalos, P. (Eds.), Smart City Networks. Springer Optimization and Its
Applications, vol. 125 Springer, Cham.
Desdemoustier, J., Crutzen, N., Giffinger, R., 2018. Municipalities' understanding of the Smart City
concept: an exploratory analysis in Belgium. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.10.029 (in press).
Dupont, L., Hubert, J., Guidat, C., Camargo, M., 2018. Understanding user representations, a new
development path for supporting Smart City policy: evaluation of the electric car use in Lorraine
Region. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.10.027 (in press).
Dustdar, S., Nastić, S., Šćekić, O., 2017. Smart Cities: The Internet of Things, People and Systems.
Springer. Ellul, J., 2012. Le système technician. Cherche midi.
Engelbert, J., van Zoonen, L., Hirzalla, F., 2018. Excluding citizens from the European smart city: the
discourse practices of pursuing and granting smartness. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.08.020 (in press).
Escolar, S., Villanueva, F.J., Santofimia, M., Alises, D.V., del Toro Garcia, X., Lopez, J.C., 2018. A
multiple-attribute decision making-based approach for Smart City rankings design. Technol. Forecast.
Soc. Chang. https://doi.org/10.1016/j.techfore.2018.07.024 (in press).
European Innovation Partnership (EIP), 2013. Smart cities and communities: operational
implementation plan. In: Available at, https://www.smartcities.at/assets/Uploads/ operational-
European Union, 2016. General Data Protection Regulation. Available at: https://eur-lex.
Florida, R., 2014. The Rise of the Creative Class: Revisited, Revised and Expanded. Basic Books.
Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanović, N., Meijers, E., 2007. Smart
Cities: Ranking of European medium-sized Cities. Centre of Regional Science (SRF), Vienna
University of Technology, Vienna, Austria Available at. http://www. smart-
Glanzel, W., Thijs, B., 2011. Using ‘core documents’ for the representation of clusters and topics.
Scientometrics 88, 297–309.
Goldin, C., 2016. Human capital. In: Diebolt, C., Haupert, M. (Eds.), Handbook of Cliometrics.
Springer Verlag, Heidelberg, Germany, pp. 55–86.
Greenfield, A., 2013. Against the Smart City: A Pamphlet. Do Projects, New York, NY.
Grimaldi, D., Fernandez, V., 2015. The alignment of University curricula with the building of a smart
city: a case study from Barcelona. Technol. Forecast. Soc. Chang. 123, 298–306.
Grimaldi, D., Fernandez, V., Carrasco, C., 2018. Heuristic for the localization of new shops based on
business and social criteria. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2016.03.011 (in press).
Hall, R.E., 2000. The vision of a smart city. In: The 2nd International Life Extension Technology
Workshop, 28 September, Paris, France.
Hamidi, S., Zandiatashbar, A., Bonakdar, A., 2018. The relationship between regional compactness
and regional innovation capacity (RIC): empirical evidence from a national study. Technol. Forecast.
Soc. Chang. https://doi.org/10.1016/j.techfore. 2018.07.026 (in press).
Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., Williams, P.,
2010. Foundations for smarter cities. IBM J. Res. Dev. 54 (4), 1–16.
Hopkins, J., McKay, J., 2018. Investigating ‘anywhere working’ as a mechanism for alleviating traffic
congestion in smart cities. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.032 (in press).
Hutchison, W., Bedford, N., Bedford, S., 2011. Ukraine's global strategy in the post-crisis economy:
developing an intelligent nation to achieve a competitive advantage. Innov. Market. 7, 46–53.
Ijaz, S., Shah, M.A., Khan, A., Ahmed, M., 2016. Smart cities: a survey on security concerns. Int. J.
Adv. Comput. Sci. Appl. 7 (2), 612–625. International Standards Organization (ISO), 2018. ISO
37122 – sustainable development in communities – indicators for Smart Cities. Available at:
Jacobides, M.G., Cennamo, C., Gawer, A., 2018. Towards a theory of ecosystems. Strateg. Manag. J.
Jamil, M.S., Jamil, M.A., Mazhar, A., Ikram, A., Ahmed, A., Munawar, U., 2015. Smart environment
monitoring system by employing wireless sensor networks on vehicles for pollution free smart cities.
Procedia Eng. 107, 480–484.
Kennedy, C., Cuddihy, J., Engel-Yan, J., 2007. The changing metabolism of cities. J. Ind. Ecol. 11,
Kennedy, C., Pincetl, S., Bunje, P., 2011. The study of urban metabolism and its applications to urban
planning and design. Environ. Pollut. 159, 1965–1973.
Kitchin, R., 2014. The real-time city? Big data and smart urbanism. GeoJournal 79 (1), 1–14.
Knight, E., Paroutis, S., 2017. Becoming salient: the TMT leader's role in shaping the interpretive
context of paradoxical tensions. Organ. Stud. 38 (3–4), 403–432.
Koutitas, G., 2018. The smart grid: anchor of the Smart City. In: McClellan, S., Jimenez, J., Koutitas,
G. (Eds.), Smart Cities. Springer, Cham.
Kraus, S., Richter, C., Papagiannidis, S., Durst, S., 2015. Innovating and exploiting entrepreneurial
opportunities in smart cities: evidence from Germany. Creat. Innov. Manag. 24 (4), 601–616.
Kretschmer, T., Claussen, J., 2016. Generational transitions in platform markets—the role of
backward compatibility. Strategy Sci. 1 (2), 90–104.
Kumar, V.T.M., Dahiya, B., 2017. Smart economy in smart cities. In: Kumar, V.T.M. (Ed.), Smart
Economy in Smart Cities. Springer, Singapore, pp. 3–76.
Kumaraswamy, A., Garud, R., Ansari, S., 2018. Perspectives on disruptive innovations. J. Manag.
Stud. 55 (7), 1025–1042.
Lee, J.H., Hancock, M.G., Hu, M.-C., 2014. Towards an effective framework for building smart cities:
lessons from Seoul and San Francisco. Technol. Forecast. Soc. Chang. 89 (1), 80–99.
Lehr, T., 2018. Smart cities: vision on-the-ground. In: McClellan, S., Jimenez, J., Koutitas, G. (Eds.),
Smart Cities. Springer, Cham.
Lewis, M.W., 2000. Exploring paradox: toward a more comprehensive guide. Acad. Manag. Rev. 25,
Lex, S., Cali, D., Rasmussen, M.K., Bacher, P., Bachalarz, M., Madsen, H., 2018. A crossdisciplinary
path to healthy and energy efficient buildings. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.023 (in press).
Leydesdorff, L., Deakin, M., 2011. The triple-helix model of smart cities: a neo-evolutionary
perspective. J. Urban Technol. 18 (2), 53–63.
Lim, C., Kim, K.-J., Maglio, P.P., 2018. Smart cities with big data: reference models, challenges, and
considerations. Cities 82, 86–99.
Lindkvist, C., Nielsen, B.F., Hans-Martin, N., Juhasw-Nagy, E., Lobaccaro, G., Wyckmans, A., 2018.
Intermediaries for knowledge transfer in integrated energy planning of urban districts. Technol.
Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore. 2018.07.020 (in press).
Luger, M.I., Goldstein, H.A., 1991. Technology in the Garden: Research Parks and Regional
Economic Development. University of North Carolina Press.
Lyon, D., 2006. Theorising Surveillance: The Panopticon and Beyond. (Willand, Cullompton).
Marsal-Llacuna, M.L., Colomer-Llinàs, J., Meléndez-Frigola, J., 2015. Lessons in urban monitoring
taken from sustainable and livable cities to better address the smart cities initiative. Technol. Forecast.
Soc. Chang. 90 (B), 611–622.
Mora, L., Deakin, M., Reid, A., 2018a. Combining co-citation clustering and text-based analysis to
reveal the main development paths of smart cities. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.019 (in press).
Mora, L., Deakin, M., Reid, A., 2018b. Strategic principles for Smart City development: a multiple
case study analysis of European best practices. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.035 (in press).
Morozov, E., Bria, F., 2018. Rethinking the Smart City: Democratizing Urban Technology. Rosa
Luxemburg Stiftung, New York, NY Available at: http://www.rosalux-nyc.org/ wp-
Moustaka, V., Theodosious, Z., Vakali, A., Kounoudes, A., Anthopoulos, L., 2018. Enhancing social
networking in smart cities: privacy and security borderlines. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.10.026 (in press).
Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G., Scorrano, F., 2014. Current trends in smart
city initiatives: some stylized facts. Cities 38, 25–36.
Nielsen, B.F., Lindkvist, C., Baer, D., 2018. Identifying and supporting exploratory and exploitative
models of innovation in municipal urban planning; key challenges from seven Norwegian energy
ambitious neighborhood pilots. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.11.007 (in press).
Nilssen, M., 2018. To the smart city and beyond? Developing a typology of smart urban innovation.
Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018. 07.060 (in press).
Ning, Z., Xia, F., Ullah, N., Kong, X., Hu, X., 2017. Vehicular social networks: enabling smart
mobility. IEEE Commun. Mag. 55 (5), 16–55.
OECD, 2017. Better Life Initiative: Measuring Well-being. Éditions OCDE, Paris Available at:
Papachroni, A., Heracleous, L., Paroutis, S., 2016. In pursuit of ambidexterity: managerial reactions to
innovation-efficiency tensions. Hum. Relat. 69 (9), 1791–1822.
Paroutis, S., Bennett, M., Heracleous, L., 2014. A strategic view on smart city technology: the case of
IBM Smarter Cities during a recession. Technol. Forecast. Soc. Chang. 89 (1), 262–272.
Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D., 2014. Sensing as a service model for
smart cities supported by internet of things. Trans. Emerg. Telecommun. Technol. 25 (1), 81–93.
Ramaswami, A., Russell, A.G., Culligan, P.J., Rahul Sharma, K., Kumar, E., 2016. Metaprinciples for
developing smart, sustainable, and healthy cities. Science 352 (6288), 940–943.
Ruhlandt, R.W.S., 2018. The governance of smart cities: a systematic literature review. Cities 81, 1–
Ryan, R.M., Deci, E.L., 2000. Self-determination theory and the facilitation of intrinsic motivation,
social development, and well-being. Am. Psychol. 55 (1), 68.
Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., Oliveira, A., 2011. Smart cities and
the future internet: Towards cooperation frameworks for open innovation. In: Domingue, J. (Ed.), The
Future Internet. FIA 2011. Lecture Notes in Computer Science. vol. 6656 Springer, Berlin,
Schiavone, F., Paolone, F., Mancini, D., 2018. Business model innovation for urban smartization.
Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore. 2018.10.028 (in press).
Sennett, R., 2012. No one likes a city that's too smart. In: The Guardian, Available at:
Sennett, R., 2018. Building and Dwelling: Ethics for the City. Farrar, Straus and Giroux, New York,
Sepasgozar, S., Hawken, S., Sargolzaei, S., Foroozanfa, M., 2018. Implementing citizen centric
technology in developing smart cities: a model for predicting the acceptance of urban technologies.
Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.09.012 (in press).
Shapiro, J.M., 2006. Smart cities: quality of life, productivity, and the growth effects of human capital.
Rev. Econ. Stat. 88 (2), 324–335.
Smith, W.K., 2014. Dynamic decision making: a model of senior leaders managing strategic
paradoxes. Acad. Manag. J. 57, 1592–1623.
Söderström, O., Till, P., Klauser, F., 2014. Smart cities as corporate storytelling. City 18, 307–320.
Stratigea, A., Papadopoulou, C.-A., Panagiotopoulou, M., 2015. Tools and technologies for planning
the development of smart cities. J. Urban Technol. 22 (2), 43–62.
Suzuki, L.R., 2017. Smart cities IoT: enablers and technology road map. In: Rassia, S., Pardalos, P.
(Eds.), Smart City Networks. Springer Optimization and Its Applications, vol. 125 Springer, Cham.
Tanguy, M., Kumar, V., 2018. Measuring the extent to which Londoners are willing to pay for public
art in their city. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.11.016 (in
Tokoro, N., 2015. The Smart City and the Co-creation of Value: A Source of New Competitiveness in
a Low-Carbon Society. Springer.
Toppeta, D.J., 2010. The Smart City Vision: How and ICT Can Build Smart, “Livable”, Sustainable
Cities. The Innovation Knowledge Foundation Available at:
Trencher, G., 2018. Towards the smart city 2.0: empirical evidence of using smartness as a tool for
tackling social challenges. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.033 (in press).
Van den Buuse, D., Kolk, A., 2018. An exploration of smart city approaches by international ICT
firms. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore. 2018.07.029 (in press).
Van der Graaf, S., Ballon, P., 2018. Navigating platform urbanism. Technol. Forecast. Soc. Chang.
https://doi.org/10.1016/j.techfore.2018.07.027 (in press).
Van Eck, N., Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric
mapping. Scientometrics 84, 523–538.
Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N., Nelson, L.E., 2009. Helping CIOs
understand “smart city” initiatives. Growth 17 (2), 1–17.
Weddle, R.L., 2009. Research triangle park: past success and the global challenge. In: Wessner, C.
(Ed.), Understanding Research, Science and Technology Parks: Global Best Practices—Summary of a
Symposium. The National Academies Press; National Research Council, Washington DC.
Wells, H.G., 1897. A story of the days to come. In: The Pall Mall Magazine, Available at:
West, G., 2017. Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life
in Organisms, Cities, Economies, and Companies. Penguin Press, New York, NY.
Yu, J., Wen, Y., Jin, J., Zhang, Y., 2018. Towards a service-dominant platform for public value co-
creation in a smart city: evidence from two metropolitan cities in China. Technol. Forecast. Soc.
Chang. https://doi.org/10.1016/j.techfore.2018.11.017 (in press).
Zygiaris, S., 2013. Smart city reference model: Assisting planners to conceptualize the building of
smart city innovation ecosystems. J. Knowl. Econ. 4 (2), 217–231.