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Smart Cities Will it survive the hype?
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Smart Cities Will it survive the hype?
Abstract
This paper addresses Arcautes (2014) comment that smart cities
will never leave the
Trough of Disillusionment of the hype cycle. Using the diffusion
of innovations theory as a
lens, I argue that smart cities will be able to leave the Trough
of Disillusionment as it builds
improved versions of its technology, make use of smart
technologies that are already heading
towards the Plateau of Productivity and overcomes its technical
difficulties through small
experiments.
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The first smart cities were heralded by technology companies
more than 10 years ago as
solutions to problems in cities. Smart city developments
included Koreas New Songdo, United
Arab Emirates Masdar City and Portugals PlanIT Valley
(Greenfield, 2013), and many existing
cities have announced smart city initiatives. As the first smart
cities are being completed,
criticisms of smart cities have surfaced (Townsend, 2013;
Greenfield, 2013). One particular
criticism points out that smart city technologies have been
unable to address the problems that
cities face, and referencing the Gartner hype cycle (Fenn &
Raskino, 2008), concludes that smart
cities and its technologies will never be widely adopted
(Arcaute, 2014). Is smart cities all hype?
Will it become obsolete without fulfilling its potential? In
this paper, I discuss the theory of the
Gartner hype cycle and outline criticisms of smart cities that
indicate it will never exit the
Trough of Disillusionment. Using the diffusion of innovations
theory as a lens, I argue that
smart cities will eventually leave the Trough of
Disillusionment, as there are signs that smart
cities and its technologies will continue to improve and address
problems that cities face. The
way smart city technologies can be trialled via small
experiments and are complementary to
other smart technologies also support the notion that smart
cities will head towards mainstream
adoption.
Criticism of smart cities
The proportion of the worlds population in urban areas has
increased from 30% in 1950
to 54% in 2014, and is projected to increase to 66% by 2050
(United Nations, 2014). The rapid
flow of people to cities create issues for cities in both
developed and developing countries, which
are trying to build enough infrastructure, boost economic
development to support new residents
in a sustainable manner. Cities also seek to manage the rising
inequality in cities. Smart cities
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and its associated technologies are seen as promising solutions,
helping cities to make efficient
use of its resources, to generate income, and to find novel
solutions for better living.
While there are many conceptions of smart cities amongst
technology companies,
governments and academics, most definitions include the notion
that smart cities are places
where information technology is used to address problems in the
city (Townsend, 2013;
Greenfield, 2013). Giffinger et al (2007) note that smart cities
seek to perform well in 6 areas:
economy, social and human capital, governance, transport and ICT
infrastructure, environment
and quality of life.
Smart cities have received strong criticism recently from 2
observers who have followed
the industry closely. Townsend (2013) criticized the vision of
the corporate leaders who created
the first smart cities, who focused on automation, efficiency
and optimization. Using Koreas
Songdo as a case study, Townsend concluded that Ciscos
technology accomplishments in
Songdo have been lackluster and that it has destroyed much of
its natural environment.
Townsend commented that while Songdo has potential, Ciscos
ambitions to make it a
networked and automated smart city would only be fulfilled in
the distant future. Greenfield
(2013) echoed Townsends criticism, and argues that the current
rhetoric for smart cities has the
same characteristics as urban planning techniques that had
already been discredited in the
twentieth century. Greenfield compared the smart cities movement
to Corbusian urbanism and
identified many similar characteristics such as a top-down
approach to managing the city,
deploying simplistic and rigid systems for the sole benefit of
administrators, and making the look
and feel of a city uniform. Greenfield cites the results of
Corbusians urbanism as conclusive
proof that the corporate vision of smart cities will fail.
Greenfield reflects both his and
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Townsends assessment of the first smart city developments when
he says that From the
vantage point of the present, it is clear that the canonical
Masdar City, New Songdo and PlanIT
Valley are, by most any reasonable measure, failed projects.
Against this backdrop of criticism, Arcaute (2014) commented
that smart cities are now
in the Trough of Disillusionment in the hype cycle and would
never leave. These criticisms
support the notion that expectations of smart cities have
dropped drastically since its initial hype,
and that smart cities and its technologies would not be able to
regain the confidence of the
public.
Smart cities and the hype cycle
The hype cycle is a model developed by Gartner, an information
technology research and
advisory company. It explains how the expectations and
visibility of a particular technology are
raised and lowered over its life cycle. Fenn & Raskino
(2008) theorize that expectations for a
particular technology comprises expectations formed by media
hype, and expectations of the
industry. In the case of media hype, when a new technology
bursts on the scene, it is not well
understood but is seen to have enormous potential. Media
exposure increases public expectations
rapidly till it reaches a peak where the technology falls short
of the hype and disappointment
ensues, with sharply decreased expectations. Industry
expectations are based on the technology
S-curve which has been used to describe how performance and
adoption of technology increases
over time. Taylor and Taylor (2012) describe how performance and
adoption of the technology is
slow in the beginning as the industry figures out how to apply
the technology. Performance and
adoption then increases more quickly as technical problems are
resolved, and slows down as the
natural limits of the technology are approached. Fenn &
Raskino (2008) believe that the
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expectations of the industry will follow the S-curve, as the
industrys expectations of the
technology should correspond to the performance of the
technology. The hype cycle thus
combines expectations built up by media hype with industry
expectations and charts the overall
expectations over time. It is used to give organisations a view
of how a technology or application
will evolve over time and as a tool for thinking about whether
to invest in these technologies.
Fenn and Raskino (2008) outline the five phases that
technologies in the hype cycle go
through. Figure 1 depicts each of the five phases:
(a) The technology trigger, which is when a new technology
arrives on the scene. While
the technology shows promise, its performance is low and there
are many technical
obstacles to overcome. Regardless, there is increasing positive
publicity and rising
public expectations;
(b) Media hype and public expectations will eventually reach the
technologys peak of
inflated expectations;
(c) Some failures surface and technical difficulties become
better understood. Although
the technologys performance continues to improve in this phase,
negative publicity
increases and public expectations are lowered due to previously
inflated expectations.
Expectations of the technology start to sink into a trough of
disillusionment;
(d) As technical barriers are overcome and improved versions of
the technology appear,
the technology begins its ascent on the slope of enlightenment.
Organisations also
better understand the benefits of and applications for the
technology. Adoption of the
technology increases in this phase.
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(e) As the relevance and applicability of the technology becomes
clear, it is adopted by
the mainstream. The performance of the technology improves
gradually in this phase
as incremental improvements are made and approaches its plateau
of productivity.
While technologies may experience the first three phases of the
hype cycle, it is possible for
technologies to be mired in the trough of disillusionment, and
become obsolete without starting
on the slope of enlightenment.
Empirical investigations of the hype cycle are limited and
inconclusive. For example,
Steinhart and Liefer (2010) used search behaviours from Google
Insight to measure the hype of
three technologies (tidal power, integrated gastification
combined cycle and photovoltaic
generation) over time and found that the hype of these
technologies did not fit the hype cycle
model. On the other hand, Jun (2012) studied the hype cycle in
the context of hybrid
cars/vehicles and found that news reports and search traffic
indicated that hybrid cars/vehicles
could be modelled by the hype cycle pattern. Academics note that
the hype cycle has weak
theoretical foundations (Leary, 2008; Steinhart & Leifer,
2010). Although the hype cycle lacks
rigour and empirical support, it is often used in investment
decisions by technology practitioners
(Leary, 2008).
Arcaute (2014)s comment on smart cities places smart cities in
the trough of
disillusionment of the hype cycle. Greenfields (2013) and
Townsends (2013) criticism provide
evidence that there is a sense of disillusionment with the first
generation of smart cities promoted
and built by information technology companies such as Cisco
Systems, IBM and Siemens. Even
before the first generation of smart cities are built, they have
been deemed as failures by both
Greenfield and Townsend. Given that there will be practical
difficulties and unexpected negative
consequences when these smart cities become operational,
Greenfields and Townsends
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criticism could be just the start of the negative press for
smart cities as it slides into the trough of
disillusionment. Under the hype cycle model, such negative
publicity is expected, and the
industry would have little influence over overall
expectations.
Figure 1: Gartner Hype Cycle
Smart cities and the diffusion of innovations theory
In the later stages of the hype cycle, expectations based on
hype become less influential
in driving overall expectations. Expectations are instead driven
more by the technology S-curve,
which is a measure of performance of the technology. In the
later stages adoption of the
technology is also an indicator of performance of the technology
(Taylor & Taylor, 2012). As
such, whether a particular technology leaves the trough of
disillusionment is more dependent on
the adoption and performance of the technology, and less
dependent on past negative publicity.
The theory of diffusion of innovations provides a framework for
evaluating whether
smart cities will leave the trough of disillusionment. Rogers
(2003) developed the theory to
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explain how technologies are adopted by people/organisations
over time. He believed 4 elements
influenced whether technologies were adopted: the
characteristics of the technology itself,
communication channels, time, and the social system. The
diffusion of innovations theory is
particularly apt for discussing the latter stages of the hype
cycle as the technology S-curve in the
hype cycle is based on the theory. While the theory covers many
elements, I focus on the
characteristics of the technology itself to illustrate why smart
cities will increasingly be adopted,
leave the trough of disillusionment and head towards the plateau
of productivity in the hype
cycle.
The relative advantage or impact of the technology is commonly
considered to drive
adoption rates (Rogers 2003; Wejnert 2002; Greenhalgh et al
2004). The more a particular
technology is perceived to be better than its alternatives or
the incumbent technology, the more
rapid the adoption rate (Roger 2003). Arcautes (2014)
observation points out that failing to
address problems in cities will result in perceptions that smart
cities and its technologies have
little or no advantages compared to other
technologies/innovations. This would dent the chances
of smart cities leaving the trough of disillusionment in the
hype cycle. However, the negative
criticisms have so far been directed at the first smart cities,
which can be thought of as the first
version of smart cities. The hype cycle model allows for
improved versions of technology to
appear over time (Fenn & Raskino, 2008). These improved
versions overcome some of the
teething issues of the first versions and in the case of smart
cities, would be better placed to solve
problems in cities.
There are signs that the improved versions of smart cities are
being built, in the form of
Townsend (2013) and Greenfield (2013) alternative visions for
smart cities. Both believe that
smart cities should be led by the people and organized in a
bottom-up manner, instead of the top-
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down approach envisioned by information technology companies.
Both believe that smart city
technologies should aid residents in creating solutions for
challenges in the city, and cities should
encourage civic participation and involvement. Greenfield, for
instance, believes that residents
should be empowered with data-gathering, analysis and
visualization tools, networked
technologies and open data to better understand their city and
solve problems. Townsends vision
is for smart cities to preserve opportunities for spontaneity,
serendipity, and sociability and be
open and participatory. He believes that the increasing adoption
of the smartphones, the rising
number of things connected to the internet, and the rapid
increase in urban population create the
conditions for residents to engage in making smart cities better
places and addresses challenges
in cities. Parts of their vision are being implemented today, in
the form of commitments from
cities to release more data to understand issues in the city and
hackathons where residents form
groups to develop solutions to problems in cities. As more
parties embark on building smart
cities, further alternative visions will surface, each improving
upon previous versions and finding
better solutions to problems of cities. The final version of
smart cities adopted by the mainstream
may not look like Songdo or PlanIT Valley, but they would still
be places where information
technology is used to address problems in the city.
Beyond the technologys relative advantage and impact to address
problems, complexity
and trialability of the technology are other important
characteristics that drive adoption
(Greenhalgh et al, 2004). Technologies that are easier to use
and understand will be adopted
more rapidly (Denis et al, 2002; Rogers, 2003), and technologies
that can be broken down into
more manageable parts and adopted incrementally or tested on a
limited basis will be more
likely to be adopted (Greenhalgh et al, 2004).
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While the discussion has been centred on smart cities as a
technology/innovation as a
whole so far, it is useful to note that smart cities can also be
seen as a collection of different
smart technologies. Gartners (2014) hype cycle report for smart
city technologies explicitly
recognises a number of smart technologies, each with a place in
their own hype cycle. For
example, Gartner believes that Real-Time Parking is on the rise
to its peak of expectations,
electric vehicles is sliding into the trough of disillusionment,
and location- and Condition-
Sensing Technologies is climbing the slope of enlightenment. In
this sense, smart cities is a
technology that can be broken down into more manageable parts,
with some smart technologies
being adopted first while the remaining smart technologies
address their technical issues.
By relying on a collection of smart technologies, smart cities
have a stronger chance of
leaving the trough of disillusionment. Depending on a variety of
smart technologies means that
the aims of smart cities could be achieved by different
combinations of smart technologies.
Smart technologies that have been adopted more quickly could
also pave the way for other smart
technologies to be adopted, through creating greater relative
advantage for associated smart
technologies, establishing standards in particular areas,
building confidence in related smart
technologies. The Global Positioning System, for example, has
helped mobile health monitoring
technology keep track of users and created opportunities to
build more effective car sharing
services based on location data generated by the system. Many of
the smart technologies are
amenable to being experimented with on a smaller scale as well.
Technologies such as intelligent
lampposts, consumer energy storage and smart workspaces can be
run as pilot projects on a
limited basis, such as within particular neighbourhoods or a
particular subset of the population.
This would help the industry identify and remove technical
challenges with the technology
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before introducing them on a larger scale, which increases the
chance of adoption (Greenhalgh et
al, 2004).
Conclusion
In this paper, I have illustrated how the diffusion of
innovations theory is a useful lens for
analysing whether smart cities will leave the trough of
disillusionment in the hype cycle, and
argued based on the characteristics of smart cities that it will
eventually be adopted by the
mainstream, though the form it takes will be different from what
was envisioned by information
technology companies when creating the first smart cities. This
is further complicated by the
complexity of urban systems and human behaviour, and the scale
of problems that smart cities
and its technologies are trying to solve.
Beyond the characteristics of the technology, the diffusion of
innovations theory states
that the social system, communication channels and time
influence adoption of technologies as
well (Rogers, 2003). Given the number of factors and
interrelationships between these factors,
diffusion is a complex process and an analysis including all
components would provide further
insight to whether smart cities will truly leave the trough of
disillusionment. Empirical studies
(see Jun, 2012; Steinhart and Leifer, 2010) comparing smart
cities and its technologies to other
forms of technology tracked by Gartner could also be useful is
establishing where smart cities
are in the hype cycle.
Regardless of whether and how smart cities are adopted, there is
no doubt that solutions
will have to be found for increasing urbanization and its
associated issues, such as infrastructure
capacity, inequality, economic development and sustainability.
Hype is inevitable as we search
for solutions to cities problems. It is important for
technologists, professionals, government and
residents to work to overcome the technical difficulties in new
technologies, and participate in
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making cities better places to live in. Hype may build
excitement and optimism, but it is the hard
work in the trenches that help technologies fulfil their
potential.
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