Smart Cities - Will It Survive the Hype

Smart Cities Will it survive the hype?

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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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Smart Cities - Will It Survive the Hype

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