lombardi-giordano-farouh-yousef 2012.pdf
TRANSCRIPT
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
1/15
This article was downloaded by: [University of Liverpool]On: 15 July 2014, At: 04:48Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Innovation: The European Journal of
Social Science ResearchPublication details, including instructions for authors and
subscription information:
http://www.tandfonline.com/loi/ciej20
Modelling the smart city performancePatrizia Lombardi
a, Silvia Giordano
b, Hend Farouh
c& Wael
Yousefd
a
Politecnico di Torino, Department of Housing and Cities , Turin ,ItalybSITI Innovation Research Centre of Politecnico di Torino , Turin ,
ItalycHousing and Building National Research Center (HBRC) , Giza ,
EgyptdDepartment of Urban Planning , Al-Azhar University , Cairo ,
Egypt
Published online: 19 Apr 2012.
To cite this article:Patrizia Lombardi , Silvia Giordano , Hend Farouh & Wael Yousef (2012)
Modelling the smart city performance, Innovation: The European Journal of Social Science
Research, 25:2, 137-149, DOI: 10.1080/13511610.2012.660325
To link to this article: http://dx.doi.org/10.1080/13511610.2012.660325
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (theContent) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or
arising out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
http://www.tandfonline.com/loi/ciej20http://dx.doi.org/10.1080/13511610.2012.660325http://www.tandfonline.com/action/showCitFormats?doi=10.1080/13511610.2012.660325http://www.tandfonline.com/loi/ciej20 -
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
2/15
Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions -
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
3/15
Modelling the smart city performance
Patrizia Lombardia*, Silvia Giordanob, Hend Farouhc and Wael Yousefd
aPolitecnico di Torino, Department of Housing and Cities, Turin, Italy; bSITI InnovationResearch Centre of Politecnico di Torino, Turin, Italy; cHousing and Building National ResearchCenter (HBRC), Giza, Egypt; dDepartment of Urban Planning, Al-Azhar University, Cairo,Egypt
(Received 13 October 2010; final version received 10 October 2011)
This paper aims to offer a profound analysis of the interrelations between smart
city components connecting the cornerstones of the triple helix. The triple helixmodel has emerged as a reference framework for the analysis of knowledge-basedinnovation systems, and relates the multiple and reciprocal relationships betweenthe three main agencies in the process of knowledge creation and capitalization:university, industry and government. This analysis of the triple helix will beaugmented using the Analytic Network Process to model, cluster and beginmeasuring the performance of smart cities. The model obtained allows interac-tions and feedbacks within and between clusters, providing a process to deriveratio scales priorities from elements. This offers a more truthful and realisticrepresentation for supporting policy-making. The application of this model is stillto be developed, but a full list of indicators, available at urban level, has beenidentified and selected from literature review.
Keywords: Analytic Network Process; smart city components; triple helixapproach
Introduction
The application of information and communications technology (ICT) in the context
of future cities is often indicated by the notion of smart city. This concept has been
quite fashionable in the policy arena in recent years. Compared with the concept of
digital city or intelligent city (Lombardi et al. 2009), the main focus is not limited
to the role of ICT infrastructure but is mainly on the role of human capital/edu-
cation, social and relational capital, and environmental issues. These are consideredimportant drivers of urban growth.
In order to explore the concept of a smart city, a revised triple helix model
has been recently proposed by Lombardi et al. (2012) focusing on the production
of knowledge by universities and government and the production of innovations
that are patented by industry and universities as an index of intellectual capital
(Etzkowitz 2008, Caragliu et al. 2009, Deakin 2010). This model presupposes that
the three helices operate in a complex urban environment, where market demand,
governance, civic involvement and citizens characteristics, along with cultural and
social capital endowments, shape the relationships between the traditional helices of
university, industry and government.
*Corresponding author. Email: [email protected]
InnovationThe European Journal of Social Science Research
Vol. 25, No. 2, June 2012, 137149
ISSN 1351-1610 print/ISSN 1469-8412 online
# 2012 ICCR Foundation
http://dx.doi.org/10.1080/13511610.2012.660325
http://www.tandfonline.com
http://dx.doi.org/10.1080/13511610.2012.660325http://www.tandfonline.com/http://www.tandfonline.com/http://dx.doi.org/10.1080/13511610.2012.660325 -
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
4/15
The results of the above study has shown the analysis to baseline the develop-
ment of smart cities in terms of their dual roles as generators of intellectual capital,
creators of wealth and regulators of standards (university, industry, civil society
and government), as well as supporting the social learning and knowledge-transfer
abilities that are needed to meet the requirements of their regional innovation
systems.
Although this analysis has been a useful start for understanding the main
governance component of smart cities, it does not consider the other recognized
aspects related to ecological sustainability. In addition, it does not recognize the
number of relationships and feedbacks between categories that are dependent upon
the interconnected and systemic nature of the aspects involved.
This paper proposes a different model that involves the civil society as one of
the main key actors, alongside universities, industry and government (Etzkowitz
and Zhou 2006). This new framework is used for classifying smart city performance
indicators and for structuring an ANP (Analytic Network Process) exercise (Saaty
2005) with the aim of investigating the relations between smart cities components,actors and strategies to which they are moving. This exercise was conducted within
a focus group, involving a number of experts in different disciplines.
Smart city components and performance indicators
The concept of the smart city has been quite fashionable in the policy arena in
recent years, with the aim of drawing a distinction from the terms digital cityand
intelligent city. Its main focus is still on the role of ICT infrastructure, but much
research has also been carried out on the role of human capital/education, social
and relational capital, and environmental interests as important drivers of urban
growth. (Komninos 2002, Shapiro 2008, Deakin 2010).
Although there is no agreement on the exact definition of a smart city, a number
of main dimensions of a smart city have been identified through a literature review
(Giffingeret al. 2007, Van Soom 2009, Fusco Girardet al. 2009) and include: smart
economy; smart mobility; smart environment; smart people; smart living; and smart
governance.
These six dimensions connect with traditional regional and neoclassical theories
of urban growth and development. In particular, the dimensions are based on theories
of regional competitiveness, transport and ICT economics, natural resources, human
and social capital, quality of life, and participation of citizens in the governance
of cities.
Although the term smart city is not very widely used yet in spatial planning
literature or urban studies, it is still possible to identify various aspects of it as a basis
for further elaboration. However, it should be noted that in the literature the term
is not used in a holistic way; rather it is used with reference to various aspects that
range from a smart city as an IT-district to a smart city as regarding the education
(or smartness) of its inhabitants.
In association with economy, smart city is used to describe a city with a smart
industry. This implies especially industries in the fields of ICT as well as other
industries employing ICT in their production processes. The name smart city isalso used for business parks or districts comprising companies within this field
(Giffingeret al. 2007, Fusco Girard et al. 2009, Caragliuet al. 2009).
138 P. Lombardiet al.
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
5/15
The term is also used in relation to the education of a city s inhabitants. A smart
city therefore has smart inhabitants in terms of their educational grade. In addition,
the term is referred to the relation between the city government administration and its
citizens. Good governance or smart governance is often referred to as the use of new
channels of communication for the citizens, e.g. e-governance or e-democracy
(Florida 2002, Benner 2003, Torres et al. 2005, Lombardi et al. 2009).
The term smart city is furthermore used to discuss the use of modern tech-
nology in everyday urban life. This includes not only ICT but also, and especially,
modern transport technologies. Logistics as well as new transport systems are
smart systems that improve urban traffic and inhabitants mobility. Moreover,
various other aspects referring to life in a city are mentioned in connection to the
term smart city, like security/safety, green, efficient and sustainable energy (Benner
2003, Komninos 2007, Giffinger et al. 2007, Caragliu et al. 2009).
To sum up, there are several fields of activity that are described in the literature
in relation to the term smart city: industry, education, participation, technical
infrastructure and various soft factors (Giffinger et al. 2007).
The triple helix model has recently emerged as a reference framework for the
analysis of knowledge-based innovation systems, and relates the multiple and
reciprocal relationships between the three main agencies in the process of knowledge
creation and capitalization: universities, industry and government (see for a recent
overview Etzkowitz 2008, Deakin 2010, Lombardiet al. 2012). In the context of the
present analysis, this paper will focus on this model as a starting point for the
assessment of the performance of smart cities. In order to link the evaluation of smart
city components and the three main helices of the model, a modified triple helix
framework is proposed that adds another unifying factor to the analysis, namely civil
society (Etzkowitz and Zhou 2006).
The advanced model presupposes that the four helices operate in a complex
urban environment, where civic involvement along with cultural and social capital
endowments shape the relationships between the traditional helices of university,
industry and government. The interplay between these actors and forces determines
the success of a city in moving on a smart development path.
This framework can be operationalized by focusing on the measurement of the
four helices and linking these to the main dimensions of a smart city (smart economy,
smart mobility, smart environment, smart people, smart living and smart govern-
ance). The result of this exercise is the development of a novel framework for
classifying smart city performance indicators, as shown in Table 1.As one can see, both the main components/activities and the main actors/helices
of a smart city are represented. The identified clusters are: smart governance(related
to participation);smart human capital(related to people); smart environment(related
to natural resources);smart living(related to the quality of life); and smart economy
(related to competitiveness).
The sources of data for Table 1 were both a literature review, including EU
projects reports and the Urban Audit dataset, and indicators selected from statistics
of the European Commission, the European Green City Index, TISSUE, Trends
and Indicators for Monitoring the EU Thematic Strategy on Sustainable Develop-
ment of Urban Environment and the smart cities ranking of European medium-sizedcities. This includes more than 60 indicators classified in the five clusters. These
60 indicators were selected based on a questionnaire and two focus groups
Innovation The European Journal of Social Science Research 139
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
6/15
Table 1. Smart city components, triple helix and performance indicators.
Revised triple
helix
Clusters
Smart Governance Smart Economy
Smart Human Capital
Indicators Smart Liv
University No. of universities and
research centers in the city
Public expenditure on
R&D percentage of
GDP per head of city
population
Percentage of population
aged 1564 with
secondary-level education
living in Urban Audit
Percentage of
professors and
researchers inv
international p
and exchange
No. of courses entirely
downloadable from the
internet/total no. courses
Public expenditure on
education percentage
of GDP per head of city
population
Percentage of population
aged 1564 with higher
education living in Urban
Audit
Number of gra
international m
per year
Number of research
grants funded by
international projects
Percentage of inhabitants
working in education and
in research & development
sector
Percentage of
accessible cour
people with dis
(PWD)
Governement e-Government on-line
availability (percentage of
the 20 basic services that
are fully available online)
GDP per head of city
population
Voter turnout in national
and EU parliamentary
elections
Proportion of t
in for recreatio
sports and leisu
Debt of municipal
authority per resident
Share of female city
representatives
Green space (m
which the publ
access, per capi
Percentage of households
with computers
Median or average
disposable annualhousehold income
City representatives per
resident
Number of pub
libraries
Unemployment rate Number of the
and cinemas
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
7/15
Table 1 (Continued)
Revised triple
helix
Clusters
Smart Governance Smart Economy
Smart Human Capital
Indicators Smart Liv
Percentage of households
with Internet access at
home
Energy intensity of the
economy gross inland
consumption of energy
divided by GDP
Health care
expenditure
percentage of G
capita
Tourist overnig
in registered
accommodatio
year per residen
Civil society e-Government usage by
individuals (percentage
individuals aged 1674
who have used the
Internet, in the last 3
months, for interaction
with public authorities)
Percentage of projects
funded by civil society
Foreign language skills Total book loa
other media pe
resident
Participation in life-long
learning (%)
Museum visits
inhabitant
Individual level of
computer skills
Theater and cin
attendance per
inhabitant
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
8/15
Table 1 (Continued)
Revised triple
helix
Clusters
Smart Governance Smart Economy
Smart Human Capital
Indicators Smart Liv
Individual level of internet
skills
Industry Number of research grants
funded by companies,
foundations, institutes/No
annual scholarships
Employment rate in:
High Tech and
creative industries
Renewable energy
and energy efficieny
systems
Financial
intermediation and
business activities
Culture and
entertainment industry
Commercial services
Transport and
communication
Hotels and
restaurants
Patent applications per
inhabitant
Number of ent
adopting ISO 1
standards
All companies (total
number)
Employment rate in
knowledge-intensive
sectors
Proportion of p
undertaking in
based training
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
9/15
Table 1 (Continued)
Revised triple
helix
Clusters
Smart Governance Smart Economy
Smart Human Capital
Indicators Smart Liv
Number of local units
manufacturing High
Tech & ICT products
Companies with
headquarters in the city
quoted on national stockmarket
Components of domestic
material consumption
GDP, Gross domestic product.
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
10/15
with specialists and professionals to select the most relevant indicators to the
proposed clusters.
Furthermore, the authors of this study have identified the relations between
the smart city components by way of an ANP. The next section illustrates the
development of the ANP model that is used for investigating the interrelations
between smart city components and actors and finally for verifying whether the cities
are smart, and if not, whether they are moving in the right direction.
Assessing the smart citys performance
A city is a complex system. Its complexity is due to the individuals unpredictable
interrelations. As complex systems, cities have unpredictable behaviors and, when
some actions are set up, reactions and feedbacks can be obtained. Cities are places
where different interrelated ecosystems live, and they are also communications
systems (Abler et al. 1970). The relationships can be effected by tangible and
intangible infrastructures and they can produce tangible or intangible networks.Tangible networks are created by transport and telecommunications infrastructures,
which can be named hard infrastructures (Wakelin 1990). The intangible net-
works, as networks of capital (economic and human), are produced by the soft
infrastructures, which comprise education and governance, and by the economic
infrastructures (Wakelin 1990). Because of its network nature, a city should be
described using a more truthful and realistic model representation based on a
network system with the expression of relations between elements. Moreover, the
Analytic Network Process, an advanced version of the Analytic Hierarchy Process,
seems more appropriate for supporting decision-making in this field.
The ANP model consists of clusters (i.e. groups of homogeneous elements ofa decision problem), elements (i.e. nodes of the network), interrelationships between
clusters, and interrelationships between elements. It allows interactions and feedback
within and between clusters and provides a process to derive ratio scales priorities
from the elements (Saaty 2005). Synthetically, the ANP methodology involves the
following main steps (Saaty 2005, Saaty and Vargas 2006).
I. Structuring the decision-making model. This activity involves an identifica-
tion of both of the elements constituting the decision problem and their
relationships. The network model is constituted by various clusters of ele-
ments, and alternatives or options from which to choose. Each element can
have influence and inter-dependence relations: this can be a source, that is,
an origin of a path of influence, or a sink, that is, a destination of paths
of influence. There are two kinds of interdependences: between elements
related to different clusters (outer or external connections) and within the
same cluster (inner or internal relations). The latter relation is identified
as a loop.
II. Developing pairwise comparison of both elements and clusters to establish
relations within the structure. In this step, a series of pairwise comparisons are
made by participants in the decision-making process (usually experts,
managers and citizens representatives) to establish the relative importance
of decision elements with respect to each component of the network. In pair-wise comparisons, a ratio scale of 19 is used (named the fundamental scale
or Saaty scale). The numerical judgments established at each level of the
144 P. Lombardiet al.
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
11/15
network form pair matrixes that are used to derive weighted priority vectors of
elements (Saaty 2001).
III. Achievement of the final priorities. In order to obtain the global priority
vector of the elements, including the alternatives, the mathematical approach
encompasses the use of supermatrices (portioned matrices composed of
submatrices consisting of priority weight vectors of the elements that have
been evaluated). A final supermatrix is obtained at the end of the process,
containing the global priority vector of the elements.
As required by step I, a complex model was developed that involves all of the men-
tioned clusters of a smart city, i.e. Smart Governance (related to participation);
Smart Human Capital (related to people); Smart Environment (related to natural
resources); Smart Living (related to the quality of life); and Smart Economy (related
to competitiveness).
The relationships between indicators (and clusters) have been identified using
a control hierarchy (Saaty 2001) composed of the four axes of the adopted triplehelix, i.e. universities, industry, government and civil society. Figure 1 shows this
control hierarchy. Each axis is organized by a subnetwork consisting of:
the five clusters representing the above-mentioned smart city component/
activities including the relative selected indicators;
a cluster of alternatives made by four policy visions (or prototypes) of smart
cities as derived from the Urban Europe Joint Programme Initiatives
(see report by Nijkamp and Kourtik 2011): Connected City (smart logistic
and sustainable mobility), Entrepreneurial City (economic vitality), Liveable
City (ecological sustainability) and the Pioneer City (social participationand social capital).
As an example, Figure 2 shows the Civil Society subnetwork, where both the
Smart Governance and Smart Economy clusters include only one element,
respectively: E-gov usage by individuals and Percentage of projects funded by
civil society. This nodes organization allows inner connections in the other clus-
ters, as in the Smart Human Capital, where the Foreign language skills influence
Figure 1. The main network.
Innovation The European Journal of Social Science Research 145
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
12/15
other nodes, such as Individual level of computer skills and Individual level
of internet skills.
Bidirectional relationships are identified as follows:
Smart Economy and Smart Environment, where the Percentage of projects
funded by civil societyis the direct cause of the Relationship to percentage of
citizens engaged in environmental and sustainability oriented activities;
Smart Human Capital and Smart Living, where the Participation in life-
long learning is connected by all smart living nodes (Museum visits per
inhabitant, Theater and cinema attendance per inhabitant and Total book
loans and other media per resident).
Additional mono-directional connections can be identified between the otherclusters.
As required in Step II of the ANP methodology, an assessment exercise was
conducted with the aim of developing pairwise comparisons, of both elements
(or nodes) and clusters. Figure 3 shows one of several pair matrixes that were
Figure 2. The Civil Society subnetwork.
Figure 3. Pairwise cluster comparison using Saatys scale.
146 P. Lombardiet al.
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
13/15
composed and used to derive weighted priority vectors of elements (Saaty 2001).
In each pairwise comparisons matrix, a ratio scale of 19 is used. In particular,
Figure 3 shows the cluster comparison matrix for the alternatives.
The achievement of the final priorities of all the elements included in the model
is obtained in Step III. This includes the overall priorities of the alternatives
obtained by synthesizing the priorities of the alternatives from all the subnetworks.
The final results are:
(1) Entrepreneurial City (48%);
(2) Pioneer City (20%);
(3) Liveable City (17%);
(4) Connected City (13%).
The Entrepreneurial City. This image assumes that, in the current and
future global and local competition, Europe can only survive if it is able to
maximize its innovative and creative potential in order to gain access toemerging markets outside Europe; cities are then spearheads of Europes
globalization policy.
The Pioneer City. This image refers to the innovative melting pot
character of urban areas in the future, which will show an unprecedented
cultural diversity and fragmentation of lifestyles in European cities; this
will prompt not only big challenges, but also great opportunities for smart
and creative initiatives in future cities, through which Europe can become
a global pioneer.
The Liveable City.The final image addresses the view that cites are not only
energy consumers (and hence environmental polluters), but may
throughsmart environmental and energy initiatives (e.g. recycling, waste recupera-
tion) act as engines for ecologically benign strategies, so that cities may
become climate-neutral agents in a future space-economy; cities in Europe
are then attractive places to live and work.
The Connected City. The image of a connected city refers to the fact that
in an interlinked (from local to global) world, cities can no longer be
economic islands in themselves (no fortresses), but have to seek their
development opportunities in the development of advanced transporta-
tion infrastructures, smart logistic systems and accessible communication
systems through which cities become nodes or hubs in polycentric net-works (including knowledge and innovation networks).
Conclusions and further steps
This paper has illustrated an on-going study in the field of smart cities evaluation.
The analysis started from a revised notion of triple helix considering that civil society
usually plays a prominent role toward the realization of sustainable development
in cities (Etzkowitz and Zhou 2006).
In order to assess the connections between smart city development and this
institutionalization of the triple helix, an ANP model was developed. This interrelatedmodel was used for investigating the relations between smart city components, actors
and visions, or strategies to which the smart cities are moving.
Innovation The European Journal of Social Science Research 147
-
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
14/15
The results show that the Entrepreneurial City is the policy vision with higher
priorities in all the sectors considered in the model, i.e. universities, government, industry
and civil society. Some relevant urban planning and policy implications of this vision are:
A high degree of entrepreneurial activities and a constant flow of new firm
creation are prerequisites for finding a new role within the new global eco-
nomic landscape. Innovation and creativeness are thus the necessary ingre-
dients for entrepreneurial cities in Europe.
Special emphasis has to be given to new architectures, building technologies,
intra-urban mobility solutions, public space management, e.g. for lighting
or citizen information management, integrated urban energy planning and
management, and ICT-based solutions that offer various opportunities for
new urban design and management.
New requirements for efficient, effective and reliable infrastructures (such as
energy, ICT, water, waste treatment and management) may occur. Since an
appropriate infrastructure is essential for cities attractiveness for companiesand people alike and therefore their economic development, emphasis has to
be given to the determination of these requirements within the scope of cities
as complex systems.
The ANP not only underlines the complexity of the reference system, but it also
improves the relationships and the inter-connections between all the constituting
elements of the smart cities model. The main innovative features of the model are:
the introduction of the civil society as a crucial stakeholder that empowers the
classical triple helix model composed of university
government
industry; the use of the four aforementioned helices, representing the main stakeholders
operating in a smart urban development, as control criteria for modeling
the decision making proble, rather than implying the traditional benefits
opportunitiescostsrisks control hierarchy (Saaty 2005);
a more truthful and realistic city model representation based on a network
system with the expression of relationships between elements;
the development of the model as well as the assessment exercise is the result
of a participative process, involving expertise on urban planning, sustainable
development evaluation, urban sociology and urban economy;
measurement of asmart city policy vision, considered as an holistic, inter-
related, multistakeholders concept, based on both quantitative indicators and
experts view.
The results obtained from this exercise are interesting but clearly the model requires
further implementation and improvement. The main limitations are as follows:
influence of the focus group in the definition of the indicatorsrelationships
and weights;
high number of indicators and, consequently, high number of pairwise
comparisons, resulting in a large amount of time required to achieve a result;
technical problems still included in the beta version of the software used fordeveloping stages 2 and 3 of the ANP application (available online from www.
superdecisions.com).
148 P. Lombardiet al.
http://www.superdecisions.com/http://www.superdecisions.com/http://www.superdecisions.com/http://www.superdecisions.com/ -
8/10/2019 Lombardi-Giordano-Farouh-Yousef 2012.pdf
15/15
In conclusion, the assessment exercise illustrated in this paper is a pilot study. It still
requires the development of a testing exercise with the participation of main city
stakeholders, offering a reflexive learning opportunity for the cities to measure what
options exist to improve their performance. This will be the future task of the
authors.
References
Abler, R., Adams, J.S., and Gould, P., 1971. Spatial organization: the geographers view of theworld. Englewood Cliffs, NJ: Prentice-Hall.
Benner, C., 2003. Learning communities in a learning region: the soft infrastructure of cross-firm learning networks. Environment and planning A, 35, 18091830.
Caragliu, A., Del Bo, C., and Nijkamp, P., 2009, March. Smart cities in Europe. Paperpresented to the Creating Smarter Cities Conference, Edinburgh Napier University.
Deakin, M., 2010. SCRAN: the smart cities (inter) regional academic network supportingthe development of a trans-national comparator for the standardisation of egovernmentservices.
In: C. Reddick, ed.
Comparative e-government: an examination of e-governmentacross countries. Berlin: Springer, 425446.Ezkowitz, H., 2008. The triple helix: university, industry and government. Routledge, London.Etzkowitz, H. and Zhou, C., 2006. Triple Helix twins: innovation and sustainability. Science
and public policy, 33 (1), 7783.Florida, R., 2002. The rise of the creative class. New York: Basic Books.Fusco Girad, L., Lombardi, P., and Nijkamp, P., 2009. Creative urban design and development.
(Special issue.)International journal of services technology and management, 13 (23), 111115.Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., and Meijers, E.,
2007, October. Smart cities ranking of European medium-sized cities. Centre of RegionalScience, Vienna. Available from: http://www.smart-cities.eu[Accessed November 25, 2010].
Komninos, N., 2002. Intelligent cities: innovation, knowledge systems and digital spaces.London: Spon Press.
Lombardi, P., Cooper, I., Paskaleva, K., and Deakin, M., 2009. The challenge of designinguser-centric e-services: european dimensions. In: C. Reddick, ed. Strategies for locale-government adoption and implementation: comparative studies. Hershey, PA: Idea GroupPublishing.
Lombardi, P., Giordano, S., Caragliu, A., Del Bo, C., Deakin, M., Nijkamp, P., Kourtit, K.,and Farouh, H., 2012. An advanced triple-helix network model for smart citiesperformance. In: Y. Ozge, ed. Green and ecological technologies for urban planning: creatingsmart cities. Hershey, PA: IGI Global, 5973.
Nijkamp, P. and Kourtik, K. 2011. Joint Programming Initiative (JPI) on Urban Europe.Global challenges and local responses in the urban century. A scoping document, 27, VUUniversity Amsterdam.
Saaty, T.L., 2005. Theory and applications of the analytic network process. Pittsburgh,
PA: RWS.Saaty, T.L. and Vargas, L.G., 2006. Decision making with the analytic network process:
economic, political, social and technological applications with benefits, opportunities, costs andrisks. New York: Springer.
Shapiro, J.M., 2008. Smart cities: quality of life, productivity, and the growth effects of humancapital.The review of economics and statistics, 88 (2), 324335.
Torres, L., Vicente, P., and Basilio, A., 2005. e-Government developments on delivering publicservices among EU cities. Government information quarterly, 22, 217238.
Van Soom, E. 2009. Measuring levels of supply and demand for e-services and e-government:a toolkit for cities [online]. Smart Cities Research Brief, no. 3. Available from: http://www.smartcities.info/research-briefs [Accessed February 25, 2009].
Wakelin, M., 1990. Globalization of regional development. In: D.V. Gibson, G. Kozmetsky,and R.W. Smilor, eds. The technopolis phenomenon. Smart cities, fast systems, globalnetworks. Lanham, MD: Rowman & Littlefield.
Innovation The European Journal of Social Science Research 149
http://www.smart-cities.eu/http://www.smartcities.info/research-briefshttp://www.smartcities.info/research-briefshttp://www.smartcities.info/research-briefshttp://www.smartcities.info/research-briefshttp://www.smart-cities.eu/