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Overcoming the dichotomous nature of smart city research is fundamental to provide cities with a clear understanding of how smart city development should be approached. This paper introduces a research methodology for conducting the multiple-case study analyses neces-sary to meet this challenge. After being presented, the practical feasibility, effectiveness and logistics of such a methodology are tested by examining the activities that Vienna has imple-mented to deliver its smart city development strategy. The results of this pilot study show how the application of the proposed methodology can help smart city researchers codify the knowledge produced from multiple smart city experiences using a common protocol. This in turn allows them to: (1) coordinate efforts when investigating the strategic principles that drive smart city development and test the divergent hypotheses emerging from the scientific litera-ture; (2) share the results of this investigation and hypothesis testing by conducting exten-sive cross-case analyses among multiple studies able to capture the generic qualities of the findings; (3) gain consensus on the way to think about, conceptualize and standardize the analysis of smart city developments; and (4) develop innovative monitoring and evaluation systems for smart city development strategies by reflecting upon the lessons learned from current practices.
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How to Overcome the Dichotomous Nature of Smart City
Research: Proposed Methodology and Results of a Pilot Study
Luca Mora
a
, Mark Deakin
a
, Alasdair Reid
a
, and Margarita Angelidou
b
a
Edinburgh Napier University, Edinburgh, UK;
b
Aristotle University of Thessaloniki, Thessaloniki, Greece
ABSTRACT
Overcoming the dichotomous nature of smart city research is
fundamental to providing cities with a clear understanding of how
smart city development should be approached. This paper
introduces a research methodology for conducting the multiple-
case study analyses necessary to meet this challenge. After
presenting the methodology, we test the practical feasibility,
eectiveness, and logistics of such a methodology by examining
the activities that Vienna has implemented in building its smart city
development strategy. The results of this pilot study show how the
application of the proposed methodology can help smart city
researchers codify the knowledge produced from multiple smart
city experiences, using a common protocol. This in turn allows
them to: (1) coordinate eorts when investigating the strategic
principles that drive smart city development and test the divergent
hypotheses emerging from the scientic literature; (2) share the
results of this investigation and hypothesis testing by conducting
extensive cross-case analyses among multiple studies able to
capture the generic qualities of the ndings; (3) gain consensus on
the way to think about, conceptualize, and standardize the analysis
of smart city developments; and (4) develop innovative monitoring
and evaluation systems for smart city development strategies by
reecting upon the lessons learned from current practices.
KEYWORDS
Smart city; urban innovation;
sustainable urban
development; research
methodology; Vienna
Introduction
Mora et al. (2017;2018a;2018b) and Komninos and Mora (2018) reveal the presence of a
deeply rooted division in the underlying structure of smart city research and show that
such a division surfaces as a set of dichotomies that question how smart city development
1
should be approached. Their research demonstrates that these dichotomies present them-
selves as divergent hypotheses on what strategic principles need to be considered when
designing and implementing the actions required to support the development of smart
cities. It also suggests that this division generates uncertainty over the way ahead and
creates a knowledge gap that needs to be closed in order for smart cities to move as a
set of groundbreaking urban innovations to demonstrate the transformative and disrup-
tive role technology [has] in solving urban issuesand supporting urban sustainability
(March, 2016: 1694).
© 2018 The Society of Urban Technology
CONTACT Luca Mora l.mora@napier.ac.uk Edinburgh Napier University, The Business School, Edinburgh, UK
JOURNAL OF URBAN TECHNOLOGY
2019, VOL. 26, NO. 2, 89128
https://doi.org/10.1080/10630732.2018.1525265
This paper considers multiple-case-study research with a deductive approach as one of
the most suitable methods for testing the divergent strategic principles for smart city
development that each dichotomy stands for. The ambiguity surrounding smart city
research demonstrates that a critical synthesis of the literature produced to date is
missing, and the empirical knowledge needed to close the gap that currently exists
between theory and practice has yet to be generated. Multiple-case study analyses that
are able to investigate the smart city phenomenon under dierent conditions are required
to bring about such knowledge and delineate what strategic principles drive smart city
development. The use of a common research design can help smart city researchers coor-
dinate their eorts towards the achievement of this common objective.
2
Following such a line of reasoning, this paper introduces a research methodology that
can be deployed for conducting large-scale multiple-case study analyses of smart city
development strategies and acquiring the scientic knowledge necessary to overcome
the dichotomies at the heart of smart city research. In addition, it reports on the results
of a pilot study that is instrumental in examining the practical feasibility, eectiveness,
and logistics of the proposed methodology. During the pilot study, the smart city devel-
opment strategy implemented by the city of Vienna was examined.
The paper is structured in four sections. The rst provides an extensive discussion of the
four dichotomies that Mora et al. (2017;2018a;2018b) have identied. This discussion is
based on the review of the most recent literature on smart cities and oers an understanding
of the division within smart city research. The design of the methodology proposed for
testing the divergent strategic principles that each dichotomy puts forward is presented in
the second section, while its functioning is examined in the third, which reports on the
results of the pilot study and the authorsexperience of conducting it. Structuring the
paper in this fashion makes it possible to show how the proposed methodology can be
deployed and how its components work together, ensuring its practical feasibility and eec-
tiveness. The logistics of the pilot study also allow the singular nature of this case study to be
addressed by: (1) evaluating the sustainability of the research process and its practicability;
(2) providing recommendations that are based on direct experience; and (3) detecting prac-
tical issues and limitations that may aect a larger sample of case study analyses and provide
possible solutions. These three aspects are discussed in the last and conclusive section of the
paper, which addresses the lessons learned from the pilot study.
The Dichotomous Nature of Smart City Research
Transforming urban areas into smart cities is an ambition that local and regional govern-
ments are trying to realize by developing strategies that make it possible to tackle urban
sustainability by means of ICT solutions. Cases of smart city development strategies can
be found in communities all over the world, and their developments have been captured
by an increasing number of studies. Some of these studies and the smart city cases they
examine are listed in Table 1.
However, despite a growing interest in smart cities and almost three decades of litera-
ture analyzing their development, a clear explanation of what needs to be done in order for
urban environments to succeed in designing and implementing strategies for supporting
smart city transformations is still missing. In the literature currently available, dierent
development paths can be identied (Mora et al., 2017;2018a;2018b; Komninos and
90 L. MORA ET AL.
Mora, 2018), whose presence generates uncertainty on how to approach smart city devel-
opment. This is because these paths suggest strategic principles that are divergent in
nature, making it dicult to establish whether smart city development should be based
on a: (1) technology-led or holistic strategy; (2) double or quadruple-helix model of col-
laboration; (3) top-down or bottom-up approach; (4) mono-dimensional or integrated
intervention logic. The questions arising from each dichotomy mark a knowledge gap
Table 1. Some of the smart city developments under investigation
Reference Smart city cases
Aina 2017 Yanbu Industrial City; Jubail Industrial City; King Abdullah Economic City; Riyadh; Jeddah;
Dammam; Makkah; Madinah (Asia)
Alawadhi et al. 2012 Mexico City, Philadelphia, Quebec City, Seattle (North America)
Anderson et al. 2012 Cape Town (Africa); Dongtan, Gujarat International Financial Tech-City, Jubail, Lavasa, Masdar,
Shanghai, Shenyang, Songdo, Suwon, Taoyuan, Tianjin, Urumqi, Wuxi (Asia); Ballarat, Gold
Coast City, Ipswich (Australia); Amsterdam, Besançon, Birmingham, Bottrop, Copenhagen,
Dublin, Eindhoven, Gdansk, Issy-les-moulineaux, Kalundborg, Lyon, Malaga, Malmö, PlanIT
Valley, Rotterdam, Sopron, Tallinn, Trikala, Trondheim (Europe); Bristol, Chattanooga,
Cleveland, Dakota County, Dublin, Moncton, Ottawa, Quebec City, Toronto, Windsor-Essex,
Winnipeg (North America); Curitiba, Pedra Branca, Porto Alegre, Recife (South America)
Angelidou 2014 Singapore, Songdo IBD (Asia); Amsterdam, Barcelona, Malta, Thessaloniki (Europe); New York
City (North America); Rio de Janeiro (South America)
Angelidou 2017 Konza (Africa); Cyberjaya, Singapore, King Abdullah Economic City, Masdar, Skolkovo, Songdo
(Asia); Amsterdam, Barcelona, London, PlanIT Valley, Stockholm (Europe); Chicago,
New York (North America); Rio de Janeiro (South America)
ARUP 2013 Hong Kong (Asia); Barcelona, Stockholm (Europe); Boston, Chicago (North America); Rio de
Janeiro (South America)
Bakici et al. 2013 Barcelona (Europe)
Bolici and Mora 2015 Amsterdam, Barcelona (Europe)
Cardullo and Kitchin 2017 Dublin (Europe)
Cisco Systems 2012 Busan, Singapore (Asia); Barcelona, Oulu, Rivas-Vaciamadrid, Stockholm (Europe); Boston,
San Francisco, South Bend (North America); Rio de Janeiro (South America)
Coletta et al. 2017 Dublin (Europe)
Cowley et al. 2017 Bristol, Glasgow, London, Manchester, Milton Keynes, Peterborough (Europe)
Cugurullo 2013 Masdar (Asia)
Dameri 2014 Amsterdam, Genoa (Europe)
Datta 2015 Dholera (Asia)
Ferrer 2017 Barcelona (Europe)
Fietkiewicz and Stock
2015
Kyoto, Osaka, Tokyo, Yokohama (Asia)
Gupta and Hall 2017 Agartala; Agra; Ahmedabad; Ajmer; Aligarh; Allahabad; Amravati; Amritsar; Aurangabad;
Bareilly; Belagavi; Belgaum; Bhagalpur; Bhopal; Bhubaneswar; Bidhannagar; Biharsharif;
Bilaspur; Chandigarh; Chennai; Coimbatore; Dahod; Davangere; Dharamshala; Dindigul;
Erode; Faridabad; Gandhinagar; Ghaziabad; Guwahati; Gwalior; Haldia; Hubballi-Dharwad;
Imphal; Indore; Jabalpur; Jaipur; Jalandhar; Jhansi; Kakinada; Kalyan-Dombivali; Kanpur;
Karnal; Kochi; Kohima; Kota; Lucknow; Ludhiana; Madurai; Mangaluru; Moradabad; Nagpur;
Namchi; Nashik; NDMC; New Town Kolkata; Oulgaret; Panaji; Portblair; Pune; Raipur; Rajkot;
Rampur; Ranchi; Rourkela; Sagar; Saharanpur; Salem; Shillong; Shivamogga; Solapur; Surat;
Thane; Thanjavur; Thoothukudi; Tiruchirappalli; Tirupati; Tiruppur; Tumakaru; Udaipur;
Ujjain; Vadodara; Varanasi; Vellore; Visakhapatnam; Warangal (Asia)
Komninos 2011 Hong Kong (Asia); Amsterdam, Milton Keynes (Europe)
Lee et al. 2014 Seoul (Asia); San Francisco (North America)
Leydesdorand Deakin
(2011)
Edinburgh (Europe); Montreal (North America)
Mora and Bolici 2016 Barcelona (Europe)
Mora and Bolici 2017 Amsterdam (Europe)
Mora et al. 2018c Amsterdan, Barcelona, Helsinki, Vienna (Europe)
Sauer 2012 Amsterdam (Europe)
Shwayri 2013 Songdo (Asia)
Vanolo 2014 Bari, Bologna, Turin, Genoa, Milan, Naples (Europe)
Zygiaris 2013 Amsterdam, Barcelona, Edinburgh (Europe)
JOURNAL OF URBAN TECHNOLOGY 91
that current research on smart cities is unable to close, and which can be demonstrated by
reviewing the literature produced to date (see Table 2).
When trying to dene whether a successful smart city development strategy is
technology-led or holistic, smart city researchers provide two dierent answers
(Niaros, 2016;Moraetal.,2017). On the one hand, according to the literature pro-
duced by ICT companies such as IBM, Cisco Systems, Siemens, ABB, Hitachi, and
Fujitsu, smart city development is driven only by information and communication
technologies. On the other hand, a large body of literature suggests this vision is
inadequate to support smart city development because it conceives smart cities as
technological objects rather than complex socio-technical systems in which techno-
logical development needs to be aligned with human, social, cultural, economic,
and environmental factors.
In addition, as pointed out by Soderstrom et al. (2014), McNeill (2016) and Paroutis et al.
(2014), ICT companies also suggest smart city development strategies require a narrow col-
laborative model in which the interaction is only between service providers selling their
smart city solutions to local governments. However, a large number of researchers consider
the double-helix structure of this collaborative model unable to provide the intellectual
capital that is necessary to drive smart city development. Their research calls for a much
more open and inclusive collaborative ecosystem based on a quadruple-helix structure
3
where all the city stakeholders representing research, industry, and government are involved,
along with civil society organizations and citizens.
4
This division also surfaces in relation to the third question: is the most suitable
approach for developing smart cities top-down or bottom-up? In the rst case, the city
government denes both a long-term vision and a strategic framework for supporting
smart city development. Whereas the bottom-up approach is deregulated, based on self-
organization, and founded on grassroots movements. In addition, it puts civil society in
the drivers seat, suggesting the direct involvement of the people in the development of
ICT-driven solutions for urban sustainability and their integration in the urban environ-
ment is what determines whether strategies are successful or not. The researchers cham-
pioning the bottom-up approach also highlight the importance of a radical shift from top-
down urban innovation processes and movement towards an open and bottom-up process
of urban innovation.
The last dichotomy concerns the intervention logic to be considered when implement-
ing smart city development strategies. According to Manville et al. (2014), smart city
development requires an integrated and multi-dimensional approach and a successful
smart city development strategy covers a large number of policy domains. This assump-
tion is in line with the assessment system that Vienna University of Technology, Univer-
sity of Ljubljana, and Delft University of Technology applied in 2007 to compare a large
group of medium-sized European cities and evaluate their performance as smart cities.
The following six domains were considered: living; economy; people; environment; mobi-
lity; and governance (Ginger et al., 2007). This multi-dimensional intervention logic is
also supported by IBM Corporation (2017a;2017b) and Cisco Systems (2016a;2016b) and
their operating systems for smart cities: comprehensive ICT platforms that integrate a
collection of digital solutions and applications for improving the management of
systems for energy and utilities, parking, environmental protection, safety and security,
transportation, education and healthcare. In contrast to this, the European Commission
92 L. MORA ET AL.
Table 2. Capturing the dichotomous nature of smart city research in the literature published between
1992 and 2018
Hypotheses: H1.1. Techno-led; H1.2. Holistic; H2.1. Top-down; H2.2. Bottom-up; H3.1. Double Helix;
H3.2. Quadruple Helix; H4.1. Mono-dimensional; H4.2. Integrated.
Dichotomy
1
Dichotomy
2
Dichotomy
3
Dichotomy
4
Reference H1.1 H1.2 H2.1 H2.2 H3.1 H3.2 H4.1 H4.2
ABB, 2013 XXX
Alawadhi et al., 2012 X
Amato et al., 2012a XXX
Amato et al., 2012b XXX
Amato et al., 2012c XXX
Angelidou, 2017 XXX X
Angelidou and Psaltoglou, 2017 X
Baccarne et al., 2014a X
Baccarne et al., 2014b X
Bergvall-Kåreborn et al., 2009 XX
Bolici and Mora, 2015 XXX X
Brech et al., 2011 XXX
Breuer et al., 2014 XX
Caragliu et al., 2011 X
Carvalho, 2015 X
Chen-Ritzo et al., 2009 XXX
Christopoulou et al., 2014 X
Cisco Systems, 2016a X
Cisco Systems, 2016b X
Concilio and Rizzo, 2016 X
Cosgrove et al., 2011 XXX
Cugurullo, 2013 X
Dameri, 2014 X
Dameri, 2017 X
Deakin, 2014 XX
Deakin and Al Wear, 2011 X
Deakin and Leydesdor,2014 X
Dirks and Keeling, 2009 XXX
Dirks et al., 2009 XXX
Dirks et al., 2010 XXX
Ersoy, 2017 X
European Commission, 2009 X
European Commission, 2011 X
European Commission, 2012a X
European Commission, 2012b X
European Commission, 2016 X
European Innovation Partnership on Smart Cities and
Communities, 2013
X
Exner, 2015 XX
Gardner and Hespanhol, 2018 X
Ginger et al., 2007 X
Gooch et al., 2015 X
Grossi and Pianezzi, 2017 X
Harrison et al., 2010 XXX
Harrison et al., 2011 XXX
Hemment and Townsend, 2013 XX
Hollands, 2008 X
Hollands, 2015 X
Hollands, 2016 X
IBM Corporation, 2017a XXX X
IBM Corporation, 2017b XXX X
Katz and Ruano, 2011 XXX
Kehoe, 2011 XXX
Kitchin, 2014 X
Kohno et al., 2011 XXX
(Continued)
JOURNAL OF URBAN TECHNOLOGY 93
promotes a mono-dimensional vision of the smart city, which is described as a low-carbon
and resource ecient urban environment fully committed to invest in IT solutions for
smart transport, smart buildings, and smart grids.
This extensive literature review exposes the hidden contradictions of the debate on smart
cities and the four dichotomies that the research conducted into the subject has generated.
Each dichotomy surfaces from divergent hypotheses concerning what strategic principles
drive smart city development. These hypotheses are listed in Table 3 and the scienticknowl-
edge required to empirically test their validity can be acquired by conducting multiple-case
study analyses. The following section provides a comprehensive and thorough description
of the methodology that this paper proposes to organize and carry out such analyses.
A Research Methodology for Investigating Smart Cities
Case-study research involves the empirical investigation of a current phenomenon within
its real-life context and can be applied to meet four dierent purposes: (1) to provide
Table 2. Continued
Dichotomy
1
Dichotomy
2
Dichotomy
3
Dichotomy
4
Reference H1.1 H1.2 H2.1 H2.2 H3.1 H3.2 H4.1 H4.2
Komninos, 2014 XXX
Kourtit et al., 2014 XXX
Kurebayashi et al., 2011 XXX
Lee and Hancock, 2012 X
Lee et al., 2014 XX
Leydesdorand Deakin, 2011 XX
Manville et al., 2014 XX
McNeill, 2016 X
Mora and Bolici, 2016 XXX X
Mora and Bolici, 2017 XXX X
Niaros, 2016 X
Paul et al., 2011 XXX
Pollio, 2016 X
Ratti and Townsend, 2011 X
Reddy Kummitha and Crutzen, 2017 X
Ruano et al., 2011 XXX
Schaefer et al., 2011 XXX
Schaers et al., 2012 X
Schuurman et al., 2012 XX
Schuurman et al., 2016 XX
Selada, 2017 XX
Shin, 2007 XX
Shin, 2009 XX
Shin and Kim, 2010 XX
Siemens, 2014 XXX
Soderstrom et al., 2014 XX
Sujata et al., 2016 X
Tamai, 2014 XXX
Townsend, 2013 XX
van Waart et al., 2016 X
van Winden and van den Buuse, 2017 X
Viitanen and Kingston, 2014 X
Yigitcanlar and Kamruzzaman, 2018 X
Yigitcanlar and Lee, 2014 X
Yoshikawa et al., 2011 XXX
Zygiaris, 2013 X
94 L. MORA ET AL.
descriptions; (2) to build new theories; (3) to rene existing theories; and (4) to test the
validity of existing theories (Eisenhardt, 1989; George and Bennett, 2005; Robson, 1993;
Yin, 2009). The research methodology that this paper proposes focuses attention on the
last approach: its aim is to activate a theory-testing process able to assess the validity of
the divergent hypotheses emerging from each dichotomy by means of multiple-case
study analyses of smart city development strategies. This methodology is built on the
most relevant literature describing how case study research should be approached (Cres-
well, 2009; Eisenhardt 1989; Eisenhardt and Graebner, 2007; George and Bennett, 2005;
Gerring, 2004; Gibbert et al., 2008; Miles and Huberman, 1994; Patton, 1990; Robson,
1993; Seawright and Gerring, 2008; Shakir, 2002; Stake, 1978,1995,1998; Yin, 2009;
2012) and is composed of three phases: (1) multiple-case study selection, (2) data collec-
tion, and (3) data processing and analysis.
Phase 1: Multiple-Case Study Selection
The selection of the appropriate cases is key to a successful multiple-case study analysis.
Considering the purpose of this study, the selection process needed to rely on a theoretical
sampling approach and not a random selection. Theoretical sampling means that case
studies, as experiments conducted in a laboratory, are not randomly sampled from a popu-
lation, but chosen for the likelihood that they will oer theoretical insight(Eisenhardt
and Graebner, 2007: 27).
Starting from an initial population of cities in which a strategy for supporting smart
city development has been designed and implemented, the researcher is required to
nd and focus attention on extreme cases: unusual manifestations of the smart city
phenomenon, which show either outstanding success or failure in approaching such
development. These cases are the most suitable to conrm or disprove the initial
hypotheses by observing what strategic principles have driven the selected cities. The
selection of extreme cases depends on how the researcher wants to approach the repli-
cation logic. The available options are the following: (1) literal replication: the cases are
chosen due to their similar settings and are expected to provide similar results and (2)
theoretical replication: the selected cases have dierent settings and variations are
expected in the results of the analysis.
The rst approach is based on the selection and comparison of either (1) cities that have
successfully approached smart city development and the ICT-driven approach to urban
Table 3. The four dichotomies emerging from smart city research and the divergent strategic principles
they underpin
Dichotomies Strategic principles
Dichotomy 1: Technology-led or holistic Hypothesis 1.1: Technology-led strategy
Hypothesis 1.2: Holistic strategy
Dichotomy 2: Top-down or bottom-up approach Hypothesis 2.1: Top-down approach
Hypothesis 2.2: Bottom-up approach
Dichotomy 3: Double or quadruple-helix model of collaboration Hypothesis 3.1: Double-helix model of collaboration
Hypothesis 3.2: Quadruple-helix model of
collaboration
Dichotomy 4: Mono-dimensional or integrated intervention
logic
Hypothesis 4.1: Mono-dimensional intervention logic
Hypothesis 4.2: Integrated intervention logic
JOURNAL OF URBAN TECHNOLOGY 95
sustainability it promotes, or (2) cities in which the transition process has proven to be
unsuccessful. The literal replication is exemplied by Ornetzeder and Rohracher (2013),
who investigate the internal dynamics and structural conditions necessary to develop suc-
cessful grassroots innovations in the elds of energy and transport. The investigation is
conducted by comparing three outstanding cases of sustainable grassroots innovations.
Aschemann-Witzel et al. (2017) propose something similar in their investigation into
key success factors in initiatives designed to reduce consumer-related food waste.
Additional examples are provided by Mora and Bolici (2016;2017) and Bolici and
Mora (2015), wherein the selection of two leading cases of smart cities makes it possible
to outline a preliminary roadmap describing the design and implementation process of
smart city development strategies.
By following a theoretical replication logic, attention is instead focused on both types of
extreme cases and the researcher tests its initial hypotheses by comparing successful and
unsuccessful samples. For example, Li et al. (2012) investigate the role of publicprivate
partnerships in residential browneld redevelopment by analyzing two case studies that
have produced opposite results. This approach has also helped Brunia et al. (2016: 30),
who compare workspaces with opposite employee satisfaction scores in order to explore
what factors explain the high or low percentages of satised employees in oces with
shared activity-based workplaces.
Both approaches are considered as suitable for this research methodology, as they
are complementary in nature. However, despite the approach chosen for conducting
the case study analysis, it is important to note that the multiple-case study selection
process and the number of replications always determine the external validity of the
analysis and the extent to which the ndings can be generalized. The number of repli-
cations depends upon the certainty the researcher wants to achieve and the greater
certainty lies with the larger number of cases(Yin, 2009: 58). Research by Eisenhardt
(1989) suggests a number of cases between 4 and 10 is ideal to have a good basis for
analytical generalization.
5
In addition, analytical generalization is also aected by two contextual conditions: the
geographical distribution of the selected cases and their size.
6
A more heterogeneous
sample determines a broader generalization of the results. For example, Calzada (2017)
investigates the governance strategies of leading smart city transformations by comparing
four European cities located in two dierent countries: Glasgow and Bristol in the United
Kingdom and Barcelona and Bilbao in Spain. This approach makes it possible to improve
the common understanding of such eects in Europe, but additional research is required
to test whether the ndings are of wider signicance because they also apply to other ter-
ritorial contexts.
Phase 2: Data Collection
The researcher follows a replication logic and subjects all the selected cases to the same
analytical process, which starts with the data collection phase. To establish what strategic
principles have led the cities towards becoming successful or unsuccessful examples of
smart cities, two databases are required. The rst one includes a list of all the activities
undertaken by each city to implement the smart city development strategy, to be organized
and classied in four categories:
96 L. MORA ET AL.
A. Community Building. Activities supporting the construction of an open and inclusive
collaborative environment able to support the design and implementation of the smart city
development strategy. This is done by raising citizen engagement in the smart city eld;
stimulating user-driven innovation and community-led urban development; increasing
public awareness and digital literacy; informing the citys stakeholders; and improving
their level of understanding about smart city development and the benets it can generate.
B. Strategic Framework. Activities aiming to develop the citys strategic framework for
guiding and regulating smart city development. The output of these activities includes:
(1) action plans, programs, guidelines, roadmaps, recommendations, governmental acts,
and policy documents; (2) measures proposing standards and technical requirements,
along with assessment methods; and (3) workgroups managing the general course of
the smart city development strategys operations.
C. Services and Applications. Activities which allow new ICT services and applications to
be integrated within the city.
D. Digital Infrastructure. Activities aiming to develop the technological infrastructure
necessary to use and benet from the available ICT services and applications. Examples
of activities include the integration of urban operating systems and the construction or
extension of high-speed broadband networks and public Wi-Fi networks.
This classication system makes it possible to group together all the activities according
to the objectives and outcomes towards which eorts are directed. In case of activities pro-
ducing outcomes that belong to multiple categories, they need to be included in more than
one group. By analyzing the percentage of activities belonging to each group, the
researcher will be able to determine whether the smart city development strategies
under investigation are: (1) holistic or technology-led [Dichotomy 1] and (2) developed
by means of either a top-down or bottom-up approach [Dichotomy 2].
The activities belonging to the category [C] Services and applicationsshall then be
assigned to one or more application domains to investigate the smart city development
strategys intervention logic [Dichotomy 4]. These activities allow the integration of
new digital solutions within the urban environment and can be classied according to
the objectives pursued through their implementation. The classication system is com-
posed of 11 application domains, which are described in Table 4 and selected by
merging the classication systems for smart technologies proposed to date (Ginger
et al., 2007; Manville et al., 2014; Neirotti et al., 2014; Reviglio et al., 2013; Cisco
Systems, 2016a;2016b).
7
This typology makes it possible to build a classication system
as broad as possible.
After classifying all the activities, the researcher focuses attention on the structure of
each smart city development strategys inter-organizational collaborative network
[Dichotomy 3]. The aim is to build a second database describing such a network and estab-
lish whether the model of collaboration is based on a double or quadruple-helix approach.
To meet this aim, the organizations that have collaborated in developing the activities pre-
viously mapped need to be identied and classied. In addition, each activity needs to be
JOURNAL OF URBAN TECHNOLOGY 97
analyzed to establish whether citizens have been involved in the implementation process.
The following classication system is provided to group the organizations by type:
.Research: universities and other research and educational institutions
.Industry: businesses which are involved in consultancy activities and/or in the distri-
bution of goods and services
.Government: local, regional, and national governmental authorities, along with their
majority-owned subsidiaries and external agencies
.Civil Society: civil society organizations
.Other: organizations that do not belong to the previous categories or where the infor-
mation necessary to complete the classication is not available.
These classication systems allow for the activities and organizations to be mapped and
analyzed by cross-referencing the qualitative data extracted from multiple sources.
Digital records reporting on the smart city development strategy under investigation
and produced by the city government should be considered as primary sources.
These include the following examples: agendas, minutes of meetings, press releases,
news and newsletters, conference presentations, conference speeches obtained from
either presentersnotes or videos of the events; reports, brochures, governmental
acts, policy papers and documents, and webpages. Additional data can also be acquired
from digital records produced by organizations that are either collaborating with the
city government in implementing the citys smart city development strategy, or not
involved but interested in providing data describing such a strategy. These sources
can be considered secondary and can include, for example, reports produced by con-
sultancy rms, news and articles published in online magazines, and any type of scho-
larly publications. This approach strengths the quality of the research process because
the multiple-case study analysis is conducted by combining data and information
which are extracted from multiple sources and provided by both internal and external
observers.
8
Table 4. Classication system for application domains
Application domains Objectives
C.01. Energy networks To increase the eciency and sustainability of either street lighting, or networks for
producing, storing and distributing energy
C.02. Air To ensure a better air quality in outdoor environments
C.03. Water To improve water resource management
C.04. Waste To improve waste management processes
C.05. Mobility and
transport
To provide city users with more sustainable and accessible transport systems and address
mobility issues
C.06. Buildings and districts To improve the eciency, accessibility and management systems of buildings and districts
C.07. Health and Social
Inclusion
To improve the quality, accessibility and organization of health services and support social
inclusion
C.08. Cultural heritage To ensure a better protection of both tangible and intangible cultural heritage and enhance
their cultural value
C.09. Education To increase the quality of teaching-learning processes delivered by education systems
C.10. Public safety and
security
To ensure safety and security in urban spaces and face safety challenges
C.11. E-government To increase the convenience and accessibility of public services and information to city users
C.12. Other ICT services and applications aiming at producing benets dierent than those related to the
previous application domains
98 L. MORA ET AL.
Primary and secondary sources can be found by conducting multiple keyword search
queries which aim to scan the World Wide Web. The following search string is suggested:
[name of the city under investigation] smart city.If the case study is located in a non-
English-speaking country, the search string should be adapted in accordance with the local
language. For example, in the case of Barcelona, data items reporting on the citys smart
city development strategy frequently use the term ciudad inteligenteinstead of smart
city(City of Barcelona, 2012;2013).
Phase 3: Data Processing and Analysis
Coding is suggested as a method to organize the large volume of unstructured qualitative
data collected from the data items and facilitate the identication and classication of both
activities and organizations, along with their progressive analysis.
Coding is how you dene what the data you are analyzing is about. It involves identifying and
recording one or more passages of text or other data items such as the part of pictures that, in
some sense, exemplify the same theoretical or descriptive idea. Usually, several passages are
identied and they are then linked with a name for that ideathe code. Thus, all the text and
so on that is about the same thing or exemplies the same thing is coded with the same name.
(Gibbs, 2007: 38)
The coding process can be conducted by following the procedure suggested by Eisenhardt
(1989), Gibbs (2007), Robson (1993), and Strauss and Corbin (1990). Qualitative data
analysis and research software programs, such as Atlas.ti, NVivo, and QDA Miner, can
be deployed as supporting tools. After being collected, the digital records need to be
reviewed repeatedly to identify the activities that every city that has been selected as a
case study has implemented to enable smart city development. Each activity is assigned
a code, identifying sections of text or other data items that describe the following attri-
butes: objectives of the activity; generated or expected outcomes; and organizations
involved in its development.
9
The coding process is expected to result in a detailed report in which the activities of
each city are listed and the data necessary to study them is presented in a structured
and well-organized form. These data are then used to populate the two databases and
acquire the knowledge necessary to test the divergent hypotheses emerging from the
four dichotomies.
Pilot Study: Testing the Research Methodology
A small-scale preliminary study is conducted to assess the practical feasibility, eective-
ness, and logistics of the proposed methodology. This makes it possible to reveal practical
issues and limitations and propose changes that are able to solve them. In addition, this
pilot study oers the possibility of showing researchers how the research methodology
can be deployed by way of a practical example.
The pilot study is split into three phases. During the rst phase, the strategy for mul-
tiple-case study selection is veried by simulating the sampling process of a multiple-case
study analysis that: (1) focuses attention on large European cities (population between
500,000 and 5,000,000 inhabitants);
10
and (2) is based on a literal replication logic. The
sampling process results in the identication of 10 extreme cases: large European cities
JOURNAL OF URBAN TECHNOLOGY 99
that have successfully approached the implementation of strategies for supporting smart
city development. One of these extreme cases is then selected to run the second and
third testing phases, in which the approach proposed for collecting, processing, and ana-
lyzing the data is examined.
Testing Phase 1: Multiple-Case Study Selection
An initial population of cities in the selected range of inhabitants and belonging to the
European Unions member states is dened by combining the census statistics of each
country.
11
In accessing such data, 60 candidate case studies were identied, and the follow-
ing were selected as extreme cases: Amsterdam in the Netherlands; London, Birmingham,
Glasgow and Manchester in the United Kingdom; Copenhagen in Denmark; Barcelona
and Madrid in Spain; Vienna in Austria; and Helsinki in Finland. These 10 cities were
selected due to their success in the eld of smart cities and heterogeneous geographical
distribution. Together, they cover six dierent European countries, and this provides
the basis for a broad generalization of the results.
The success of each candidate case study in the eld of smart city development was
evaluated by means of an online search phase, which was conducted to identify and
review the literature reporting on: (1) comprehensive comparative analyses of smart
city cases and smart city rankings in which one or more candidate case study was
shown to be a leading example; and (2) competitions in which these cities have received
awards for their smart city development strategies. The data resulting from this search
support the identication of the ten above-mentioned extreme cases:
.After comparing infrastructural, social, and economic factors characterizing a large
sample of cities, Kotkin (2009) has included Amsterdam in the top 10 worlds smart cities
.Amsterdam, Copenhagen, and Vienna are respectively among the winners of the World
Smart Cities Awards 2012, 2014, and 2016. In addition, along with Barcelona, Amster-
dam is also one of the nalists of the 2015 edition
12
.The Intelligent Community Forum (ICF)s team of analysts has named Barcelona as
one of the best Smart Communities of 2012, recognizing the citys leadership role in
supporting urban development and innovation by leveraging the potential of ICT sol-
utions and infrastructures
13
.Amsterdams smart city development strategy has been selected as a Benchmark of
Excellenceby the European Commission and described as a best practice to be repli-
cated in other urban contexts (Velthausz, 2011)
.Amsterdam was awarded the European City Star Award 2011 by the European Com-
mission, which highlighted the capability of its smart city development strategy to
demonstrate how cities can be successful in bringing together public parties, private
organizations, and citizens in order to take advantage of ICT for urban development
purposes (I amsterdam, 2011; Amsterdam Smart City, 2011)
.According to the Smart City Index Rankings developed in 2011 and 2012 by IDC
(International Data Corporation), Barcelona and Madrid are both among the top
ve smart cities in Spain (Achaerandio et al., 2011;2012)
.In a recent study benchmarking Austrian smart cities, IDC (2016) has also included
Vienna among the most advanced smart city cases
100 L. MORA ET AL.
.The European Commission nominated Barcelona as the 2014 European Capital of
Innovation for its smart city development strategy. According to the jury, this strategy
showed how the use of ICT could bring the city government closer to its citizens (Euro-
pean Commission, 2014a)
.According to a new study commissioned by Huawei and conducted by Navigant Con-
sulting (Woods et al., 2016), London, Birmingham, Glasgow, and Manchester are the
United Kingdoms leading examples of smart cities
.Vienna heads the Smart City Strategy Index developed by the consulting rm Roland
Berger, while Stockholm and Copenhagen are among the top-performing European
cities (Zelt et al., 2017)
.Considering the data provided by Manville et al. (2014), the Directorate-General for
Internal Policies of the European Parliament has recognized Amsterdam, Barcelona,
Copenhagen, Helsinki, Manchester, and Vienna as six of the most successful smart
cities in Europe and the most suitable cases for further in-depth analyses.
Testing Phase 2: Data Collection
The practical feasibility and eectiveness of the data collection, processing, and analysis
processes were tested by using the case of Vienna, which was randomly selected among
the 10 extreme cases. This made it possible to continue with the pilot study and start
searching for the digital records from which to collect the qualitative data necessary to
conduct the analysis.
The data collection process was composed of a series of searches,
14
each one pursuing a
specic aim, in which Google and multiple search strings were deployed:
.Search 1: Vienna smart cityOR Wien smart city
.Search 2: Vienna smart cityOR Wien smart citysite:smartcity.wien.gv.at
.Search 3: Vienna smart cityOR Wien smart citysite:www.wien.gv.at
.Search 4: Vienna smart cityOR Wien smart city-www.wien.gv.at
-smartcity.wien.gv.at
Search 1 was conducted in order to nd the main online repositories in which the city
government of Vienna stores the digital records reporting on the citys smart city devel-
opment strategy. The pages displayed by the search engine in response to the query
show that the city governments repositories storing most of the data items are the City
Councils online information service (https://www.wien.gv.at) and Smart City Wien, the
ocial website of the Viennas smart city development strategy (https://smartcity.wien.
gv.at).
Both repositories were searched (Search 2 and Search 3) and this made it possible to
detect 365 digital records, which included press releases, news, newsletters, webpages,
interviews, conference presentations, reports, posts on social media websites, policy
papers and documents, and governmental acts. After being identied, every data item
was downloaded and labelled using consecutive numbers. In addition, an excel spread-
sheet is created in which the itemsUniform Resource Locators (URLs) were listed in
order to check the presence of duplicates, which were eliminated as soon as detected.
JOURNAL OF URBAN TECHNOLOGY 101
This list was then expanded upon by adding 114 new digital records produced by
organizations that were either collaborating with the city government in implementing
Viennas smart city development strategy or interested in communicating information
describing the program of activities that were undertaken. These organizations were con-
sultancy rms, publishing companies, research centers, universities, national and regional
governmental authorities, and non-governmental institutions, and the digital records they
produced include scholarly publications, articles found in online magazines and newspa-
pers, posts, press releases, reports, and videos. These data items were considered as sec-
ondary sources and were collected with Search 4, in which Google was asked to
automatically eliminate any results from the two city governmentsonline repositories
previously examined.
Testing Phase 3: Data Processing and Analysis
Overall, 99.3 percent of the collected digital records were text documents, while the
remaining items were digital videos. Considering the high number of data items, their
analysis was conducted by using Atlas.ti as a supporting tool. Atlas.ti works with a large
range of media; however, in order to be processed, les need to be formatted according
to the systemsrequirements.Therefore,textdocumentsstoredinformatsdierent
from .txt, .doc, .docx, .odt, and .pdf were all converted. However, no changes were required
for the video les.
After being prepared and uploaded onto Atlas.ti, the digital records were reviewed in a
systematic way to identify the activities that Vienna has developed to implement its smart
city development strategy. Each activity was assigned a code that described the following
attributes of the data: objectives of the activity; generated or expected outcomes; and enti-
ties involved in its development. The coding process resulted in a detailed report in which
the activities were listed one by one and the data necessary to study them were presented in
a structured and well-organized form. The report was generated by using Atlas.tis output
function called Codebook, and was used to create two databases, in which activities and
organizations were classied (see Appendix B and Appendix C). It is important to note
that the information necessary to classify each organization was obtained from their
ocial websites, because the data provided by the digital records were insucient to com-
plete this task.
With the coding process, 54 activities were mapped (See Figure 1) and their analysis
makes it possible to understand how Vienna has approached smart city development
and test the validity of the hypotheses each dichotomy stands on.
Dichotomy 1: Technology-Led or Holistic Strategy
Viennas smart city development strategy gives equal weight to: (1) the deployment of
technological advancements leading to either the resolution or mitigation of urban sus-
tainability issues; and (2) the development of both a collaborative environment and a stra-
tegic framework for supporting the deployment of these technological advancements. This
strategy is therefore based on a holistic vision of smart cities, which are not considered as
technology-only focused systems resulting from the massive combination of sets of inter-
connected ICT components, but socio-technical systems in which technological develop-
ment is aligned with human, social, cultural, economic, and environmental factors.
102 L. MORA ET AL.
The accuracy of this statement is evidenced by the data in Figure 2, in which the per-
centage of activities by group of categories is compared and appears to be balanced. The
rst group includes those activities belonging to at least one of the rst two categories, i.e.,
[A] Community buildingand [B] Strategic framework,both of which focus attention
on the non-technological factors of smart city development. For example, with the Euro-
pean projects CLUE, TRANSFORM, and Urban Learning, Vienna has improved its capa-
bility of delivering policy and programs for supporting the deployment of ICT solutions
able to reduce carbon emissions (Brandt et al., 2014; Hartmann et al., 2015; Hemis
et al., 2017; CLUE Project Partners, 2014). In collaboration with a consortium composed
of 28 partners, which includes city governments, businesses, and universities, Vienna has
also launched Smart Together, a project aimed at testing new approaches for fostering
user-centric innovation, collaboration, and citizen engagement in smart city
development.
15
On the contrary, the second group of activities include projects and initiatives in which
the deployment of ICT services, applications, and infrastructures within the urban
environment is among the objectives or the only objective. Examples of technological sol-
utions and infrastructures include: decision-supporting tools for managing urban energy
and mobility systems (Bednar et al., 2016; Marguerite et al., 2016); electric vehicles, char-
ging infrastructures and info-mobility systems (Wiener Modellregion and Climate and
Figure 1. Viennas smart city development strategy: number of activities by category
Figure 2. Viennas smart city development strategy: number of activities by group of categories
JOURNAL OF URBAN TECHNOLOGY 103
Energy Fund, 2014); a large-scale network of Wi-Fi access points in public spaces and
leisure areas to provide citizens and tourists with location-based information and free-
of-charge access to the Internet; mobile apps allowing the city government to receive feed-
back and send up-to-date information and instructions on how to proceed in case of
dangerous situations; and QR codes for accessing digital contents related to local facilities
via mobile devices.
16
Dichotomy 2: Top-Down or Bottom-Up Approach
Viennas smart city development strategy is holistic and keeps a balance between top-
down and bottom-up approaches. The city government is the most active organization
belonging to the smart city ecosystem of Vienna and has contributed to develop about
50 percent of the total activities (See Appendix C). This means that Viennas smart city
development is boosted by a signicant number of bottom-up activities and, what is
more, the analysis of the objectives and outcomes related to the work undertaken by
the city government demonstrates that it is clearly aimed at promoting this bottom-up
development process. The city government provides leadership and its actions are oriented
towards the construction of: (1) a decentralized development process; (2) an open, inclus-
ive, and cohesive collaborative ecosystem; and (3) the strategic framework for regulating
the smart city transformation of the entire city and bringing the dierent organizations
belonging to this ecosystem into a harmonious and ecient relationship.
To achieve this aim, the city government:
.sets up a participatory process for developing the Smart City Wien Framework Strategy,
i.e., a strategic document that lays down Viennas guidelines for smart city develop-
ment. This document provides a long-term vision that extends to 2050 and establishes
what objectives need to be achieved and the expected results. In addition, it identies
the application domains to focus attention on and describes the governance and moni-
toring systems that need to be adopted and the strategic principles to follow. The use of
a participatory approach ensures the Framework Strategy represents a single vision that
city stakeholders all agree on (City of Vienna 2014)
.collects ideas, comments, and feedback about the citys ICT requirements from public
and private sector organizations and citizens in order to develop a Digital Agenda with
projects for handling Viennas most pressing urban challenges (Heissenberger and
Schuhböck, 2015)
.increases Viennas know-how on urban technologies and smart city development by
collaborating in delivering new planning and operational tools, recommendations,
guidelines, standards and technical requirements, and evaluation and assessment
methods
17
.assigns the role of Smart City Wien Agency to TINA Vienna GmbH,
18
which becomes
the central coordination point for all internal and external stakeholders. It should
cover the areas of coordination, stakeholder management, inquiry management, and
communication and would record, evaluate, and initiate projects on behalf of all rel-
evant partners within and outside the City of Vienna. The objective lies in the interdis-
ciplinary promotion of networking between municipal administration, research,
business, and industry(City of Vienna, 2014: 88)
104 L. MORA ET AL.
.collaborates in organizing forums, conferences, workshops, and meetings dealing with
smart city development in order to: generate interest; inform the community; engage
new stakeholders and make the collaborative ecosystem larger; stimulate collaboration;
and raise public awareness of the potential benets ICTs can produce in urban environ-
ments (City of Vienna, 2016; Digital City Wien 2015;2016)
.makes public data freely accessible to support developers interested in building new
applications and digital services.
19
Dichotomy 3: Double or Quadruple-Helix Model of Collaboration
The smart city collaborative ecosystem of Vienna is analyzed and graphically visualized by
using the open-source software Gephi. The result is the network illustrated in Figure 3, in
which the organizations mapped during the coding process are represented as nodes with
a diameter that is directly proportional to the number of activities they have worked on.
Every edge connects the organizations which have collaborated in implementing at least
one activity. The stronger the degree of collaboration between two organizations, the
higher the thickness of the edge connecting them. Colors are assigned according to the
organization types.
The data describing the networksstructure shows that Vienna has approached smart
city development by means of a triple-helix collaborative model: the collaboration among
industry, government, and research is the engine behind Viennas smart city development
strategy. With participation at 56 percent, businesses are the most active organizations and
are followed by institutions for education and research and governmental authorities,
which both represent approximately 19 percent of the collaborative network. The remain-
ing 4 percent are civil society organizations, which are by far the least represented organ-
ization type.
However, despite this data, it is important to note that a number of activities suggest
Vienna has made an eort to strengthen the participation possibilities of civil society by
increasing citizensactive involvement in the implementation process of its smart city
development strategy (See Figure 4). This intent is clearly expressed in the strategic frame-
works that the city government has developed to guide and regulate the development of
Vienna as a smart city (City of Vienna, 2014). For example, the city government has
invited Viennas citizens to take part in the planning phase of the smart city development
strategy and support the production of the strategic framework by attending a series of
stakeholdersforums, along with representatives from universities, governmental auth-
orities, the business sector, and civil society organizations. These forums have been orga-
nized regularly and conceived as discussion events for exchanging ideas and ensuring
greater transparency, participation, and collaboration in the smart city eld (Hofstetter
and Vogl, 2011; Climate and Energy Fund, 2013; City of Vienna et al., 2011; 2013; City
of Vienna, 2012;2013a;2013b;2014;2016).
In addition, a number of projects have activated collaborative processes in which citi-
zens are asked to participate in the design and testing phases of ICT solutions and infra-
structures to be deployed in the urban environment. The aim is to improve the capability
of ICT to meet local communitiesneeds. This is the case of Smarter Together and Smart
Cities Demo Aspern, in which Living Labs are used as collaborative platforms for attract-
ing high public attention and foster user-centric innovation (Aspern Smart City Research,
JOURNAL OF URBAN TECHNOLOGY 105
2015; Muhlmann, 2017).
20
All of this provides evidence of an attempt to move from a
triple to a quadruple-helix collaborative model.
Dichotomy 4: Mono-Dimensional or Integrated Intervention Logic
The data obtained from the analysis of the activities belonging to the category [C] Ser-
vices and applicationsshows that Vienna has adopted an integrated intervention logic
Figure 3. Viennas smart city development strategy: smart city collaborative ecosystem
Figure 4. Viennas smart city development strategy: citizen participation
106 L. MORA ET AL.
and its smart city development strategy covers a mix of application domains (See Figure 5).
The citys interest in smart city development is mainly oriented towards smart transport,
smart building, and smart grid solutions for low-carbon and energy-ecient urban
environments. Most of the ICT services and applications supporting Viennas smart
city transformation are deployed to ght climate change and boost energy eciency in
mobility and transport (C.05), buildings and city districts (C.06), and power infrastruc-
tures (C.01).
This approach is aligned with the European Commissions interpretation of smart
cities. However, Viennas smart city development strategy makes a signicant eort to
extend such an interpretation by seeking to use digital technologies for addressing
additional sustainability issues related to other policy domains. For example, technological
solutions are brought into action to improve the management of natural resources other
than energy, such as air, waste, and water; stimulate the use of public transports by pro-
viding citizens with real-time information; stimulate social inclusion and citizen collabor-
ation by oering up-to-date overviews of where co-design activities are implemented in
Vienna and opening public data; and increase the quality of assistance services for
elderly people by helping them to easily access the online world.
21
Discussion and Conclusions
Overcoming the dichotomous nature of smart city research is fundamental in order to
provide cities with a clear understanding of how smart city development should be
approached, and to support them in delivering the program of activities needed to
Figure 5. Viennas smart city development strategy: activities by application domain
JOURNAL OF URBAN TECHNOLOGY 107
enable such a development. This paper introduces a research methodology for conducting
the deductive multiple-case study analyses necessary to meet this challenge. In addition, it
reports on the practical feasibility, eectiveness, logistics, and replicability of this method-
ology by analyzing the implementation process of the smart city development strategy
proposed by Vienna.
The results of the pilot study show how the proposed research methodology can be
deployed to capture and codify the knowledge produced from multiple smart city experi-
ences. This common protocol makes it possible for smart city researchers to (1) coordinate
eorts in investigating which strategic principles drive smart city development and test the
divergent hypotheses emerging from the scientic literature; (2) share the results of this
investigation and hypothesis-testing by conducting extensive cross-case analyses among
multiple studies able to capture the generic qualities of the ndings; and (3) collaborate
in developing a platform able to generate agreement over the way to think about, concep-
tualize, and standardize the analysis of smart city developments. The standard of reporting
that this methodology lays down also makes it possible to account for and make transpar-
ent the transformation that cities undergo in the process of becoming smart. This makes it
easier for researchers to capitalize on the lessons learned from extreme cases and develop
new and innovative monitoring and evaluation systems for assessing the operational value
of smart city development strategies.
This study clearly demonstrates that smart cities emerge from a program of associated
activities and, by adopting a reverse engineering approach, these programs can be decon-
structed into building blocks whose analysis enables the structure of any smart city devel-
opment strategy to be interpreted. This is the rationale behind the research methodology
for multiple-case study analyses that this paper reports on and whose validity, eective-
ness, and replicability are demonstrated by the results of the pilot study.
The pilot study reveals the strategic principles that Vienna has decided to choose in
order to enable smart city development. These principles have allowed Vienna to rst
address the dichotomous nature of smart city research and overcome the ambiguity sur-
rounding the methodology driving smart city development. This is achieved by:
(1) embracing a holistic vision of smart cities, which are considered as socio-technical
systems in which technological development is aligned with human, social, cultural,
economic, and environmental factors
(2) balancing top-down and bottom-up approaches
(3) instituting the industry-government-research relationships of the triple-helix colla-
borative model, while making eorts to strengthen the participation of civil society
and progressively moving towards a quadruple-helix model of stakeholder
engagement
(4) assembling an integrated intervention logic that cuts across the multitude of appli-
cation domains which smart city development represents.
Finally, in terms of logistics, the pilot study clearly shows that the sampling approach, data
collection, and analytics of the protocol are all adequate, as is the classication system of
the smart city activities that has been deployed. It also demonstrates that samples are avail-
able when looking for extreme cases of smart cities, and the literature reporting on devel-
opment strategies can provide a great deal of qualitative data able to support
108 L. MORA ET AL.
comprehensive analytical processes. In addition, it is important to point out that no rel-
evant issues serving to raise questions as to the value of the protocol have been detected
when conducting the pilot study. However, researchers need to be aware that the
coding process is particularly time-consuming and resource intensive, because it requires
a systematic analysis of qualitative data which is not only large in scale, but also complex to
approach in terms of content analysis. The use of digital supporting tools (Atlas.ti) has
been fundamental to managing this phase of the analysis and maintaining a chain of evi-
dence supporting the progressive identication of new activities. The contribution of mul-
tiple researchers in coding the activities is also extremely helpful when conducting the
content analysis, because this reciprocal exercise makes it possible to check the data
items in rotation and improve the quality of the coding process.
Notes
1. In this article, smart city development is considered as an ICT-driven approach to urban sus-
tainability, and smart cities are dened as follows: cities in which issues limiting sustainable
urban development are tackled by means of ICT-related solutions (Mora et al., 2018b). The
term cityis here used to refer to any type of urban area, irrespective of its population size.
2. Case study research can be approached by using either single- or multiple-case studies.
However, as Yin (2009) and Eisenhardt (1989) suggest, multiple-case study analyses
provide broader opportunities to generalize the theoretical prepositions under investigation
than examinations based on single-case studies.
3. The quadruple-helix collaborative model is driven by the university-industry-government
relations composing the triple-helix model (Etzkowitz and Leydesdor,2000) and adds
civil society, i.e., civil society organizations and citizens, as the fourth element of the colla-
borative ecosystem (Arnkil et al., 2010; Cavallini et al., 2016).
4. United Nations (1998), Food and Agriculture Organization (2013), World Economic Forum
(2013), and European Commission (2014b) dene civil society organizations as a broad cat-
egory that encompasses non-governmental organizations (NGOs), community groups, char-
ities, trusts, foundations, advocacy groups, faith-based organizations, and national and
international non-state associations.
5. Case study research does not allow for statistical generalization but analytical generalization.
As Yin (2009: 38) points out, in case study research the mode of generalization is analytic
generalization, in which a previously developed theory is used as a template with which to
compare the empirical results of the case study. If two or more cases are shown to support
the same theory, replication may be claimed. The empirical results may be considered yet
more potent if two or more cases support the same theory but do not support an equally
plausible, rival theory.
6. For the selection of the cases, we suggest using the classication system developed by the
European Commission and Organization for Economic Co-operation and Development
(OECD), in which cities are divided by number of inhabitants. The classication system is
composed of the following six categories: S (50,000100,000); M (100,000250,000); L
(250,000500,000); XL (500,0001,000,000); XXL (1,000,0005,000,000); Global cities
(More than 5,000,000) (Dijkstra and Poelman, 2012).
7. See also IBM: http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/smarterplanet.
8. Interviews can be considered as an additional source of information, but it is important to
remember that this data collection method requires a great deal of resources and preparation;
key informants need to be selected with precision and a protocol needs to be designed to
guarantee a high degree of data reliability (Robson, 1993; Yin, 2009). Therefore, we
suggest conducting interviews only when indispensable, in particular whether: (1) digital
records are scarce or do not provide sucient data on the program of activities of the
JOURNAL OF URBAN TECHNOLOGY 109
smart city development strategies under investigation, or (2) further validation of data
through cross verication is needed.
9. Data items dierent from texts include pictures, maps, technical drawings, and specic sec-
tions of audio and video les.
10. In the classication system developed by European Commission and OECD, cities with a
population between 500,000 and 5 million inhabitants belong to the XL and XXL categories
(Dijkstra and Poelman, 2012).
11. The census statistics are presented in the Appendix A.
12. The World Smart Cities Awards is a competition that the Smart City Expo World Congress
organizes annually to identify the most ambitious strategies, the most advanced projects,
and the most innovative initiatives around the world fostering the development of the smart
city concept. The complete lists of winners and nalists of each edition of the World Smart
Cities Awards are available on the Smart City Expo World Congresswebsite: http://www.
smartcityexpo.com.
13. The Intelligent Community Forum is an independent think tank based in New York City. All
the information on its Awards Program and the winners of each edition can be found at:
http://www.intelligentcommunity.org.
14. The search phase was conducted in April 2016.
15. Additional information can be found in the projectsocial website: http://smarter-together.
eu.
16. Additional data on mobile applications and Wireless LAN hotspots currently available in
Vienna is provided by the city government: https://www.wien.gv.at.
17. See the following projects: CityOpt (http://www.cityopt.eu); TRANSFORM (http://
urbantransform.eu); PUMAS (www.pumasproject.eu); TRANSFORM+ (http://www.transform-
plus.at); EU-GUGLE (http://eu-gugle.eu); and INNOSPIRIT (http://www.innospirit.org).
18. TINA Vienna GmbH is part of Wien Holding GmbH (2012), a holding company of the City
of Vienna that carries out community tasks.
19. Data are provided through the online platform Open Government Wien (https://open.wien.
gv.at/site/open-data/).
20. For further information, see also (1) the website of the Austrian Institute of Technology
(AIT), which has collaborated in delivering the project Smart Cities Demo Aspern
(https://www.ait.ac.at/en/research-elds/smart-grids/projects/smart-cities-demo-aspern);
(2) the ocial website of the project Smarter together (http://smarter-together.eu).
21. See the following projects: CO2 Neutrale; SternE; SeniorTab; Citybike Wien; E-Taxis; SMILE;
AnachB; Wien.at live-App; Open Government Data; and Wien Gestalten (https://smartcity.
wien.gv.at).
Disclosure Statement
No potential conict of interest was reported by the authors.
Notes on Contributors
Luca Mora is a lecturer in urban innovation dynamics at the business school of Edinburgh Napier
Universitys School of Engineering and the Built Environment.
Mark Deakin is professor of built environment and head of the Centre for Smart Cities at Napier
Universitys School of Engineering and the Built Environment.
Alasdair Reid is a research fellow at the Centre for Smart Cities of Edinburgh Napier Universitys
School of Engineering and the Built Environment.
Margarita Angelidou is a senior researcher at URENIO Research, Aristotle University of Thessaloniki.
110 L. MORA ET AL.
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Appendix A
Table A. Census statistics of the EU States members: cities with a population between 1 and 5 million
inhabitants
EU
CODE COUNTRY
CITIES
CENSUS
YEAR DATA SOURCE
AT Austria 1 2013 Statistics Austria (http://www.statistik.at)
BE Belgium 1 2013 Statistics Belgium (http://www.statistik.at)
BG Bulgaria 1 2012 National Statistical Institute (http://www.nsi.bg)
HR Croatia 1 2011 Croatian Bureau of Statistics (http://www.dzs.hr)
CY Cyprus 0 2011 Statistical Service (http://www.cystat.gov.cy)
CZ Czech
Republic
1 2012 Czech Statistical Oce (http://www.czso.cz)
DK Denmark 1 2013 Ministry of Social Aairs and the Interior (http://www.noegletal.dk)
EE Estonia 0 2011 Statistics Estonia (http://www.stat.ee)
FI Finland 1 2013 Population Register Centre (http://vrk.)
FR France 2 2010 National Institute for Statistics and Economic Studies (http://www.
insee.fr)
DE Germany 13 2011 Federal Statistical Oce (https://www.destatis.de)
GR Greece 1 2011 Hellenic Statistical Authority (http://www.statistics.gr)
HU Hungary 1 2012 Hungarian Central Statistical Oce (http://statinfo.ksh.hu)
IE Ireland 1 2011 Central Statistics Oce (http://www.cso.ie)
IT Italy 6 2013 Ancitel (http://portale.ancitel.it)
LV Latvia 1 2011 Central Statistical Bureau of Latvia (http://www.csb.gov.lv)
LT Lithuania 1 2013 Statistics Lithuania (http://www.stat.gov.lt)
LU Luxembourg 0 2014 National Institute of Statistics and Economic Studies of the Grand
Duchy of Luxembourg (http://www.statistiques.public.lu)
MT Malta 0 2011 National Statistics Oce (http://nso.gov.mt)
NL The
Netherlands
3 2014 Statistics Netherlands (http://cbs.nl)
PL Poland 5 2014 Central Statistical Oce of Poland (http://stat.gov.pl)
PT Portugal 1 2011 National Institute of Statistics (http://www.ine.pt)
RO Romania 2 2013 National Institute of Statistics (http://www.insse.ro)
SK Slovakia 1 2011 Statistical Oce of the Slovak Republic (http://slovak.statistics.sk)
SI Slovenia 0 2013 Statistical Oce of the Republic of Slovenia (http://stat.sl)
ES Spain 6 2012 Spanish Statistical Oce (http://www.ine.es)
SE Sweden 2 2012 Statistics Sweden (http://scb.se)
UK United
Kingdom
7 2011 Oce for National Statistics (http://ons.gov.uk)
JOURNAL OF URBAN TECHNOLOGY 121
Table B. Viennas smart city development strategy: activities by category and application domain
A: Community Building; B: Strategic Framework; C. Services and Applications; D. Digital infrastructure; C.01. Energy networks; C.02. Air; C.03. Water; C.04. Waste; C.05.
Mobility and transport; C.06. Buildings and districts; C.07. Health and Social Inclusion; C.08. Cultural heritage; C.09. Education; C.10. Public safety and security; C.11. E-
government; C.12. Other.
Activity Category Application domain
ID Code Name A B C D C.01 C.02 C.03 C.04 C.05 C.06 C.07 C.08 C.09 C.10 C.11 C.12
ACT.0001 AnachB smart von A nach B X X X
ACT.0002 Aspern.mobil X
ACT.0003 Boutiquehotel Stadthalle: Stadthotel mit Null-Energie-Bilanz X X
ACT.0004 CASE (Competencies for A sustainable Socio-Economic development)
ACT.0005 Citybike Wien X X X
ACT.0006 CITYOPT X X X X X
ACT.0007 CLUE (Climate Neutral Urban Districts in Europe)
ACT.0008 CO2 neutrale Post X X X X X
ACT.0009 Die MA 48 Mist App X X
ACT.0010 Digital Agenda Wien X X
ACT.0011 DigitalCity.Wien
ACT.0012 E-Mobility on Demand X X
ACT.0013 E-Taxis X X X
ACT.0014 Energiespar-Bim X X
ACT.0015 EOS Energie aus Klärschlamm X X
ACT.0016 EU-GUGLE: Sustainable renovation models for smarter cities X X
ACT.0017 Forschungsprojekt SMART.MONITOR
ACT.0018 INNOSPIRIT
ACT.0019 INWAPO
ACT.0020 IREEN
ACT.0021 LED-Technik in der öentlichen Beleuchtung X X
ACT.0022 Open Government Data X X
ACT.0023 Optihubs
ACT.0024 Photovoltaik-Dachgarten X X
ACT.0025 Arrowhead X X X
ACT.0026 Programm Klimaaktiv Erneuerbare Wärme X X
ACT.0027 Smart City Wien Project X X
ACT.0028 PUMAS X X X X
ACT.0029 SeniorTab X X
ACT.0030 Skopje Urban Transport
ACT.0031 SCDA (Smart Cities Demo Aspern) X X X
Appendix B
122 L. MORA ET AL.
ACT.0032 Smart City Wien Framework Strategy
ACT.0033 Smart Hubs 2.0
ACT.0034 Smart Services
ACT.0035 Smart Verteilerkreis X X X X
ACT.0036 Smarter Together X X X X
ACT.0037 SMILE (Die Mobilitätsplattform der Zukunft) X X X
ACT.0038 Social City Wien - Plattform für gesellschaftliche Innovation
ACT.0039 SternE - Erneuerbare Energie in der Hauptkläranlage X X
ACT.0040 Technologiezentrum aspern IQ X X
ACT.0041 TINA Vienna GmbH X
ACT.0042 TRANSFORM+ X X
ACT.0043 TRANSFORMation Agenda for Low Carbon Cities X X
ACT.0044 Trinkwasserkraftwerke X X
ACT.0045 URBAN LEARNING X
ACT.0046 URBEM-DK X X X X X
ACT.0047 Virtual Oce X X
ACT.0048 Weatherpark (Windkomfort-Optimierung für den Stadtteil Hauptbahnhof
Wien)
XX
ACT.0049 Wien Gestalten X X X
ACT.0050 wien.at live-App X X X
ACT.0051 wien.at Public WLAN X
ACT.0052 Digital Salon
ACT.0053 DigitalCity.Wien Action Day
ACT.0054 ZENEM (Zukünftige Energienetze mit Elktromobilität)
JOURNAL OF URBAN TECHNOLOGY 123
Appendix C
Table C. Viennas smart city development strategy: collaborative ecosystem
RES: Research; IND: Industry; GOV: Government; CIV: Civil Society; OTH: Other.
Organization Location N° of
ID Code Name Type City Country Activities
ORG.0001 City of Vienna GOV Wien Austria 24
ORG.0002 TINA Vienna GmbH GOV Wien Austria 15
ORG.0003 AIT Austrian Institute of Technology RES Wien Austria 11
ORG.0004 Vienna University of Technology RES Wien Austria 10
ORG.0005 Neue Urbane Mobilitat Wien GmbH GOV Wien Austria 9
ORG.0006 Siemens Aktiengesellschaft Oesterreich IND Wien Austria 5
ORG.0007 Wien Energie Stromnetz GmbH GOV Wien Austria 5
ORG.0008 VTT Technical Research Centre of Finland RES Espoo Finland 4
ORG.0009 Wien 3420 Aspern Development GmbH IND Wien Austria 4
ORG.0010 Wiener Linien GmbH GOV Wien Austria 4
ORG.0011 Wiener Netze Gmbh GOV Wien Austria