St. Petersburg University
Graduate School of Management
Master in Information Technologies and Innovation Management
PERFORMANCE MANAGEMENT & MEASUREMENT MODEL
FOR SMART CITIES
Master’s Thesis by the 2nd year student
Concentration – MITIM
Svetlana Kuznetsova
Research advisor:
Dr. Tatjana Samsonowa,
Associate Professor
St. Petersburg
2016
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Кузнецова Светлана Айдаровна, студент второго курса магистратуры
направления «Менеджмент», заявляю, что в моей магистерской диссертации на тему
«Модель управления и оценки эффективности деятельности интеллектуальных городов»,
представленной в службу обеспечения программ магистратуры для последующей
передачи в государственную аттестационную комиссию для публичной защиты, не
содержится элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских
диссертаций имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что
«ВКР выполняется индивидуально каждым студентом под руководством назначенного ему
научного руководителя», и п. 51 Устава федерального государственного бюджетного
образовательного учреждения высшего профессионального образования «СанктПетербургский государственный университет» о том, что «студент подлежит отчислению
из Санкт-Петербургского университета за представление курсовой или выпускной
квалификационной работы, выполненной другим лицом (лицами)».
_______________________________________________ (Подпись студента)
_______________25.05.2016_______________________ (Дата)
STATEMENT ABOUT THE INDEPENDENT CHARACTER OF
THE MASTER THESIS
I, Kuznetsova Svetlana, (second) year master student, program «Management», state that
my master thesis on the topic «Performance Management & Measurement Model for Smart
Cities», which is presented to the Master Office to be submitted to the Official Defense
Committee for the public defense, does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major
curriculum programs of higher and secondary professional education at St.Petersburg University
«A master thesis must be completed by each of the degree candidates individually under the
supervision of his or her advisor», and according to paragraph 51 of Charter of the Federal State
Institution of Higher Professional Education Saint-Petersburg State University «a student can be
expelled from St. Petersburg University for submitting of the course or graduation qualification
work developed by other person (persons)».
________________________________________________(Student’s signature)
_______________25.05.2016________________________ (Date)
2
АННОТАЦИЯ
ФИО студента
Тема магистерской
диссертации
Факультет
Программа
Год окончания
ФИО научного руководителя
Описание целей, задач и
основных результатов
исследования
Ключевые слова
Кузнецова Светлана Айдаровна
Модель управления и оценки эффективности
деятельности интеллектуальных городов
Высшая школа менеджмента,
Санкт-Петербургский государственный университет
38.04.02 «Менеджмент»
2016
Самсонова Татьяна Александровна,
доктор общественно-политических наук
Целью исследования является создание модели
(целостного подхода), включающей в себя набор
качественных и количественных показателей, которые
могут быть использованы для оценки эффективности
функционирования интеллектуальных городов.
Задачи текущей магистерской диссертации
включают в себя детальный обзор литературы на тему
«Умный город» для определения основных понятий;
проведение интервью с представителями умных городов;
с о зд а н и е ко н ц е п туа л ь н о й м од е л и у п р а в л е н и я
эффективностью умных городов, основанной на
экспертных интервью; проведение массового анкетного
опроса с целью проверки концептуальной модели и
окончательной доработки результатов при помощи
модели управления и измерения эффективности умных
городов.
На основании данных, собранных в ходе интервью
с представителями интеллектуальных городов, а также
опроса представителей IT компаний и университетов,
была представлена пятиуровневая модель управления
эффективностью деятельности умных городов. Основная
ценность представленной модели заключается в
сопоставлении целей умного города с конкретными
каче ственными и количе ственными ключевыми
показателями эффективности.
Представленная модель управления и измерения
эффективности деятельности умных городов может быть
использована на практике админист рацией
рассмотренных умных городов, а также представителями
бизне са, частными лицами и представителями
администрации других умных городов.
Умный город, оценка эффективности, ключевые
показатели эффективности (КПЭ), модель управления,
оценка эффективности, информационнокоммуникационные технологии (ИКТ)
3
ABSTRACT
Master Student’s Name
Master Thesis Title
Faculty
Program
Year
Academic Advisor’s Name
Description of the goal, tasks
and main results
Keywords
Svetlana Kuznetsova
Performance Management and Measurement Model for Smart
Cities
Graduate School of Management,
Saint Petersburg University
38.04.02 «Management»
2016
Dr. Tatjana Samsonowa, Associate Professor
The goal of the current research is to create the model
(holistic approach) with both qualitative and quantitative
indicators that can be used for assessing performance of smart
cities.
The objectives of the current master thesis include indepth literature review on “smart city” concept definitions;
conducting expert interviews with the representatives of
smart cities; creating the conceptual model for performance
management of smart cities based on expert interviews;
making mass survey in order to check the conceptual model
and finalizing results through the performance management
and measurement model for smart cities.
Based on the data collected with expert interviews and
survey a five-level performance management model for smart
cities is suggested. The key value of the model provided is
mapping goals of smart city with concrete qualitative and
quantitative KPIs that can be used to assess the performance
of the smart urban settlement.
The constructed performance management and
measurement model can be used in practice by both internal
(smart city managers) and external stakeholders (business,
individuals and city managers of other smart cities).
Smart city, performance measurement, key performance
indicators (KPI), performance management and measurement
model, Information and Communication Technologies (ICT)
4
TABLE OF CONTENTS
INTRODUCTION........................................................................................................................... 6
1.
2.
3.
STATE-OF-THE-ART IN “SMART CITY” CONCEPT.........................................................9
1.1.
Theoretical background of “smart city” concept.............................................................. 9
1.2.
Smart cities in practice....................................................................................................14
1.3.
Performance management and measurement background..............................................18
1.4.
Identifying the research gap............................................................................................24
1.5.
Summary of chapter I......................................................................................................27
METHODOLOGY.................................................................................................................29
2.1.
Data collection methods..................................................................................................29
2.2.
A five-level Performance Management Model description............................................ 32
2.3.
Data collection process and sample description............................................................. 35
2.4.
Summary of chapter II.................................................................................................... 39
RESEARCH FINDINGS....................................................................................................... 42
3.1.
Goals and smart city domains......................................................................................... 42
3.2.
Mapping goals and KPIs................................................................................................. 47
3.3.
Managerial implications and recommendations............................................................. 52
3.4.
Summary of chapter III................................................................................................... 55
CONCLUSIONS........................................................................................................................... 57
APPENDICES............................................................................................................................... 60
Appendix 1. Interview Guideline...............................................................................................60
Appendix 2. Case studies........................................................................................................... 61
Appendix 2.1. Case study: City of Copenhagen.................................................................... 61
Appendix 2.2. Case study: Hong Kong..................................................................................69
Appendix 2.3. Case study: City of Vienna............................................................................. 77
Appendix 2.4. Case study: City of Innopolis......................................................................... 84
Appendix 3. Online Questionnaire............................................................................................ 88
Appendix 4. Map of smart city domains and concrete KPIs..................................................... 95
REFERENCES.............................................................................................................................. 99
5
INTRODUCTION
One of the most popular and trendy words nowadays is the word “smart” that is
understood by people all over the world as “intelligent”, “innovative”, and “high-tech”.
In the XXI century things around us are becoming smarter and our life is getting more
comfortable, safe and exciting due to the rapid development of Information and Communication
Technologies (ICT), digitalization of services and the deep integration of smart devices and
gadgets (such as smartphones, ultrabooks, tablet computers, etc.) into our daily life.
The development of smart technologies is quite rash. Just several years ago specific smart
devices were considered as something extraordinary, however today the development of whole
smart cities is already observed. Smart city development started from “smart houses” (Park et al.,
2003; Ricquebourg et al., 2006) and nowadays continues as evolution of “smart cities” in which
people will be relieved from widely spread problems such as traffic jams, queues, dependence on
energy, etc. (Hollands, 2008; Nam and Pardo, 2011; Chourabi et al., 2012).
At present smart technologies are actively used for private and business purposes in
various industries but in several years such technologies will become a crucial part of all the
spheres of our life dramatically changing the direction of social development and turning us into
the smart society.
Despite the fact that there is a great amount of research on smart city concepts and also a
large number of practical examples of modern and innovative cities of new generation, the
question of analyzing performance and measuring effectiveness of these “smart cities” is still
open.
The goal of current master thesis is to create the model (holistic approach) with both
qualitative and quantitative indicators that can be used for assessing performance of smart cities.
Research questions to be answered within the current master thesis were formulated as
the following ones:
What is the common practice in regard to performance management of smart cities? Are
there common smart city goals, and are there common key performance indicators (KPIs)
to assess performance?
Is there a generic performance management approach to describe the common practice of
performance management of smart cities? How are goals and KPIs reflected in this
approach?
Can the holistic performance management model be provided based on the practices of
real smart cities?
6
Can there any recommendation be derived from the suggested approach to advise on the
implementation and further development of a performance management and
measurement model in smart city?
To achieve the research goal, the master thesis will address the following objectives:
conducting in-depth literature review on “smart city” concepts and definitions;
conducting interviews with experts in planning and construction of smart cities;
creating the conceptual model for performance measurement of smart cities based on
expert interviews;
making mass questionnaires to validate the conceptual model;
finalizing results through the performance measurement model for smart cities.
The first chapter of the master thesis presents the state-of-the-art in the smart city concept
and reviews the theoretical background of the topic, “smart city” definitions, approaches to smart
city initiatives, examples of smart cities in the Russian Federation and all over the world.
Moreover, the importance of the performance management and measurement model for “smart
cities” is justified.
The second chapter describes the methodological framework of research which results are
presented in the third chapter. The main tools to be applied in this research include in-depth
interviews with experts from the current scientific, questionnaires that allow collecting the
feedback and statistical results to create the base for the performance management and
measurement model for smart cities. Furthermore, the five-level performance management
model (Samsonowa, 2012) created for R&D departments is adapted for measuring the
performance of smart cities.
The third chapter gives the overview of key findings of research and final results
achieved. Results from expert interviews present a basis for building the preliminary
performance management and measurement model. All the data collected from expert interviews
and survey is analyzed and used to build the final performance management and measurement
model. Information gained from expert interviews with smart cities representatives are presented
in the form of case studies for each smart city. Moreover, the final model maps goals of smart
cities with KPIs classes and specific qualitative and quantitative ratios to assess the smart city
performance.
Finally, conclusions and practical recommendations are given based on the final
performance management and measurement model for smart cities. Moreover, the model
presented can be adapted to various smart cities depending on their goals, principles, economic,
cultural and political peculiarities. Current master thesis brings both theoretical and practical
7
value since it contains not just the in-depth literature analysis of the smart city concept, but also
describes specific KPIs that can be used by smart city initiatives to assess their performance.
8
1. STATE-OF-THE-ART IN “SMART CITY” CONCEPT
The current chapter will present the state-of-the-art in smart city concept. It will provide
in-depth analysis of smart city phenomenon with comparison of the range of smart city
definitions while presenting common components of various viewpoints. Furthermore, the set of
practical examples of implementation and realization of smart city strategy will be presented.
Such practical experiences provided will include both world and Russian smart cities. Moreover,
the literature analysis of performance management and performance measurement concepts will
be also suggested for better understanding of the final Master thesis outcomes.
1.1.
Theoretical background of “smart city” concept
Nowadays the level of migration is continuously increasing all over the world. According
to United Nations (2014), by 2050 more than 60% of population will be living in small towns,
cities or megalopolises. This clearly defined tendency of the growing urbanization and increasing
complexity of the daily life in cities make government develop new strategies on urban
development and take care of citizens’ comfort.
To fight challenges many large cities face today such as traffic congestion, pollution,
depletion of natural resources, increased workload on the existing infrastructure, etc., city
managers are trying to adopt technology-based approaches that cause the concept of smart city to
appear.
In spite of the fact that there are already several examples of smart city projects being
implemented, there is still no universal definition of the smart city. To identify the common
features of smart cities outlined by scholars and researchers, several sources with specific
interpretations of the smart city concept were compared (Table 1).
In many articles that cover the current topic (Hall, 2000; Cosgrave et al., 2013), smart
city is considered as the urban center of the future that is efficient, environmentally friendly and
safe. It means that all the structures inside such a city are designed and constructed with use of
integrated, advanced materials of high-quality, electronic sensor devices, and overall
computerized systems that combine databases and decision-making algorithms. This definition
illustrates that smart city touches all parts of urban life such as healthcare, education,
infrastructure (buildings, tunnels, bridges, seaports, railway stations, airports, roads), transport
and logistics, energy, environment, etc. Also, as the smart city concept is closely related to
sustainable development three important aspects must be considered. Firstly, needs of the
population are fulfilled through increasing the life quality; secondly, the impact on environment
9
must not exceed established norms (effective and reasonable use of natural resources is crucial);
and, finally, the population must not endanger the life of future generations.
The European Commission states that a smart city is a place where traditional services
and networks provided for citizens can be characterized as highly qualified and more efficient
through the use of telecommunication and digital technologies “for the benefit of its inhabitants
and business”. The example of smart city projects carried out by the European Commission is the
European Innovation Partnership on Smart Cities and Communities (EIP) initiated in July, 2012
to accelerate the development of “smart cities” all over the world. This EIP is going to
implement smart technologies into such areas as urban transport networks, water supply, waste
of disposal facilities, lighting and heating of buildings. It also comprises a more responsive and
interactive city administration for meeting the ageing population’s needs and creating safer
public spaces (Digital Single Market, 2016).
British Standard Institute defines smart city as quite effective integration of such systems
as physical, human and digital in the built environment in order to deliver inclusive, prosperous
and sustainable future of its citizens (Gktoday.in, 2016).
IBM emphasizes that all types of smart cities are based on new innovative technologies
and insights with the aim of transforming their systems, operations and service delivery.
Furthermore, the concept of “smart city” is closely connected with high competition among
cities for new residents, visitors, investors and businesses what leads to providing the vibrant
economic climate and the high quality of life. IBM even puts the emphasis on three dimensions:
planning and management, infrastructure and people. Planning and management makes the
accent on efficient daily management that helps city stay safe and vital for its citizens and
businesses. Infrastructure includes key fundamental services such as roads, bridges, utilities and
mass transit systems that keep city livable and desirable. As for the people, all innovations and
smart services support the needs of citizens through social programs, healthcare, education, etc.
(Smarter Planet, 2016).
Cisco describes smart cities along with additional term that is smart and connected
communities (S+CC). All in all, Cisco believes that serious problems such as increased
population, polarized economic growth, increased greenhouse-gas emissions, decreased budgets
and others can be solved with scalable solutions that take advantage of Information and
Communication Technologies (ICT) in order to reduce costs, increase efficiency, and enhance
the quality of life. Moreover, from the Cisco point of view, the main barrier to implementing
such kind of solutions is the complexity of how cities are financed, regulated, planned and
operated. On the other side, smart cities present the great opportunity to integrate various
10
physical infrastructures: transportation systems, utilities, real estate, city services, etc. (Falconer
and Mitchell, 2012).
Although smart city definitions are numerous, it is possible to outline some common
features attributed to smart cities by scholars and researchers. In general, smart city provides an
intelligent way of managing its structures and systems such as energy, transport, healthcare,
buildings, homes and also the environment. It means that “smart city” generally operates in the
complex urban environment, causing the collaboration between complex technological systems,
city infrastructure, human behavior, economy, political and social structures (Gaur et al., 2015).
Table 1
Definitions of “smart city”
Source
Hall (2000)
Washburn and Sindhu
(2009)
Caragliu et al. (2011)
Angelidou (2014)
Perbolia et al. (2014)
Cisco Systems, Inc. (2014)
The British Standards
Institution (2014)
The European Commission
(2014)
Definition
“The urban center of the future, made safe, secure
environmentally green, and efficient because all structures whether for power, water, transportation, etc. are designed,
constructed, and maintained making use of advanced, integrated
materials, sensors, electronics, and networks which are interfaced
with computerized systems comprised of databases, tracking, and
decision-making algorithms”.
“The use of ICT [makes] the critical infrastructure components
and services of a city – which include city administration,
education, healthcare, public safety, real estate, transportation, and
utilities, more intelligent, interconnected, and efficient”.
“A city is smart when investments in human and social capital
and traditional (transport) and modern (ICT) communication
infrastructure fuel sustainable economic growth and a high quality
of life, with a wise management of natural resources, through
participatory governance”.
“All urban settlements that make a conscious effort to capitalize
on the new Information and Communications Technology (ICT)
landscape in a strategic way, seeking to achieve prosperity,
effectiveness and competitiveness on multiple socio-economic
levels”.
“Smart City proposes a holistic vision of future communities
where new intelligent technological tools, services and
applications are integrated in a unique platform, providing
interoperability and coordination between such sectors as
building, energy, environment, government, living, mobility,
education, health”.
“An integrated urban information and communication technology
(ICT) overlay on a city that can support delivery of connected
urban services and allow for efficient management of those
services on a global scale”.
“Effective integration of physical, digital and human systems in
the built environment to deliver a sustainable, prosperous and
inclusive future for its citizens”.
“A city seeking to address public issues via ICT-based solutions
on the basis of multi-stakeholder, municipally based partnership”.
11
The Smart Cities Council
(2015)
City that “uses information and communications technology (ICT)
to enhance its livability, workability and sustainability”.
Table 1 (continuation)
“Smarter cities of all sizes are capitalizing on new technologies
and insights to transform their systems, operations and service
delivery (water, transportation, energy) and improve citizens’ life
through social programs, healthcare, and education”.
IBM Corporation (2016)
Some of these definitions are more focused on specific aspects such as technologies, data
or citizens while others have a broader focus. In order to formulate the definition of smart city to
be used for the current research, all definitions above were compared and the most popular and
common features were identified (Table 2).
Table 2
Comparison of “smart city” definitions
Source
Hall (2000)
Caragliu et al.
(2009)
Washburn and
Sindhu (2009)
Angelidou M.
(2014)
Perboli et al.
(2014)
Cisco Systems,
Inc. (2014)
The British
Standards
Institution (2014)
The European
Commission
(2014)
The Smart Cities
Council (2015)
IBM Corporation
(2016)
Composite elements mentioned in “smart city” definitions
Sustainable
The use of
High quality
Effective
Green & Eco
development
ICT
of citizens’
government environment
life
management
˅
˅
(˅)
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
˅
(˅)
˅
˅
* ˅ component is explicitly mentioned in the definition
(˅) component is not explicitly mentioned, but is implied in the definition
All in all, smart city can be identified as an urban settlement that provides the highquality life for its citizens (in spheres such as transportation, healthcare, education, public safety,
etc.) through effective government management and the efficient use of Information and
Communication Technologies (ICT).
12
To summarize all the points discussed above and make the current topic more clear all the
information on the “smart city” concept was combined in the mind map (Figure 1). This mind
map shows that smart technologies can be implemented in various areas of citizens’ life,
facilities and institutions such as hospitals, public transport systems (metro, buses, trams, etc.),
schools, universities, roads, buildings, shopping centers and many others. Implementation of
technologies and innovations allow eliminating problems the increasing population face today
and making the daily life of people more comfortable and safe along with the rising level of
urbanization. Tools such as available computer centers, applications for smartphones and tablets,
electronic sensor devices in the city streets and the single operational control center are great
means to achieve goals that are set to make the city smart.
Figure 1 Mind map of smart city
Concerning smart city initiatives, they differ from each other depending on economic,
political, and cultural peculiarities of the country. In particular, approaches to smart city
initiatives can be divided into two different groups. The first group is presented with “top-down”
approaches that focus on various technologies, master planning, efficiency, integrating data and
information from different systems into a single operational center. “Bottom-up” approaches
focus on citizens and the means of how the local population use mobile applications, social
media, computer centers, sensor devices, etc. (all the types of innovative modern technologies) in
order to solve daily problems through creating absolutely new solutions able to change the
situation in the city (Nam and Pardo, 2011; Komninos et al., 2012; Kitchin, 2014).
It is very important to mention while making the link between theory and practice in the
smart city concept that with a view to assist in creating cities of the future the Smart Cities
Council was formed. The Smart Cities Council is the industry coalition formed in order to
accelerate the movement to smart and sustainable cities. The Council states that the smart city is
13
one that has digital technologies crossing all functions and systems in this city
(Smartcitiescouncil.com, 2016). The vision of the Smart Cities Council is closely connected with
the definition above: the participants and members envision the world where the intelligent
design and digital technologies are combined to create sustainable smart cities with high-quality
living and high-quality jobs. Consequently, three core values of such new cities are outlined:
Livability (cities without pollution and congestion, with clean and healthy living
conditions),
Workability (cities providing infrastructure for completing globally high-quality
Sustainability (cities providing various services without stealing from next
jobs)
generations) (Smartcitiescouncil.com, 2016).
While to create intelligent modern cities the Smart Cities Council also closely
collaborates with key business partners such as Cisco, General Electric, IBM, Microsoft,
MasterCard and many others.
1.2.
Smart cities in practice
The beginning of XXI century became the incremental step in development of the smart
city concept with the implementation of projects on intelligent cities in practice. The decision of
how the process of continuous smart city functioning is organized refers to the cities themselves.
Such organizational decisions are under control of the city administration or even single separate
initiatives. Based on the real examples of smart cities, they can be divided into two types.
(Figure 2).
Smart
city
Construction
type
Management
type
Independence
type
Newly-built
(from scratch)
Existing city
Single smart
solutions from
city boards
Indepent
initiative
Smart city
initiative
Newly elected
government
Under
administrative
control of
"mother" town
Initiative in the
administrative
structure
14
Figure 2 Smart city types
The first group of intelligent settlements includes cities that already exist through
developing and implementing smart strategies that can be assigned with the status of smart city.
Nowadays, several European cities are the perfect illustration of these intelligent cities.
The European Parliament that took smart city development in Europe under control aims
to develop the single smart city concept for the European Union and outline characteristics or
components of the intelligent city. It was already mentioned previously that the European
Parliament draws the great attention to ICT-based solutions while defining smart city. It assumes
that smart city has characteristics such as Smart Governance, Smart People, Smart Living, Smart
Mobility, Smart Economy and Smart Environment.
Based on the smart city definition and six key characteristics of smart cities the European
Parliament supposes that the status of smart city can be assigned to those having at least one key
characteristic announced by the European Parliament. It means that by 2011 240 out of 468 EU
cities (51% of the total number) had one or more smart city characteristics and were classified as
smart cities.
The distribution of intelligent settlements is consistent for the whole Europe. The
countries with the highest absolute number of smart cities include the United Kingdom, Spain
and Italy, while the countries that have the highest proportion of smart cities in Europe are Italy,
Austria, Denmark, Norway, Sweden, Estonia, and Slovenia (European Parliament, 2014).
In 2014 the European Parliament announced the six most successful European cities with
smart city initiatives. This set of cities is based on the fact how cities perform in the context of
their country’s national priorities alongside with socio-economical and political circumstances
and the alignment of each city to the European strategy and targets for future development. This
list includes Amsterdam (the Netherlands), Barcelona (Spain), Copenhagen (Denmark), Helsinki
(Finland), Manchester (the United Kingdom), and Vienna (Austria) (European Parliament, 2014).
Actually, each smart city can be independent in smart solutions and projects that it
implements to enhance the performance of all city areas. For example, Amsterdam provides a
great amount of projects in city domains such as smart mobility, smart society, smart living,
smart economy, and infrastructure (Amsterdam Smart City, 2016). Barcelona Smart City
initiative puts efforts on finding solutions in the city areas of public and social services,
environment, mobility, companies and businesses, research and innovation, communication
infrastructure, tourism, and citizen cooperation (Smartcity.bcn.cat, 2016).
The second group of intelligent cities includes those smart cities that are built from the
scratch as totally new projects for creating better living circumstances for its future citizens and
also positioned as cities of the completely new generation. By now there are only several
15
practical examples of these cities as planning and construction processes take the long period to
be realized.
Songdo International Business District (IBD) is a new smart city that is built from the
scratch and that occupies the territory of approximately 6 km 2 on the northeast of South Korea.
The story of Songdo starts in 2001 when Incheon Metropolitan City 1 invites Gale International,
the privately owned real estate development company based in New York to tour the project site.
On the fourth year the master plan of the future smart city was completed and signed. The very
first construction works were finished in 2009 with the opening of the Central Park, the first
residential and commercial project. During ten years Songdo was successful in constructing and
providing its residents with four international university campuses, schools, wide roads, higher
number of bicycle paths and walkways, the 65-floor Northeast Asia Trade Tower and green
spaces amounting to 40% of the whole city area.
Actually, Songdo IBD is an urban settlement designed for the people who work and live
there. This city aims to combine the ideal mix of residential, business, retail, cultural and
recreational environments. Nowadays, there are over 1,000 retail and hospitality businesses
operating, over 1,600 global and domestic companies located in Songdo. Moreover, as this smart
city is not just for businesses but also for the better life of its citizens, there are over 20,000
residential units occupied and 36,000 residents living in this South Korean intelligent settlement
(Songdoibd.com, 2016).
Masdar City is a planned city project in Abu Dhabi, the United Arab Emirates (UAE).
The construction works were under control of Masdar, a renewable energy company also based
in the UAE, while major capital investments referred to the Government of Abu Dhabi. The city
master plan was signed in 2008 and the same year construction works started. Actually, the city
is designed to become a hub for cleantech companies that specialize in recycling, IT, green
transportation, green chemistry, and electric motors. Nowadays, there are several thousand
residents living and also working on the territory of this city. Moreover, Masdar City still
continues to add new schools, universities, businesses, restaurants, accommodation and much
more while creating the diversity of modern city with the plan to provide facilities for 40,000
citizens in the nearest future (Masdar.ae, 2014).
As for the Russian experience in smart city development, smart city initiatives mainly
relate to projecting and constructing of new urban settlements from the very scratch. There are
several smart city projects in the Russian Federation mainly on the construction stage.
In particular, there are already two initiatives of intelligent cities from the Agency of
Investment Development of the Republic of Tatarstan such as Smart City Kazan and Innopolis
1 The Incheon Metropolitan City is a city located on the northwest of South Korea
16
(Tida.tatarstan.ru, 2016). Moreover, Skolkovo City is also the new urban settlement located in
Moscow on the territory of the Skolkovo Innovation Center.
Smart City Kazan is the holistic urban planning according with the smart city concept
alongside with the project of green city development that was presented in 2012. At present, the
construction works are still in progress with the aim of developing four key city areas that are the
central business precinct, the knowledge and education precinct, the special economic zone, the
enterprise precinct, and Parkland. The overall population is predicted to be about 58,800 citizens.
Moreover, the approximate number of jobs created will amount to 39,000; a number of
residential units will reach 16,620; and total building gross floor area (GFA) is predicted to be 7
mln m2 (Kazansmartcity.com, 2016). Overall, Kazan Smart City will be designed and constructed
based on five key principles including eco-urbanism, smart growth, smart location, low carbon,
inclusive and identity (Kazansmartcity.com, 2016).
Innopolis is also a newly built city located on the territory of the Republic of Tatarstan.
The city economy is based on the high-tech industries. A unique safe environment alongside with
the modern residential infrastructure, broad opportunities for education and professional
development have been created in Innopolis. In June, 2012 the Prime Minister of the Russian
Federation together with the President of the Republic of Tatarstan signed the agreement for the
construction of the new intelligent city. The master plan of Innopolis was proposed by architects
from Singapore. The foundation ceremony took place in 2015. Nowadays, the city provides
multifamily houses and townhouses for 5,000 people, schools, kindergarten designed for 225
children, the special economic zone for business representatives, the Innopolis University
focusing on information technologies. In the following years the existing infrastructure will be
expanded (Innopolis.ru, 2016).
Another smart city in the Russian Federation named “Skolkovo” is located close to
Moscow. The very first objects appeared on the territory of the innovation center already in
2012, however, the major part of the construction works are planned to be finished in 2017. In
this center of innovations constructors expect to provide optimal opportunities for research
organizations and businesses and also to create the attractive city environment that will be
comfortable both for residents and guests. The key elements of Skolkovo will be the University
and the TechnoPark besides the Congress center, numerous office and laboratory buildings, sport
centers and shopping malls. Additional attention is also paid to parks and other public places
where citizens will spend their free time. For the convenience, local jobs will be created near
housing estates so that employees will economize on the commuting time. Skolkovo search for
options to implement on its territory all modern solutions aimed to improve the overall urban
environment (Skolkovo Community, 2016).
17
Despite the fact that there are several practical examples of smart cities and smart city
strategies being implemented, the phenomenon of smart city is relatively new. That explains the
lack of instruments, systems and models to be used for evaluating the performance of smart
cities. In order to better outline the research gap for the current research, the literature review of
the performance management concept was also conducted.
1.3.
Performance management and measurement background
Since the goal of the current research is to create the model that can be used to measure
the performance of smart cities, alongside with the definition of smart city the terms of
performance management and performance measurement were identified.
For the better understanding of the state-of-the-art in performance management the
literature review was conducted (Table 3). Mainly, performance management is regarded as a
continuous process with several step swith various types of actions such as performance
planning, performance monitoring, and performance assessment (Brudan, 2010; Suhardi, 2015).
In spite of the fact that the performance management process can be different to some
extent in various companies and departments, its overall goal is generally the same. Performance
management aims to initiate positive changes in the organizational process and results
(Amaratunga and Baldry, 2002; Tantardini and Kroll, 2015). While in some cases performance
management leads to changes in the strategy and the review of organizational goals (Striteska,
2012; Waal, 2013).
Table 3
Definitions of “performance management”
Source
Amaratunga and Baldry
(2002)
Krause (2005)
Brudan (2010)
Osmani and Maliqi (2012)
Definition
“The use of performance measurement information to effect
positive change in organizational culture, systems and processes,
by helping to set agreed-upon performance goals, allocating and
prioritizing resources, informing managers to either confirm or
change current policy or programme directions to meet those
goals, and sharing results of performance in pursuing those goals”.
“Performance management encompasses all activities that are
aimed at the optimization of stakeholder benefits through the
constant improvement of the players’ professional competence
and social skills, and at the same time, that minimize the financial,
physical, temporal, emotional and social effort”.
“Performance management is the overarching process that deals
with performance. It reflects the approach one entity has towards
performance and it includes sub processes such as: strategy
definition (planning/goal setting), strategy execution, training and
performance measurement”.
“Performance Management identifies the organization's
18
Striteska (2012)
Waal (2013)
Ammons (2015)
Striteska and Jelinkova
(2015)
Suhardi (2015)
objectives, the necessary results to achieve these objectives, the
ways to be effective these objectives, and the drivers to achieve
them”.
“Performance management is a strategic approach to management
which provides managers, employees and stakeholders at different
levels with instruments necessary in order to regularly plan,
continuously monitor, periodically measure and review
performance of an organization”.
“Performance management is defined as the process in which
steering of the organization takes place through systematic
definition of mission, strategy, and objectives of the organization,
making these measurable through critical success factors and key
performance indicators in order to be able to take corrective and
preventive actions to keep the organization on track to great
performance”.
“Performance management actions begin with observing the
current state of performance, proceed to committing to the pursuit
of a more favorable level of performance, and culminate in taking
steps to achieve the targeted level”.
“Defining a strategy that expresses what the company wants to
achieve in the future and through which activities”, while
“monitoring and measuring plays a key role”.
“Performance management is a series of activities starting from
performance planning, monitoring/review of performance,
performance assessment and follow-up in the form of rewards and
punishment”.
Through the comparison of various definitions of performance management, the
composite components included in these definitions were identified (Table 4). The analysis of
performance management definitions allowed identifying that in most cases this process has
setting goals as the first step. It assumes the short-term or long-term planning of future actions
and results to be achieved. In order to assess the performance, specific ratios and key
performance indicators (KPIs) are used based on the goals previously set and the data collected
(Waal, 2013; Tantardini and Kroll, 2015). Continuous monitoring and analysis is necessary to
summarize KPIs’ values and use them more efficiently to take further decisions. Finding the
ways and methods to improve effectiveness and efficiency of the company’s actions and
programs is both the final step of performance management and also its key objective (Osmani
and Maliqi, 2012; Waal, 2013). Finally, the decisions about punishment in case of low
organizational performance or rewards in case of high organizational performance can be made
(Suhardi, 2015).
Table 4
Comparison of “performance management” definitions
Source
Composite elements mentioned in “performance management” definitions
Planning/ goals
Performance
Monitoring/
Performance
19
setting
Amaratunga
and Baldry
(2002)
Krause (2005)
Brudan (2010)
Osmani and
Maliqi (2012)
Striteska
(2012)
Waal (2013)
Ammons
(2015)
Striteska and
Jelinkova
(2015)
Suhardi
(2015)
Tantardini and
Kroll (2015)
measurement
Analysis
review/ Finding
ways to improve
performance
˅
˅
˅
˅
(˅)
˅
˅
(˅)
˅
˅
˅
˅
(˅)
˅
˅
˅
˅
˅
˅
˅
(˅)
˅
˅
˅
˅
˅
˅
˅
˅
˅
(˅)
˅
˅
˅
* ˅ component is explicitly mentioned in the definition
(˅) component is not explicitly mentioned, but is implied in the definition
Based on the literature review presented above it is assumed that performance
management is a process that includes such activities as goals setting, periodical measurement of
performance through various KPIs alongside with their continuous analysis, and taking further
decisions to improve effectiveness and efficiency.
It should be emphasized that performance measurement is considered as a substantial part
of performance management (Striteska, 2012; Suhardi, 2015). Analysis of various literature
sources (Table 5) gives better understanding of basic and additional features of performance
measurement and shows that performance measurement is the process and one of the
fundamental organizational functions that allows assessing the performance of company,
department or even the individual person (Aracioglu et al., 2013; Zamecnik and Rajnoha, 2015).
Performance measurement as a tool to measure effectiveness and efficiency (Neely et al., 1995;
Rezaei et al., 2011) generally consists of such steps as data collection, calculation of key
performance indicators (KPIs) to assess the performance, and final reference to analysis of ratios
evaluated (Nappi and Rozenfeld, 2015).
Moreover, performance measurement and reporting as substantial organizational function
can be realized through two levels (Atkinson et al., 1997; Kerssens-Van Drongelen and Fisscher,
2003). The first level is connected with a company as a whole that presents results of current
position and its performance to external stakeholders. While the second level takes place inside
20
the company between its managers and subordinates. However, at both levels it can be three
different types of actors who are evaluators (external stakeholders or company’s managers),
“evaluatees” (company as whole or middle managers), and assessors (external auditors or
internal controllers).
Table 5
Definitions of “performance measurement”
Source
Neely et al. (1995)
Atkinson et al. (1997)
Kerssens-Van
Drongelen and Fisscher
(2003)
Moullin (2007)
Rezaei et al. (2011)
Aracioglu et al. (2013)
Ivanov and Avasilcai
(2014)
Balabonienė and
Večerskienė (2015)
Nappi and Rozenfeld
(2015)
Definition
“Performance measurement can be defined as the process of
quantifying the efficiency and effectiveness of action; the process of
quantifying action, where measurement is the process of quantification
and action correlates with performance”.
“Our approach to performance measurement focuses on one output of
strategic planning: senior management’s choice of the nature and
scope of the contracts that it negotiates, both
explicitly and implicitly, with its stakeholders. The performance
measurement system is the tool the company uses to monitor those
contractual relationships”.
“Performance measurement and reporting takes place at 2 levels: (1)
company as a whole, reporting to external stakeholders, (2) within the
company, between managers and their subordinates. At both levels
there are 3 types of actors: (a) evaluators (e.g. managers, external
stakeholders), (b) “evaluatees” (e.g. middle managers, company), (c)
assessor, which is the person or institution assessing the effectiveness
and efficiency of performance measurement and reporting process and
its outputs (e.g. controllers, external accountant audits)”.
“Evaluating how well organizations are managed and the value they
deliver for customers and other stakeholders”.
Table 2 (continuation)
“A set of metrics used to quantify both the efficiency and effectiveness
of actions. Performance measurement methods are attractive to
researchers. Performance measurement helps to bring more scientific
analysis into a decision-making process. It underlines the change
towards management by information and knowledge, instead of
primarily relying on experiences and judgment”.
“Performance measurement is one of the fundamental management
functions. Evaluating performance, reviewing changes in the
surrounding environment and making adjustments are normal and
necessary parts of the strategic management process”.
“The main role of performance measurement is to assess the current
position of the organization and also to help managers create and
implement a better strategy”.
“The performance measurement is the tool that describes the
organization’s improvement because it is not possible to any
organization to act effectively without having its performance
measured”.
“Performance measurement is the process of quantifying efficiency
and effectiveness of actions. To this end, performance indicators
21
should be chosen, implemented, and monitored. Performance
indicators are the metric used to quantify the efficiency and/or
effectiveness of actions of part or of an entire process or a system in
relation to a pattern or target. These performance indicators are
essential elements for planning and strategic control cycles”
“The term “Performance Measurement (Business Performance
Measurement, Corporate Performance Measurement or Enterprise
Performance Measurement)” means the creation and use of usually
several indicators of various dimensions (e.g., cost, time, quality,
innovation capacity, customer satisfaction), which are used to assess
effectiveness and efficiency of the performance and performance
potentials of different objects in the enterprise, the so-called levels of
performance (e.g., organizational units of various sizes, staff,
processes)”.
Zamecnik and Rajnoha
(2015)
Based on the literature review of performance measurement the range of composite
elements is presented below (Table 6), starting from the key goal of performance measurement
that is to assess effectiveness and efficiency of actions or existing strategy with the link to the
further decision-making process based on the values of quantitative and qualitative indicators.
Moreover, based on the reported results some decisions to improve daily operations alongside
with strategic decisions to influence the long-term performance in positive way can be made
(Atkinson et al., 1997; Rezaei et al., 2011).
Table 6
Comparison of “performance measurement” definitions
Source
Neely et al.
(1995)
Atkinson et al.
(1997)
Kerssens-Van
Drongelen and
Fisscher
(2003)
Moullin
(2007)
Rezaei et al.
Composite elements mentioned in “performance measurement” definitions
Assessing
Performance
Performance
Strategic
Link to
effectiveness quantitative
qualitative
planning
decision
and efficiency indicators
indicators
making
˅
˅
(˅)
˅
(˅)
˅
˅
(˅)
˅
˅
˅
(˅)
˅
˅
22
(2011)
Aracioglu et
al. (2013)
Ivanov and
Avasilcai
(2014)
Balaboniene
and
Vecerskiene
(2015)
Nappi and
Rozenfeld
(2015)
Zamecnik and
Rajnoha
(2015)
(˅)
(˅)
˅
˅
˅
˅
(˅)
˅
(˅)
˅
˅
˅
˅
˅
˅
˅
(˅)
˅
(˅)
* ˅ component is explicitly mentioned in the definition
(˅) component is not explicitly mentioned, but is implied in the definition
Finally, based on the analysis of the set of definitions it is assumed that performance
measurement is the process of data analysis and its interpretation with the use of both
quantitative and qualitative indicators that has the aim of assessing effectiveness and efficiency
of actions and taking reasonable decisions.
The model refers to the specific draft or basic scheme that can be implemented and used
in the future for certain situations. In general, the model should present some patterns or
standards that can be applied in practice.
All in all, in order to develop the performance management model for smart cities (that
also includes performance measurement aspects), it is necessary to collect information about
short-term and long-term goals of intelligent cities and to identify which KPIs are used to assess
the performance of smart cities and how successful they are in helping to achieve the goals
previously set.
1.4.
Identifying the research gap
Nowadays it can be quite difficult to analyze critically the research made and scientific
articles about performance measurement of smart city activities and also classify them into
different groups according with approach peculiarities as there are still not so many literatures
devoted to this topic. It is connected also with the fact that in practice there is no one single clear
understanding of how ideal intellectual city should look like. Scientists, IT managers and all the
staff who are involved in the process of building the cities of new generation sometimes do not
know which way of organizing some activities or implementing some new technologies will be
23
correct and more effective one. And if there is no clear picture about how the process of creating
smart city and its strategy realization should look like, it can be very complicated to assess the
effectiveness and smartness of activities inside this town and also it can be quite difficult to
measure the overall performance of the whole city.
Despite this evident problem, there are few scientific sources that provide some ideas
about performance measurement of smart cities. These existing sources recommend a number of
indicators and ratios to be used for giving feedback on the effectiveness of smart systems and
smart technologies. All these indicators and ratios can be divided into two groups by their type qualitative and quantitative.
Qualitative data deals with descriptions, can be observed but cannot be measured or
calculated. That makes the process of information analysis more complicated and subjective.
However, quantitative data is always presented with some numbers or ratios so this type of data
can be easily measured, presented in figures and used to construct some models.
The business model matrix with the qualitative indicators for smart cities was presented
in 2015 (Walravens, 2015). The current matrix can be used as an analytical tool in some cases for
innovation ecosystems or even collaborative arrangements that provide customers with solutions
combined by several companies with their individual offerings (Adner, 2006). This matrix is
applicable for mobile services and applications what limits its practical adaptation. All the
indicators presented in this business model matrix are divided and organized into two levels. The
first level presents the connection with smart city goals that are set by policy makers while the
second organizational level corresponds to ways of how governments are organized to reach
those goals (Table 7).
The governance parameters related to the value network include good governance
(political motivation in offering various services to citizens, rights of citizens and their
protection) and stakeholder management (involvement of stakeholders in the process of bringing
a service to end user). The governance parameters related to the technical architecture are
technology governance (the importance of transparency and participation in making any
technological choices by public entities) and public data ownership (controlling the terms under
which the data is open to various actors). The public value parameters related to the financial
architecture are return on public investment (the expected value generated by a public
investment) and public partnership model (building of financial relationships between public and
private participants in the value network). The public value parameters related to the value
proposition are public value creation (examination of public value from the end user perspective)
and public value evaluation (the question if an evaluation is performed of the public value the
government sets out to create).
24
Table 7
Business model matrix (Walravens, 2015)
Value Network
Business Design
Parameters
Control Parameters
Value Parameters
Investment
structure
User involvement
Ownership VS
Consortium
Exclusive VS Other
influence
Modular VS
integrated
Concentrated VS
distributed
Enabled,
Encouraged,
Dissuaded or
Blocked
Revenue model
Intended value
Direct VS indirect
Price/ Quality
Lock-in Effects
Interoperability
Revenue sharing
Positioning
Enabled,
Encouraged,
Dissuaded or
Blocked
Yes or no
Complements VS
substitutes
Branding
Direct VS mediated
Profile & Identity
management
Distribution of
intelligence
Centralized VS
distributed
Governance parameters
Good governance
Organizational
Value Proposition
Modularity
Integrated VS
Disintegrated
Control over
customers
Policy goals
Financial
Architecture
Control over assets
Vertical Integration
Public design
parameters
Technical
Architecture
Harmonising
existing policy goals
& regulation
Accountability &
trust
Stakeholder
management
Choices in (public)
stakeholder
involvement
Public value parameters
Technology
governance
ROPI
Public value
creation
Inclusive VS
exclusive
Open VS closed data
Expectations on
financial returns
Multiplier effects
Public value
justification
Market failure
motivation
Public data
ownership
Definition of
conditions under
which and with
whom data is shared
Public partnership
model
Public value
evaluation
PPP, PFI, PC…
Yes or no
Public value testing
Another example of existing performance management models to be used for smart cities
is the Civil Society sub-network that has been presented as the model to evaluate policy visions
of smart cities (Nijkamp and Kourtik, 2011). As the figure below presents (Figure 3), there are
four alternative city visions: the Connected City (advanced transportation infrastructures, smart
logistic systems), the Entrepreneurial City (globalization policy, economic vitality), the Livable
City (smart environmental and energy initiatives), and the Pioneer City (unprecedented cultural
diversity and fragmentation of lifestyles). Moreover, the Civil-Society sub-network presents also
five domains that can be crucial for giving the city a status of being smart urban settlement.
These domains include smart governance, smart economy, smart human capital, smart living and
smart environment. One of the key disadvantages of the current model is the lack of specific
ratios that can be used to assess the level of the development of presented areas. So the CivilSociety sub-network presents just the components that state what city can be identified as
“smart” and what kind of criteria it should meet in order to be effective.
25
Figure 3 The Civil Society sub-network (Nijkamp and Kourtik, 2011)
Based on the analysis of the existing performance management models and approaches to
evaluating the performance of smart cities the research gap was identified. The research gap is
derived from the consequence of key disadvantages of performance management approaches
presented above. First of all, there are no quantitative ratios presented in the structure of existing
approaches to measure how the goals of smart city are achieved. Secondly, there is a poor
mapping of smart city goals and KPIs to be used to assess the concrete goals.
To make the following steps much clearer and easier the research questions were
formulated for the current research:
What is the general practice in performance management of smart cities? Are there
common smart city goals, and are there common KPIs to evaluate performance?
Is there a generic performance management approach to describe the common practice of
performance management of smart cities? What is the place of smart city goals and KPIs
in this approach?
Can the holistic performance management model be provided based on the practices of
real smart cities?
Can there any recommendation be suggested from the presented approach to advise on
the implementation and further development of a performance management model in
smart city?
Summarizing the information above and finalizing the literature review made, it should
be emphasized that the research gap is connected with the lack of instruments to be used for
performance management and measurement of smart cities. Considering the disadvantages of
existing models, the key goal of current research is to identify indicators and ratios (both
26
qualitative and quantitative) that can be used to measure smart city performance and to combine
them into the holistic performance management model that will be useful for the practical aims.
1.5.
Summary of chapter I
First of all, in order to be more aware of the smart city context, in-depth analysis of the
current topic was made. It was found out that nowadays there is the tendency of the rapid
urbanization. According to United Nations study, more than 60% of the total world population
will live in cities by 2050. Such the intense urban population growth became the reason for
several urban problems that include traffic jams, air and water pollution and overconsumption of
natural resources. Smart city is considered to be one of the most effective ways to solve such
various urban problems. However, as the phenomenon of smart city is relatively new, there is
still a lack of instruments that can be used to measure its performance.
The detailed literature review of the current topic reveled that there is still no one single
definition of smart city. Based on the analysis of different approaches common components of
smart city were defined which include the active use of ICT, the high-quality life of citizens,
sustainable development, green environment, and smart government systems. To find out the
different strategies of smart city development and management systems, practical examples of
European, Asian and Russian smart cities were considered.
After the literature review all the information, definitions and thoughts about “smart city”
concept were summarized in the graph (Figure 4) presented below to make the concept easier to
understand. Today the “smart cities” topic is mainly connected and identified with green and eco
environment, safety of citizens, smart transportation and logistics systems, city design, planning
of daily operations, smart healthcare (making the process of hospital services easier and the
process of treatment faster and more effective), smart education in kindergartens, schools and
universities, smart infrastructure that makes the life inside the city faster and more comfortable,
etc.
27
Smart
Infrastructure
Smart
Education
Smart
Healthcare
Green
Environment
"Smart City"
Concept
City Planning
and
Operations
Public Safety
Smart
Logistics
Figure 4 “Smart City” concept vision
Moreover, for the better understanding of the final results of the current research the
literature review of performance management concept was conducted. Existing performance
measurement models for smart cities were analyzed what helped to identify the research gap as
current models use only qualitative ratios and do not make the clear connection between goals
and KPIs.
Considering the lack of performance management systems for smart cities the goal of the
current research was set as creating the model with KPIs that can be used to assess the
performance of smart cities.
In order to achieve this goal the range of research questions were formulated. First of all,
it is necessary to find out if any common practices for performance management of smart cities
exist. Secondly, it should be researched if a holistic performance management model can be
provided based on the practices and if any recommendations can be given based on the final
model suggested.
The detailed analysis of performance management provided the understanding of future
results of the current research as performance measurement is a part of the performance
management process alongside with goal setting, reviewing and performance control.
28
2. METHODOLOGY
The second chapter will describe methodology of the current research. The detailed
information about data collection methods, data collection process and samples for expert
interviews and questionnaires will be provided in the current chapter. Furthermore, the detailed
description of the five-level Performance Management Model (Samsonowa, 2012) created for
R&D departments of ICT companies will be provided alongside with justification to use this
model for performance management of smart cities.
2.1.
Data collection methods
Nowadays, there are various types of researches that differ in topics and aspects.
However, these researches are common in the fact that they all need some data to be collected
and derive results, recommendations or conclusions based on information gathered.
In general, all the kinds of research can be divided into three categories that are
quantitative, qualitative and mixed ones that combine both quantitative and qualitative research
(Williams, 2007). Quantitative research uses statistical, computational or mathematical
techniques in order to investigate the observable phenomena in the systematic empirical way.
Main objectives of such quantitative research are to develop mathematical theories and
hypotheses that explain phenomena and that can be used in the future to make predictions
(Creswell, 2003). Qualitative research has aim to explore some topic through non-numerical
tools and expert evaluations (Williams, 2007).
The current research presents the combination of research types that include the case
study as the method to conduct qualitative research and the survey in the form of the
questionnaire as the method of mixed research including both quantitative and qualitative
research.
The instruments chosen for the research are valid and reliable. The validity and reliability
can be explained through the type of research that aims to identify practices of developing the
list of criteria of city “smartness”, performance measurement indicators already existing and
analyzing the current situation and further suggest the performance management and
measurement model for smart cities.
The case study as the first step of the research process is based on the published report
that investigates a certain phenomenon that has been studied over time (Thomas, 2011). The case
study as a research strategy relies on multiple source of evidence (Creswell, 2009). The data
sources for case studies include expert interviews and available documents. Since the interview
is a process of exchanging views between two or more individuals on a topic of mutual interest
29
(Kvale, 1996), the interview can be identified as the systematic way of talking with and listening
to people or as a way of collecting data from people through conversations.
There are several reasons to use interviews as the data collection method and the research
instrument (Gray, 2004):
a need to attain highly personalized data;
importance of good return rate;
opportunities are required for probing;
respondents are not fluent in the native language of the country, or where they have
difficulties with written language.
Four major types of interviews are widely spread nowadays differing from each other by
types of questions, aims and the general format of interview. These types are:
the structured interview (the standardized interview where the same questions with the
same wording and in the same sequence are asked from respondents (Bryman, 2001)),
the unstructured interview (the non-directed interview; the flexible method in which
interviews are different and do not require certain guidelines);
the semi-structured interview (the non-standardized interview not only with guidelines,
but also with the possibility to ask additional questions);
the non-directive interview (the interview with no preset topics to pursue and no preplanned questions during which the interviewee leads the conversation) (Gray, 2004).
To accomplish research the semi-structured form of interviews with experts in the field of
smart cities construction and development was chosen. Such interviews are non-standardized and
frequently used in the qualitative analysis. In this case the researcher has a list of key themes to
be covered, issues and questions to be discussed. An interview guide is used but additional
questions still can be asked (Appendix 1). Within each topic the interviewer is free to conduct the
conversation based on own preferences, explain questions or ask to clarify some answers if
needed (Corbetta, 2003).
This type of interview suits current research the most because it gives more freedom in
comparison with a structured interview in which the interviewer has to adhere to a detailed
interview guide (Kajornboon, 2004). Another advantage of semi-structured interviews is the fact
that the researcher has the opportunity to prompt and go deeper into the given question, while
key questions from the interview guideline must not be missed in order to construct the better
performance management and measurement model. In addition, the researcher can explain or
rephrase the question if the respondent is unsure about the meaning of the posed question.
Table 8
30
Intentional and extensional definitions of the interview
Intentional definition
A conversation in which one person
elicits information from another
person
Extensional definition
Telephone interview
Face-to-face interview
Panel interview
Group interview
All in all, the interview as a method to be used for accomplishing current research is
described in the Table 8 by means of intentional and extensional definitions. Concluding all the
points above, it is possible to define the interview as a conversation in which one person has the
aim to elicit some necessary information from another person he or she is talking with. The
extensional definition provides several examples of interviews such as telephone interviews faceto-face interviews, panel interviews, or group interviews.
After the interview with experts the conceptual performance management model is going
will be created. This model will be validated with a data collection tool such as a questionnaire
oriented on the large sample. The questionnaire is a data collection instrument that represents the
systematically prepared form or document with a number of questions intentionally designed to
detect responses from respondents or research informants with the purpose of collecting
necessary data or information (Bryman and Bell, 2003).
There are two main types of questionnaires: unstructured (open ended forms) and
structured (closed forms) different in the forms of questions that constitute the questionnaire.
The unstructured questionnaire also often named as the unrestricted type of questionnaire
or the open-ended one calls for free responses in the own words of the respondent. Such forms of
open-ended questions give the respondent the opportunity to express his or her point of view
through a set of options. This type of questionnaires requires more time and may not be suitable
for some researches since some of respondents may refuse to participate in the questionnaire
(that will immediately decrease a number of answers).
The current research assumes to make the questionnaire for quite a wide sample of
respondents in order to gain more valid results. Due to this reason the second type of
questionnaires, the structured one was chosen to fulfill current research. Structured
questionnaires give more control or guidance for the answer of the respondent. This type can be
described as a closed form because the questions require the respondent to give answers like
‘yes’ or ‘no’ or simply check an item out of a list of given responses. Those questions that require
‘yes’ or ‘no’ answers are also named as dichotomous questions. There also can be multiplechoice questions for which the respondent selects the answer that is very close to his or her
opinion. It means that the choice of the respondent is limited to the set of options provided,
31
however, such kind of questionnaires saves the respondent’s time and do not require extended
answers. To conclude, the structured questionnaire of the closed form was chosen as a research
tool with the objective to check the model built previously based on expert interviews.
Table 9
Intentional and extensional definitions of the questionnaire
Intentional definition
A series of questions with the aim
of extracting necessary information
Extensional definition
Open ended questionnaire
Closed questionnaire
The same as for interviews the table with intentional and extensional definitions (Table 9)
is presented above. The questionnaire can be identified as a paper with a series of questions with
the aim of extracting some necessary information to be used later for the research (or other)
objectives. Examples of questionnaires are presented through the extensional definition: the most
popular forms of questionnaires are open-ended questionnaires (unstructured) and closed
questionnaires (structured).
Expert interviews and questionnaires that were chosen as data collection methods for
current research allow identifying not theoretical but practical gaps, advantages and
disadvantages of real models and give the understanding of how the optimal model should look
like. Finally, interviews and questionnaires give the opportunity to extract data that is not
available in open sources.
2.2.
A five-level Performance Management Model description
As a basis for the Performance Management Model for smart cities a five-level
Performance Management Model (Samsonowa, 2012) presented for research and development
(R&D) departments of ICT companies and including five levels will be used (Figure 5). This
model is considered as a model comprising the relations between inputs, activities, outputs and
outcomes of a separate research department inside an enterprise.
Performance measurement relates to a continuous weighting of a defined company’s
business goal on the one side and the degree of its achievement on the other side. Fundamentally,
goals and the goal setting process can be considered at three various levels: the strategic level
(answering the question of where to go), the tactical level (answering the question of what to do)
and the operational level (answering the question of how to do).
32
Figure 5 A five-level Performance Management Model (Samsonowa, 2012)
In general, all the levels can be divided into two parts. The first part named as the
Performance Management System (PMgS) consists of three levels. Level one and level two are
presented with Company goals and Research goals. They can be combined because both present
the Goal level and set strategic goals that answer the question of ‘where to go?’ for the company
on a whole. These goals that are usually quite broad and long-term define the vision of the
company or the entire function. Research goals are more narrow and precise because they
depend on the exact area and peculiarities of the project. It is obvious that in this case research
goals (the second level of model) follow objectives of the organization and do not contradict
company’s goals, vision and mission.
The Activity level of PMgS represents eleven Performance clusters that include
technology transfer, future business opportunities, research portfolio management, intellectual
property creation, operational excellence, talent pool, image, publications, presence in scientific
community, collaboration with academia, collaboration with partners and customers.
The second part of Model is presented through Performance Measurement System
(PMsS) that consists of KPI Classes and Concrete KPIs. As it is possible to understand from the
names these levels are combined as KPI ones. After the step when key activities are defined,
criteria to assess those activities need to be identified. All in all, final level requires defining and
using of concrete methods (indictors, ratios, etc.) that can measure performance.
33
In order to be used later during empirical research this five-level Performance
Management Model for R&D departments, presented previously, should be reviewed from the
prospect of smart city. While it is also important to understand the background if this model can
be transferred to the practice of smart cities. Adaptation of the model from organizational
(departmental) use to city administration is relevant due to the s imilar logic in functioning and
development of different units while city can be considered as an organizational unit. Moreover,
R&D industry is close to smart city and smart initiatives because of the active use of ICT as
instruments to enhance performance and improve efficiency and effectiveness of organizational
unit.
Furthermore, such drawbacks of existing performance measurement models for smart
cities as absence of quantitative ratios and lack of correlation between smart city goals and KPIs
makes the performance evaluating quite subjective and incomplete. That justifies the choice of
the methods applied for the current research as one of the key advantages of this five-level
Performance Management Model is direct mapping of KPIs to specific goals that are going to be
assessed. Overall, final Performance Management Model for smart cities also consists of five
levels as original Model that is used for R&D departments (Samsonowa, 2012).
The very first level of company’s goals can be implemented for the country level since
each government strives to develop steadily, protect rights of citizens and become politically and
economically stable.
The second research level explains smart city goals as smart cities represent the part of
the country and specific cities have their own goals and objectives to be reached. While the goals
of different smart cities can be very similar as these cities belong to one common category, some
components can still differ and depend on the economic, political, and social peculiarities of the
city, its vision and mission.
The Activity level is closely connected with key functions of smart cities distributed
among different city committees. Nowadays, almost all cities have the similar structure of the
administration. It usually includes the Committee of economic development and finance, the
Education Committee, the Committee on Industry, the Committee on Social Policy, the
Construction Committee, the Committee on Transport and Logistics, the Committee on Culture,
the Committee on Public Security. Smart city objectives and domains of each Committee are
different and depend on functions and areas the Committee deals with.
The last two levels present performance measurement system which is going to be one of
the outputs of the Master thesis in the form of performance management model. So criteria for
performance assessment and specific indicators or ratios depend on performance goals and their
peculiarities that Committee sets. These KPIs are presented through empirical study in Chapter
34
III. They are also divided into groups depending on indicators’ characteristics and types of
activities of exact city Committee.
The correct company’s goals, which any performing department attempts to achieve,
should contain targets to be reached, as well as some elements of time at which the milestones or
target to that time are reached and also rules about a preference ordering about the ways to get
there. The three elements indicate organizational goals and the definition of performance rest on
the definition of a causal model linking inputs and outcomes through selected causal
relationships. For example, as it is possible to see the performance clusters (that are the third
level within the five-level Performance Management Model), represent the important link
connecting the two ends: organizational goals and performance measurement.
The same logic can be implemented for smart cities. The correct country goals, which any
existing city inside this country attempts to achieve, should contain some certain list of targets to
be reached. The objectives and plans of Committees should follow vision and both short- and
long-term plans of the single smart city and must not contradict them.
2.3.
Data collection process and sample description
For the purpose of efficient data collection during expert interview an interview guide
was created. In general, the interview guide that should avoid any ambiguity presents a list of
questions, topics and issues the researcher would like to cover during the interview. Prior to
interviews, convenient guidelines for their conduction were created to make the interview output
more reliable.
The interview guide with the draft of questions to be asked is presented in Appendix 1.
The interview with representatives of smart cities consists of several parts. The very first part
includes questions about personal data of experts such as job titles, positions in the smart city
administration and responsibilities. This information is important for the better understanding of
how exact respondents participate in smart city development and what exact area the respondent
manages.
The second block of questions is closely connected with the first part of the five-level
Performance Management Model presented before. The aim of this block is to identify what
goals the smart city sets to be achieved. Also, it is necessary to understand if different
committees of the particular smart city have a different list of goals to be accomplished till the
end of the month or the year. It may be assumed that goals of various committees should follow
general goals of the smart city and not contradicting the city’s vision of development. The point
for measuring the goal achievement will be identified.
35
Another block of questions has the aim to gain information about performance
measurement tools or the whole performance measurement system being applied in the particular
smart city. The respondents were asked to describe the performance measurement tools or
system, the process of how the city evaluates the goals achieved or how it plans to work in the
future (depending on the stage of development of the smart city). It was also meaningful to
examine the origin of such tools or system, in particular, whether the smart city created the
performance measurement system on its own or used the experience of other cities. The
problems and disadvantages of the current system could be analyzed to define how they could be
eliminated in the future to avoid these mistakes and drawbacks while creating the Performance
Measurement Model as the output of current research. During the research process the number of
key performance indicators (KPIs) that this smart city uses were identified and what they exactly
measure was revealed. The insight into distinguishing short- and long-term KPIs gave a clear
understanding of how different indicators can be used for evaluation of effectiveness of smart
city activities. Furthermore, the frequency of collecting and analyzing various indicators and
ratios was identified. Finally, forecast aspects for future development in performance
measurement models were considered.
If the smart city does not use any performance measurement tools, models or specific
ratios to evaluate its performance, it was useful to reveal and analyze the underlying reasons for
that situation and define the ideal model to be used for performance measurement of the smart
city in the future.
The last set of interview questions concentrates on analyzing how KPIs of separate
Committees are derived from KPIs of the smart city and how they reflect the critical success
factors of the smart city.
The further step after expert interviews and analysis of documents available is creating
conceptual performance management model to be used for smart cities. Logic models as one of
the techniques to combine and synthesize results of multi case studies can be based on the
number of cases from two and more (Yin, 2003). The use of models as analytic technique
assumes creating theoretical models based on practical examples of several case studies. The
sample for case studies includes existing smart cities that work continuously on the
implementation and realization smart city strategy. The expert interviews to collect data for case
studies were conducted with the representatives of smart city administration or managers of
smart city initiatives (Table 10).
36
Table 10
Sample description for expert interviews
Expert name
Hans Christian Christiansen
City name
Copenhagen
Eva Pangerl
Vienna
Po Chi Wu
The respondent wished to
keep the name unknown
Hong Kong
Innopolis
Respondent position
Senior Adviser, The Technical and
Environmental Administration, City
Development/ Municipal planning
Municipality of the City of Vienna,
Department MA 18 Urban Development and
Planning, Smart City Wien
Vice-Chairman, Invotech
Smart city manager
The additional method of data collection, the questionnaire used to validate the
performance management model built based on case studies data consisted of both close and
open-ended questions to give the opportunity for respondents to express their own opinion and
fill in the model with their suggestions. The types of questions included into the survey are:
multiple choice questions with the comment box;
questions with the matrix or rating scale with the comment box.
As the questionnaire was supposed to be conducted among 100 respondents it should not
have taken too much to get more feedback. Furthermore, with the aim of getting more valuable
and adequate results the respondent was aware about smart cities development, functioning,
design and construction. The sample for questionnaire included representatives of IT companies
that conduct separate smart projects and create solutions for intelligent urban settlements,
representatives of IT and research universities, both international and Russian companies (Table
11).
Table 11
Sample description for questionnaire
Company name
IBM
Country
USA
AGT
Switzerland
ITMO
Russia
AECOM
USA
Ingria
Russia
JetBrains
Czech
Republic
Brief description
producer of computer hardware, middleware and software,
and consultant in services in nanotechnology
IoT and Social data management, Big Data integration and
advanced analytics
St. Petersburg National Research University of Information
technologies, mechanics and optics
worldwide provider of Professional Technical Services and
Management Support Services to markets of Infrastructure,
Transportation, Facilities, Environmental, Energy, and Water
business incubator as a structural unit of St.Petersburg
Technopark managing high-tech projects
software development company that targets software
developers and project managers
37
IAC SPb
Russia
Leontief Center
Russia
Cisco
USA
Start
Russia
Development
St. Petersburg Russia
Polytechnic
University
U n i v e r s i t y o f Greece
Thessaly
AEM-technology
Russia
NEOTEX
Russia
IzhoraRemServis
Russia
K r y l o v S t a t e Russia
Research Center
ITERANET
Russia
St.Petersburg
University of
Technology and
Design
EMC
Smart City
Council
Intel
Russia
Siemens
Germany
USA
USA
USA
Table 11 (continuation)
an enterprise working in the field of informatization and
providing information and analytical support of local
authorities of the City of St. Petersburg
independent research and consulting organization in the areas
of science, information technologies and telecommunication
technology company that designs, manufactures and
sells networking equipment
implementation of development projects in the urban and
suburban real estate
National research technical University
the University with Schools of Humanities and Social
Sciences, Engineering, A g r i c u l t u r a l S c i e n c e s , Health
Sciences, Physical Education and Sport Sciences, and
Sciences
mechanical engineering division of ROSATOM state
corporation in the field of power engineering
services directly related to the transfer and commercialization
of technology and other related services
integrated solutions for industrial enterprises in the field of
maintenance and repair
one of the world’s major ship research & design centres with
century-long efforts in the field of innovative R&Ds for the
benefit of national shipbuilding
a full-featured system integrator and service in the field of
communication and computer technologies
the largest university in the Russian artistic and technological
profile
computer data storage company
an advisor and market accelerator that promotes the move to
smart, sustainable cities
multinational technology company p r o d u c i n g
motherboard chipsets, network interface controllers and other
devices related to communications and computing
engineering company with the principal divisions of
Industry, Energy, Healthcare, and Infrastructure & Cities
The respondents of the questionnaire can be described with the origin of the company or
University. All the sample representatives are divided into Russian and non-Russian organization
that illustrates the presence of Russian smart city context in the current research (Figure 6). From
the figure provided below it is possible to conclude that more than half or survey respondents
present Russian companies and Universities.
38
40.00%
60.00%
Russian
Non-Russian
Figure 6 Location of organization headquarter
All the methods described above were supposed to facilitate distinguishing performance
management and measurement tools or systems that are used in practice, motives for their use,
existing problems and possible ways to create the optimal model.
2.4.
Summary of chapter II
The current chapter of the master thesis covers the description of methods applied for
data collection and creation of the performance management and measurement model described
further in Chapter III. Key tools used for data collection are qualitative including expert
interviews and questionnaires oriented on the wider sample and used to prove the results of the
conceptual model.
As the base for the final Performance Management and Measurement Model a five-level
Performance Management Model (Samsonowa, 2012) was used. This Model was initially
developed for research departments of a company, however, in the context of the current master
thesis the Model was implemented for smart cities.
In general, a five-level Performance Management Model consists of two parts that are
Performance Management System (three levels) and Performance Measurement System (two
levels). Towards the performance management model for smart city, the first level of the Model
presents global goals of the government as legal unit. The second level explains smart city goals
as smart city as a single unit of the country has the set of the goals and objectives that also
should not contradict the country goals. Activity level is closely connected with key functions of
smart cities that are distributed among different city committees and presents through various
smart city domains. The last two levels present performance measurement system which is going
to be one of the outputs of the Master Thesis in the form of Performance Measurement Model.
So criteria for performance assessment and specific indicators or ratios depend on performance
goals and their peculiarities that Committee sets.
39
Qualitative methods of data collection used in research had the aim to explore the
problem through non-numerical tools and expert evaluations. Interviews and questionnaires gave
the possibility to analyze the current situation with existing practice of using performance
measurement models and tools in smart cities that have already been developed or just will be
created. The instruments chosen for the research are valid and reliable.
Interviews were held with experts from smart cities construction and development fields.
During the research process it was identified whether the status “smart” could be attributed to the
city of new generation, what qualitative or quantitative indicators and practices already exist for
measuring performance and success of smart cities both in Russia and abroad.
Interviews with experts
Case studies based on interviews and documents available
Conceptual model based on case studies results
Questionnaires aimed to check the concepttual model
Final performance management & measurement model created
Figure 7 Plan of empirical study
As it is summarized with the Figure 7 above the first step of data collection represents
interviews with experts who manage new smart cities projects in Russia and also abroad. The
interview guideline is presented in Appendix 1 that helps to understand the interview key topics
and most important questions. After interviews preliminary model is going to be created with
qualitative and quantitative indicators that can assess performance of smart cities.
In order to check conceptual model questionnaires are organized for wider sample of
respondents who are also dealing in the smart cities development areas. Questionnaire is
supposed to be structured one that means it consists of closed multiple choice questions with
possibility to add the own answer of respondent if it is not found in the range of suggested. The
multiple choice questions and matrix forms are better options to be used for the survey structure
as all the questions are based on the conceptual performance management model for smart cities
constructed previously from case studies.
The final step after interviews and questionnaires conducted is creating Performance
management & measurement model that can be used in practice to evaluate effectiveness of
40
operations inside smart cities. This model is key output of current Master Thesis that has both
theoretical and practical value. The results of empirical study are presented in Chapter III below.
41
3. RESEARCH FINDINGS
The current chapter will present key findings of research alongside with final results
achieved. Firstly, goals and smart city domains found from case studies of smart cities and
questionnaire results will be described. Secondly, detailed specification of KPI classes to be used
to assess specific domains of smart cities will be suggested. Finally, managerial implications and
recommendations for smart city managers will be proposed in the third chapter.
3.1.
Goals and smart city domains
Data on practical performance management and measurement models and also concrete
KPIs that were used by smart cities was collected following two key steps. Based on the
structure and components of 5-level Performance Management Model (Samsonowa, 2012) a set
of questions was developed and presented in the chapter II of current master thesis. The results
of expert interviews present performance goals of smart city initiatives and also specific KPIs
that are used to assess the performance of smart city.
Based on detailed responses of the smart cities representatives it was summarized which
strategy smart cities were following; what performance goals were set to follow the strategy of
making the city “smarter”; what qualitative and quantitative indicators were used by the smart
city administration to assess the city performance. Moreover, in-depth research of performance
goals from different smart cities experiences allows to understand that all the smart city
initiatives, in general, can be quite similar in the sense that these smart cities aim to serve and
improve similar city areas and domains in spite of the fact that those smart city initiatives can be
different due to political, economical, and cultural peculiarities.
The overall strategy and key goals of a smart city are presented as the Smart City Goal
part of the Goal level in the final five-level Performance Management Model to be used for
smart cities (Figure 8). According to analysis of the practical examples this kind of the smart city
goal is quite individual and highly depends on city specifics.
Figure 8 Goal level of Performance Management Model for smart cities
42
Copenhagen sets the ambitious plan to become the world’s first carbon neutral capital by
the year 2025. Such a plan requires long-term actions; however, it is still realistic as the statistics
show that in 2011 Copenhagen was able to reduce CO2 emissions by more than 20% compared to
2005. In order to follow this smart city goal, the Climate plan was developed and introduced by
the City Council in 2009. This official framework presents the detailed action plan with specific
objectives of how the goal of carbon neutrality can be achieved successfully by 2025. Mainly,
the Copenhagen smart city goal is going to be achieved through smart energy consumption and
production alongside enhancing the level of green mobility, smart water usage and also attracting
citizens to participate actively in smart city projects and solutions (CPH 2025 Climate Plan,
2012).
Hong Kong in its “Smarter Hong Kong, Smarter Living” Strategy outlines the framework
for the City to leverage on new technologies to stimulate further continuous economic
development, build platforms for collaboration, communication and sharing, deliver integrated eservices to the citizens and foster the ICT industry. It is a living strategy that grows and evolves
continuously alongside the global trends and aspirations of the Hong Kong community (Public
Consultation on 2014 Digital 21 Strategy, 2013).
The key objective of the Vienna smart city that is also announced as the long-term smart
strategy to be implemented till 2050 is to provide the best quality of living for all its citizens
along with maximum resource saving brought about by extensive and complex innovations. That
assumes Vienna to become the city of green spaces and renewable energy, one of the most
successful and attractive hubs in Europe for young people from all over the world, and also the
city of equal opportunities for citizens of all the generations (Smart City Wien, 2014).
The case studies for each smart city of Copenhagen, Vienna, Hong Kong and Innopolis
summarize the data collected from expert interviews and documents available about smart city
strategy implementation (Appendix 2). All case studies have the same structure which is used to
create the final Five-level Performance Management Model for smart cities (Table 12).
Table 12
Case study outline
Case element
City Profile
Smart city initiative profile
Smart city domains
Smart city KPIs
Mapping goals and KPIs
Conclusions
Description of content
Brief description of the city itself
Description of management systems of smart city initiatives
Domains of smart city goals and functioning
List of KPI classes and concrete KPIs
List of KPIs to assess specific smart city goals
Analysis of the existing instruments to assess smart city
performance alongside with drawbacks of current performance
measurement systems
43
All in all, analysis and comparison of goals of Copenhagen, Hong Kong, Vienna and
Innopolis served as the basis for indication of performance goals that can be used in the Activity
level of the 5-level final Performance Management Model. In general, the list of six performance
goals is defined through identification and analysis of both common points and differences of
three smart cities used in the sample. The six smart city domains identified include the following
(Figure 9):
Green environment;
Resources and energy;
Quality of citizens living;
ICT innovations;
Business and entrepreneurs;
Smart government.
Figure 9 Performance goals of smart cities
The use of resources and energy in an effective way as a single performance goal of smart
city can be also allocated into different domain. Basically, smart resources assume the way how
natural and artificial resources and city assets are used. It is also closely connected with
sustainable development of the city on a whole that means interrelation between effective uses of
resources in the current period of time and saving these resources in the appropriate condition for
the future generation. One of the way to deal with the current goal domain is also the active use
of renewable energy sources as it is presented in the case of the Copenhagen smart city strategy
(City of Copenhagen: City Hall, 2014).
The effective implementation of the strategy of smart resources and energy depends not
only on solutions from the city management side but on citizens’ inclusion. The effective use of
energy assumes saving watts per capita to serve housing services such as heating, cooling, etc.
Maintenance of green environment is another great part in the list of smart city
performance goals. This domain like the effective use of resources and energy correlates with
sustainable development of smart city and preserving the environment healthy and green not
only for present but also for next generations.
44
In general, key environmental problems include air pollution, water pollution, and a lack
of green areas around the city territories. In order to deal with these problems specific green
solutions should to be implemented in the context of the city development strategy. For example,
such smart green projects and solutions can be connected with waste recycling, development of
the culture of using CO2-free modes such as bicycles or electrical cars.
Enhancing the quality of citizens living is one of the key aims of smart cities considered
as a reason for smart city initiatives to be implemented all over the world. This performance goal
of smart cities basically includes improving various city services in order to make the life of
citizens easier and more comfortable. That can assume availability and the higher quality of
healthcare services, transport and logistics services, level of education.
Also the quality of citizens’ living can have strong correlation with intensity and
availability of ICT tools that aim to serve some public services. It is explained by the fact that
such innovative instruments and electronic devices tend to make the most of procedures much
easier and less time-consuming.
Implementation of ICT innovations in various city areas is a straight forward
consequence of the goal of enhancing the quality of citizens living. Such ICT innovations are
very important to be actively implemented and used by citizens in various public services.
However, it also relates to businesses and even private life. From the expert interviews it was
identified that the smart city strategy of Hong Kong mostly relies on active implementation of
ICT tools into different areas of the city (Hong Kong: Commerce and Economic Development
Bureau, 2014).
Supporting the business and enterprise in case with smart cities assumes not just finance
support and investments into businesses from the government and city administration side. It is
mostly connected with creating a range of IT services for companies to communicate with each
other, with consumers and also with city management. Such IT services can include multi-sided
platforms where one side is the city administration or cloud services.
Development of smart government systems actually takes a very important place in the set
of smart city goals and the overall strategy as a great number of smart projects and solutions can
be initiated by city managers. While following a number of steps to achieve the current goal of
smart government some aspects need to be considered. For example, they can reflect how open
the government agencies are in terms of communications and data sharing with citizens that is
also connected with administration flexibility and dynamics. Furthermore, the intensity of
various ICT tools use in government functioning plays a very important role with the aim of
making a range of administration procedures much easier and less time-consuming.
45
The questionnaire results prove the conceptual five-level Performance Management
Model built based on the findings from Copenhagen, Hong Kong, Vienna, and Innopolis case
studies. All smart city domains are marked as important for the successful realization and
implementation of the smart city strategy in practice and also should be assessed by city
managers for further development (Figure 10). The top three domains with high priority to be
evaluated include Quality of citizens living, Resources and energy, and ICT innovations. While
domains with highest percentage of answers to be considered as the goals of low priority are
“Smart government” and “Business and enterprise”. Besides, no more additional smart city goals
are suggested by survey respondents what can justify the completeness of the Activity level of
the Model.
Smart government
56%
12%
32%
37%
34%
Business and enterprise
29%
11%
ICT innovations
66%
23%
Quality of citizens living
5%
Resources and energy
4%
81%
14%
78%
18%
Green environment
32%
27%
0
0.1
0.2
Low priority
Medium priority
High priority
0.3
41%
0.4
0.5
0.6
0.7
0.8
0.9
Figure 10 The importance of smart city domains
However, it is quite important to remember that smart city goals need to be reconsidered
periodically in order to match with higher level goals o such as country and regional goals. In
addition, the systematical smart city goals reconsideration allows moving with the times while
considering all the economic, political, social, and ecological circumstances not only in the
particular city but also in the concrete country or even globally. According to the questionnaire
findings the dominant group of responses with the 42% of answers assumes to reconsider smart
city goals once every three years (Figure 11). The further most populous groups are respondents
who believe that smart city goals should be reconsidered once every two years (29%) or once a
year (14%). While also several answers in the category of “other” assume to reconsider goals of
intellectual city once every 10-15 years that makes the link between smart city goals and its
strategy closer.
46
7.00% 8.00%
14.00%
42.00%
29.00%
More than once a year
Once a year
Once every two years
Once every three years
Other
I find it difficult to answer
Figure 11 Frequency to reconsider smart city goals
The results of the mass survey with the representatives of both Russian and international
IT companies and computer science universities proved the model built based on expert
interviews. All the performance smart city goals were mentioned as the important ones and also
relevant for the intelligent urban settlements almost in the same proportions.
3.2.
Mapping goals and KPIs
As it is mentioned in the original five-level Performance Management Model for R&D
departments (Samsonowa, 2012), the Performance Management System consists of Goal level
and Activity levels that include performance goals. While the Performance Measurement System
of the model is presented with the KPI level where KPI Classes are connected with performance
goals founded out previously and are divided into concrete KPIs.
Based on the data collected through expert interviews and the questionnaire six
performance goals of smart city were identified. Furthermore, each performance goal can be
assessed with a range of qualitative and quantitative KPIs. To make the performance
measurement process more detailed, the concrete KPIs are divided into KPI classes according to
their common domains what allows ensuring that all the city areas are covered. All in all, KPI
classes and Concrete KPIs are the parts of the KPI level of the final Performance Management
Model and present the Performance Measurement System of this model (Figure 12).
47
Figure 12 A five-level Performance Management Model for smart cities
The first performance goal which is “Using of resources and energy in effective way” is
mapped with four KPI classes. First of all, as one of this goal component is the effective use of
energy, the first KPI cluster tends to combine ratios that aim to evaluate effectiveness of energy
use. For the city that strives to decrease losses in energy consumption it is important to control
the energy consumption of each citizen in watt during the certain period of time (f. e., daily,
weekly, monthly or annually). This ratio depends mostly on the personal attitude and
contribution of each citizen into the city’s smart strategy realization. Moreover, that concerns not
only how the basic energy sources are used by citizens but also the intensity of renewable energy
sources use in gross energy consumption. It leads to the development of the renewable energy
infrastructure and switching from consumption of standard energy sources to consumption of
electricity produced by wind turbines.
However, by the resources smart cities mean not only natural resources but also other city
assets that include residential and commercial buildings on the city territory. That justifies the
necessity to distinguish the layout and overall convenience of buildings as a separate KPI class.
The buildings present a part of the city infrastructure used by citizens. It assumes that these
buildings in urban areas should be attractive for the citizens. Moreover, all the city constructions
should operate effectively in terms of energy consumption for different in-house services such as
cooling, space heating, water heating that makes connection with the previous KPI cluster of the
effective energy use.
48
The overall city infrastructure is another important city asset. It reflects how well the
Internet infrastructure works with the density level of Wi-Fi points around the city area and the
space covered with Wi-Fi points. Moreover, the development of sport facilities and special sport
zones play the very important role in ensuring the higher quality of citizens’ life. City
infrastructure development also includes availability of parks and green zones for the city
population alongside with the total square meters covered by green zones.
Four KPI class suggested for the assessment of the performance goal such as the effective
use of resources and energy is the layout and the overall level of development of transport
systems as they are considered to be one of the most important city resources. For individuals it
is valuable to spend less time in traffic jams that can be achieved through high-quality roads and
highly-developed transport modes. Ratios of this KPI cluster aim to assess the functionality of
the integrated public transport system in terms of its availability for citizens, the public transport
density and convenience of switching from one mode to another for citizens.
The second group of KPI classes aims to be used for evaluating the performance goal
such as “Maintenance of green environment”. Despite the fact that nowadays green environment
presents a huge and broad topic, the experience of Copenhagen, Hong Kong, Vienna and
Innopolis allowed to divide this group of indicators into three classes based on their common
domains. First of all, the big part of the green strategy is waste recycling that demands efforts of
the city administration, availability of city infrastructures and also the citizens culture and habits
to recycle the household waste.
Moreover, the main reason for air pollution is the huge amount of emissions from the
city transport. Replacing gasoline cars with electrical vehicles can solve this environmental
problem and reduce the overall level of air pollution. CO 2-free transport modes such as cycling
can become one of the alternatives to get closer to the green city. Furthermore, a higher number
of electrical cars is also able to decrease CO 2-emissions into the atmosphere.
Besides decreasing water pollution, the rational use of water resources plays one of the
key roles in the green and eco-friendly environment of the city. Like the energy consumption
case, the solution to this problem also depends both on city managers and citizens. The ecofriendly usage can be calculated by means of KPIs such as daily water losses in liters per capita
and the bacteria level in harbor.
One of the most important functions of smart cities is connected with enhancing the
quality of citizens living as citizens are considered to be the major stakeholders of any urban
settlement. For the interest of citizens, the quality of public services is very important. That
includes the level of education with availability of schools, colleges, universities and the wide
range of educational programmes provided. Moreover, the level of health care services as one of
49
the public services reflects the level of city smartness and advancement so the access to basic
health care services is very important to evaluate. Also, the higher quality of citizens’ life
depends not only on physical facilities, but also on citizens’ psychological state. This case
reflects the importance of gender equality and higher safety of living conditions.
All the KPIs that can be used to evaluate the degree of ICT innovations implementation
in various city areas are divided into four KPI classes. These classes include d evelopment of ICT
talents (f. e., a number of local graduates from ICT programmes; intensity of university and ICT
industry collaboration in terms of a number of contracts and agreements), density of electronic
devices around the city area (f. e., a number of connected electronic sensors), city attractiveness
for top international researchers, professors and students from science research and technical
universities, and also a volume of investments in R&D projects (f. e., foreign investments in €;
investments from local government in €).
Supporting businesses and enterprises in the context of smart city involves the intense use
of different ICT instruments provided by the city government to enhance the easier collaboration
and communication between both business and city management sides. This domain assumes
availability of multi-sided platforms with participation of city management and cloud services.
Moreover, it is important to support not just large-scaled corporations but also small and medium
enterprises (SME) and startups in different industries through creating special governmental
portals that aim to support such SMEs with legal, regulatory and other issues.
Development of smart government systems assumes the city administration being open to
citizens in communication and sharing the information. For achieving this goal, the availability
of multi-sided government services is very important to ensure the constant contact with
businesses and individuals. These communication platforms can be presented through
government mobile websites and mobile applications. To conclude, the use of ICT innovations is
beneficial for the intense communication between city managers since it makes the public
procedures less time-consuming.
The questionnaire as the final step of research methodology proves the results of expert
interviews. Still, the additional KPIs relevant to smart city domains were suggested by
respondents (Table 13). These new KPIs provided tend to complete all six smart city domains
that were identified previously through the case study method.
50
Table 13
KPIs suggested from the questionnaire
Smart city domain
Green environment
Resources and energy
Quality of citizens living
ICT innovations
Business and entrepreneurs
Smart government
KPI
Share of certified companies
Local food production
Brownfield use
Grey and rain water use
Net migration
Unemployment rate
Youth unemployment rate
Creative industry
Innovation hubs in the city
Density of connected electronic sensors
distribution
New business registered
Cross-departmental integration
Measure
% of companies
% of tonnes
% of km2
% of houses
Number
% of citizens
% of citizens
% of people
Number
% of city area
covered
Number/ per year
Likert scale
To gain a deeper understanding of the smart city goals context the questions about
frequency of reconsidering KPIs used to assess the specific goals were also asked during the
survey (Figure 13). The questionnaire results demonstrate that dominant category within
reconsideration of KPIs is “once every three years” (39%). The second most populous group is
respondents who believe that smart city KPIs should be reconsidered once every two years
(35%). All in all, the findings of reconsidering KPIs match with the results of reconsidering
smart city goals and repeat the previous tendency as both actions present the parts of smart city
strategy.
2.00%
8.00%
More than once a year
16.00%
39.00%
Once a year
Once every two years
Once every three year
Other
35.00%
I find it difficult to answer
Figure 13 Frequency to reconsider KPIs that are used to assess goals
The question of recalculating KPIs is also one of the most important and arguable. The
assumptions made previously based on responses of Copenhagen, Hong Kong, Vienna and
Innopolis representatives suggest that in most cases the frequency of calculating and analyzing
KPIs depends on the smart city domain and even on concrete smart projects, solutions and their
peculiarities. On the other side, the more frequent KPI calculation alongside with continuous
51
evaluation provides a deeper understanding of the current status in smart city goals achievement.
According to the questionnaire results the dominant group of answers about frequency of
recalculating and analysis of KPIs that are used to assess smart city goals is “once a year” with
the 49% of votes. The following response categories are “more than once a year” and “other”
with 24% and 14% respectively (Figure 14). The comments of the “other” answers category
suggests that the frequency to recalculate KPIs values depends on the smart city domain and
specific city goal/ area that is going to be evaluated.
2.00%
14.00%
24.00%
More than once a year
Once a year
4.00%
Once every two years
7.00%
Once every three year
Other
I find it difficult to answer
49.00%
Figure 14 Frequency to recalculate KPIs that are used to assess goals
The detailed results gathered both from case studies and questionnaires are presented
through the list of concrete KPIs to be used for different smart city domains (Appendix 4). All in
all, each of the smart city domains is mapped with three or four KPI classes that include concrete
KPIs to be used to assess the specific domain actions. KPIs from each class are presented with
both qualitative and quantitative ratios that are all relevant to evaluate the concrete smart city
goal. KPI classes contain from two to nine concrete KPIs. The total number of KPIs that can be
used to assess six smart city domains is 96 with the highest number of KPIs for the “Resources
and energy” domain. Moreover, from the total number of KPIs included into the final
Performance Management Model the amount of qualitative indicators is 19, that demonstrates
the dominance of quantitative ratios (comparing with previously identified performance
management models for smart cities).
3.3.
Managerial implications and recommendations
The major practical value of a five-level Performance Management Model suggested for
smart cities is the mapping of specific performance goals of smart cities with both concrete
qualitative and quantitative KPIs relevant for assessing each performance goal. The final
Performance Management Model provided can be used in practice by both internal and external
stakeholders of smart city that have different aims and reasons to use KPIs values of exact smart
52
city. Figure 16 presents the stakeholder groups that can be interested in the results of
performance evaluation of the concrete smart city (Figure 15). The internal stakeholders include
smart city managers and administration that control intelligent city activities and are responsible
for realization and development of the smart city strategy and separate smart solutions. The
group of external stakeholders includes businesses and individuals that can be interested in
investments into specific smart city areas, solutions and projects and also city managers of other
smart cities that have the opportunity to benchmark their own practice with external ones.
To assess how smart city
goals city
are achieved
Smart
managers
To find out drawbacks in
strategy implementation
Figure 15
To make a decision to Business and
invest in smart projects and individuals
External
stakeholders
solutions
To benchmark external
practices in performance City managers
of other cities
management and
measurement
Managerial implications for different groups of smart city stakeholders
First of all, with the help of the suggested framework city managers are able to measure
how smart city goals have been achieved during the certain period of time and also assess the
progress of the work done. However, it is important to remember that performance management
assumes not just measuring specific ratios. The results of such analysis are used in order to find
the ways for further improvement of the efficiency and effectiveness of the existing programs
(Tantardini and Kroll, 2015).
It also means that continuous monitoring and detailed analysis of a set of concrete KPIs
values can become the basis to summarize and make some conclusions about some drawbacks in
strategy implementation not evident from the very surface analysis.
Secondly, as it was already mentioned above the results and findings of the current
research can be used not only by city managers but also by the external stakeholders. In general,
the group of external stakeholders that can be interested in performance of the specific smart city
includes companies, entrepreneurs, individuals and city managers of others smart cities that
already exist or just start implementing their smart strategy. For example, the results of
performance analysis of smart city can become the basis for the investment decision made by
businesses and individuals if they would like to support specific smart city projects or solutions.
53
From this perspective, the more successful the specific project is, the more it will be attractive
for investors to be included in their portfolio.
Moreover, external stakeholders such as representatives of other smart cities can be
interested in the results of current research. It is useful to benchmark different practices in
performance management and measurement as it gives the opportinity to use some valuable
ideas and KPIs that are relevant for another smart city.
The theoretical contribution of current research is presented with the range of theoretical
values added. First of all, the common components of smart city definitions are identified
through the comparison of several scientific sources that also provided the possibility to
construct the smart city definition to be used for current research. Besides, case studies on smart
cities of Copenhagen, Hong Kong, Vienna and Innopolis were created based on the expert
interviews with the representatives of smart cities and on documents and reports with the
relevant information about smart city strategies implementation of Copenhagen, Hong Kong,
Vienna and Innopolis available. Finally, based on the five-level Performance Management Model
for R&D departments the Performance Management Model for smart cities that maps six smart
city domains with concrete KPIs was provided.
The five-level Performance Management Model suggested as a final outcome of current
research gives the opportunity to provide the set of recommendations for smart city managers.
The recommendations are based on the structure of the final model itself and also on its semantic
content.
One of the key milestones in successful realization and the smart city strategy
implementation is setting a clear range of performance goals for different city areas that will
make the process of performance management much easier in the future. Moreover, the
performance goals should result from the smart city long-term goals and do not contradict with
the smart city strategy, mission and vision. That follows from the link between the second and
the third levels of a five-level model that present smart city goals and performance goals
respectively. The very first step of the performance management process is planning and goals
setting.
Moreover, it is better to divide concrete qualitative and quantitative KPIs into different
clusters for a more detailed further analysis and an easier control process. These clusters should
be relevant for concrete city goals in order to make the performance measurement process more
complete. In its turn, this recommendation is based on the link between the third and the forth
levels of final performance management and measurement model that are presented as
performance goals and KPIs classes respectively.
54
Furthermore, performance management should be considered by the smart city
administration as a continuous process. From one side, it can make the overall assessment
process more time and efforts consuming as the data to be collected requires time and staff to be
involved in this performance management process. From the other side, that leads to higher
benefits for the smart city administration as the earlier the problems are found out the less costly
it will be for city managers to eliminate them and reduce the negative effect of various
circumstances.
3.4.
Summary of chapter III
The final chapter of current master thesis presents the empirical results of the research
through creating a five-level Performance Management Model for smart cities based on the case
studies of Copenhagen, Vienna, Hong Kong and Innopolis and also the results of the
questionnaire. So the sample for the case study includes three smart cities from European and
Asian regions while the sample for the questionnaire includes more than 100 representatives
from both international and Russian IT companies and research universities.
There are six smart city domains and also KPI classes that are mapped with concrete
performance goals. The six domains include green environment, resources and energy, quality of
citizens living, ICT innovations, business and entrepreneurs, and smart government. Each smart
city domain is mapped with three or four KPI classes depending on the nature of the domain. The
single KPI class includes from two to nine concrete key performance indicators that are relevant
to the specific smart city goal.
Furthermore, the survey findings proved the importance of all the smart city domains
provided. Moreover, some additional concrete KPIs are suggested that complete all six smart city
domains provided from the case study results. It was also found from the questionnaire results
that the frequency to analyze KPIs depends on goal specifics and city areas.
Based on the final results of current research the range of recommendations for smart city
managers was provided. One of the most important points is connected with setting clear goals to
be achieved within the certain period of time. These goals can be divided between city
committees that makes the process of performance management much easier.
One more recommendation is based on the key advantage of the final model suggested
that propose to divide concrete qualitative and quantitative KPIs into separate classes according
to their domains and map these classes with relevant smart city goals. Moreover, performance
management should have the constant basis that helps to find out problems and ways to solve
them in time.
55
All in all, the final Performance Management Model provided can be used in practice by
both internal and external stakeholders of smart city. Based on the KPIs values city managers are
able to measure how smart city goals have been achieved. Furthermore, the analysis of KPIs
calculated helps to find out drawbacks in strategy implementation.
Moreover, the results of performance analysis of smart city can become the basis for the
investment decision made by businesses and individuals that are willing to support specific smart
city projects or solutions in form of sponsorship or to gain profit.
One more external stakeholder side that can use the research results are city managers of
other smart cities. They can be interested in benchmarking their own practice of smart city
strategy implementation with the external practice in performance management and
measurement while using some valuable ideas and KPIs that are relevant for their smart city and
suitable for their smart city peculiarities.
56
CONCLUSIONS
In both developing and developed countries cities are considered to be the key elements
of the social and economic development. According to the United Nations study, more than 60%
of the total world population will live in cities by 2050 (United Nations, 2014). The intense
population growth became the reason of urban problems such as traffic congestion, environment
pollution, overconsumption of natural resources, and others. For nowadays, the smart city
initiative is one of the most effective ways to solve various urban problems. However, as the
phenomenon of smart city is relatively new there is still a lack of instruments that can be used to
measure its performance.
Considering the lack of performance management systems to be used for smart cities the
goal of current master thesis was set as creating the model with KPIs that can be used to assess
the performance of smart cities. In order to achieve this goal, the range of research questions
were also set. First of all, it was necessary to find out if any common practices for performance
management of smart cities exist. Secondly, if a holistic performance management model can be
provided based on the practices? And, finally, can any recommendations be given based on the
final model suggested?
The current master thesis includes in-depth literature review of the smart city concept
comparing and analyzing different definitions of smart city and identifying common components
as there is still no one common definition for smart city. The practical experience of smart city
strategies implementation in Europe, Asia and Russia was analyzed. However, as the practice of
smart city strategy implementation is still not so widely spread all over the world there is a lack
of instruments to be used for performance evaluation of smart city.
The literature review of existing performance management approaches for smart cities
helped to find out the research gap as the current models include only qualitative indicators and
do not make a clear connection between smart city goals/domains and KPIs to assess the relevant
goals. Considering the drawbacks of existing performance management approaches for smart
cities the five-level Performance Management Model was suggested to be used for performance
evaluation of smart cities. The framework used to create the final Performance Management
Model for smart cities was the five-level Performance Management Model provided previously
for R&D departments of ICT companies (Samsonowa, 2012). This Model was chosen due to its
main advantage which is mapping performance goals and relevant KPI classes. The smart city
itself can be observed as the organizational unit as the performance management process for
various units can be quite identical.
57
The model was constructed based on the case studies of Copenhagen, Hong Kong,
Vienna and Innopolis, and also on the results of questionnaires with representatives of Russian
and international IT companies and research universities. The key advantage of the final model
suggested is mapping smart city domains with KPI classes that are relevant to assess specific
goals. There are both qualitative and quantitative KPIs presented for each smart city domain.
The very first level represents the country level as every government sets its own goals in
order to develop steadily, protect rights of citizens and be politically and economically stable.
The second level explains smart city goals and its overall strategy as smart cities represent the
part of the country and also the specific city has its own goals and objectives to be reached. The
Activity level contains six smart city domains that reflect smart city goals and functions
distributed usually among different city committees. These smart city domains include “Green
environment”, “Resources and energy”, “Quality of citizens living”, “ICT innovations”,
“Business and entrepreneurs”, and “Smart government”. The last two levels present the
performance measurement system and contain KPI classes with concrete KPIs inside each class.
Each domain is mapped with three or four KPI classes and includes both qualitative and
quantitative KPIs. All in all, 96 concrete key performance indicators are presented to be used for
performance evaluation of smart city.
In order to finalize the results of current research and also to prove that the preliminary
goal was achieved the explicit answers to research questions are provided below.
RQ 1 – What is the common practice in regard to performance management of smart cities? Are
there common smart city goals, and are there common key performance indicators (KPIs) to
assess performance?
The smart cities that are successful in implementation and realization of the smart city
strategy have practices regarding the performance management of smart city. In spite on the fact
that the strategy and city goals can be different and quite individual for the separate smart city
the domains to achieve the goals are similar for different smart city practices. Based on the case
studies of Copenhagen, Vienna, Hong Kong and Innopolis smart cities six key smart city
domains were identified that include green environment, resources and energy, quality of citizens
living, ICT innovations, business and entrepreneurs, and smart government.
Despite the fact that smart cities in practice can be similar in city domains and smart city
characteristics, performance measurement systems differ from each other. KPIs are divided into
classes based on their domains while concrete KPIs to be used for performance evaluation of
smart city are different.
58
RQ 2 – Is there a generic performance management approach to describe the common practice
of performance management of smart cities? How are goals and KPIs reflected in this
approach?
During the literature review on performance management of smart cities only two
examples of performance management approaches were found out. However, the key
disadvantages of existing approaches used for performance evaluation of smart cities are the lack
of quantitative KPIs and also the lack of clear connection between smart city goals and exact
KPIs to assess these goals.
RQ 3 – Can the holistic performance management model be provided based on the practices of
real smart cities?
The analysis of smart city practical experience provided understanding that different
smart city practices can be similar in their domains to make the city “smarter” in spite of their
economic, political and cultural peculiarities. These six domains defined are closely correlated to
the city areas. So based on the similarities of smart city strategy implementation the holistic
performance management model can be suggested to be used by various smart cities for
performance evaluation.
RQ 4 – Can there any recommendation be derived from the suggested approach to advise on the
implementation and further development of a performance management and measurement model
in smart city?
As the final five-level Performance Management Model is built considering the practical
experience of different smart city strategies implementation, smart cities experience in
performance management and the personal opinion and experience of representatives of IT
companies and research universities, the set of recommendations was provided. These
recommendations can be used by smart city managers to improve the existing performance
management system or develop the new one.
Moreover, the results of current research can be considered as a platform for future
research. First of all, in the literature review part some practical examples of implementations
and realization of the smart city strategy both worldwide and in Russia were provided. However,
the final Performance Management Model is suggested based on the data collected from expert
interviews in European and Asian smart cities and on the experience and personal opinions of
representatives of ICT companies and computer science universities that are involved in separate
smart solution projects of different domains.
Finally, it is also important to remember that the final five-level Performance
Management Model provided can be adapted by any smart city initiatives with fulfilling the set
of KPIs based on its smart city domains and also economic, political and cultural peculiarities.
59
APPENDICES
Appendix 1. Interview Guideline
Table 14
Questions for the expert interviews with smart city representatives
Question Block
General Info
Smart city goals
Do you have performance
management tools?
Are the KPIs of Committee derived
from smart city goals?
Questions
Smart city name
Job title of respondent
Department/ Committee
Area of activities
Structure of smart city administration
Can you name key smart city goals?
Do different Committees have their own goals?
How the goal achievement is measured?
Yes/ No/ Why?
Short description
Did you create the system by yourself or is it other
smart cities experience?
Problems/ Disadvantages?
Number of KPIs? What are they?
Do you make difference between short- and long-term
KPIs?
How often do you collect and analyze KPIs?
How often do you reconsider the KPIs?
How often do you synchronize KPIs with goals?
How do you derive KPI of Committee from the overall
smart city KPIs?
What is the reason for measuring the performance?
Appendix 2. Case studies
Appendix 2.1. Case study: City of Copenhagen
City of Copenhagen Profile
Copenhagen is the capital and the biggest city of Denmark in terms of population. As the
capital Copenhagen also plays the role of political, economical and cultural center of Denmark.
Since the 21st century, Copenhagen has undergone the strong urban development, facilitated by
investment in its infrastructure and institutions. Moreover, with Copenhagen Stock Exchange the
city is known as one of the major financial centers of Northern Europe. However, being one of
the most progressive and attractive cities in both Denmark and Europe leads to the intense
growth of urban population with approximately 1,000 new inhabitants every month. To prevent
the negative circumstances of overpopulation in Copenhagen the city administration has already
launched smart city solutions. Alongside with recent developments in the pharmaceutical
industry and the city service sector, the number of initiatives in clean technology was launched.
These initiatives aim to support the city’s goal to become carbon-neutral by 2025 (Københavns
Kommune, 2016).
The strategy of becoming green, healthier and smarter city is accompanied with both
smart initiatives and rewards for successfully implemented projects. According to Siemens
Green Index in 2009 Copenhagen was identified as the greenest major city in Europe among all
other 30 European cities (Siemens.com, 2016). In 2014 Copenhagen was named “European
Green Capital” by the European Commission (Ec.europa.eu, 2016), and “World’s Healthiest
City” by CNN (Stateofgreen.com, 2016).
Smart City Initiative Profile
The Copenhagen Smart city initiative that is under control of the city administration
attracts citizens to solve problems and develop new ideas or decisions to make their city
“smarter”. Moreover, the Copenhagen Solutions Lab (CSL) was founded in 2014 with the focus
to implement smart city projects and innovations in close collaboration with knowledge
institutions and companies as well as citizens (Cphsolutionslab.dk, 2016).
CSL represents the City of Copenhagen’s incubator for various smart city initiatives. To
make the process of creating and launching smart city ideas and projects more efficiently CSL
works in close collaboration with all the departments of the Copenhagen administration.
Working focus points within the Lab include reduction of carbon emissions, new various
information technology solutions, implementation of sensors that are able to provide citizens
61
with real time data and information on current events, situations and activities in the city
(Cphsolutionslab.dk, 2016).
Smart City Goals and Domains
The key perspective of Copenhagen is to make the City more effective and successful in
terms of energy consumption. By 2025 Copenhagen wants to become the world’s first carbon
neutral capital. In 2009 the City administration developed and adopted the Climate plan that
explains steps of how to make the green, smart and carbon neutral city. The CPH 2025 Climate
plan includes objectives, efforts and initiatives adopted by the City Council that must be
launched in order to achieve the goal of carbon neutrality.
According to the City strategy, Copenhagen aims to become the world’s first carbon
neutral capital by 2025, while the city’s universities and businesses will lead the development of
green solutions generating sustainable green growth and employment.
Main domains of Copenhagen considered for achieving the smart city goal of carbon
neutrality are mobility (smart and eco-friendly transport systems), water (effective and smart
water consumption), energy and resources (effective use of existing resources while reducing
wastes), and strategy (urban planning and infrastructure) (Table 15).
Table 15
Smart city of Copenhagen domains
Domains
Mobility
Water
Energy and Resources
Strategy
Description
Smart and eco-friendly transport systems
Effective and smart water consumption
Effective use of existing resources while reducing wastes
Urban planning and infrastructure
Smart City KPIs
All the key performance indicators (KPIs) used to assess performance are divided into 10
KPI classes with the number of concrete KPIs inside each class ranging from three to six (Table
16).
Table 16
The list of KPIs used by smart city of Copenhagen
KPI class
Building
Cooling
Concrete KPI
Attractiveness of urban areas for citizens
Health and quality of life
Pay back period of investments in energy savings
CO2 emissions with the use of district cooling
Electricity consumption with the use of district
cooling per single citizen
Sulphur dioxide emissions with the use of district
cooling
Measure
Likert scale
Likert scale
Number of years
Tonnes/ per year
Watt/ per year
Tonnes/ per year
62
Cycling
Harbour
Nitrogen oxide emissions with the use of district
cooling
Expenditure on energy imports
Quantity of cycling Copenhageners and commuters
Net social gain for every km travelled by bike
instead of car
Health care costs saved for single citizen
Distance travelled in bicycle
Bicycle parking facilities
Bacteria level
Diversity of flora and fauna
Heating
Proximity of citizens to blue recreational space
Heating provided from the district heating grid
Energy generated from waste
Energy generated from biomass
Integrated
public transport
Urban planning
Waste recycling
Water usage
Wind power
Heating costs per single consumer
Quantity of car trips
CO2 emissions
Quality of life from convenient public transport
Time saved due to less traffic congestion
Convenience of transfer between each mode
Access to regional green areas by metro improves
the quality of life
Space for sports and social activities
Attractiveness for residents, businesses and tourists
Waste volume recycled
Waste volume incinerated
Household recycling level
Greenhouse gas emissions from incineration
Daily water consumption
Daily water losses
Production costs of local businesses through greater
water efficiency
Demand for bottled water
Quantity of wind turbines
Electricity production from wind power
Sulphur dioxide emissions
Nitrogen oxide emissions
CO2 emissions
Tonnes/ per year
€/ per year
Number
€/ per km cycled
€/ per km cycled
km/ per workday
Number
Biochemical oxygen
demand (BOD)
Number of different
species
Likert scale
% of total heating
% of t ot al energy
generated
% of t ot al energy
generated
€/ per year
Number
Tonnes/ per year
Likert scale
Hours/ per day
Likert scale
Likert scale
km2
Likert scale
Tonnes/ per year
Tonnes/ per year
% of total household
waste
m3
Liters/ per capita
Liters/ per capita
€/ per year
Liters/ per capita
Number
% of total electricity
production
Tonnes/ per year
Tonnes/ per year
Tonnes/ per year
63
Mapping Goals and KPIs
Table 17
Map of smart city of Copenhagen domains and KPIs
KPI classes
Building
Cooling
Smart city domains
Mobility
Water
Energy and resources
Strategy
1. Attractiveness of urban
areas for citizens
2. Health and quality of life
3. P a y b a c k p e r i o d o f
investments in energy
savings
1. CO2 emissions with the
use of district cooling
2. Electricity consumption
with the use of district
cooling per single citizen
3. Sulphur dioxide emissions
with the use of district
cooling
4. Nitrogen oxide emissions
with the use of district
cooling
5. Expenditure on energy
imports
64
Cycling
Harbour
Heating
1. Q u a n t i t y o f c y c l i n g
Copenhageners and
commuters
2. Health expenses saved by
cycling
3. Net social gain for every
km travelled by bike
instead of car
4. Health care costs saved for
single citizen
5. Dis tance travelled in
bicycle
6. Bicycle parking facilities
1. Bacteria level
2. Diversity of flora and
fauna
3. Proximity of citizens to
blue recreational space
1. Heating provided from the
district heating grid
2. Energy generated from
waste
3. Energy generated from
biomass
4. Heating costs per single
consumer
65
Integrated public
transport
1. Quantity of car trips
2. CO2 emissions
3. Q u a l i t y o f l i f e f r o m
convenient public transport
4. Time saved due to less
traffic congestion
5. Convenience of transfer
between each mode
Urban planning
1. Access to regional green
areas by metro improves
the quality of life
2. Space for sports and social
activities
3. Attractiveness for
residents, businesses and
tourists
Waste recycling
Water usage
1.
2.
3.
4.
Waste volume recycled
Waste volume incinerated
Household recycling level
Greenhouse gas emissions
from incineration
1. Daily water consumption
2. Daily water losses
3. Energy consumption from
water services
4. Production costs of local
businesses through greater
water efficiency
5. Demand for bottled water
66
Wind power
1. Quantity of wind turbines
2. Electricity production from
wind power
3. Sulphur dioxide emissions
4. Nitrogen oxide emissions
5. CO2 emissions
67
Conclusions
Copenhagen as any political, economical and social center in Europe faces a lot of
problems related to increased population, resources consumption and workload on the existing
infrastructure. Copenhagen was one of the pioneers in Europe that realized the necessity of smart
city development to optimize city processes and increase the welfare of citizens. During the last
years there have been several complex smart city initiatives being introduced in Copenhagen
what makes it very urgent to find the ways of how to manage and measure smart city
development.
According to the City Strategy, the Copenhagen’s aim with the highest priority is to
become the first carbon capital in the world by 2025. The interrelated goals that follow from this
vision break down to four directions:
1. Introduction of smart transport systems (mobility);
2. Effective water consumption (water);
3. Effective use of resources with minimized wastes (energy and resources);
4. Urban planning and infrastructure (strategy).
The case study of Copenhagen allowed mapping these goals with specific city areas and
KPIs that enable smart city stakeholders to manage and measure the performance of Copenhagen
as a smart city.
The case study also showed that Copenhagen uses specific KPIs to measure the
achievement of each goal of smart city development. The scope of these KPIs is quite large what
illustrates that the city regards the effective management and performance of smart city
development as a complex task. These KPIs include both qualitative and quantitative indicators
to make the process of performance management and measurement more effective and unbiased.
Quantitative KPIs include such indicators as number of new facilities and places, the amount of
consumed resources and the amount of expenses. Qualitative KPIs relate mainly to the level of
the citizens satisfaction from the use of smart city services and facilities and the level of
attractiveness of specific smart city initiatives.
Despite the fact that Copenhagen succeeded in developing the set of KPIs to manage and
measure the smart city performance, not every goal of smart city development is mapped with
target smart city areas and appropriate KPIs. This can result in the problem of poor interrelation
of goals that were derived from the primary aim of Copenhagen as a smart city. Finally, for each
goal city used the unbalanced number of KPIs. The main gap is identified with the goal of the
strategic level – effective urban planning and infrastructure.
Appendix 2.2. Case study: Hong Kong
City of Hong Kong Profile
Hong Kong is a special administrative region of the Republic of China situated on the
southern coast of the country. It is one of the leading financial centers not only in Asia but all
over the world. There are more than 7 million citizens of various nationalities living in Hong
Kong at present that makes this city one of the world’s most densely populated metropolises.
Limited land space resulted in the dense infrastructure and made the territory of Hong Kong a
centre of modern architecture. Hong Kong also has a highly developed public
transportation network that relies on mass transit by road or rail services.
However, being in the list of the top five of countries and territories by the population
density Hong Kong encounters the range of urban problems (Data.worldbank.org, 2016).
In 1998 the Digital 21 Strategy was launched as one of the first steps of Hong Kong in
the overall development of Information and Communication Technologies (ICT). Later the
International Institute for Management Development twice considered Hong Kong as the first in
technological infrastructure development in 2012 and 2013 (Gov.hk, 2016).
Smart City Initiative Profile
Invotech is a non-profit initiative launched in 2013 and dedicated to promoting and
supporting positive changes in Hong Kong through innovation and technology. One of the
Invotech goals is to engage individuals, entrepreneurs, corporations, associations, academies and
the government to leverage their collective efforts in order to improve Hong Kong. Invotech
seeks to create and sustain new ecosystems that help Hong Kong evolve and become more
innovative.
The Smart Hong Kong initiative of Invotech is designed to bring together likeminded
people to achieve a sustainably prosperous future. Finding the balance in the ecosystem means
reaching the consensus, a common understanding of what "mutually beneficial" really means. A
free sharing of ideas, knowledge and dreams among all the stakeholders in the society is the only
way forward (Invotech, 2016).
Smart City Goals and Domains
The central aspects of the Smart Hong Kong initiative include environment, public
services, citizens’ empowerment, business initiation and ICT industry support (Table 18).
69
Table 18
Smart city of Hong Kong domains
Domains
Environment
Public services
Empowering citizens
Igniting business
ICT industry support
Description
Sustainable development without degrading ecology
Developing of e-government services that offer convenient and
efficient option for public while they are dealing with the
Government
Enhancing basic capabilities of citizens to operate in an ICT-rich
environment
Boosting operational efficiency and business opportunities
Growing local ICT industry as a sheer enabler supporting other
industries while also facilitating ICT sector to consolidate as a
major economic contributor
Smart City KPIs
All the key performance indicators (KPIs) used to assess performance by the smart city of
Hong Kong are divided into 11 KPI classes with the number of concrete KPIs inside each class
ranging from one to five (Table 19).
Table 19
The list of KPIs used by smart city of Hong Kong
KPI class
Concrete KPI
City-wide Wi-Fi Quantity of Wi-Fi hotspots
Quantity of locations with Wi-Fi hotspots
Internet connection speed
Wi-Fi penetration rate
Digital identity Quantity of active digital certificate users
(individual)
User-friendly digital certificates
e-Learning
Availability of online learning resources
Quantity of Wi-Fi hotspots at schools
Availability of ICT education in school curriculum
Establishing
innovative
platforms
ICT Talent
development
Infrastructure
Quantity of patents granted to the six local territory
institutions engaged in technological research and
development
Quantity of economically active spin-off companies
Income generated from intellectual property rights
Quantity of local graduates from ICT programmes
Intensity of university-ICT industry collaboration
Rewards for ICT innovations
Quantity of connected sensors in warehouse
management
Quantity of connected sensors in luggage handling
Quantity of connected sensors in trade and logistics
Measure
Number
Number
MB/ sec
m2
Number
Likert scale
Likert scale
Number
% of schools providing
ICT education
Number
Number
€/ per year
Number/ per year
Number of partner
agreements/ per year
Number/ per year
Number
Number
Number
70
Multi-platform
Government
services
Paperless
solutions
SME Cloud
services
Startup
ecosystem
Waste recycling
Quantity of connected sensors in livestock control
Quantity of government mobile websites
Quantity of government mobile apps
Number
Number
Number
Costs saved due to the less paper usage
Storage space saved
Information sharing intensity
Quantity of cloud services for SMEs
Intensity of mobile apps use
Availability of online portal to serve startups
€/ per year
m2
Likert scale
Number
Likert scale
Likert scale
Waste generation per capita
Quantity of waste separation bins in public spaces
Quantity of waste separation bins in commercial
and industrial buildings
Percentage of population using recycling bins
Disposal of household garbage per capita
kg/ per day
Number
Number
% of total population
kg/ per day
71
Mapping goals and KPIs
Table 20
Map of smart city of Hong Kong domains and KPIs
KPI classes
City-wide Wi-Fi
Digital identity
e-Learning
Smart city domains
Environment
Public services
Empowering citizens
Igniting business
1. Quantity of Wi-Fi
hotspots
2. Quantity of
locations with Wi-Fi
hotspots
3. Internet connection
speed
4. Wi-Fi penetration
rate
1. Quantity of active
digital certificate
users (individual)
2. User-friendly digital
certificates
1. Availability of
online learning
resources
2. Quantity of Wi-Fi
hotspots at schools
3. Availability of ICT
education in school
curriculum
ICT industry support
Establishing
innovative
platforms
ICT Talent
development
1. Quantity of patents
granted to the six
local territory
institutions engaged
in technological
research and
development
2. Quantity of
economically active
spin-off companies
3. Income generated
from intellectual
property rights
1. Quantity of local
graduates from ICT
programmes
2. Intensity of
university-ICT
industry
collaboration
3. Rewards for ICT
innovations
73
Infrastructure
Multi-platform
Government
services
Paperless
solutions
SME Cloud
services
Startup
ecosystem
1. Quantity of
connected sensors
in warehouse
management
2. Quantity of
connected sensors
in luggage handling
3. Quantity of
connected sensors
in trade and
logistics
4. Quantity of
connected sensors
in livestock control
1. Quantity of
government mobile
websites
2. Quantity of
government mobile
apps
1. Costs saved due to
the less paper usage
2. Storage space saved
3. Information sharing
intensity
1. Quantity of cloud
services for SMEs
2. Intensity of mobile
apps use
1. Availability of
online portal to
serve startups
74
Waste recycling
1. Waste generation
per capita
2. Quantity of waste
separation bins in
public spaces
3. Quantity of waste
separation bins in
commercial and
industrial buildings
4. Percentage of
population using
recycling bins
5. Disposal of
household garbage
per capita
75
Conclusions
Hong Kong as one of the most rapidly developing cities in both China and all over the
world and as one of the top five territories by population density tends to face the range of urban
problems (Data.worldbank.org, 2016). Hong Kong actively promotes ICT development that aims
to support various city areas thus enhancing the quality of citizens living and eliminating
negative circumstances of urban problems. Latest technologies such as Cloud computing, Big
data analytics, Internet of Things, Wireless and multi-platforms are going to be adopted in
different sectors.
Moreover, Invotech as the Smart City initiative aims to create and implement smart
projects and innovative ideas that enhance public services, support businesses, and improve the
eco-environment and the quality of citizens’ life in Hong Kong. The list of Hong Kong smart city
domains and objectives includes the following ones:
1. Sustainable development without degrading the ecology (environment);
2. Developing of e-government services that offer convenient and efficient options for
public while dealing with the government (public services);
3. Enhancing basic capabilities of citizens to operate in an ICT-enriched environment
(empowering citizens);
4. Boosting operational efficiency and business opportunities (igniting business);
5. Growing the local ICT industry as an enabler for supporting other industries and also
facilitating the ICT sector to consolidate as a major economic contributor (ICT industry
support).
In the context of the Digital 21 Strategy “Smarter Hong Kong, Smarter Living” theme
specific methods and ratios to measure the performance of smart initiatives are provided. They
are both qualitative (i.e., availability of online portals to serve startups, availability of online
learning resources) and quantitative ones (i.e., waste generation per capita, quantity of
government mobile websites, quantity of local graduates from ICT programmes) relevant to
specific smart city domains of Hong Kong.
However, the number of KPIs that are used to assess the smart city performance is
unbalanced for different smart city domains. Moreover, the biggest part of KPIs tends to evaluate
how ICT serves various Hong Kong areas that can provide the limited picture about the
performance of the smart city.
Appendix 2.3. Case study: City of Vienna
City of Vienna Profile
Vienna is the federal capital of Austria and also one of nine federal provinces of Austria
located in the eastern part of the country. Population of Vienna amounts to 1.8 million people;
together with suburbs reaches the level of 2.3 million (AUSTRIA, 2016). Thus, Vienna is the
largest city by population in Austria, occupying the eleventh place among the largest cities in the
European Union. Moreover, the City of Vienna also takes roles of the cultural, economic and
political center of Austria.
Vienna is one of the most successful cities all over the world where the infrastructure,
innovations and the quality of life are highly concerned (Mercer.com, 2016). That is why the
government of Vienna took a decision to establish the new initiative named Smart City Wien
with the goal to hold the current position and further develop the concept of “smart city”. By
2014 the smart city of Vienna has already succeeded in becoming one of the six most successful
European cities with smart city initiatives (European Parliament, 2014).
Smart City Initiative Profile
Smart City Wien is the initiative that was launched in 2011 under the control of Mayor
Michael Häupl in the city of Vienna with long-term goals to improve the design and
development of the federal capital. This initiative aims to cover all the areas of the city while
making the life quality of its citizens much better. Various activities in the context of this smart
city initiative relate to infrastructure, energy, mobility and other key areas of urban development
(Smartcity.wien.gv.at, 2016).
Smart City Wien stands for “intelligent city” that assumes the deployment of innovative
and intelligent solutions for citizens and also the sustainable, effective and responsible use of
resources. As the Initiative touches all areas of city life it means that all municipal departments in
collaboration with many science and business experts have contributed to the development of
basic principles and objectives of Smart City Wien (Smart City Wien, 2014).
Smart City Goals and Domains
The key long-term objective of the Smart City Wien Initiative is to significantly reduce
the resource consumption in Vienna while continuing to offer the high quality of living, safety
and security for all citizens by 2050. The central aspects of the Smart City Wien Framework
Strategy include resources, innovation and quality of living (Table 21).
77
Table 21
Smart city of Vienna domains
Domain
Resources
Innovation
Description
Radical resource preservation
Development and productive use of innovations/ new
technologies
High and socially balanced quality of living
Quality of living
Smart City KPIs
Nowadays, there are no fixed KPIs that aim to measure Smart City Wien Framework
strategy. While the City of Vienna is a part of such research projects as CityKeys (European
research project) and Smart.Monitor (national research project) that deal with Smart City KPIs.
All the KPIs provided below are suggested based on the analysis of Smart City Wien Framework
Strategy targets and are going to be adapted in the future (Table 22). All the key performance
indicators (KPIs) are divided into 10 KPI classes with the number of concrete KPIs inside each
class ranging from two to five.
Table 22
The list of KPIs used by smart city of Vienna
KPI class
Buildings
Economy
Education
Concrete KPI
Energy consumption of existing buildings for space
heating/cooling/water heating
Cost optimization
Measure
Watt/ per service
Purchasing power
GDP/ per capita
Quantity of hearquarters of international companies
Quantity of new enterprises
Direct investment flows
Technology-intensive products sales
Quantity of whole-day and integrated schools
Quantity of high-quality childcare offerings
Final energy consumption
Number
persons/ year
€/ year
% in the expert volume
Number
Number
% of citizens with
higher education
Watt/ per capita
Primary energy input
Watt/ per capita
Educational level
Energy
Affordability of healthcare services
% of gross energy
consumption
% of total city square
tonnes of CO2
equivalents
Likert scale
Quantity and quality of their leisure time
Likert scale
Renewable sources of energy
Environment
Share of green spaces
Savings achieved by municipal waste management
Healthcare
€/ per new structure
78
Infrastructure
Openness of government
Quantity of pilot projects with ICT enterprises are
to serve as showcases for the city and its economy
Public space Wi-Fi coverage
Mobility
CO2-free modes (walking and cycling)
Motorised individual traffic (MIT)
CO2-free commercial traffic originating and
terminating
Research,
Attractive research and innovation hub
Technology and Quantity of research units of international
Innovation
corporations
Quantity of international top researchers
Quantity of international students
Social inclusion Safety of life conditions
Attractiveness of housing environment
Absence of gender superiority
Likert scale
Number
% of m2
% of total vehicles
% of total vehicles
% of total commercial
traffic
Rating in Europe
Number
Number
Number
Likert scale
Likert scale
Likert scale
79
Mapping Goals and KPIs
Table 23
Map of smart city of Vienna domains and KPIs
KPI classes
Buildings
Smart city domains
Resources
1. Energy consumption of existing
buildings for space heating/ cooling/
water heating
2. Cost optimization
Economy
Environment
Healthcare
Quality of living
1. Purchasing power
2. Quantity of hearquarters of
international companies
3. Quantity of new enterprises
4. Direct investment flows
5. Technology-intensive products sales
1. Quantity of whole-day and integrated
schools
2. Quantity of high-quality childcare
offerings
3. Educational level
Education
Energy
Innovations
1. Final energy consumption
2. Primary energy input
3. Renewable sources of energy
1. Share of green spaces
2. Savings achieved by municipal waste
management
1. Affordability of healthcare services
2. Quantity and quality of their leisure
time
Infrastructure
Mobility
Research,
Technology and
Innovation
Social inclusion
1. Openness of government
2. Quantity of pilot projects with ICT
enterprises are to serve as showcases
for the city and its economy
3. Public space Wi-Fi coverage
1. CO2-free modes (walking and
cycling)
2. Motorised individual traffic (MIT)
3. CO2-free commercial traffic
originating and terminating
4. Energy consumption by passenger
traffic
1. Attractive research and innovation
hub
2. Quantity of research units of
international corporations
3. Quantity of international top
researchers
4. Quantity of international students
1. Safety of life conditions
2. Attractiveness of housing
environment
3. Absence of gender superiority
81
Conclusions
Being one of the most developed cities in Europe and the political and economical center
of Austria, the City of Vienna faces the problem of overpopulation that leads to overconsumption
of natural resources and environment pollution. The Smart City Wien initiative was launched in
2011 as a project that brings together all relevant fields of knowledge and involves all relevant
stakeholders with the objective of solving current urban problems and continuously building on
city’s strengths. Nowadays, the City of Vienna is included in the list of the six most successful
European cities with smart city initiatives based on the city activity in the context of country’s
priorities (European Parliament, 2014).
Furthermore, Vienna is one of the participants of CITYkeys projects founded in February,
2015 by the European Union HORIZON 2020 programme with the goal to develop KPIs and
data collection procedures for monitoring of smart city solutions across European cities
(Citykeys-project.eu, 2016).
Following the long-term goal of providing the high quality of living, safety and security
for its citizens through reducing resource consumption by 2050, the smart city of Vienna
identifies and follows three key domains that include:
1. Radical resource preservation (resources);
2. Development through the effective and efficient use of innovations and new
technologies (innovations);
3. Maintenance of the high and socially balanced quality of citizens living (quality of
living).
In order to continuously track how the smart city plan performs and measure the level of
goals achievement, the Smart City Wien Framework Strategy provides specific ways and means
to reach the results and objectives set. Moreover, the range of specific KPIs is also suggested to
assess how the smart city goals have been achieved.
The KPIs are divided into separate classes relevant to each single city domain. Every KPI
class contains both qualitative and quantitative indicators that give the possibility to get the
clearer understanding of the current picture of the smart city of Vienna. Qualitative KPIs mainly
include ratios such as affordability of various public services for the citizens, their personal
comfort and feeling of safety while staying and living in the City of Vienna (i.e., affordability of
healthcare services, safety of life conditions, absence of gender superiority, etc.). Quantitative
KPIs can be measured in certain numbers and contain such indicators as energy consumption,
share of green spaces, public space Wi-Fi coverage and others.
Furthermore, in-depth analysis of current practice of smart city of Vienna in performance
management provides possibility to suggest some recommendations for further improvement.
83
Firstly, the list of domains can still be completed with green environment and smart governance
alongside with concrete KPIs to measure these objectives. Moreover, the KPI class such as
“social inclusion” can be enhanced also with quantitative KPIs, since only qualitative ones make
the assessment more subjective and incomplete.
84
Appendix 2.4. Case study: City of Innopolis
City of Innopolis Profile
Innopolis is a new built city founded in 2015 which is located on the territory of the
Republic of Tatarstan. While the story of Innopolis starts from 2012 when the Prime Minister of
the Russian Federation together with the President of the Republic of Tatarstan signed agreement
for the construction of new intelligent city. The very first construction works already started in
the end of year 2012 according with the master plan of architects from Singapore. Two years
later the Innopolis urban settlement acquired the status of the city of Innopolis.
Nowadays, the city economy is based on the high-tech industries being. A unique safe
environment alongside with modern residential infrastructure, broad opportunities for education
and professional development have been created in Innopolis. The city provides multifamily
houses and townhouses for its population, school of the Innopolis University, the kindergarten
designed for 225 children, special economic zone for the companies-residents while the city
infrastructure still will be expanded (Innopolis.ru, 2016).
Smart City Initiative Profile
The management of the city is under control of the Mayor’s office. The Mayor of
Innopolis was elected by the residents in December 2014. The responsibilities of the Mayor’s
Office of Innopolis are focused on creating a unique ecosystem with modern residential
infrastructure, a safe environment both for working and living, and huge opportunities for
education and professional development of Innopolis residents (Innopolis.ru, 2016).
Smart City Goals and Domains
The key goal of Innopolis is connected with creating one of the best platforms for the
comfort work and living of IT-specialists while providing the sustainable development of the
city. The smart city domains can be divided into five categories presented below (Table 24).
Table 24
Smart city of Innopolis domains
Domain
Quality of citizens living
ICT development
Environment
Infrastructure
Business support
Description
Providing wide opportunities for education and professional
development
Creating the platform for the comfort work and living of
specialists in IT sphere
Safe environment for living and working
Modern, high-quality residential infrastructure
Active attraction of business while providing them with
advantages of being a resident company of Innopolis
85
Smart City KPIs
There are four KPI classes that is distinguished from the interview results with the
Innopolis representative and also from the analysis of information available for free access. Each
KPI class include from one to two concrete KPIs (Table 25).
Table 25
The list of KPIs used by smart city of Innopolis
KPI class
Education
Healthcare
Social comfort
Business
ecosystem
Concrete KPI
Quantity of students
Availability of healthcare services
Net migration
Satisfaction of citizens
Quantity of companies employees
Quantity of companies-residents
Measure
Number
Likert scale
Number
Likert scale
Number
Number
86
Mapping Goals and KPIs
Table 26
Map of smart city of Innopolis domains and KPIs
KPI classes
Education
Healthcare
Social comfort
Business
ecosystem
Smart city domains
Quality of citizens
living
1. Quantity of students
1. Availability of
healthcare services
1. Net migration
2. Satisfaction of
citizens
ICT development
Environment
Infrastructure
Business support
1. Q u a n t i t y
of
companies
employees
2. Quantity of
companies-residents
87
Conclusions
The city of Innopolis is planned as the intelligent city of new generation while being one
of the first projects of smart city construction in Russian Federation. In general, Innopolis is
considered to be a high-tech city and also a platform for the work and constant development of
IT-specialists. All in all, the list of the Innopolis goals includes five key directions:
1. Providing wide opportunities for education and professional development (quality of
citizens living);
2. Creating the platform for the comfort work and living of specialists in IT sphere (ICT
development);
3. Maintenance of safe environment for living and working (environment);
4. Providing modern, high-quality residential infrastructure for citizens (infrastructure);
5. Active attraction of business while providing them with advantages of being a resident
company of Innopolis (business support).
The performance management of Innopolis is concentrated around the satisfaction of the
city population while “happiness of the citizens” is mentioned as a key performance indicator
being considered by the city managers. The instruments used by city administration to gather
data about daily life of citizens, their problems, complaint or suggestions are Concierge Service
24/7 and Zendesk. Concierge Service 24/7 is a service created by Innopolis itself and aims to be
the communication tool between city management and its population. Zendesk is adapted system
that offers for its clients a wide range of customer service tools (Zendesk, 2016).
However, considering the fact that Innopolis as a smart city project was officially opened
just in 2015, there is still a lack of high-qualified performance management model to be used for
the goals evaluation. Not all the domains of Innopolis smart city is mapped with concrete KPIs
that are used to evaluate city performance. Moreover, the list of KPIs existed is narrow and do
not demonstrate the detailed picture of Innopolis functioning.
All in all, from the case of Innopolis and review of smart city context in Russia it is
possible to summarize that the experience of intelligent cities in our country is not as rich as
European and Asian smart cities have. The lack of KPIs to be used for performance evaluation of
Innopolis is connected with the fact of recent foundation of the city. The positive tendencies
present and are expressed in continuous communication with citizens while also gathering
information about the work of various urban services.
Appendix 3. Online Questionnaire
“Performance Management & Measurement Model for Smart Cities”
Thank you for your participation in our survey.
Survey Profile and Objectives:
The current survey is a part of data collection for Master thesis that aims to create a
model (holistic approach) for assessing performance of smart cities with both qualitative and
quantitative ratios. The online questionnaire includes the following sections:
Section 1 collects information about goals that are set by smart cities and the importance
and relevance of these goals for smart city development.
Section 2 collects information about specific dimensions of KPIs that can be used to
assess performance of smart cities and the level of goals achievement.
To complete the questionnaire you will need approximately 20 minutes. We also kindly
ask you to complete the survey within 40 days.
Thank you for your valuable input.
Respondent Profile:
Please fill the form below with your contact information. It gives us possibility to share
with you the final results of our research.
Name:
Company:
Country:
Email:
Survey Approach:
All the responses from current survey will be analyzed and presented in a matrix form
where exact KPIs map the smart city goals alongside with the importance of these single KPIs
and goals for high smart city performance.
KPI 1
KPI 2
KPI 3
KPI N
Goal 1
Goal 2
Goal 3
Goal N
(importance degree)
(importance degree)
(importance degree)
(importance degree)
+++
+
++
+
+
+++
++
89
Section 1. Goals of smart cities
In this section you will be provided with goals that are most commonly used in smart
cities strategies and plans for the further development. Please indicate the importance (priority)
of these goals based on your personal opinion and experience. If the goal should not be assessed
by smart city please match the first column. It is also possible to add the goal that you can feel is
missed in the current list.
Goal
Should not be S h o u l d
assessed
assessed –
low priority
be Should be Should
a s s e s s e d – assessed –
medium priority
be
high priority
Using
of
resources and
energy
in
effective way
Maintenance of
green
environment
Enhancing the
quality of
citizens living
Implementation
of
ICT
innovations in
various city areas
Supporting the
business and
enterprise
Development of
smart
government
systems
Other goal:
Other goal:
How often smart city should reconsider its goals? Please select the most appropriate answer
from your point of view.
More than once a year
Once a year
Once every two years
Once every three year
Other: ________
I find it difficult to answer
90
Section 2. KPIs to assess the performance of smart city
In this section for those goals that were matched by you as important ones for smart cities
we will provide set of possible KPIs. Please select more relevant ones to assess each goal
achievement based on your own opinion and experience. If some KPIs are not relevant please
select the first column.
Please select the KPIs from the table below that are relevant to the goal “Using of
resources and energy in effective way” and identify their importance. It is also possible to add
KPI that you can feel is missed in the current list.
Goal: “Using of resources and energy in effective way”
K P I s a s s e s s i n g Not relevant
Relevant –
R e l e v a n t – Relevant –
the…
less important
important
very important
… effectiveness of
energy usage (f. e.,
energy consumption
in watt per capita; %
of renewable sources
of energy in gross
energy consumption)
… layout and
convenience of
buildings (f. e.,
attractiveness of
urban areas for
c i t i z e n s ; energy
consumption of
existing buildings for
space
heating/cooling/wate
r heating in watt per
service)
… level of city
infrastructure
development (f. e., %
of public space Wi-Fi
coverage; space for
sports and social
activities in m2)
… layout of transport
systems (f. e., time
saved due to less
traffic congestion for
every single citizen
per day; convenience
of transfer between
each mode)
Other KPI:
Other KPI:
91
Please select the KPIs from the table below that are relevant to the goal “Maintenance of
green environment” and identify their importance. It is also possible to add KPI that you can
feel is missed in the current list.
KPIs assessing the…
Goal: “Maintenance of green environment”
Not relevant
Relevant –
R e l e v a n t – Relevant –
less important
important
very important
… intensity of waste
recycling (f. e., waste
generation per capita
in kg; greenhouse gas
emissions from
incineration in m3)
… volume of
electrical (ecofriendly) vehicles (f.
e . , % o f CO2-free
modes (walking and
cycling) from total
vehicles; % of
electrical cars from
total vehicles)
… water eco-friendly
usage (f. e., daily
water losses in liters
per capita; bacteria
level in harbour)
Other KPI:
Other KPI:
Please select the KPIs from the table below that are relevant to the goal “Enhancing the
quality of citizens living” and identify their importance. It is also possible to add KPI that you
can feel is missed in the current list.
KPIs assessing the…
Goal: “Enhancing the quality of citizens living”
Not relevant
Relevant –
R e l e v a n t – Relevant –
less important
important
very important
… degree of social
comfort (f. e., absence
of gender superiority;
safety of life
conditions)
… level of citizens’
digital identity (f. e.,
number of active
digital individual
certificate users)
… level of education
92
(f. e., % of citizens
with
higher
education; number of
whole-day and
integrated schools)
… level of health care
services (% of
population with
access to basic health
care services)
Other KPI:
Other KPI:
Please select the KPIs from the table below that are relevant to the goal “Implementation
of ICT innovations in various city areas” and identify their importance. It is also possible to add
KPI that you can feel is missed in the current list.
Goal: “Implementation of ICT innovations in various city areas”
KPIs assessing the… Not relevant
Relevant –
R e l e v a n t – Relevant –
less important
important
very important
… development of
ICT talents (f. e.,
number of local
graduates from ICT
programmes; intensity
of univers ity-ICT
industry collaboration
in number of
contracts and
agreements)
… density of
electronic devices
around the city area
(f. e., number of
connected electronic
sensors)
… attractiveness of
city for foreign
researchers (number
of research units of
international
corporations; number
of international top
researchers and
students)
… volume of
investments in R&D
projects (f. e., foreign
investments in €;
investments from
93
local government in
€)
Other KPI:
Other KPI:
Please select the KPIs from the table below that are relevant to the goal “Supporting the
business and enterprise” and identify their importance. It is also possible to add KPI that you
can feel is missed in the current list.
KPIs assessing the…
Goal: “Supporting the business and enterprise”
Not relevant
Relevant –
R e l e v a n t – Relevant –
less important
important
very important
… intensity of
innovative multisided platforms usage
(f. e., number of
platforms; number of
business participants
in platforms)
… intensity of cloud
services usage (f. e.,
number of cloud
services for SMEs;
intensity of mobile
apps use)
… startup ecosystem
(f. e., availability of
online portal to serve
startups)
Other KPI:
Other KPI:
Please select the KPIs from the table below that are relevant to the goal “Development of
smart government systems” and identify their importance. It is also possible to add KPI that you
can feel is missed in the current list.
Goal: “Development of smart government systems”
KPIs assessing the… Not relevant
Relevant –
R e l e v a n t – Relevant –
less important
important
very important
… degree of
government openness
(f. e., availability of
free contacts with city
administration)
… availability of
multi-platform
government services
(f. e., number of
government mobile
94
websites; number of
government mobile
apps)
… number of
connected sensors in
public services (f. e.,
number of connected
sensors in luggage
handling; number of
connected sensors in
trade and logistics)
Other KPI:
Other KPI:
How often smart city should reconsider KPIs that are used to assess goals? Please select the
most appropriate answer from your point of view.
More than once a year
Once a year
Once every two years
Once every three year
Other: ________
I find it difficult to answer
How often smart city should recalculate KPIs that are used to assess goals? Please select the
most appropriate answer from your point of view.
More than once a year
Once a year
Once every two years
Once every three year
Other: ________
I find it difficult to answer
Your responses have been submitted.
Thank you very much for your participation in our research.
95
Appendix 4. Map of smart city domains and concrete KPIs
Table 27
KPIs for “Green environment” domain
KPI class
Intensity of waste
recycling
Volume of electrical
(eco-friendly)
vehicles
Water eco-friendly
usage
Concrete KPIs
1. Savings achieved by municipal waste
management
2. Waste generation
3. Waste volume incinerated
4. Greenhouse gas emissions from
incineration
5. Quantity of waste separation bins in
public spaces
6. Quantity of waste separation bins in
commercial and industrial buildings
7. Percentage of population using
recycling bins
8. Disposal of household garbage
1. CO2-free modes (walking and
cycling)
2. Motorised individual traffic (MIT)
3. CO2-free commercial traffic
originating and terminating
1. Daily water losses
2. Daily water consumption
3. Demand for bottled water
4. Production costs of local business
through greater water efficiency
5. Bacteria level in harbor
6. Diversity of flora and fauna
7. Proximity of citizens to blue
recreational space
Measure
tonnes of CO2 equivalents
kg/ per capita
tonnes
m3
Number
Number
% of total population
kg/ per capita
% of total vehicles
% of total vehicles
% of total commercial
traffic
Liters/ per capita
Liters/ per capita
Liters/ per capita
€/ per year
Biochemical oxygen
demand (BOD)
Number of different species
Likert scale
Table 28
KPIs for “Resources and energy” domain
KPI class
Effectiveness of
energy usage
Concrete KPIs
1. Final energy consumption
2. Primary energy input
3. Renewable sources of energy
4.
5.
6.
7.
8.
Local food production
Brownfield use
Grey and rain water use
Quantity of wind turbines
Electricity production from wind
power
9. Electricity consumption with the use
of district cooling
Measure
Watt/ per capita
Watt/ per capita
% of gross energy
consumption
% of tonnes
% of km2
% of houses
Number
% of total electricity
production
Watt/ per capita
96
Layout and
convenience of
buildings
Level of city
infrastructure
development
Layout of transport
systems
1. Energy consumption of existing
buildings for space heating/ cooling/
water heating
2. Cost optimization
3. Attractiveness of urban areas for
citizens
4. Pay back period of investments in
energy savings
5. Heating provided from the district
heating grid
6. Heating energy generated from waste
7. Heating energy generated from
biomass
8. Heating costs per single consumer
1. Public space Wi-Fi coverage
2. Space for sports and social activities
3. Share of green spaces
4. Attractiveness for residents,
businesses and tourists
5. Quantity of pilot projects with ICT
enterprises are to serve as showcases
for the city and its economy
5. Quantity of Wi-Fi hotspots
6. Quantity of locations with Wi-Fi
hotspots
7. Internet connection speed
8. Wi-Fi penetration rate
1. Time saved due to less traffic
congestion
2. Convenience of transfer between each
mode
3. Quantity of car trips
4. Quality of life from convenient public
transport
5. Bicycle parking facilities
Watt/ per service
€/ per new structure
Likert scale
Number of years
% of total heating
% of total energy generated
% of total energy generated
€/ per year
% of m2
m2
% of total city square
Likert scale
Number
Number
Number
MB/ sec
m2
Hours/ per day for single
citizen
Likert scale
Number
Likert scale
Number
Table 29
KPIs for “Quality of citizens living” domain
KPI class
Degree of social
comfort
Level of citizens’
digital identity
Level of education
Concrete KPIs
1. Safety of life conditions
2. Attractiveness of housing
environment
3. Absence of gender superiority
4. Net migration
1. Quantity of active digital individual
certificate users
2. Availability of user-friendly digital
certificates
1. Quantity of whole-day and integrated
schools
Measure
Likert scale
Likert scale
Likert scale
Number
Number
Likert scale
Number
97
2. Quantity of high-quality childcare
offerings
3. Educational level
Level of health care
services
4. Availability of online learning
resources
5. Quantity of Wi-Fi hotspots at schools
6. Availability of ICT education in
school curriculum
1. Affordability of healthcare services
2. Quantity and quality of their leisure
time
3. Health care costs saved for single
citizen
Number
% of citizens with higher
education
Likert scale
Number
% of schools providing ICT
education/ courses
Likert scale
Likert scale
€
Table 30
KPIs for “ICT innovations” domain
KPI class
Development of ICT
talents
Density of electronic
devices around the
city area
Attractiveness of city
for foreign
researchers
Volume of
investments in R&D
projects
Concrete KPIs
1. Quantity of local graduates from ICT
programmes
2. Intensity of university-ICT industry
collaboration
3. Rewards for ICT innovations
4. Creative industry participants
1. Quantity of connected electronic
sensors
2. Density of connected electronic
sensors distribution
1. Attractive research and innovation
hub
2. Quantity of innovation hubs
3. Quantity of research units of
international corporations
4. Quantity of international top
researchers
5. Quantity of international students
1. Foreign investments
2. Investments from local government
Measure
Number
Number of partner
agreements
Number
% of citizens
Number
% of city area covered
Rating in country/ region/
world/ Likert scale
Number
Number
Number
Number
€
€
Table 31
KPIs for “Business and entrepreneurs” domain
KPI class
Intensity of
innovative multisided platforms usage
Concrete KPIs
1. Quantity of platforms
2. Quantity of business participants in
platforms
3. Income generated from intellectual
Measure
Number
Number
€
98
4.
Intensity of cloud
services usage
Startup and business
ecosystem
1.
2.
1.
2.
3.
4.
property rights
Quantity of economically active spinoff companies
Quantity of cloud services for SMEs
Intensity of mobile apps use
New business registered
Direct investment flows
Quantity of headquarters of
international companies
Availability of online portal to serve
startups
Number
Number
Likert scale
Number
€
Number
Likert scale
Table 32
KPIs for “Smart government” domain
KPI class
Degree of
government openness
Availability of multiplatform government
services
Number of connected
sensors in public
services
Concrete KPIs
1. Openness of government
2. Availability of free contacts with city
administration
3. Cross-departmental integration
4. Information sharing intensity
1. Quantity of government mobile
websites
2. Quantity of government mobile apps
3. Costs saved due to the less paper
usage
4. Storage space saved
1. Quantity of connected sensors in
warehouse management
2. Quantity of connected sensors in
luggage handling
3. Quantity of connected sensors in
trade and logistics
4. Quantity of connected sensors in
livestock control
Measure
Likert scale
Likert scale
Likert scale
Likert scale
Number
Number
€
m2
Number
Number
Number
Number
99
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