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1
2nd International EIBURS-TAIPS conference on:
“Innovation in the public sector
and the development of e-services”
Patterns of public eService development
across European cities
Davide Arduini, Annaflavia Bianchi, Alessandra
Cepparulo, Luigi Reggi and Antonello Zanfei
Eiburs-TAIPS Team, University of Urbino, Italy
antonello.zanfei@uniurb.it
University of Urbino
April 18-19th, 2013
•Focus and motivation
•Novelty of this line of research
•Research questions
•Background literature
•Data and indicators
•Cross-country comparisons of eService development
•Analysis of eService development at the city level
• City characteristics and the development of eServices
•Conclusions and implications
Outline
This presentation evaluates public eService diffusion
as part of a smart growth strategy in Europe
• eService development across Europe is a key aspect of
innovation in the public sector and contributes to EU long term
competitiveness.
• extant benchmarking and studies can hardly account for the
actual patterns of public eService development for several
reasons:
- they most often focus on eGovernment, largely disregarding
other web based public activities
- even when attention is given to other eService categories, these
are not examined with comparable methods
- comparative studies are mostly focused on national patterns,
with limited attention to the regional or local level of analysis
Focus
… and motivation
• A novel dataset (EIBURS-TAIPS Database) providing
comparable data at the following levels:
- across four service categories (eGovernment, eProcurement,
eHealth and Intelligent Transport Systems)
- across countries: EU15 member states
- across 229 large and medium sized cities in EU15 countries
• An in-depth analysis of heterogeneity of public eService
diffusion at all three levels (across service categories, nations
and cities)
• An exploratory analysis of the characteristics of European
cities associated with public eService development
•A focus on the links between public eServices and the
“smartness” of cities
Novelty of this research line
• How heterogeneous is public sector innovation across EU15
nations in terms of four public eServices ?
•Is heterogeneity higher across countries or cities?
•Does heterogeneity persist when considering clusters of
cities with comparable levels of socio-economic development
across Europe?
•Are Smart cities also best performers in terms of public
eService development?
•What characteristics of European cities are associated with
public eService development?
Research questions
Background literature (1/4)
•Fast growing literature on the diffusion of public eServices
(Arduini&Zanfei 2011)
•Frequent use of composite indicators (CI)in this field (European
Commission 2001-2010, UN 2001-2010)
•Most studies focus on eGovernment, very few deal with other public e-
services and none assesses national or regional performances across
different public e-services.
•While there is a relatively long tradition of CIs designed at the national
level, their use at the regional and local level is still largely under-developed.
•Recent exceptions:
•EC (2009) for eProcurement, and CapGemini (2010) for eGovernment have
introduced for the first time a regional focus of analysis.
•Academic papers have developed CIs using data at the local level, but based on
individual services and with low impact at the policy making level (Baldersheim
et al. 2008, Flak et al. 2005, Arduini et al. 2010, Codagnone &Villanueva 2011).
•T
•The focus on individual services at the country level, combined with data
constraints encountered at a more disaggregated level, has long led to:
•Erroneously conclude that evidence on e-service diffusion in one area of public
sector activity can be used to make inference on e-service diffusion in other
areas.
•disregard the extreme heterogeneity of regions in terms of e-service
development
Background literature (2/4)
•Need to better capture heterogeneity in public sector innovation
•Two academic traditions have been feeding the discussion concerning urban
innovation and the role of public sector in it: 1) Systemic theory of innovation;
and 2) the literature of Smart cities
1)Systemic theory of innovation
•The systemic theory of innovation was initially formulated at the national level
Lundvall (1992), Nelson (1993) and Edquist (2005, 2008)
•Gradual shift towards the regional and local levels
(Braczyk et al., 1997; Cooke and Morgan, 1998; Asheim and Coenen, 2005)
•Innovation as an evolutionary process, as a result of complex interactions
among different actors, including public institutions
•Public technology procurement literature emphasises the roles of public sector
in systemic innovation (Edquist et al. 2000; Hommen and Rolfstam, 2009)
•Purchaser and end users of technology
•Catalysers of innovation
Differences in the nature, behaviour and organisation of players involved,
including the public sector, combined with the characteristics of technologies
determine a high heterogeneity of innovation processes across countries
and regions (Fagerberg, 2005; Tether and Metcalfe, 2004)
Background literature (3/4)
2) The literature of Smart cities
•One distinctive feature of smart cities is their performance in the field of
innovation (Arribas et al. 2012; Deakin, 2012; Capello et al., 2012)
•Smart cities defined as territories combining: the creativity of talented
individuals, the development of institutions that enhance learning and
innovation, and of digital spaces facilitating knowledge transfer(Eger, 1997;
Graham and Marvin, 2001, Komninos, 2006; Sotarauta, 2010, Boulton et al.,
2012)
•The support of local government innovation policies is fundamental to the
design and implementation of smart city initiatives (Lindskog, 2004; Lepouras et
al., 2007; Ingram et al., 2009; Giffinger and Gudrun, 2010)
•City smartness will likely feed-back on local government capacity to innovate
their organisation and activities, including their services
Smarter cities are likely to have more innovative public sector and more
advanced public services
Heterogeneity of innovation within regions and across cities as a result of
different mix of city level characteristics and evolution
Background literature (4/4)
Based on these converging streams of literature
we should thus observe:
•A high heterogeneity in public sector innovation
across nations
•Heterogeneity in terms of overall eService diffusion
•Heterogeneity in terms of eService portfolio and
specialisation
•Heterogeneity is even higher at the local level,
reflecting the variety of actors and local government
roles
•Cities will differ in terms of eServices reflecting their
smartness
Data and indicators
Our study combines two datasets:
1) EIBURS-TAIPS Dataset (source: University of Urbino)
•Data characteristics: information collected by the TAIPS team
through website-surfing to monitor public e-services provided by
local public transport companies, municipalities and hospitals at
the city level (15-EU). Data refer to availability of eServices in
2012, corrected to account for standard quality measures.
•Sample design: 229 cities representing the EU15 subset of the
322 cities monitored in Eurostat’s Urban Audit dataset
•Variables: info on the provision of 23 eServices classified into
four categories
ITS/Infomobility (based on ITIC-Between methodology, 2010)
eHealth (Based on Empirica methodology, 2008; and Deloitte
methodology, 2011)
eProcurement (based on IDC methodology, 2010)
eGovernment (based on Capgemini methodology, 2010)
2) Urban Audit Dataset (source: Eurostat) :
•Data characteristics: comparable information on 322 cities, out
of which the EU15 sample of 229 cities is derived
•Sample design: cities included correspond to
20% of the national population
the geographic distribution of population within the country (peripheral,
central)
the size distribution within countries (medium-sized cities having a
population of 50,000 – 250,000 inhabitants, large cities with >250 000)
Time coverage: six waves
1989 - 1993; 1994 - 1998; 1999 - 2002; 2003 - 2006; 2007 - 2009
Variables:
demography, social aspects, economic aspects, civic involvement,
training and education, environment, ICT, travel and transport,
information society, culture and recreation
Data and indicators
Data and indicators: E-HEALTH
Unit of analysis : hospitals
Service
list
Videoconferencing/Video
consultations between patients and
doctors
Dedicated and formal use of facilities such as
consultations between patients (either at home or
outside the hospital) and hospital medical staff (for
clinical purposes)
Electronic Patient Records (EPR)
A computer-based patient record system which
contains patient-centric, electronically-maintained
information about an individual’s health status and
care. The system allows online access to patients
e-booking Electronic appointment booking system
Online clinical tests
Computer-based system for electronic transmission of
results of
clinical tests. The system allows online access to
patients
e-referrals Hospitals offering the possibility to external health
actors to make appointments for their patients
Telemedicine service (tele-
homecare/tele-monitoring)
The provision of social care at a distance to a patient
in his/her home, supported by means of
telecommunications and computerized systems
Online chronic disease
management
Home care services using ICT can contribute to the
management of long duration/slow progression
diseases
Online ticket payment Hospitals offering web based payment systems for
visits and clinical tests
Data and indicators : ITS/INFOMOBILITY
Category Unit of analysis : Local public transport companies
Service
list
Public Informed Mobility
Online info to users while
travelling
Public transport companies providing online information to
users (e.g. waiting times, strikes, delays, failures, etc.)
Online time table
consultation
Public transport companies offering the possibility to consult
the online timetable of public transport network
Online travel planning Public transport companies offering timetables with route
planning (travel planner) on the web
Online ticket purchase Public transport companies offering web based payment
systems
Private Informed Mobility
Info to car drivers while
travelling
Public transport companies providing online information to
travelers about traffic or parking
Electronic road or parking
toll
Public transport companies offering a electronic ticketing
system of parking spaces
Data and indicators :E-PROCUREMENT
Unit of analysis: Municipality
Servic
e
list
eProcurement Visibility
Publication of general information on public
procurement
General information on public procurement made available on
the municipality websites
Publication of notices to official electronic notice boards Official electronic board on the municipality websites where
procurement notices are made
Link to e-procurement services Link to a web page (owned by the municipality or by external
parties) providing eProcurement services
eProcurement (Pre-Award Phase)
e-NOTIFICATION Publication of tenders and procurement notices on the web
Online registration of supplier Creation of user accounts and profiles with related roles
e-mail alerts for suppliers Possibility for the suppliers to receive email alerts about
forthcoming calls and notices of their interests
e-SUBMISSION
Assistance services to the supplier
E-mail, chat, audio/videoconferencing communication for
Question and Answer sessions between eProcurement
operators and bidders
Online supplier help session help services to assist suppliers in the preparation of online
tender
e-AWARDS
Online information about awarded contracts The website publishes the contracts awarded and their
winner
e-auctions Availability of tools to carry out real-time price competitions
eProcurement (Post-Award Phase)
e-ORDERING
e-catalogues Online order from e-catalogues through eProcurement
website
Electronic market Electronic market hosted by the eProcurement website, for
online interaction between buyers and suppliers
e-INVOICING
e-invoicing service E-invoicing services managed by the eProcurement website
e-PAYMENT
e-payment service Online payment services, managed by the eProcurement
website
Data and indicators: EGOVERNMENT
Unit of analysis : Municipality
Service
list
Online local taxes Declaration, payment, notification of assessment
Online registration school Standard procedure to register children at kindergarden
Online registration of
residence
Standard procedure to register the residence in a local
area of town
On line payment fines Standard procedure to pay fines at municipal police office
Online personal documents Standard procedure to obtain an international passport and
an identity card
Online public library
Standard procedure to consult the catalogue(s) of a public
library to obtain specific information regarding a specific
carrier (Book, CD, etc)
Online birth/marriage
certificates Standard procedure to obtain a birth or marriage certificate
Online registration of a new
company Standard procedure to start a new company
Measuring service availability and quality
CI pillar eService Availability eService Quality
E-HEALTH 8 eServices considered Not measured
INFOMOBILITY 6 eServices considered Presence/absence of quality
features including: multi-
channel delivery, advanced
functions and applications
E-
PROCUREMENT
1 eService considered =
eProcurement
Presence/absence of quality
features associated with each
phase (visibility, pre-award,
post-award phases)
E-
GOVERNMENT
8 eServices considered Interactivity stages, normalized
0-100% (see CapGemini,
2010)
Construction of a Composite Indicator (CI) -1
An indicator of public eServices development is
calculated as the average of a city’s performance in the
four domains considered (eHealth, Infomobility,
eGovernment, eProcurement)
For each domain, we used existing analytical
frameworks in order to: (a) define the different
dimensions of phenomena studied, including standard
measures of quality of eServices available (e.g.
interactivity); (b) define the nested structure of the
various sub‐groups that will guide the aggregation
process; (c) select the underlying basic indicators
The indicators obtained are then normalized (MIN-MAX
method) in order to make the scores of each city in the
four domains fully comparable.
Construction of a Composite Indicator (CI) -2
CI pillar Framework Weighting method Aggregation
E-HEALTH Based on
Empirica, 2008;
and Deloitte,
2011
Multiple Correspondence
Analyis (MCA) applied to the
basic indicators. Suitable for
dichotomous variables
(OCED, 2008).
Arithmetic
mean
INFO MOBILITY Based on ITIC-
Between, 2010
Non-linear Principal
Component Analysis (PCA)
applied to the basic
indicators. Suitable for
qualitative variables (OECD,
2008).
Arithmetic
mean
E-
PROCUREMEN
T
Based on IDC,
2010
Equal weights Arithmetic
mean
E-
GOVERNMENT
Based on
Capgemini, 2010
Weights proportional to
eServices interactivity as in
Capgemini, 2010
Arithmetic
mean
Final aggregation = Arithmetic mean
Cross-country comparisons of
eService development
Country index vs EU average index
0
0.2
0.4
0.6
0.8
AT
BE
DK
DE
IE
EL
ES
FR
IT
UK
NL
PT
FI
SE
E-services index
EU15
Heterogeneity across eServices and across
countries
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
Sweden
SE
EU15
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
Denmark
DK
EU15
Front runners
Group of countries
with all or most e-
services supplied
above the EU
average
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
United Kingdom
UK
EU15
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
Luxembourg
LU
EU15
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
Netherlands
NL
EU15
Good Performers
Group of countries with one
or two e-services supplied
above the EU average
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
ePr ocurement
Ireland
IE
EU15
Heterogeneity across eServices and across countries
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
Germany
D
EU15
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
Spain
ES
EU15
Group of countries with one e-service supplied above the EU
average
-0.1
0.1
0.3
0.5
0.7
eGOV
Infomob
eHealth
eProcurement
Belgium
BE
EU15
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
France
FR
EU15
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
Finland
FI
EU15
0
0.2
0.4
0.6
0.8
1
eGOV
Infomob
eHealth
eProcurement
Portugal
PT
EU15
Lagging behind
Group of countries with average or below average performance
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
Greece
GR
EU15
0
0.2
0.4
0.6
0.8
eGOV
Infomob
eHealth
eProcurement
Italy
IT
EU15
Analysis of eService
development at the city level
26
Comparing heterogeneity across countries and across
municipalities
EU average
To compare the municipalities in terms of their e-service
diffusion and sophistication, we need to refer to clusters of
homogenous municipalities
To do so we follow a three step procedure:
1. Drawing data from Urban Audit, we use PCA to identify a
few “summary variables” (components) that can be held to
be representative of different aspects of municipalities
2. We identify the clusters of municipalities based on the
above mentioned components
3. Using Eiburs-TAIPS data on eGov, Infomobility,
eProcurement and eHealth at the city level we illustrate
how clusters can be characterised in terms of eService
development
These comparisons are possible for 148 cities only,
due to data constraints
Comparing eServices across cities
First step: Principal Component
Analysis
Demographic characteristics:
Percentage of residents over 65
Population density: total resident pop. per square km
Infrastructural characteristics
Length of public transport network / land area
Percentage of households with Internet access at home
Civil society
Participation rate at city elections
Number of female elected city representatives
Human capital
Prop. of working age population qualified at level 5 or 6 ISCED
Economic Characteristics:
Gross Domestic Product per inhabitant in PPS of NUTS43
Unemployment rate
Sectoral specialization:
No. Manufacturing (and service?)Companies (all sectors?)
Number of persons employed in provision of ICT services
Prop. of employment in financial and business services (NACE Rev.1.1 J-K)
Environmental sensibility:
Annual amount of solid waste (domestic and commercial) that is recycled
Attractiveness:
Total annual tourist overnight stays in registered accommodation
Validated PCA -2
Cluste
r
No.
obs
characteristics municipalities
1 7 -Industrial and infrastructural
development: Medium high
- Share of financial and
business service
employment: Low
Valencia
Sevilla
Las Palmas
Palma de Mallorca
Pamplona/Iruña
Porto
Helsinki
2 1 -Industrial and infrastructural
development: High
- Share of financial and
business service
employment: High
Stockholm
3 49 -Industrial and infrastructural
development: Medium-low
- Share of financial and
business service
employment: Medium
Bonn,Karlsruhe,Mainz,Kiel,Saarbrucken
Potsdam,Koblenz,Rostock,Strasbourg
Nantes,Lille,Montpellier,Rennes,Orléans
Grenoble,Aix-en-Provence,Marseille
Catania,Cremona,Trento,Perugia
Ancona,L'Aquila,Campobasso,Caserta
Catanzaro,Reggio di Calabria,Sassari
Foggia,Salerno,Apeldoorn,Malmö,Linköpi
ng
Cluster No.
obs
characteristics municipalities
4 1 -Industrial and infrastructural
development: Very High
- Share of financial and
business service employment:
Low
Barcelona
5 2 -Industrial and infrastructural
development: Medium
- Share of financial and
business service employment:
High
Frankfurt am Main
Luxembourg (city)
6 9 -Industrial and infrastructural
development: Medium High
- Share of financial and
business service employment:
Medium High
Wien,Bruxelles /
Brussel,Hamburg,München
Napoli,Torino,Firenze,Amsterdam,Li
sboa
7 30 -Industrial and infrastructural
development: Medium low
- Share of financial and
business service employment:
Medium High
Köln,Antwerpen,Essen,Stuttgart,Lei
pzig,
Dresden,Dortmund,Düsseldorf,Han
nover,Nürnberg,Wiesbaden,Lyon,T
oulouse
Palermo,Genova,Bari,Bologna,Ven
ezia
Verona,Trieste,Pescara,Potenza,Ca
gliari
Padova,Brescia,Modena,'s-
Gravenhage
Rotterdam,Utrecht,Göteborg, Köln
Cluster No.
obs
characteristics municipalities
8 49 -Industrial and
infrastructural
development: Medium
low
- Share of financial and
business service
employment: low
Charleroi,Liège,Brugge,Namur
Aarhus,Aalborg,Bochum,Halle an der
Saale
Magdeburg,Moers,Trier,Freiburg im
Breisgau,Málaga,Murcia
Valladolid,Vitoria/Gasteiz,Oviedo,Alicante
/Alacant,Vigo,Saint-Etienne,Le
Havre,Amiens,Nancy
Metz,Reims,Dijon,Poitiers,Clermont-
Ferrand
Caen,Limoges,Besançon
Ajaccio,Saint Denis,Fort-de-France
Tours,Lens – Liévin,Nijmegen
Braga,Funchal,Coimbra,Setúbal,Ponta
Delgada,Aveiro,Faro,Tampere,Turku
Jönköping,Belfast,Derry
Comparison of the municipalities
across and within clusters in terms
of eServices supplied
Clusters index vs Cluster average
index
Cluster 3 – “medium low industrial/infrastructural
development and medium low share of business
services”
Ranking of cities in terms of eServices
Southern
Europe
Northern
Europe
Southern
Europe
Cluster 6 – “medium high industrial/infrastructural
development and medium high share of business services”
Ranking of cities in terms of eServices
Correlation among E-services index- Smart index
and its components
Smart
index
Smart
econom
y
Smart
living
Smart
peopl
e
Smart
governan
ce
Smart
mobilit
y
Smart
environme
nt
eServic
es
index
0.4* 0.4* 0.47* 0.5* 0.3 0.5* -0.25
Source: European Smart Cities ( Vienna University of Technology , Delft University
of Technology and the University of Ljubljana).
*significance level 5%
Given the results of correlations we
search for drivers of public eServices
based on the empirical literature on
determinants of Smart city development
cf. European Smart Cities (2007), Caragliu, Del
Bo, Nijkamp(2011), Caragliu & Del Bo (2012)
In search of determinants of eService
index
Determinants of the development of smart cities
- Gross Domestic Product of
city/region/country (Euro)
- New business that have registered in
the reference year*
- Self-employment rate
- Proportion in part time
- Proportion of population aged 15-64
qualified at tertiary level (ISCED 5-6)
living in Urban Audit cities - %
- Total book loans and other media per
resident*
- Length of public transport network
per inhabitant
- Number of stops of public
transport
- Number of deaths in road
accidents
- Number of tourist overnight stays in
registered accommodation per year per
resident population
- Total number of recorded crimes per
1000 population
- Number of hospital beds
- Cinema attendance (per year)*
- Theatre attendance (per year)*
- Number of museum visitors (per year)
Smart economy component
Smart mobility component
Smart people component
Smart living component * Large number of missing values
Correlations check among our index
and the potential determinants
ρ
Gdppp 0.2958*
Log(Selfemploy) -0.4156*
Sqrt(Propparttime) 0.3981*
Sqrt(Bedhospital) -0.2487*
Log(Museum) 0.3502*
Isced56 0.3143*
Log(Tourist) 0.2033*
Log( public transport
stops) 0.2344*
1/sqrt(Lenght transport
per inhabitant) 0.108
Crime 0.1616*
Sqrt(Road accidents) 0.0874
i
i
j
i
j
i
j
i
itySmartMobileSmartPeoplgSmartLivinmySmartEconoESindex
εββββ
++⋅+⋅+⋅=
4321
Our research question thus translates in an empirical model of the
form:
where ESindex is the composite indicator of
eService development
subscript i refers to cities and supra-script j refers to
the a specific component of city smartness
Variables
Gdppp: Gross Domestic Product purchasing power parity
Selfemploy:Self-employment rate
Propparttime:Proportion in part time on total workforce
bedhosital:Number of hospital beds
Museum:Number of museum visitors (per year)
Isced56: saher of population qualified at tertiary level (ISCED
5-6)
Tourist:Number of tourist overnight stays in registered per year
Public transport stops(Stopbsn):Number of stops of public
transport
Length transport: km of public transportation network per
resident population
Crime: Total number of recorded crimes per 1000 population
Road accident: Deaths in road accidents per year
Testing determinants of e-services
index -1
* p<0.05, ** p<0.01, *** p<0.001
p-values in parentheses
R-sq 0.937 0.935 0.940
N 110 110 110
(0.005)
prop 0.0600**
(0.990)
self 0.000344
(0.001) (0.000) (0.013)
isced56 0.00544*** 0.00684*** 0.00410*
(0.000) (0.000) (0.000)
stopbusnm 0.0445*** 0.0462*** 0.0399***
(0.522) (0.802) (0.085)
bedhosnm -0.00903 -0.00428 -0.0272
(0.091)
gdppps 0.00000183
index_a~e index_a~e index_a~e
(1) (2) (3)
* p<0.05, ** p<0.01, *** p<0.001
p-values in parentheses
R-sq 0.937 0.938 0.937
N 113 113 113
(0.769)
prop 0.00630
(0.242)
self -0.0252
(0.027) (0.022) (0.022)
isced56 0.00402* 0.00397* 0.00413*
(0.000) (0.000) (0.000)
stopbusnm 0.0406*** 0.0518*** 0.0401***
(0.011) (0.006) (0.024)
crime 0.000890* 0.000928** 0.000875*
(0.646)
gdppps 0.000000508
index_a~e index_a~e index_a~e
(1) (2) (3)
* p<0.05, ** p<0.01, *** p<0.001
p-values in parentheses
R-sq 0.936 0.940 0.936
N 107 107 107
(0.801)
prop 0.00558
(0.014)
self -0.0585*
(0.014) (0.087) (0.012)
isced56 0.00441* 0.00297 0.00449*
(0.445) (0.114) (0.453)
stopbusnm 0.0104 0.0223 0.0103
(0.015) (0.001) (0.033)
logmuseu 0.0217* 0.0305*** 0.0214*
(0.710)
gdppps 0.000000431
index_a~e index_a~e index_a~e
(1) (2) (3)
* p<0.05, ** p<0.01, *** p<0.001
p-values in parentheses
R-sq 0.941 0.941 0.941
N 105 105 105
(0.264)
prop 0.0208
(0.135)
self -0.0318
(0.001) (0.000) (0.004)
isced56 0.00513** 0.00568*** 0.00492**
(0.000) (0.000) (0.000)
stopbusnm 0.0454*** 0.0600*** 0.0421***
(0.965) (0.764) (0.706)
tour -0.000715 0.00470 0.00597
(0.261)
gdppps 0.00000125
index_a~e index_a~e index_a~e
(1) (2) (3)
Testing determinants of e-services
index -2
CONCLUSIONS
•This presentation fills three gaps:
–Coverage of public eServices beyond eGovernment with
comparable data
–Comparing eService development across countries and cities
–Linking eServices with smartness of cities
•Heterogeneity in Public eService development is high across
countries and across service categories
•Heterogeneity is even greater when examined at the city level
and across clusters of relatively homogeneous cities
•Cities from nordic and central European countries are largely
ranking high, but there is heterogeneity also across these cities
a regional and sub-regional approach needed
•“Smart cities” also exhibit high levels of eService development
•Smart city characteristics that are most associated with public
eService diffusion are: human capital and transportation
infrastructure development
Thanks
City sample
Code Tot cities
50 000 – 250 000
ab. > 250 000 ab.
AT 5 3 2
BE 7 4 3
DK 4 2 2
DE 40 18 22
IE 5 4 1
EL 6 7 2
ES 23 7 16
FR 35 15 20
IT 32 20 12
LU 1 1
NL 15 11 4
PT 9 8 1
FI 4 3 1
SE 8 5 3
UK 30 12 18
TOT 229 122 107
Data and indicators
Descriptive statistics
eprocurement 229 .6388128 .2440753 0 1
ehealth 229 .187901 .2463106 0 1
infomob 229 .4696298 .2191175 0 1
egov 229 .6156161 .2136404 0 1
Variable Obs Mean Std. Dev. Min Max
PCA -3
Step 2: cluster analysis
Cluster 7 – “medium low industrial/infrastructural
development and medium high share of business services”
Ranking of cities in terms of eServices
Central
Europe Northern
Europe
Souther
n
Europe
Cluster 5 – “medium industrial/infrastructural
development and high share of business
services”
Ranking of cities in terms of eServices
Ranking: single point clusters: 2
and 4
Northern
Europe
Southern
Europe
Cluster 1 – “medium high industrial/infrastructural
development and low share of business services”
Ranking of cities in terms of eServices
Ranking of outliers
Southern
Europe
Central
Europe