1. 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

2. •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

3. 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

4. • 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

5. • 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

6. 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

7. 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)

8. 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

9. 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

10. 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)

11. 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

12. 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

13. 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

14. Data and indicators :E-PROCUREMENT
Unit of analysis: Municipality
Service
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

15. 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

16. 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)

17. 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.

18. 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-PROCUREMENT 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

19. Cross-country comparisons of
eService development

20. 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

21. 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

22. 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
eProcurement
Ireland
IE
EU15

23. 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

24. 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

25. Analysis of eService
development at the city level

26. 26
Comparing heterogeneity across countries and across municipalities
EU average

27. 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

28. 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

29. Validated PCA -2

30. Cluster 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,Linköping

31. 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,Lisb
oa
7 30 -Industrial and infrastructural
development: Medium low
- Share of financial and business
service employment: Medium
High
Köln,Antwerpen,Essen,Stuttgart,Leipzig
,
Dresden,Dortmund,Düsseldorf,Hannov
er,Nürnberg,Wiesbaden,Lyon,Toulouse
Palermo,Genova,Bari,Bologna,Venezia
Verona,Trieste,Pescara,Potenza,Cagliar
i
Padova,Brescia,Modena,'s-Gravenhage

32. 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/Al
acant,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

33. Comparison of the municipalities
across and within clusters in terms of
eServices supplied

34. Clusters index vs Cluster average index

35. 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

36. Southern
Europe
Cluster 6 – “medium high industrial/infrastructural
development and medium high share of business services”
Ranking of cities in terms of eServices

37. Correlation among E-services index- Smart index and its
components
Smart
index
Smart
economy
Smart
living
Smart
people
Smart
governance
Smart
mobility
Smart
environmen
t
eServices
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%

38. 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

39. 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

40. 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
ii
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

41. 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)

42. * 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

43. 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

44. Thanks

45. 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

46. 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

47. PCA -3

48. Step 2: cluster analysis

49. 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
Southern
Europe

50. Cluster 5 – “medium industrial/infrastructural
development and high share of business services”
Ranking of cities in terms of eServices

51. Ranking: single point clusters: 2 and 4
Northern
Europe

52. Southern
Europe
Cluster 1 – “medium high industrial/infrastructural
development and low share of business services”
Ranking of cities in terms of eServices

53. Ranking of outliers
Southern
Europe
Central
Europe