1. 2nd
International EIBURS-TAIPS conference on:
“Innovation in the public sector
and the development of e-services”
University of Urbino
April 18th
-19th
, 2013
Determinants and effects of infomobility at the city level
Davide Arduini, Marco Biagetti, Luigi Reggi and Paolo Seri
EIBURS-TAIPS team, University of Urbino

2. 2nd International EIBURS-TAIPS conference on:
““““Innovation in the public sector
and the development of e-services””””
Determinants and effects of infomobility
at the city-level
1
Davide Arduini, Marco Biagetti, Luigi Reggi, Paolo Seri
EIBURS-TAIPS team
paolo.seri@uniurb.it
University of Urbino
April 19th, 2013

3. Plan of the talk
Research questions: 1) developing a model to explore the
influence of some urban characteristics on the
provision/diffusion of Infomobility services; 2) analysing the
relationships between urban pollution and ITS development
• Definition of Infomobility/Intelligent Transport Systems
(ITS)
2
(ITS)
• Literature review
• Data and the econometric model
• Results
• Conclusion

4. Definition
• The concept of Infomobility/Intelligent Transport Systems (ITS), provided by the
European Commission (2003): "Intelligent Transport Systems: Intelligence at the
Service of Transport Networks“, include the following systems:
1) Advanced information for users; 2) Traffic control, navigation surveillance and guidance; 3)
Accident management; 4) Vehicle safety and control systems, as much as electronic payment and
enforcement; 5) Operation of green zones/low emission zones; 6) Intermodality for both
passenger and freight transport; 7) Interoperability standards, e.g. for ticketing
3
passenger and freight transport; 7) Interoperability standards, e.g. for ticketing
• The availability and adoption of ITS/Infomobility applications not only provides new
and flexible transport services but also a range of information services that have the
potential to increase the accessibility and usability of transport services, reduce
inequalities and increase economic participation and access to public services
The combination of transportation accessibility, usability and availability culminate
in the increased capacity of all citizens to participate in the local economy, access
public services and to be active members in their community
ICT and Intelligent Transport Systems are improving all of these areas and are
breaking down geographical barriers as well

5. Literature review of Smart Cities (1/5)
• An increasing literature (Caragliu, Del Bo, Nijkamp, 2011; Arribas, Kourtit, Nijkamp,
2012; Deakin, 2012; Lombardi, Giordano, Farouh, Yousef, 2012) has highlighted that
there are several urban characteristics which are described in relation to the concept
“Smart City”: a) smart economy (related to competitiveness); b) smart people (related
to human capital); c) smart governance (related to participation); d) smart
environment (related to natural resources); e) smart living (related to the quality of
life)
• “Smart City” is furthermore used to discuss the use of modern transport technologies
4
• “Smart City” is furthermore used to discuss the use of modern transport technologies
(f) in everyday urban life (Komninos, 2008; Hollands, 2008; Alkandari et al., 2012)
Intelligent transport systems/Infomobility contribute to the rational exploitation of existing
infrastructure without resorting to the establishment of new facilities: 1) improve the
economic productivity of current and future systems; 2) environmental protection; 3) improve
the level of traffic safety; 4) increase the prosperity of travelers, commuters and residents; 5)
increase the operational efficiency of the transportation system; 6) reduce commuting time
and cost; 7) predict the movement of traffic and events that may affect the future
• These six features connect with traditional regional and theories of urban growth and
development

6. Literature review of Smart Cities (2/5)
Determinants of “Smart Cities” in the literature
Urban
characteristics
Indicators
Smart
economy
R&D expenditure; Employment rate in knowledge-intensive sectors; New businesses registered;
GDP per employed person; Unemployment rate; % of employed in providing ICT services and
products; etc
Smart
people
Top research centres, top universities; Population qualified at levels 5-6 ISCED; Share of people
working in creative industries; etc.
5
people working in creative industries; etc.
Smart
governance
Expenditure of the municipal per resident; Availability of new channels of communication for the
citizens (e.g. eGovernment, eHealth, etc.); Satisfaction with quality of public and social services;
etc.
Smart
environment
Accumulated ozone concentration; Green space share; Efficient use of water, Efficient use of
electricity; etc
Smart
living
Museums visits per inhabitant; Theatre attendance per inhabitant; Satisfaction with quality of
health system; Importance as tourist location; Overnights per year per resident; Poverty rate; etc.
Smart
mobility
Public transport network per inhabitant; Broadband internet access in households; Traffic safety;
Availability of ICT and modern and sustainable transport systems; etc.

7. Literature review of Smart Cities (3/5)
• In sum, the application of Intelligent transport systems/Infomobility in “Smart Cities”
can produce various benefits (Harrison and Donnely, 2010)
Reducing resource consumption, notably energy and water, hence contributing to reductions
in CO2 emissions
Improving the utilization of existing infrastructure capacity, hence improving quality of life
and reducing the need for traditional construction projects
Making new services available to citizens, commuters and travelers, such as real-time
6
Making new services available to citizens, commuters and travelers, such as real-time
guidance on how best to exploit multiple transportation modalities
Improving commercial enterprises through the publication of real-time data on the operation
of city services
Revealing how demands for energy, water and transportation peak at a city scale so that city
managers can collaborate to smooth these peaks and to improve resilience
Drivers receive better information about traffic and road conditions and make decisions
about which routes to follow

8. Health end-point Units (per year) EU25 Italy
Mortality – life expectancy
reduction
Months 8.6 9.0
Mortality – long term exposure Life years lost x1,000 3618 498
Mortality – long term exposure Number of premature
deaths x1,000
348 51
Infant mortality Cases x1,000 0.6 0.08
Chronic bronchitis Cases x1000 163 24
Respiratory hospital cases x1000 62 9
Literature review: traffic pollution and health (4/5)
Traffic
pollution still
harmful to
health in
many parts of
Europe.
7
Respiratory hospital
admissions
cases x1000 62 9
Cardiac hospital admissions Cases x1000 38 5
Restricted activity days Days x1000 347687 48105
Respiratory medication use
(children)
Days x1000 4218 531
Respiratory medication use
(adults)
Days x1000 27742 4003
Lower respir. symptoms
(children)
Days x1000 192756 21945
Lower respir. symptoms in
adults with chronic disease
Days x1000 285345 40548
Transport in
Europe is
responsible for
damaging
levels of air
pollutants and
a quarter of EU
greenhouse gas
emissions.
Source:CAFE 2005

9. Literature review: how intelligent transport systems can
reduce pollution (5/5)
2) providing real time Information about air pollution to the public
- spontaneous changes in mobility behavior
1) infomobility easier use of public transport changes in mobility behavior
reduction of urban pollution
Three main channels:
8
- spontaneous changes in mobility behavior
- traffic restrictions from local autorities
3) speed control traffic signals
- Kan, A. and de Barros, A.G., (2007) “The role of intelligent transport systems in reducing the
impact of traffic pollution on the environment and health”
- Bell, M. C. (2006). Environmental factors in intelligent transportation systems. IEE Proceedings:
Intelligent Transportation Systems, 153(2), 113-128.
- Coelho, M. C., Farias, T. L., & Rouphail, N. M. (2005). Impact of speed control traffic signals on
pollutant emissions. Transportation Research, Part D (Transport and Environment),10(4), 323-40.

10. Aim of the paper
• Drawing on Smart City’s framework, we aim to develop a model to explore the
influence of some urban characteristics of “ Smart Cities ”””” on the
provision/diffusion of Infomobility services
9
• We aim to apply this framework to 140 European cities, employing an unusually
detailed and statistically consistent dataset on public e-services at the city-level
• We analyse the relationships between urban pollution and ITS development

11. Data collection (1/3)
1) Urban Audit Dataset (source: Eurostat)
• Aim: providing reliable information, comparable amongst 322 cities in 27 Member States, plus 47 cities
from Switzerland, Norway, Croatia and Turkey
• Sample design: cities were chosen on the basis of the following criteria:
the selected cities in each country should correspond to approximately 20% of the national
population
the participating cities in each country should represent about 20% of the population in that
country
10
country
the participating cities should reflect a good geographic distribution within the country (peripheral,
central)
coverage should reflect a sufficient number of medium-sized cities (medium-sized cities having a
population of 50000 – 250000 inhabitants, large cities with >250 000)
Time coverage: five waves
1989 - 1993; 1994 - 1998; 1999 - 2002; 2003 - 2006; 2007 – 2009
Variables: nine different areas of variables have been defined
demography, social aspects, economic aspects, civic involvement, training and education,
environment, travel and transport, information society, culture and recreation

12. Data collection (2/3)
2) EIBURS-TAIPS Dataset (source: University of Urbino)
• Aim: desk analysis conducted through website-surfing to monitor public e-service
availability provided by local public transport companies and municipalities at the city
level (EU-15)
• Sample design: 229 cities composing the EU15 subsample of the 322 (EU-27)
monitored in Eurostat’s Urban Audit dataset
Time coverage: 2012
11
• Time coverage: 2012
• Variables: two service categories have been considered, and data have been collected
adapting and integrating extant methodologies
ITS/Infomobility (based on ITIC-Between methodology, 2010)
eProcurement (based on IDC methodology, 2010)

13. Data collection (3/3)
• ITS/Infomobility: service list
Unit of analysis Local public transport company
Public Informed Mobility Electronic services related to public transportation (bus, metro, trains, etc.)
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
12
Service
list
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 Electronic services related to private transportation (cars, trucks, etc.)
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. The construction of Infomobility Composite Indicator (ICI)
• The framework is based on ITIC-Between, 2010 and composed of 4
basic indicators
Basic indicator
Service involved
(see slide 12)
Variable
No. of channels used to offer information services to public
transport users while travelling (call center, SMS, website, etc.)
Online info to users while travelling info_users
13
No. of different ways to access to time tables of public
transportation (download, static webpage, travel planner offered
via website, smart phone application, etc.)
Online time table consultation
Online travel planning
timetables
No. of different ways to purchase the ticket (smart card, website,
mobile phone, etc.)
Online ticket purchase tickets
No. of channels used to offer travel info on parking and traffic to
car drivers (call center, SMS, website, etc.)
Info to car drivers while travelling travel_info
Note: the service “Electronic road or parking toll” is not included in the CI since its variance is close to zero

15. The construction of Infomobility Composite Indicator (ICI)
• The methodology for computing the index is based on the JRC-OECD Manual for
constructing composite indicators (OECD, 2008. pag. 89)
• The weights are obtained through a Nonlinear Principal Component Analysis, which is
suitable for qualitative variables. See Gifi A. (1990) Nonlinear Multivariate Analysis.
John Wiley & Sons
Dimensions revealed COMPONENTS LOADINGS from non-
14
Dimension
Variance Accounted For
Total
(Eigenvalue)
% of
Variance
1 2.871 71.768
2 .747 18.665
3 .310 7.749
4 .073 1.818
Total 4.000 100.000
Dimensions revealed
Dimensions
weight1 2
tickets 0.13 0.82 0.21
Info_users 0.30 0.09 0.27
timetables 0.27 0.00 0.25
travel_info 0.30 0.08 0.27
Sum 1 1 1.00
COMPONENTS LOADINGS from non-
linear PCA SQUARED & weights
The final index is obtained as the weighted mean of the values of the 4 indicators

16. The diffusion of the Infomobility Composite Indicator (ICI)
Values of the CI in the selected cities (normalized MIN-MAX)
0,6
0,7
0,8
0,9
1
15
0
0,1
0,2
0,3
0,4
0,5
FrankfurtamMain
København
Leipzig
Berlin
Örebro
Antwerpen
Brugge
Linköping
Göteborg
Reims
Genova
FreiburgimBreisgau
Bonn
Torino
Odense
Dresden
Jönköping
Hannover
Enschede
Almere
Dijon
Trier
Roma
Nice
NewcastleuponTyne
Wolverhampton
Magdeburg
Mönchengladbach
Köln(Cologne)
Bielefeld
Wien
Essen
Wrexham
Linz
Montpellier
Innsbruck
London
PalmadeMallorca
Leeds
Cork
Galway
Bordeaux
Amsterdam
Bristol
Portsmouth
Stoke-on-trent
Regensburg
Strasbourg
Bremen
Darmstadt
Rennes
Dublin
Belfast
Firenze
Edinburgh
Saint-Etienne
Lens-Liévin
Lisboa
Valencia
Charleroi
Namur
Murcia
Liverpool
Nottingham
Lille
Trento
Poitiers
Paris
Rostock
Metz
Leicester
Salerno
Vigo
Kiel
Göttingen
Clermont-Ferrand
Tours
Córdoba
Thessaloniki
Catania
Tilburg
Breda
Volos
Setúbal
Cambridge
Lincoln
Groningen
Koblenz
Ajaccio
Verona
Cardiff
Coimbra
Potsdam
Madrid
Gijón
Trieste
Schwerin
Eindhoven
Oviedo
Irakleio
HospitaletdeLlobregat(L')
Palermo
Campobasso
Aveiro
Funchal
Catanzaro
PontaDelgada
Badajoz
Potenza
Pescara
ReggiodiCalabria
Ioannina
Logroño
Fort-de-France
Gravesham
EU15 average

17. The diffusion of the Infomobility Composite Indicator (ICI)
Average values of the CI in the selected cities, by Country (normalized MIN-MAX)
0,5
0,6
0,7
0,8
0,9
EU15 average
16
0
0,1
0,2
0,3
0,4
DK SE LU BE DE AT IE UK NL FI FR ES IT PT EL

18. The theoretical pinpoint of the analyzed model
Where Infomob is our dependent variable (composite indicator), eproc is a composite indicator of
iiiii
iiii
iiiii
carsictlocuntournightsozone
highedemplfinempltranspemplhotel
emplrpopdenspopeprocInfomob
εββββ
ββββ
ββββα
++++
++++
+++++=
1211109
8765
4321 lnln
17
Where Infomob is our dependent variable (composite indicator), eproc is a composite indicator of
eProcurement (calculated as the simple mean of 13 indicators), lnpop and lnpopdens are the
logarithms of population and population density respectively, emplr is the municipal rate of
employment, emplhotel, empltransp and emplfin are the employment in hotels-restaurants-
trade, transport-communications and financial and business sectors (in %), highed is the share of
people between 15 and 64 years of age with at least a degree (ISCED 5-6), ozone is the number of days
in a year when an excess of ozone is recorded in town, tournights is the number of tourist overnight
stays in registered accommodation per year per resident population, ictlocun is the proportion of local
units producing ICT, cars is the number of registered cars per 1,000 inhabitants.
Data are taken from the waves of Urban audit Eurostat giving priority to the last available figure.

19. Descriptive statistics
Max= Stockholm
2008
18
229 towns. 195 only theoretically available. Due to missing data of towns in some of these variables the
number of obs. On which the econometric analysis is made goes down to 140. Still it is a very high figure

20. Econometric model: results (1/4)
Positive effects are found for all of the
significant regressors even though with
different p-values
19
Adjusted R2= 0.344
P-values (+0.1 *0.05 **0.01 ***0.001)
N=140!!! It is the first time that an
analysis of this kind is performed on such
a number of towns

21. Econometric model: results (2/4)
• The provision of infomobility services is strongly related to the size of the
European cities (variable expressed in terms of total population in the city)
external pressure on Local Public Transport Companies (LPTC) to improve services can be
expected to increase with the number of city inhabitants
the perceived need for advanced communication tools between LPTC and citizens appears to
increase with size, hence with the physical and social distances to be covered within the territory
of the city in order to gain access to service providers
20
• Another important factor affecting the availability of Infomobility tools include
the economic structure of the European cities, with a positive correlation of
firms and workers in knowledge intensive services (financial and business
sectors)
• We observe that the availability of Infomobility services is affected by the
presence of other innovative actors in the same city
Among these actors are the municipalities offering eProcurement services
This result proves that when a high number of innovators are located in a given area, knowledge
spillovers will be facilitated and greater incentives are created that push less dynamic institutions
to enter the innovation race

22. Econometric model: results (3/4)
• The presence of local ICT producers in the city is also positively correlated with
Infomobility development
Local Public Transport Companies located in cities with higher shares of local ICT producers are in
a better position to gain access to relevant technology, including both hardware and software
Where public and private markets overlap, as in the case of voice or image transmission over IP
and value added services to business enterprises, a competitive presence of ICT service
providers stimulates the public organizations to expand the range of services offered through
21
providers stimulates the public organizations to expand the range of services offered through
their city networks
• The level of pollution has an impact on the development of ITS (see next slides)
• Finally, we find a positive correlation of Infomobility Index with the employment
rates in the European cities
It appears that Local Public Transport Companies that are located in dynamic areas tend to
intensify their provision of e-services
Employment rates are logically associated with the quality of social environment in which local
administrations operate and with the level and sophistication of demand for services expressed
by citizens and firms

23. Econometric model: results (4/4)
The model is well specified (Reset test is ok) and is robust to changes in the scale of
measurement (i.e. use of logs for some variables or percentage for
others), homoskedasticity is verified through Breusch-Pagan test, normality of
residuals through the Shapiro-Wilk test and standard graphical procedures
(pnorm qnorm). Some influential city (9, through Cook D’s threshold of 4/n) are
the following:
1) Aarhus (Den, medium infomob)
2) Paris (Fra, medium infomob)
High, mediu
m, low
22
2) Paris (Fra, medium infomob)
3) Luxembourg (Lux, high infomob)
4) Aalborg (Den, high infomob)
5) Cremona (Ita, low infomob)
6) Edinburgh (UK, medium infomob)
7) Stockholm (Swe, high infomob)
8) Venice (Ita, high infomob)
9) Madrid (Esp, low infomob)
m, low
based on
percentiles

24. Econometric model: results for days of ozone excess (1/2)
11.522.53
Dfbetadays_ozone_excess_lat_av
Influence of city on ozone standard error
(threshold 2/sqrt(n))
23
Cremona
Bologna
Verona
CampobassoBadajozDarmstadtPotsdam RomaFirenze MalmöMülheim a.d.Ruhr Aix-en-ProvenceMainz Cagliari TurkuMadridToledo UtrechtBielefeld Caen TorinoNürnberg BariBarcelona ToulonDortmund StevenagePoitiers CatanzaroDüsseldorf Lens - Liévin NijmegenAalborg Palma de MallorcaReims GöteborgAnconaAmiensBremen Zaragoza Besançon WienToulouseHannover LyonMönchengladbachLogroñoRennes BirminghamLilleKøbenhavn HeerlenRouenSantanderNancyGöttingen LimogesBordeauxEssen Stoke-on-trentSaarbruckenAugsburg PortsmouthPointe-à-PitreOdense RegensburgHamburg Metz HelsinkiParisKöln AjaccioNapoliErfurt Saint-EtienneCataniaStuttgart GrenobleCayenneMálaga ExeterGroningenStrasbourgRostock Le HavreDijonOrléansClermont-FerrandBochum ToursBruxelles / Brussel NantesPamplona/IruñaValenciaMurcia PerugiaMarseilleKiel Trento LiverpoolMontpellierLeipzig ManchesterL'AquilaPescaraRotterdamAmsterdamDresdenSchwerin BelfastNice BredaMagdeburgMoersBerlin Bonn PalermoSaint DenisTriesteSevilla Fort-de-FranceTrierMünchen VeneziaAarhusFrankfurt am Main 's-GravenhageLuxembourg (city)PotenzaKoblenz EdinburghHalle an der SaaleWeimar MilanoFrankfurt (Oder)Karlsruhe StockholmGenovaFreiburg im BreisgauWiesbaden
-1-.50.51
Dfbetadays_ozone_excess_lat_av
0 50 100 150 200 250
Id
(threshold 2/sqrt(n))
Italian and German cities respectively
lower and increase the coefficient of
the pollution variable by a strong
amount

25. Econometric model: results for days of ozone excess (2/2)
CaenBielefeld
Malm ö
StevenageP oitiers
Dortm undDüsseldorf
M ülheim a.d.Ruhr
Turku
B arcelona
ZaragozaB esançon
Göteborg
Reims
Aalborg
København
Rouen
OdenseErfurtMálaga
DijonBruxelles / Brussel
Leipzig
S trasbourg
M oers
Bonn
Berlin
M arseille
Trier
Dresden
Frankfurt am Main
Luxembourg (city)
Magdeburg
M ontpellier
Frankfurt (Oder)
Venezia
Halle an der SaaleW eimar
W iesbaden
Karlsruhe
Genova
Sev illaNice
Freiburg im Breisgau
Milano
M urcia
.51
e(infomob_n|X)
The same story.
Italian and German
cities are influential
on the pollution
effect on
infomobility
24
Potenza
Cagliari
L'Aquila
Catanzaro
BariAncona
Roma
Koblenz
Perugia
Palma de Mallorca
Stockholm
München
Palerm o
Napoli
Nürnberg
Edinburgh
Trieste
Valencia
Caen
Belfast
Regensburg
Bielefeld
S antanderS toke-on-trent
Clerm ont-Ferrand
StevenageLens - Liév inP oitiers
Limoges
Pamplona/Iruña
Schwerin
Nantes
ManchesterLiverpool
Rennes
Rotterdam
Birmingham
's-Gravenhage
B ordeaux
Dortm und
Rostoc k
Düsseldorf
B reda
Amsterdam
Aarhus
Orléans
Saint Denis
Kiel
S aint-Etienne
Helsinki
Fort-de-France
HannoverM önchengladbach
Exeter
Tours
ZaragozaB esançon
B ochum
EssenHam burg
Nancy
Pescara
Grenoble
A jaccio
Le Havre
Groningen
Köln
P ointe-à-Pitre
Cayenne
Odense
Metz
Paris
Erfurt
Saarbrucken
Logroño
Málaga
Lyon
Toulouse
Lille
Nijmegen
Göttingen
Augsburg
Amiens
S trasbourg
Portsmouth
M adrid
M ainz
Toulon
W ien
M arseille
Heerlen
Stuttgart
Brem en
Aix-en-Provence
UtrechtToledo
Potsdam
M ontpellier
Firenze
Trento
Catania
Sev illa
DarmstadtB adajoz
Nice
V erona
M urcia
Crem ona
Torino
B ologna
Cam pobass o
-.50
e(infomob_n|X)
-40 -20 0 20 40
e( days_ozone_ex cess_lat_av | X )
coef = .00324815, se = .00169789, t = 1.91

26. Grouping Analysis (1/4)
The nations of the 140 towns in the regression
The nations of the 54 towns in the regression belonging to
the group with high pollution (days of ozone excess)
25
The nations of the 27 towns in the regression belonging to the
group with high pollution (days of ozone excess) and high
infomobility
More than 70% of the german cities
with high pollution developed a high
level of infomobility, while the same is
true for less than 40% of the Italian
cities and 30% of French cities with
high pollution.

27. Grouping Analysis (2/4)
High pollution - High infomobility High pollution – Low infomobility
26
Towns with High pollution and High infomobility show in average an higher level
of eProcurement, an higher level of employment rate and employment in the
financial and business sectors. They are also slightly bigger. Town with High
pollution and low infomobility are in average more polluted.

28. Grouping Analysis (3/4)
High pollution - High infomobility
27

29. Grouping Analysis (4/4)
High pollution - Low infomobility
28

30. Conclusion
• There is a significant heterogeneity in the infomobility diffusion between
European cities reflecting demand-pull considerations
• We showed that innovative activities of Local Public Transport Companies
(LPTC) also reflect interdependencies among a variety of actors, especially
those active in the same city (municipalities and local ICT producers)
• There are important contextual factors which complement demand and
supply factors as key drivers for innovation in the Infomobility services
• German cities are very widely represented among those belonging to the
29
• German cities are very widely represented among those belonging to the
“high infomobility-high pollution” group (15 out of 21), while Italian (and
French) cities are much less so. More than 70% of the german cities with
high pollution developed a high level of infomobility, while the same is true
for less than 40% of the Italian cities and 30% of French cities with high
pollution national variables matter
• This results illustrates that in the latter cases (Italy and France) infomobility
is carried out largely regardless of the actual need of cities to reduce
pollution. This might indicate that in many circumstances infomobility
policies are designed more at the national than at the local level, and hardly
reflect actual priority of municipalities to control pollution levels.

31. Thanks
30

32. Data collection
• eProcurement: service list
Category eProcurement Municipality
Unit of
analysis
eProcurement Visibility Measures whether the municipality make available eProcurement services to potential suppliers on
their web site
Publication of general information on public
procurement
General information about the public procurement made available on the municipality websites
Publication of notices to official electronic notice boards
Availability of an official electronic notice board on the municipality websites where the procurement
notices are made publicly available
Link to e-procurement services Availability of a link to a web page providing eProcurement services. The web page may be part of the
website owned by the municipality or part of the website owned by an external supplier
eProcurement (Pre-Award Phase)
Measures the availability of 3 sub-phases (e-NOTIFICATION, e-SUBMISSION, e-AWARDS) constituting
the eProcurement process
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
31
Service
list
Service
description
e-mail alerts for suppliers Possibility for the suppliers to receive email alerts about forthcoming calls and notices of their interests
e-SUBMISSION Submission of proposals online
Assistance services to the supplier
Availability of online communication channels (e-mail, chat, audio/videoconferencing) to carry out Q&A
(Question and Answer) sessions between the eProcurement operator and the bidders
Online supplier help session
Existence of specific user help services, finalized to the assistance of the supplier for the preparation of
the online tender
e-AWARDS Includes the publication of awarded contracts
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)
The eProcurement Post-Award Process measures the availability of 3 distinct steps (e-ORDERING, e-
INVOINCING, e-PAYMENT) constituting the procurement process after the award of the contract
e-ORDERING Automatic placement of orders online
e-catalogues
Possibility to order online from e-catalogues managed by the eProcurement website and structured
according to the type of procurement, the product/services prices and characteristics
Electronic market
Availability of an electronic market hosted by the eProcurement website, for the online interaction
between buyers and suppliers
e-INVOICING Delivery of electronic invoices
e-invoicing service Availability of e-invoicing services managed by the eProcurement website
e-PAYMENT Online payment of contracts
e-payment service Availability of online payment services, managed by the eProcurement website

33. Econometric model: post-estimation diagnostics (1/2)
Max vif: empl hotel 3.19
Mean vif: 1.87
Threshold vif: 5
Breusch-Pagan test: chi-squared (1 dof)
P-value 0.2114
Normality: SW test = -0.406 P-value 0.658
Specification RESET test =1.14 P-value 0.3366
32
Specification RESET test =1.14 P-value 0.3366
Four light outliers (studentized res >|2|)
not exceeding iqr range:
1) Cremona (Ita) minus (low infomob)
2) Paris (Fra) minus (medium infomob)
3) Mainz (Ger) minus (low infomob)
4) Wiesbaden (Ger) plus (high infomob)
Seven possible leverage points >(2k+2)/n:
1) Aarhus (Den, medium infomob)
2) Luxembourg (Lux, high infomob)
3) Venice (Ita, high infomob)
4) Edinburgh (UK, medium infomob)
5) Palma de Mallorca (Esp, medium infomob)
6) Rome (Ita, high infomob)
7) Stockholm (Swe, high infomob)

34. Econometric model: post-estimation diagnostics (2/2)
CampobassoTorino P arisAalborg
Stoc kholmRoma
Palma de Mallorca
Edinburgh
Venezia
Luxembourg (city)
A arhus
.2.3.4
Leverage
High leverage
33
Metz ReimsStrasbourg Rouen MainzToursAugsburgRostockNancy DijonK arlsruheCaenKiel TrierBielefeldErfurtBochumNantes LyonMarseilleBremenBordeauxHannoverLilleMagdeburgStuttgartKölnClermont-FerrandDresden GroningenRegensburgBes ançon 's-GravenhageLeipzigAmiens B onnDüsseldorfOrléansMönchengladbachEssen DortmundRennesSaint-EtienneHeerlenSchwerinW ienLiverpoolBirmingham LogroñoGrenoble Koblenz Potsdam W iesbadenNijmegenHalle an der SaaleMalmöNice ZaragozaMontpellierNürnberg MoersToulouseDarmstadtRotterdamLimogesA ncona Mülheim a.d.RuhrPoitiersTrentoA jaccio ToulonTriesteValenciaBelfast ToledoMálagaS antanderHamburgCataniaStoke-on-trent Fort-de-FranceLens - LiévinMünchen Freiburg im BreisgauVerona Frankfurt (Oder)A msterdamManchester BerlinK øbenhavnPerugiaExeterPorts mouth CremonaS evillaGöttingen MadridPescaraW eimarPalermoStevenage Frankfurt am MainAix-en-ProvenceUtrechtBredaMilano BarcelonaMurcia Saint DenisGöteborgBari Genova TurkuP ointe-à-Pitre B ruxelles / B russelLe HavreS aarbruckenNapoliCagliariPamplona/IruñaHelsinki FirenzePotenzaCatanzaro BolognaL'A quila B adajozOdense
Cayenne
CampobassoTorino P arisAalborg
0.1
Leverage
0 .01 .02 .03 .04
Norm alized residual squared
High residual