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2nd International EIBURS-TAIPS conference on:“Innovation in the public sector
and the development of e-services”
University of UrbinoApril 18th-19th, 2013
Determinants and effects of infomobility at the city level
Davide Arduini, Marco Biagetti, Luigi Reggi and Paolo SeriEIBURS-TAIPS team, University of Urbino
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 SeriEIBURS-TAIPS team
University of Urbino
April 19th, 2013
Plan of the talk
Research questions: 1) developing a model to explore theinfluence of some urban characteristics on theprovision/diffusion of Infomobility services; 2) analysing therelationships between urban pollution and ITS development
• Definition of Infomobility/Intelligent Transport Systems(ITS)
2
(ITS)
• Literature review
• Data and the econometric model
• Results
• Conclusion
Definition
• The concept of Infomobility/Intelligent Transport Systems (ITS), provided by theEuropean 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 andenforcement; 5) Operation of green zones/low emission zones; 6) Intermodality for bothpassenger 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 culminatein the increased capacity of all citizens to participate in the local economy, accesspublic services and to be active members in their community
� ICT and Intelligent Transport Systems are improving all of these areas and arebreaking down geographical barriers as well
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
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 andproducts; etc
Smart people
Top research centres, top universities; Population qualified at levels 5-6 ISCED; Share of peopleworking 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 thecitizens (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 ofhealth 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.
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 reductionsin CO2 emissions
� Improving the utilization of existing infrastructure capacity, hence improving quality of lifeand 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-timeguidance on how best to exploit multiple transportation modalities
� Improving commercial enterprises through the publication of real-time data on the operationof city services
� Revealing how demands for energy, water and transportation peak at a city scale so that citymanagers can collaborate to smooth these peaks and to improve resilience
� Drivers receive better information about traffic and road conditions and make decisionsabout which routes to follow
Health end-point Units (per year) EU25 ItalyMortality – life expectancy reduction
Months 8.6 9.0
Mortality – long term exposure Life years lost x1,000 3618 498Mortality – long term exposure Number of premature
deaths x1,000348 51
Infant mortality Cases x1,000 0.6 0.08Chronic bronchitis Cases x 1000 163 24Respiratory hospital cases x 1000 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 x 1000 62 9
Cardiac hospital admissions Cases x 1000 38 5Restricted activity days Days x 1000 347687 48105Respiratory medication use (children)
Days x 1000 4218 531
Respiratory medication use (adults)
Days x 1000 27742 4003
Lower respir. symptoms (children)
Days x 1000 192756 21945
Lower respir. symptoms in adults with chronic disease
Days x 1000 285345 40548
Transport in
Europe is
responsible for
damaging
levels of air
pollutants and
a quarter of EU
greenhouse gas
emissions.
Source:CAFE 2005
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.
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
Data collection (1/3)
1) Urban Audit Dataset (source: Eurostat)
• Aim: providing reliable information, comparable amongst 322 cities in 27 Member States, plus 47 citiesfrom 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 nationalpopulation
� the participating cities in each country should represent about 20% of the population in thatcountry
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 apopulation 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
Data collection (2/3)
2) EIBURS-TAIPS Dataset (source: University of Urbino)
• Aim: desk analysis conducted through website-surfing to monitor public e-serviceavailability provided by local public transport companies and municipalities at the citylevel (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 collectedadapting and integrating extant methodologies
� ITS/Infomobility (based on ITIC-Between methodology, 2010)
� eProcurement (based on IDC methodology, 2010)
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 travellingPublic transport companies providing online information to users (e.g. waiting times,
strikes, delays, failures, etc.)
Online time table consultationPublic 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 travellingPublic 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
The construction of Infomobility Composite Indicator (ICI)
• The framework is based on ITIC-Between, 2010 and composed of 4
basic indicators
Basic indicatorService 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 planningtimetables
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
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 71.7682 .747 18.6653 .310 7.7494 .073 1.818Total 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
The diffusion of the Infomobility Composite Indicator (ICI)
Values of the CI in the selected cities (normalized MIN-MAX)
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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
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DK SE LU BE DE AT IE UK NL FI FR ES IT PT EL
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
εββββββββ
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.
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
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
Econometric model: results (2/4)
• The provision of infomobility services is strongly related to the size of theEuropean cities (variable expressed in terms of total population in the city)
� external pressure on Local Public Transport Companies (LPTC) to improve services can beexpected to increase with the number of city inhabitants
� the perceived need for advanced communication tools between LPTC and citizens appears toincrease with size, hence with the physical and social distances to be covered within the territoryof the city in order to gain access to service providers
20
• Another important factor affecting the availability of Infomobility tools includethe economic structure of the European cities, with a positive correlation offirms and workers in knowledge intensive services (financial and businesssectors)
• We observe that the availability of Infomobility services is affected by thepresence 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, knowledgespillovers will be facilitated and greater incentives are created that push less dynamic institutionsto enter the innovation race
Econometric model: results (3/4)
• The presence of local ICT producers in the city is also positively correlated withInfomobility development
� Local Public Transport Companies located in cities with higher shares of local ICT producers are ina 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 IPand value added services to business enterprises, a competitive presence of ICT serviceproviders 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 throughtheir 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 employmentrates in the European cities
� It appears that Local Public Transport Companies that are located in dynamic areas tend tointensify their provision of e-services
� Employment rates are logically associated with the quality of social environment in which localadministrations operate and with the level and sophistication of demand for services expressedby citizens and firms
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
Econometric model: results for days of ozone excess (1/2)
11
.52
2.5
3D
fbet
a d
ays
_oz
on
e_ex
cess
_la
t_a
v
Influence of city on ozone standard error
(threshold 2/sqrt(n))
23
Cremona
BolognaVerona
CampobassoBadajozDarmstadtPotsdam RomaFirenze MalmöMülheim a.d.Ruhr Aix-en-ProvenceMainz Cagliari TurkuMadridToledo UtrechtBielefeld Caen TorinoNürnberg BariBarcelona ToulonDortmund StevenagePoitiers CatanzaroDüsseldorf Lens - Liév in 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
Dfb
eta
da
ys_
ozo
ne_
exce
ss_
lat_
av
0 50 100 150 200 250Id
(threshold 2/sqrt(n))
Italian and German cities respectively
lower and increase the coefficient of
the pollution variable by a strong
amount
Econometric model: results for days of ozone excess (2/2)
Ca enBielef eld
Ma lm ö
Stevena geP oitiersDortm un dDüsse ldo rf
M ülh eim a.d .Ru hr
Turku
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ReimsAalb org
Købe nh avn
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Sev illaNice
Freibu rg im Breisga u
M ilano
M urcia
.51
e(
info
mo
b_
n | X
)The same story.
Italian and German
cities are influential
on the pollution
effect on
infomobility
24
Pote nza
Cag lia ri
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Roma
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Na poli
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Ca en
Belfast
Reg en sburg
Bielef eld
S antanderS toke-o n-tre nt
C le rm on t-Fe rran d
Stevena geLe ns - Liév inP oitiers
L imo ge s
Pamplo na /Iruña
Schwerin
Nant esMa nch este rL iverpo ol
Renne s
Ro tte rd am
Birmin gh am
's-Grave nh ag e
B ordea ux
Dortm un d
Rostoc k
Düsse ldo rf
B re daAmst erdam
Aarh us
Orléa ns
Saint De nis
Kiel
S aint-Etie nneHe lsin ki
Fort-de -Fran ce
Han nove rM ön che ng la db ach
Exe ter
Tours
Zarag ozaB esan çon
B ochu m
Esse nHam burg
Nan cy
Pe scara
Greno bleA ja ccio
Le HavreGro nin gen
Köln
P ointe -à-Pitre
Ca ye nn e
Od ense
Metz
Pa ris
Erfurt
Sa arbrucken
L ogro ño
Mála ga
Lyon
Tou louse
L ille
Nijme ge n
Göttin ge nAug sbu rg
Amie ns
S trasbo urg
Portsmou th
M ad ridM ainz
To ulon
W ien
M arseille
He erle n
Stuttg art
Brem en
Aix-e n-Pro ven ce
UtrechtToled o
Po tsda m
M o ntp ellie r
Firen ze
Tre ntoCa ta nia
Sev illa
Darmsta dtB ada joz
Nice
V erona
M urcia
Crem on a
To rino
B olog na
Cam po ba ss o
-.5
0e
( in
fom
ob
_n
| X )
- 40 -20 0 20 40e( days_ozone_ex cess_la t_av | X )
coef = .00 32 48 15, se = .0 01 697 89 , t = 1.91
Grouping Analysis (1/4)
The nations of the 140 towns in the regressionThe 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
infomobilityMore 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.
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.
Grouping Analysis (3/4)
High pollution - High infomobility
27
Grouping Analysis (4/4)
High pollution - Low infomobility
28
Conclusion
• There is a significant heterogeneity in the infomobility diffusion betweenEuropean cities reflecting demand-pull considerations
• We showed that innovative activities of Local Public Transport Companies(LPTC) also reflect interdependencies among a variety of actors, especiallythose active in the same city (municipalities and local ICT producers)
• There are important contextual factors which complement demand andsupply 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 (andFrench) cities are much less so. More than 70% of the german cities withhigh pollution developed a high level of infomobility, while the same is truefor less than 40% of the Italian cities and 30% of French cities with highpollution � national variables matter
• This results illustrates that in the latter cases (Italy and France) infomobilityis carried out largely regardless of the actual need of cities to reducepollution. This might indicate that in many circumstances infomobilitypolicies are designed more at the national than at the local level, and hardlyreflect actual priority of municipalities to control pollution levels.
Thanks
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Data collection
• eProcurement: service list
Category eProcurement MunicipalityUnit 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
procurementGeneral information about the public procurement made available on the municipality websites
Publication of notices to official electronic notice boardsAvailability 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
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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 supplierAvailability 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 sessionExistence 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-cataloguesPossibility 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 marketAvailability 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
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
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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)
Econometric model: post-estimation diagnostics (2/2)
Cam pobassoTorino P arisAa lborgStoc kholmRoma
Palma de Mallorca
Edinbu rghVenezia
Luxembourg (city)
A arhus
.2.3
.4L
eve
rag
e
High leverage
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Metz Reim sStrasbourg Rouen Ma inzTou rsAugsburgRostockNancy DijonK arlsruheCaenKiel TrierBielef eldErfu rtBochumNantes LyonMarse illeBremenBordeauxHannoverLilleM agdeburgStutt ga rtKölnClermont -Fe rrandDresden GroningenRegensburgBes ançon 's-GravenhageLe ipzigAmiens B onnDüsse ldo rfOrléansMönchengladbachEssen DortmundRennesSaint -Et ienneHeerlenSchwerinW ienLive rpoo lBirmingham LogroñoGrenoble Koblenz Po tsdam W iesbadenNijmegenHalle an der SaaleMa lmöNice ZaragozaMontpellierNürnberg MoersTou louseDarmstad tRo tterdamL imogesA ncona Mü lheim a.d.Ruh rPoitie rsTren toA jaccio Tou lonTriesteVa lenciaBe lf ast ToledoMálagaS ant anderHam burgCatan iaStoke-on -trent Fo rt -de-FranceLens - LiévinMünchen Fre iburg im Bre isgauVerona Frankfurt (Ode r)A msterdamMancheste r Be rlinK øbenhavnPerugiaExete rPorts mouth CremonaS evillaGöttingen MadridPescaraW eimarPale rmoStevenage Frankfurt am M ainAix-en-ProvenceUt rechtBredaMilano BarcelonaMurcia Sain t Den isGö teborgBari Genova TurkuP ointe -à-Pitre B ruxe lles / B russe lLe HavreS aarb ruckenNapo liCag lia riPamplona /IruñaHelsinki FirenzePo tenzaCatanzaro BolognaL 'A quila B ada jozOdenseCayenne
Cam pobassoTorino P arisAa lborg
0.1
Lev
era
ge
0 .01 .02 .03 .04Norm alized residual squared
High residual