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Determinants and effects of infomobility at the city level

Determinants and effects of infomobility at the city level






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    Determinants and effects of infomobility at the city level Determinants and effects of infomobility at the city level Presentation Transcript

    • 2ndInternational EIBURS-TAIPS conference on:“Innovation in the public sectorand the development of e-services”University of UrbinoApril 18th-19th, 2013Determinants and effects of infomobility at the city levelDavide Arduini, Marco Biagetti, Luigi Reggi and Paolo SeriEIBURS-TAIPS team, University of Urbino
    • 2nd International EIBURS-TAIPS conference on:““““Innovation in the public sectorand the development of e-services””””Determinants and effects of infomobilityat the city-level1Davide Arduini, Marco Biagetti, Luigi Reggi, Paolo SeriEIBURS-TAIPS teampaolo.seri@uniurb.itUniversity of UrbinoApril 19th, 2013
    • Plan of the talkResearch 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 theService 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 ticketing3passenger and freight transport; 7) Interoperability standards, e.g. for ticketing• The availability and adoption of ITS/Infomobility applications not only provides newand flexible transport services but also a range of information services that have thepotential to increase the accessibility and usability of transport services, reduceinequalities and increase economic participation and access to public servicesThe 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 communityICT 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 thatthere are several urban characteristics which are described in relation to the concept“Smart City”: a) smart economy (related to competitiveness); b) smart people (relatedto human capital); c) smart governance (related to participation); d) smartenvironment (related to natural resources); e) smart living (related to the quality oflife)• “Smart City” is furthermore used to discuss the use of modern transport technologies4• “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 existinginfrastructure without resorting to the establishment of new facilities: 1) improve theeconomic productivity of current and future systems; 2) environmental protection; 3) improvethe 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 timeand 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 anddevelopment
    • Literature review of Smart Cities (2/5)Determinants of “Smart Cities” in the literatureUrbancharacteristicsIndicatorsSmarteconomyR&D expenditure; Employment rate in knowledge-intensive sectors; New businesses registered;GDP per employed person; Unemployment rate; % of employed in providing ICT services andproducts; etcSmartpeopleTop research centres, top universities; Population qualified at levels 5-6 ISCED; Share of peopleworking in creative industries; etc.5people working in creative industries; etc.SmartgovernanceExpenditure 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.SmartenvironmentAccumulated ozone concentration; Green space share; Efficient use of water, Efficient use ofelectricity; etcSmartlivingMuseums visits per inhabitant; Theatre attendance per inhabitant; Satisfaction with quality ofhealth system; Importance as tourist location; Overnights per year per resident; Poverty rate; etc.SmartmobilityPublic 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 emissionsImproving the utilization of existing infrastructure capacity, hence improving quality of lifeand reducing the need for traditional construction projectsMaking new services available to citizens, commuters and travelers, such as real-time6Making new services available to citizens, commuters and travelers, such as real-timeguidance on how best to exploit multiple transportation modalitiesImproving commercial enterprises through the publication of real-time data on the operationof city servicesRevealing how demands for energy, water and transportation peak at a city scale so that citymanagers can collaborate to smooth these peaks and to improve resilienceDrivers receive better information about traffic and road conditions and make decisionsabout which routes to follow
    • Health end-point Units (per year) EU25 ItalyMortality – life expectancyreductionMonths 8.6 9.0Mortality – long term exposure Life years lost x1,000 3618 498Mortality – long term exposure Number of prematuredeaths x1,000348 51Infant mortality Cases x1,000 0.6 0.08Chronic bronchitis Cases x1000 163 24Respiratory hospital cases x1000 62 9Literature review: traffic pollution and health (4/5)Trafficpollution stillharmful tohealth inmany parts ofEurope.7Respiratory hospitaladmissionscases x1000 62 9Cardiac hospital admissions Cases x1000 38 5Restricted activity days Days x1000 347687 48105Respiratory medication use(children)Days x1000 4218 531Respiratory medication use(adults)Days x1000 27742 4003Lower respir. symptoms(children)Days x1000 192756 21945Lower respir. symptoms inadults with chronic diseaseDays x1000 285345 40548Transport inEurope isresponsible fordamaginglevels of airpollutants anda quarter of EUgreenhouse gasemissions.Source:CAFE 2005
    • Literature review: how intelligent transport systems canreduce pollution (5/5)2) providing real time Information about air pollution to the public- spontaneous changes in mobility behavior1) infomobility easier use of public transport changes in mobility behaviorreduction of urban pollutionThree main channels:8- spontaneous changes in mobility behavior- traffic restrictions from local autorities3) speed control traffic signals- Kan, A. and de Barros, A.G., (2007) “The role of intelligent transport systems in reducing theimpact 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 onpollutant 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 theinfluence of some urban characteristics of “ Smart Cities ”””” on theprovision/diffusion of Infomobility services9• We aim to apply this framework to 140 European cities, employing an unusuallydetailed 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 nationalpopulationthe participating cities in each country should represent about 20% of the population in thatcountry10countrythe 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 waves1989 - 1993; 1994 - 1998; 1999 - 2002; 2003 - 2006; 2007 – 2009Variables: nine different areas of variables have been defineddemography, 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 datasetTime coverage: 201211• Time coverage: 2012• Variables: two service categories have been considered, and data have been collectedadapting and integrating extant methodologiesITS/Infomobility (based on ITIC-Between methodology, 2010)eProcurement (based on IDC methodology, 2010)
    • Data collection (3/3)• ITS/Infomobility: service listUnit of analysis Local public transport companyPublic 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 ofpublic transport network12ServicelistOnline travel planningPublic transport companies offering timetables with route planning (travel planner) onthe webOnline ticket purchase Public transport companies offering web based payment systemsPrivate 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 orparkingElectronic 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 4basic indicatorsBasic indicatorService involved(see slide 12)VariableNo. of channels used to offer information services to publictransport users while travelling (call center, SMS, website, etc.)Online info to users while travelling info_users13No. of different ways to access to time tables of publictransportation (download, static webpage, travel planner offeredvia website, smart phone application, etc.)Online time table consultationOnline travel planningtimetablesNo. of different ways to purchase the ticket (smart card, website,mobile phone, etc.)Online ticket purchase ticketsNo. of channels used to offer travel info on parking and traffic tocar drivers (call center, SMS, website, etc.)Info to car drivers while travelling travel_infoNote: 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 forconstructing composite indicators (OECD, 2008. pag. 89)• The weights are obtained through a Nonlinear Principal Component Analysis, which issuitable for qualitative variables. See Gifi A. (1990) Nonlinear Multivariate Analysis.John Wiley & SonsDimensions revealed COMPONENTS LOADINGS from non-14DimensionVariance Accounted ForTotal(Eigenvalue)% ofVariance1 2.871 71.7682 .747 18.6653 .310 7.7494 .073 1.818Total 4.000 100.000Dimensions revealedDimensionsweight1 2tickets 0.13 0.82 0.21Info_users 0.30 0.09 0.27timetables 0.27 0.00 0.25travel_info 0.30 0.08 0.27Sum 1 1 1.00COMPONENTS LOADINGS from non-linear PCA SQUARED & weightsThe 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)0,60,70,80,911500,10,20,30,40,5FrankfurtamMainKøbenhavnLeipzigBerlinÖrebroAntwerpenBruggeLinköpingGöteborgReimsGenovaFreiburgimBreisgauBonnTorinoOdenseDresdenJönköpingHannoverEnschedeAlmereDijonTrierRomaNiceNewcastleuponTyneWolverhamptonMagdeburgMönchengladbachKöln(Cologne)BielefeldWienEssenWrexhamLinzMontpellierInnsbruckLondonPalmadeMallorcaLeedsCorkGalwayBordeauxAmsterdamBristolPortsmouthStoke-on-trentRegensburgStrasbourgBremenDarmstadtRennesDublinBelfastFirenzeEdinburghSaint-EtienneLens-LiévinLisboaValenciaCharleroiNamurMurciaLiverpoolNottinghamLilleTrentoPoitiersParisRostockMetzLeicesterSalernoVigoKielGöttingenClermont-FerrandToursCórdobaThessalonikiCataniaTilburgBredaVolosSetúbalCambridgeLincolnGroningenKoblenzAjaccioVeronaCardiffCoimbraPotsdamMadridGijónTriesteSchwerinEindhovenOviedoIrakleioHospitaletdeLlobregat(L)PalermoCampobassoAveiroFunchalCatanzaroPontaDelgadaBadajozPotenzaPescaraReggiodiCalabriaIoanninaLogroñoFort-de-FranceGraveshamEU15 average
    • The diffusion of the Infomobility Composite Indicator (ICI)Average values of the CI in the selected cities, by Country (normalized MIN-MAX)0,50,60,70,80,9EU15 average1600,10,20,30,4DK SE LU BE DE AT IE UK NL FI FR ES IT PT EL
    • The theoretical pinpoint of the analyzed modelWhere Infomob is our dependent variable (composite indicator), eproc is a composite indicator ofiiiiiiiiiiiiiicarsictlocuntournightsozonehighedemplfinempltranspemplhotelemplrpopdenspopeprocInfomobεββββββββββββα+++++++++++++=121110987654321 lnln17Where Infomob is our dependent variable (composite indicator), eproc is a composite indicator ofeProcurement (calculated as the simple mean of 13 indicators), lnpop and lnpopdens are thelogarithms of population and population density respectively, emplr is the municipal rate ofemployment, emplhotel, empltransp and emplfin are the employment in hotels-restaurants-trade, transport-communications and financial and business sectors (in %), highed is the share ofpeople between 15 and 64 years of age with at least a degree (ISCED 5-6), ozone is the number of daysin a year when an excess of ozone is recorded in town, tournights is the number of tourist overnightstays in registered accommodation per year per resident population, ictlocun is the proportion of localunits 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 statisticsMax= Stockholm200818229 towns. 195 only theoretically available. Due to missing data of towns in some of these variables thenumber 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 thesignificant regressors even though withdifferent p-values19Adjusted R2= 0.344P-values (+0.1 *0.05 **0.01 ***0.001)N=140!!! It is the first time that ananalysis of this kind is performed on sucha 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 inhabitantsthe 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 providers20• 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 cityAmong these actors are the municipalities offering eProcurement servicesThis 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 developmentLocal 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 softwareWhere 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 through21providers 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 citiesIt appears that Local Public Transport Companies that are located in dynamic areas tend tointensify their provision of e-servicesEmployment 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 ofmeasurement (i.e. use of logs for some variables or percentage forothers), homoskedasticity is verified through Breusch-Pagan test, normality ofresiduals through the Shapiro-Wilk test and standard graphical procedures(pnorm qnorm). Some influential city (9, through Cook D’s threshold of 4/n) arethe following:1) Aarhus (Den, medium infomob)2) Paris (Fra, medium infomob)High, medium, low222) 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, lowbased onpercentiles
    • Econometric model: results for days of ozone excess (1/2)11.522.53Dfbetadays_ozone_excess_lat_avInfluence of city on ozone standard error(threshold 2/sqrt(n))23CremonaBolognaVeronaCampobassoBadajozDarmstadtPotsdam 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 ManchesterLAquilaPescaraRotterdamAmsterdamDresdenSchwerin 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-.50.51Dfbetadays_ozone_excess_lat_av0 50 100 150 200 250Id(threshold 2/sqrt(n))Italian and German cities respectivelylower and increase the coefficient ofthe pollution variable by a strongamount
    • Econometric model: results for days of ozone excess (2/2)CaenBielefeldMalm öStevenageP oitiersDortm undDüsseldorfM ülheim a.d.RuhrTurkuB arcelonaZaragozaB esançonGöteborgReimsAalborgKøbenhavnRouenOdenseErfurtMálagaDijonBruxelles / BrusselLeipzigS trasbourgM oersBonnBerlinM arseilleTrierDresdenFrankfurt am MainLuxembourg (city)MagdeburgM ontpellierFrankfurt (Oder)VeneziaHalle an der SaaleW eimarW iesbadenKarlsruheGenovaSev illaNiceFreiburg im BreisgauMilanoM urcia.51e(infomob_n|X)The same story.Italian and Germancities are influentialon the pollutioneffect oninfomobility24PotenzaCagliariLAquilaCatanzaroBariAnconaRomaKoblenzPerugiaPalma de MallorcaStockholmMünchenPalerm oNapoliNürnbergEdinburghTriesteValenciaCaenBelfastRegensburgBielefeldS antanderS toke-on-trentClerm ont-FerrandStevenageLens - Liév inP oitiersLimogesPamplona/IruñaSchwerinNantesManchesterLiverpoolRennesRotterdamBirminghams-GravenhageB ordeauxDortm undRostoc kDüsseldorfB redaAmsterdamAarhusOrléansSaint DenisKielS aint-EtienneHelsinkiFort-de-FranceHannoverM önchengladbachExeterToursZaragozaB esançonB ochumEssenHam burgNancyPescaraGrenobleA jaccioLe HavreGroningenKölnP ointe-à-PitreCayenneOdenseMetzParisErfurtSaarbruckenLogroñoMálagaLyonToulouseLilleNijmegenGöttingenAugsburgAmiensS trasbourgPortsmouthM adridM ainzToulonW ienM arseilleHeerlenStuttgartBrem enAix-en-ProvenceUtrechtToledoPotsdamM ontpellierFirenzeTrentoCataniaSev illaDarmstadtB adajozNiceV eronaM urciaCrem onaTorinoB olognaCam pobass o-.50e(infomob_n|X)-40 -20 0 20 40e( days_ozone_ex cess_lat_av | X )coef = .00324815, se = .00169789, 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 tothe group with high pollution (days of ozone excess)25The nations of the 27 towns in the regression belonging to thegroup with high pollution (days of ozone excess) and highinfomobilityMore than 70% of the german citieswith high pollution developed a highlevel of infomobility, while the same istrue for less than 40% of the Italiancities and 30% of French cities withhigh pollution.
    • Grouping Analysis (2/4)High pollution - High infomobility High pollution – Low infomobility26Towns with High pollution and High infomobility show in average an higher levelof eProcurement, an higher level of employment rate and employment in thefinancial and business sectors. They are also slightly bigger. Town with Highpollution and low infomobility are in average more polluted.
    • Grouping Analysis (3/4)High pollution - High infomobility27
    • Grouping Analysis (4/4)High pollution - Low infomobility28
    • 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 the29• 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.
    • Thanks30
    • Data collection• eProcurement: service listCategory eProcurement MunicipalityUnit ofanalysiseProcurement Visibility Measures whether the municipality make available eProcurement services to potential suppliers ontheir web sitePublication of general information on publicprocurementGeneral information about the public procurement made available on the municipality websitesPublication of notices to official electronic notice boardsAvailability of an official electronic notice board on the municipality websites where the procurementnotices are made publicly availableLink to e-procurement services Availability of a link to a web page providing eProcurement services. The web page may be part of thewebsite owned by the municipality or part of the website owned by an external suppliereProcurement (Pre-Award Phase)Measures the availability of 3 sub-phases (e-NOTIFICATION, e-SUBMISSION, e-AWARDS) constitutingthe eProcurement processe-NOTIFICATION Publication of tenders and procurement notices on the webOnline registration of supplier Creation of user accounts and profiles with related rolese-mail alerts for suppliers Possibility for the suppliers to receive email alerts about forthcoming calls and notices of their interests31ServicelistServicedescriptione-mail alerts for suppliers Possibility for the suppliers to receive email alerts about forthcoming calls and notices of their interestse-SUBMISSION Submission of proposals onlineAssistance 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 biddersOnline supplier help sessionExistence of specific user help services, finalized to the assistance of the supplier for the preparation ofthe online tendere-AWARDS Includes the publication of awarded contractsOnline information about awarded contracts The website publishes the contracts awarded and their winnere-auctions Availability of tools to carry out real-time price competitionseProcurement (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 contracte-ORDERING Automatic placement of orders onlinee-cataloguesPossibility to order online from e-catalogues managed by the eProcurement website and structuredaccording to the type of procurement, the product/services prices and characteristicsElectronic marketAvailability of an electronic market hosted by the eProcurement website, for the online interactionbetween buyers and supplierse-INVOICING Delivery of electronic invoicese-invoicing service Availability of e-invoicing services managed by the eProcurement websitee-PAYMENT Online payment of contractse-payment service Availability of online payment services, managed by the eProcurement website
    • Econometric model: post-estimation diagnostics (1/2)Max vif: empl hotel 3.19Mean vif: 1.87Threshold vif: 5Breusch-Pagan test: chi-squared (1 dof)P-value 0.2114Normality: SW test = -0.406 P-value 0.658Specification RESET test =1.14 P-value 0.336632Specification RESET test =1.14 P-value 0.3366Four 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)CampobassoTorino P arisAalborgStoc kholmRomaPalma de MallorcaEdinburghVeneziaLuxembourg (city)A arhus.2.3.4LeverageHigh leverage33Metz 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 BolognaLA quila B adajozOdenseCayenneCampobassoTorino P arisAalborg0.1Leverage0 .01 .02 .03 .04Norm alized residual squaredHigh residual