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Patterns of public eService development across European cities


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Patterns of public eService development across European cities

  1. 1. 12ndInternational EIBURS-TAIPS conference on:“Innovation in the public sectorand the development of e-services”Patterns of public eService developmentacross European citiesDavide Arduini, Annaflavia Bianchi, AlessandraCepparulo, Luigi Reggi and Antonello ZanfeiEiburs-TAIPS Team, University of Urbino, Italyantonello.zanfei@uniurb.itUniversity of UrbinoApril 18-19th, 2013
  2. 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 implicationsOutline
  3. 3. This presentation evaluates public eService diffusion as partof a smart growth strategy in Europe• eService development across Europe is a key aspect of innovationin the public sector and contributes to EU long termcompetitiveness.• extant benchmarking and studies can hardly account for theactual patterns of public eService development for several reasons:- they most often focus on eGovernment, largely disregarding otherweb based public activities- even when attention is given to other eService categories, these arenot examined with comparable methods- comparative studies are mostly focused on national patterns, withlimited attention to the regional or local level of analysisFocus… and motivation
  4. 4. • A novel dataset (EIBURS-TAIPS Database) providing comparabledata at the following levels:- across four service categories (eGovernment, eProcurement, eHealthand 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 diffusionat all three levels (across service categories, nations and cities)• An exploratory analysis of the characteristics of European citiesassociated with public eService development•A focus on the links between public eServices and the “smartness”of citiesNovelty of this research line
  5. 5. • How heterogeneous is public sector innovation across EU15nations in terms of four public eServices ?•Is heterogeneity higher across countries or cities?•Does heterogeneity persist when considering clusters of cities withcomparable levels of socio-economic development across Europe?•Are Smart cities also best performers in terms of public eServicedevelopment?•What characteristics of European cities are associated with publiceService development?Research questions
  6. 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-servicesand 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, theiruse at the regional and local level is still largely under-developed.• Recent exceptions:• EC (2009) for eProcurement, and CapGemini (2010) for eGovernment have introducedfor the first time a regional focus of analysis.• Academic papers have developed CIs using data at the local level, but based onindividual 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 dataconstraints encountered at a more disaggregated level, has long led to:• Erroneously conclude that evidence on e-service diffusion in one area of public sectoractivity 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. 7. Background literature (2/4)• Need to better capture heterogeneity in public sector innovation• Two academic traditions have been feeding the discussion concerning urban innovationand the role of public sector in it: 1) Systemic theory of innovation; and 2) the literatureof Smart cities1)Systemic theory of innovation• The systemic theory of innovation was initially formulated at the national levelLundvall (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 amongdifferent actors, including public institutions• Public technology procurement literature emphasises the roles of public sector insystemic 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, includingthe public sector, combined with the characteristics of technologies determine a highheterogeneity of innovation processes across countries and regions (Fagerberg, 2005;Tether and Metcalfe, 2004)
  8. 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, thedevelopment of institutions that enhance learning and innovation, and of digitalspaces 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 andimplementation of smart city initiatives (Lindskog, 2004; Lepouras et al., 2007; Ingramet al., 2009; Giffinger and Gudrun, 2010)• City smartness will likely feed-back on local government capacity to innovate theirorganisation and activities, including their servicesSmarter cities are likely to have more innovative public sector and more advancedpublic servicesHeterogeneity of innovation within regions and across cities as a result of differentmix of city level characteristics and evolution
  9. 9. Background literature (4/4)Based on these converging streams of literature weshould thus observe:•A high heterogeneity in public sector innovation acrossnations• Heterogeneity in terms of overall eService diffusion• Heterogeneity in terms of eService portfolio andspecialisation•Heterogeneity is even higher at the local level, reflectingthe variety of actors and local government roles•Cities will differ in terms of eServices reflecting theirsmartness
  10. 10. Data and indicatorsOur study combines two datasets:1) EIBURS-TAIPS Dataset (source: University of Urbino)• Data characteristics: information collected by the TAIPS team throughwebsite-surfing to monitor public e-services provided by local publictransport companies, municipalities and hospitals at the city level (15-EU). Data refer to availability of eServices in 2012, corrected to accountfor standard quality measures.• Sample design: 229 cities representing the EU15 subset of the 322 citiesmonitored in Eurostat’s Urban Audit dataset• Variables: info on the provision of 23 eServices classified into fourcategories ITS/Infomobility (based on ITIC-Between methodology, 2010) eHealth (Based on Empirica methodology, 2008; and Deloittemethodology, 2011) eProcurement (based on IDC methodology, 2010) eGovernment (based on Capgemini methodology, 2010)
  11. 11. 2) Urban Audit Dataset (source: Eurostat) :• Data characteristics: comparable information on 322 cities, out ofwhich 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 apopulation of 50,000 – 250,000 inhabitants, large cities with >250 000)Time coverage: six waves 1989 - 1993; 1994 - 1998; 1999 - 2002; 2003 - 2006; 2007 - 2009Variables: demography, social aspects, economic aspects, civic involvement, trainingand education, environment, ICT, travel and transport, information society,culture and recreationData and indicators
  12. 12. Data and indicators: E-HEALTHUnit of analysis : hospitalsServicelistVideoconferencing/Video consultationsbetween patients and doctorsDedicated and formal use of facilities such as consultationsbetween 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 containspatient-centric, electronically-maintained informationabout an individual’s health status and care. The systemallows online access to patientse-booking Electronic appointment booking systemOnline clinical testsComputer-based system for electronic transmission ofresults ofclinical tests. The system allows online access to patientse-referralsHospitals offering the possibility to external health actorsto make appointments for their patientsTelemedicine service (tele-homecare/tele-monitoring)The provision of social care at a distance to a patient inhis/her home, supported by means of telecommunicationsand computerized systemsOnline chronic disease managementHome care services using ICT can contribute to themanagement of long duration/slow progression diseasesOnline ticket paymentHospitals offering web based payment systems for visitsand clinical tests
  13. 13. Data and indicators : ITS/INFOMOBILITYCategory Unit of analysis : Local public transport companiesServicelistPublic Informed MobilityOnline info to users whiletravellingPublic 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 theonline timetable of public transport networkOnline travel planningPublic transport companies offering timetables with route planning(travel planner) on the webOnline ticket purchase Public transport companies offering web based payment systemsPrivate Informed MobilityInfo to car drivers whiletravellingPublic transport companies providing online information totravelers about traffic or parkingElectronic road or parking tollPublic transport companies offering a electronic ticketing system ofparking spaces
  14. 14. Data and indicators :E-PROCUREMENTUnit of analysis: MunicipalityServicelisteProcurement VisibilityPublication of general information on public procurementGeneral information on public procurement made available on themunicipality websitesPublication of notices to official electronic notice boardsOfficial electronic board on the municipality websites whereprocurement notices are madeLink to e-procurement services Link to a web page (owned by the municipality or by externalparties) providing eProcurement serviceseProcurement (Pre-Award Phase)e-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 suppliersPossibility for the suppliers to receive email alerts aboutforthcoming calls and notices of their interestse-SUBMISSIONAssistance services to the supplierE-mail, chat, audio/videoconferencing communication for Questionand Answer sessions between eProcurement operators and biddersOnline supplier help session help services to assist suppliers in the preparation of online tendere-AWARDSOnline 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)e-ORDERINGe-catalogues Online order from e-catalogues through eProcurement websiteElectronic marketElectronic market hosted by the eProcurement website, for onlineinteraction between buyers and supplierse-INVOICINGe-invoicing service E-invoicing services managed by the eProcurement websitee-PAYMENTe-payment service Online payment services, managed by the eProcurement website
  15. 15. Data and indicators: EGOVERNMENTUnit of analysis : MunicipalityServicelistOnline local taxes Declaration, payment, notification of assessmentOnline registration schoolStandard procedure to register children at kindergardenOnline registration of residenceStandard procedure to register the residence in a local area oftownOn line payment fines Standard procedure to pay fines at municipal police officeOnline personal documentsStandard procedure to obtain an international passport and anidentity cardOnline public libraryStandard procedure to consult the catalogue(s) of a publiclibrary to obtain specific information regarding a specific carrier(Book, CD, etc)Online birth/marriagecertificates Standard procedure to obtain a birth or marriage certificateOnline registration of a newcompanyStandard procedure to start a new company
  16. 16. Measuring service availability and qualityCI pillar eService Availability eService QualityE-HEALTH 8 eServices considered Not measuredINFOMOBILITY 6 eServices considered Presence/absence of qualityfeatures including: multi-channeldelivery, advanced functions andapplicationsE-PROCUREMENT 1 eService considered =eProcurementPresence/absence of qualityfeatures associated with eachphase (visibility, pre-award, post-award phases)E-GOVERNMENT 8 eServices considered Interactivity stages, normalized 0-100% (see CapGemini, 2010)
  17. 17. Construction of a Composite Indicator (CI) -1An indicator of public eServices development is calculated asthe average of a city’s performance in the four domainsconsidered (eHealth, Infomobility, eGovernment,eProcurement)For each domain, we used existing analytical frameworks inorder to: (a) define the different dimensions of phenomenastudied, including standard measures of quality of eServicesavailable (e.g. interactivity); (b) define the nested structureof the various sub-groups that will guide the aggregationprocess; (c) select the underlying basic indicatorsThe indicators obtained are then normalized (MIN-MAXmethod) in order to make the scores of each city in the fourdomains fully comparable.
  18. 18. Construction of a Composite Indicator (CI) -2CI pillar Framework Weighting method AggregationE-HEALTH Based onEmpirica, 2008;and Deloitte, 2011Multiple CorrespondenceAnalyis (MCA) applied to thebasic indicators. Suitable fordichotomous variables (OCED,2008).ArithmeticmeanINFO MOBILITY Based on ITIC-Between, 2010Non-linear PrincipalComponent Analysis (PCA)applied to the basic indicators.Suitable for qualitativevariables (OECD, 2008).ArithmeticmeanE-PROCUREMENT Based on IDC,2010Equal weights ArithmeticmeanE-GOVERNMENT Based onCapgemini, 2010Weights proportional toeServices interactivity as inCapgemini, 2010ArithmeticmeanFinal aggregation = Arithmetic mean
  19. 19. Cross-country comparisons ofeService development
  20. 20. Country index vs EU average index00. indexEU15
  21. 21. Heterogeneity across eServices and across countries00. runnersGroup of countries withall or most e-servicessupplied above the EUaverage00. KingdomUKEU15
  22. 22. PerformersGroup of countries with one ortwo e-services supplied abovethe EU average00.
  23. 23. Heterogeneity across eServices and across countries00. of countries with one e-service supplied above the EU average-
  24. 24. behindGroup of countries with average or below average performance00.
  25. 25. Analysis of eServicedevelopment at the city level
  26. 26. 26Comparing heterogeneity across countries and across municipalitiesEU average
  27. 27. To compare the municipalities in terms of their e-servicediffusion and sophistication, we need to refer to clusters ofhomogenous 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 berepresentative of different aspects of municipalities2. We identify the clusters of municipalities based on the abovementioned components3. Using Eiburs-TAIPS data on eGov, Infomobility, eProcurementand eHealth at the city level we illustrate how clusters can becharacterised in terms of eService developmentThese comparisons are possible for 148 cities only, due to dataconstraintsComparing eServices across cities
  28. 28. First step: Principal Component AnalysisDemographic characteristics:Percentage of residents over 65Population density: total resident pop. per square kmInfrastructural characteristicsLength of public transport network / land areaPercentage of households with Internet access at homeCivil societyParticipation rate at city electionsNumber of female elected city representativesHuman capitalProp. of working age population qualified at level 5 or 6 ISCEDEconomic Characteristics:Gross Domestic Product per inhabitant in PPS of NUTS43Unemployment rateSectoral specialization:No. Manufacturing (and service?)Companies (all sectors?)Number of persons employed in provision of ICT servicesProp. 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 recycledAttractiveness:Total annual tourist overnight stays in registered accommodation
  29. 29. Validated PCA -2
  30. 30. Cluster No.obscharacteristics municipalities1 7 -Industrial and infrastructuraldevelopment: Medium high- Share of financial and businessservice employment: LowValenciaSevillaLas PalmasPalma de MallorcaPamplona/IruñaPortoHelsinki2 1 -Industrial and infrastructuraldevelopment: High- Share of financial and businessservice employment: HighStockholm3 49 -Industrial and infrastructuraldevelopment: Medium-low- Share of financial and businessservice employment: MediumBonn,Karlsruhe,Mainz,Kiel,SaarbruckenPotsdam,Koblenz,Rostock,StrasbourgNantes,Lille,Montpellier,Rennes,OrléansGrenoble,Aix-en-Provence,MarseilleCatania,Cremona,Trento,PerugiaAncona,LAquila,Campobasso,CasertaCatanzaro,Reggio di Calabria,SassariFoggia,Salerno,Apeldoorn,Malmö,Linköping
  31. 31. Cluster No.obscharacteristics municipalities4 1 -Industrial and infrastructuraldevelopment: Very High- Share of financial and businessservice employment: LowBarcelona5 2 -Industrial and infrastructuraldevelopment: Medium- Share of financial and businessservice employment: HighFrankfurt am MainLuxembourg (city)6 9 -Industrial and infrastructuraldevelopment: Medium High- Share of financial and businessservice employment: MediumHighWien,Bruxelles /Brussel,Hamburg,MünchenNapoli,Torino,Firenze,Amsterdam,Lisboa7 30 -Industrial and infrastructuraldevelopment: Medium low- Share of financial and businessservice employment: MediumHighKöln,Antwerpen,Essen,Stuttgart,Leipzig,Dresden,Dortmund,Düsseldorf,Hannover,Nürnberg,Wiesbaden,Lyon,ToulousePalermo,Genova,Bari,Bologna,VeneziaVerona,Trieste,Pescara,Potenza,CagliariPadova,Brescia,Modena,s-Gravenhage
  32. 32. Cluster No.obscharacteristics municipalities8 49 -Industrial andinfrastructuraldevelopment: Medium low- Share of financial andbusiness serviceemployment: lowCharleroi,Liège,Brugge,NamurAarhus,Aalborg,Bochum,Halle an der SaaleMagdeburg,Moers,Trier,Freiburg imBreisgau,Málaga,MurciaValladolid,Vitoria/Gasteiz,Oviedo,Alicante/Alacant,Vigo,Saint-Etienne,LeHavre,Amiens,NancyMetz,Reims,Dijon,Poitiers,Clermont-FerrandCaen,Limoges,BesançonAjaccio,Saint Denis,Fort-de-FranceTours,Lens – Liévin,NijmegenBraga,Funchal,Coimbra,Setúbal,PontaDelgada,Aveiro,Faro,Tampere,TurkuJönköping,Belfast,Derry
  33. 33. Comparison of the municipalitiesacross and within clusters in terms ofeServices supplied
  34. 34. Clusters index vs Cluster average index
  35. 35. Cluster 3 – “medium low industrial/infrastructuraldevelopment and medium low share of business services”Ranking of cities in terms of eServicesSouthernEuropeNorthernEurope
  36. 36. SouthernEuropeCluster 6 – “medium high industrial/infrastructuraldevelopment and medium high share of business services”Ranking of cities in terms of eServices
  37. 37. Correlation among E-services index- Smart index and itscomponentsSmartindexSmarteconomySmartlivingSmartpeopleSmartgovernanceSmartmobilitySmartenvironmenteServicesindex0.4* 0.4* 0.47* 0.5* 0.3 0.5* -0.25Source: European Smart Cities ( Vienna University of Technology , Delft University ofTechnology and the University of Ljubljana).*significance level 5%
  38. 38. Given the results of correlations we search fordrivers of public eServices based on theempirical literature on determinants of Smartcity developmentcf. European Smart Cities (2007), Caragliu, Del Bo,Nijkamp(2011), Caragliu & Del Bo (2012)In search of determinants of eService index
  39. 39. Determinants of the development of smart cities- Gross Domestic Product ofcity/region/country (Euro)- New business that have registered in thereference year*- Self-employment rate- Proportion in part time- Proportion of population aged 15-64qualified at tertiary level (ISCED 5-6)living in Urban Audit cities - %- Total book loans and other media perresident*- Length of public transport networkper inhabitant- Number of stops of public transport- Number of deaths in road accidents- Number of tourist overnight stays inregistered accommodation per year perresident population- Total number of recorded crimes per 1000population- Number of hospital beds- Cinema attendance (per year)*- Theatre attendance (per year)*- Number of museum visitors (per year)Smart economy componentSmart mobility componentSmart people componentSmart living component* Large number of missing values
  40. 40. Correlations check among our index andthe potential determinantsρGdppp 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 transportstops) 0.2344*1/sqrt(Lenght transportper inhabitant) 0.108Crime 0.1616*Sqrt(Road accidents) 0.0874iijijiji itySmartMobileSmartPeoplgSmartLivinmySmartEconoESindex εββββ ++⋅+⋅+⋅= 4321Our research question thus translates in an empirical model of the form:where ESindex is the composite indicator of eServicedevelopmentsubscript i refers to cities and supra-script j refers to thea specific component of city smartnessVariablesGdppp: Gross Domestic Product purchasing power paritySelfemploy:Self-employment ratePropparttime:Proportion in part time on total workforcebedhosital:Number of hospital bedsMuseum: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 yearPublic transport stops(Stopbsn):Number of stops of public transportLength transport: km of public transportation network per residentpopulationCrime: Total number of recorded crimes per 1000 populationRoad accident: Deaths in road accidents per year
  41. 41. Testing determinants of e-services index-1* p<0.05, ** p<0.01, *** p<0.001p-values in parenthesesR-sq 0.937 0.935 0.940N 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 0.00000183index_a~e index_a~e index_a~e(1) (2) (3)* p<0.05, ** p<0.01, *** p<0.001p-values in parenthesesR-sq 0.937 0.938 0.937N 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 0.000000508index_a~e index_a~e index_a~e(1) (2) (3)
  42. 42. * p<0.05, ** p<0.01, *** p<0.001p-values in parenthesesR-sq 0.936 0.940 0.936N 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 0.000000431index_a~e index_a~e index_a~e(1) (2) (3)* p<0.05, ** p<0.01, *** p<0.001p-values in parenthesesR-sq 0.941 0.941 0.941N 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 0.00000125index_a~e index_a~e index_a~e(1) (2) (3)Testing determinants of e-services index-2
  43. 43. CONCLUSIONS• This presentation fills three gaps:– Coverage of public eServices beyond eGovernment with comparabledata– Comparing eService development across countries and cities– Linking eServices with smartness of cities• Heterogeneity in Public eService development is high acrosscountries and across service categories• Heterogeneity is even greater when examined at the city level andacross clusters of relatively homogeneous cities• Cities from nordic and central European countries are largely rankinghigh, but there is heterogeneity also across these cities  a regionaland sub-regional approach needed• “Smart cities” also exhibit high levels of eService development• Smart city characteristics that are most associated with publiceService diffusion are: human capital and transportationinfrastructure development
  44. 44. Thanks
  45. 45. City sampleCode Tot cities50 000 – 250 000ab. > 250 000 ab.AT 5 3 2BE 7 4 3DK 4 2 2DE 40 18 22IE 5 4 1EL 6 7 2ES 23 7 16FR 35 15 20IT 32 20 12LU 1 1NL 15 11 4PT 9 8 1FI 4 3 1SE 8 5 3UK 30 12 18TOT 229 122 107Data and indicators
  46. 46. Descriptive statisticseprocurement 229 .6388128 .2440753 0 1ehealth 229 .187901 .2463106 0 1infomob 229 .4696298 .2191175 0 1egov 229 .6156161 .2136404 0 1Variable Obs Mean Std. Dev. Min Max
  47. 47. PCA -3
  48. 48. Step 2: cluster analysis
  49. 49. Cluster 7 – “medium low industrial/infrastructural developmentand medium high share of business services”Ranking of cities in terms of eServicesCentralEuropeNorthernEuropeSouthernEurope
  50. 50. Cluster 5 – “medium industrial/infrastructuraldevelopment and high share of business services”Ranking of cities in terms of eServices
  51. 51. Ranking: single point clusters: 2 and 4NorthernEurope
  52. 52. SouthernEuropeCluster 1 – “medium high industrial/infrastructuraldevelopment and low share of business services”Ranking of cities in terms of eServices
  53. 53. Ranking of outliersSouthernEuropeCentralEurope