ogd new generation infrastructures evaluation based on value models
TRANSCRIPT
University of the Aegean – Department of Information and Communication Systems Engineering
Charalabidis, Y., Loukis, E., Alexopoulos, H.
University of the Aegean, Greece
INTRODUCTION: THE OPEN /BIG DATA
MOVEMENT IN THE BACKGROUND
Governments are increasingly opening to the society important data they possess, in order to be used for scientific, commercial and political purposes.
Initially a first generation of Internet-based open government data (OGD) infrastructures has been developed in many countries, influenced by the Web 1.0 paradigm, in which there is a clear distinction between content producers and content users.
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A SECOND GENERATION OF OGD INFRASTRUCTURES
Recently a second generation of more advanced OGD infrastructures is under development, which is influenced by the principles of the new Web 2.0 paradigm: elimination of the clear distinction between ‘passive’
content users/consumers and ‘active‘ content producers
They aim to support highly active users,
who assess the quality of the data they consume and mention weanesses of them and new needs they have
and often become data pro-sumers‘ = both consumers and providers of data
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THE NEED FOR AN EVALUATION METHOD The big investments in this area necessitate a systematic
evaluation of these OGD infrastructures, in order to gain a better understanding and assessment of the multi-dimensional value they generate
However, a structured and comprehensive evaluation methodology is missing.
This method contributes to filling this gap. It presents and validates a methodology for evaluating these
advanced second generation of ODG infrastructures,
based on a ‘value model approach’,
i.e. on the estimation of value models of these infrastructures from users’ ratings.
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INTRODUCTION In particular: it assesses various measures of generated
value by OGD infrastructures,
structured in three layers (associated with efficiency, effectiveness and users’ future behavior),
and also the relations among them,
leading finally to the formation of a value model of the OGD infrastructure, which enables:
a deeper understanding of the whole value generation mechanism of it
and also a rational definition of IS improvement priorities
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BACKGROUND / SYNTHESIS
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Scoping eInfrastructures
Stakeholders Data Acquisition Data Provision Communication
Literature Review
IS Evaluation TAM IS Success Models E-Services
Research Streams InsightsIS Evaluation
IS’s offer various types of benefits, both financial and non-financial, and also tangible and intangible ones, which differ among the different types of IS
it is not possible to formulate one generic IS evaluation method, which is applicable to all IS
a comprehensive methodology for evaluating a particular type of IS should include evaluation of both its efficiency and its effectiveness, taking into account its particular characteristics, capabilities and objectives
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Research Streams Insights TAM (Technology Acceptance Model)
identify the characteristics and factors affecting the attitude towards using an IS, the intention to use it and finally the extent of its actual usage
perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use
IS Success Models IS evaluation should adopt a layered approach based on the
above interrelated IS success measures (information quality, system quality, service quality, user satisfaction, actual use, perceived usefulness, individual impact and organizational impact) and on the relations among them
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Research Streams Insightse-Services Evaluation
frameworks that assess the quality of the capabilities that the e-service provides to its users
frameworks that assess the support it provides to users for performing various tasks and achieving various objectives, or users’ overall satisfaction
the above frameworks do not include advanced ways of processing the evaluation data collected from the users, in order to maximize the extraction of value-related knowledge from them
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Our Evaluation Model Approach
(a) Efficiency layer: it includes ‘efficiency’ measures, which assess the quality of the basic capabilities offered by the e-service to its users.
(b) Effectiveness layer: it includes ‘effectiveness’ measures, which assess to what extent the e-service assists the users for completing their tasks and achieving their objectives.
(c) Future behaviour layer: it includes measures assessing to what extent the e-service influences the future behaviour of its users (e.g. to what extent they intend to use the e-service again in the future, or recommend it to friends and colleagues).
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Value Model Definition
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User-level Feedback CapabilitiesSupport for
Achieving User Objectives
Support for Achieving
Provider Objecti.
FutureBehaviour
Efficiency LevelEffectiveness
LevelFut. Behavior
Level
Ease of Use
Performance
Data Processing Capabilities
Data Search & Download Capabilities
Data Provision Capabilities
Data Upload Capabilities
Provid-level Feedback Capabilities
Value Measures The total of 41 value measures (all layers) were
defined where 35 for the 1st layer 14 common value measures 15 value measures for users 06 value measures for providers
These value measures was then converted to a question to be included in questionnaires to be distributed to stakeholders
A five point Likert scale is used to measure agreement or disagreement
2 Questionnaires have been formulated
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Indicative Value Dimension – 1st Level
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Ease of Use
1.1 FriendlinessThe platform provides a user friendly and easy to use environment.
1.2 Learning Easiness It was easy to learn how to use the platform.
1.3 Aesthetics The web pages look attractive.
1.4Ease of performingtasks
It is easy to perform the tasks I want in a small number of steps.
1.5 Multilingual aspects The platform allows me to work in my own language.
1.6 PersonalizationThe platform supports user account creation in order to personalize views and information shown.
1.7 Support & TrainingThe platform provides high quality of documentation and online help.
Indicative Value Dimension – 1st Level
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Data Processing Capabilities
7.1 Data EnrichmentThe platform provides good capabilities for data enrichment (i.e. adding new elements - fields)
7.2 Data CleansingThe platform provides good capabilities for data cleansing (i.e. detecting and correcting ubiquities in a dataset)
7.3 LinkingThe platform provides good capabilities for linking datasets.
7.4 VisualisationThe platform provides good capabilities forvisualization of datasets
Indicative Value Dimension – 2nd Level
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Support for Achieving User Objectives
8.1 ACC1I think that using this platform enables me to do better research/inquiry and accomplish it more quickly
8.2 ACC2This platform allows me to draw interesting conclusions on past government activity
8.3 ACC3This platform enables me to create successful added-value electronic services
8.4 ACC4I am in general highly satisfied with this platform
Application : The ENGAGE project OGD system to evaluated: ENGAGE - A new multi-
country, multi-lingual open data infrastructure for researchers, available at www.engagedata.eu
Target user group: post-graduate students from TU Delft and Uaegean, trained in the platfom
Method of user input: electronic questionnaires
Number of valid questionnaire responses processed: 42 (when the paper was submitted, now more than 100)
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The ENGAGE System
Providing PSI to research communities and citizens in a personalised manner
Curating, Annotating, Harmonising , Visualising
Gathering data from governmental organisations and systems (the Gov Cloud)
Data Linking Semantic Annotation Anonymisation Harmonisation
Visualisation - Analytics
Search and Navigation tools
Social sciences
Data Service Provision Infrastructure
Data Curation Infrastructure
Public Sector Information Sources
Tailored data services
Research and Industry Governance and policy making
Citizens and education
Data analytics
Knowledge / Data Mining
Directory services and direct linking to
data archives
ICTCitizens
Natural Sciences and Engineering
Governance
User groups
Collaboration / Communities
Personalisation
Single point of Access
Data Quality Knowledge Mapping
Public Organisations, Repositories, Databases
Law
PolicyModelling
Automatic curation algorithms
Value Model Estimation Algorithm
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Value Dimensions Internal
Consistency Examination
Value Dimensions
Variables Calculation
Average Ratings Calculation
Regression Models
Estimation
Correlations Estimation
Value Models’ Construction
Improvement Priorities
Identification
Estimated Value Model
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Data ProvisionCapabilities
3.03
Data Search & DownloadCapabilities
3.03
User-level FeedbackCapabilities
2.97
Ease of Use3.35
Performance2.15
Data ProcessingCapabilities
3.27
Data Upload Capabilities2.93
Provider-level FeedbackCapabilities
3.44
Support for AchievingUser Object.
3.17
Support for AchievingProvider Obj.
3.12
Future Behaviour3.19
0.624
0.489
0.639
0.760
0.651
0.307
0.680
0.730
0.479
0.379
0.135
0.632
0.735
Improvement Priorities Identification Such an OGD infrastructure value model,
Enables the identification of improvement priorities,
which are the first layer OGD systems capabilities that receive low evaluation by the users,
and at the same time have high impact on higher layers’ value generation
Mapping for decision support
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Lower Ratings Group
Higher Ratings Group
data provision capabilities
data search-download cap.
data upload capabilities
performance
provider-level feedback cap.
ease of use
data processing capabilities
user-level feedback capabil.
Lower Impact Group
Higher Impact Group
data provision capabilities
user-level feedback capab.
performance
provider-level feedback cap.
data processing capabilities
ease of use
data search-download cap.
data upload capabilities
6-9/01/2014 HICSS 47 - University of the Aegean
Conclusions 1/2
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This paper has presented a methodology for determining the value generation mechanism and the improvement priorities of advanced 2nd generation open government data systems,
which are characterized by the elimination of the distinction between providers and consumers of such data.
The proposed methodology assesses a wide range of types of value generated by such OGD infrastructures for data ‘pro-sumers’,
and at the same time exploits the relations between the above types of value (which are usually not exploited and ignored by IS evaluation methodologies in general),
leading to additional useful value-related information and more insights into these advanced ODG systems,
providing valuable support for making important ODG systems investment, management and improvement decisions.
Conclusions 2/2
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An algorithm for advanced processing of users’ evaluation data has been proposed,
which leads to the estimation of the value model of the OGD infrastructure,
enabling a better understanding of the whole value generation mechanism of its,
and the identification of improvement priorities,
which are the first layer OGD systems capabilities that receive low evaluation by the users, and at the same time have high impact on higher layers’ value generated.
A first application-validation of the proposed methodology provided interesting conclusions for the OGD systems developed in ENGAGE infrastructure