Open Data Infrastructures Evaluation Framework using Value Modelling
Upcoming SlideShare
Loading in...5
×
 

Open Data Infrastructures Evaluation Framework using Value Modelling

on

  • 475 views

This is my presentation at HICSS 2014, on evaluation models for Open Data sites and portals, based on the work we are doing at the ENGAGE project

This is my presentation at HICSS 2014, on evaluation models for Open Data sites and portals, based on the work we are doing at the ENGAGE project

Statistics

Views

Total Views
475
Views on SlideShare
438
Embed Views
37

Actions

Likes
2
Downloads
7
Comments
0

3 Embeds 37

https://twitter.com 27
http://hawaiiopendata.com 9
http://www.webnotwar.ca 1

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Open Data Infrastructures Evaluation Framework using Value Modelling Open Data Infrastructures Evaluation Framework using Value Modelling Presentation Transcript

  • Charalabidis,Y., Loukis, E., Alexopoulos, H. University of the Aegean, Greece University of the Aegean – Department of Information and Communication Systems Engineering
  • 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. 2
  • 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 3 View slide
  • 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 multidimensional 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. 4 View slide
  • 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 5
  • BACKGROUND / SYNTHESIS Literature Review IS Evaluation TAM IS Success Models E-Services Scoping eInfrastructures Stakeholders Data Acquisition Data Provision Communication 6
  • Research Streams Insights IS 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 7
  • 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 8
  • Research Streams Insights e-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 valuerelated knowledge from them 9
  • 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). 10
  • Value Model Definition Data Provision Capabilities Data Search & Download Capabilities User-level Feedback Capabilities Support for Achieving User Objectives Ease of Use Future Behaviour Performance Data Processing Capabilities Data Upload Capabilities Support for Achieving Provider Objecti. Provid-level Feedback Capabilities Efficiency Level Effectiveness Level Fut. Behavior Level 11
  • 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 12
  • Indicative Value Dimension – 1st Level Ease of Use 1.1 Friendliness The 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. 1.4 Ease of performing tasks 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 Personalization The platform supports user account creation in order to personalize views and information shown. 1.7 Support & Training The platform provides high quality of documentation and online help. 13
  • Indicative Value Dimension – 1st Level Data Processing Capabilities 7.1 Data Enrichment The platform provides good capabilities for data enrichment (i.e. adding new elements - fields) 7.2 Data Cleansing The platform provides good capabilities for data cleansing (i.e. detecting and correcting ubiquities in a dataset) 7.3 Linking The platform provides good capabilities for linking datasets. 7.4 Visualisation The platform provides good capabilities for visualization of datasets 14
  • Indicative Value Dimension – 2nd Level Support for Achieving User Objectives 8.1 ACC1 I think that using this platform enables me to do better research/inquiry and accomplish it more quickly 8.2 ACC2 This platform allows me to draw interesting conclusions on past government activity 8.3 ACC3 This platform enables me to create successful added-value electronic services 8.4 ACC4 I am in general highly satisfied with this platform 15
  • Application : The ENGAGE project OGD system to evaluated: ENGAGE - A new multicountry, 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) 16
  • The ENGAGE System Social sciences ICT Natural Sciences and Engineering Governance Policy Modelling Law Providing PSI to research communities and citizens in a personalised manner Single point of Access User groups Tailored data services Data Service Provision Infrastructure Citizens Research and Industry Governance and policy making Search and Navigation tools Knowledge / Data Mining Collaboration / Communities Visualisation - Analytics Data analytics Citizens and education Personalisation Directory services and direct linking to data archives Curating, Annotating, Harmonising , Visualising Data Quality Data Curation Infrastructure Gathering data from governmental organisations and systems (the Gov Cloud) Data Linking Knowledge Mapping Semantic Annotation Automatic curation algorithms Anonymisation Public Sector Information Sources Public Organisations, Repositories, Databases Harmonisation
  • Value Model Estimation Algorithm Value Dimensions Internal Consistency Examination Value Dimensions Variables Calculation Average Ratings Calculation Value Models’ Construction Correlations Estimation Regression Models Estimation Improvement Priorities Identification 18
  • Data Provision Capabilities 3.03 Data Search & Download Capabilities 3.03 User-level Feedback Capabilities 2.97 Ease of Use 3.35 Estimated Value Model 0.639 0.760 Support for Achieving User Object. 3.17 0.651 0.624 0.730 Future Behaviour 3.19 0.379 0.735 Performance 2.15 Data Processing Capabilities 3.27 Data Upload Capabilities 2.93 0.489 0.479 0.135 0.632 Support for Achieving Provider Obj. 3.12 0.680 0.307 Provider-level Feedback Capabilities 3.44 19
  • R2 coefficients of second and third layer value dimensions’ regression models Regression Models SUO model (8 indep. variables) 0.776 SPO model (8 indep. variables) 0.599 FBE model (2 indep. variables) 0.412 FBE model (10 indep. variables) 6-9/01/2014 R2 0.647 HICSS 47 - University of the Aegean 20
  • 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 Lower Ratings Group data provision capabilities Higher Ratings Group provider-level feedback cap. Lower Impact Group data provision capabilities Higher Impact Group data processing capabilities data searchdownload cap. ease of use user-level feedback capab. ease of use data upload capabilities performance 6-9/01/2014 data processing capabilities performance data searchdownload cap. user-level feedback capabil. provider-level feedback cap. data upload capabilities HICSS 47 - University of the Aegean 22
  • Conclusions 1/2 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. 23
  • Conclusions 2/2 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 24