Open Data, Information Processing, and Datification in Government Minitrack

Permanent URI for this collection

https://hdl.handle.net/10125/42469

The public sector is data-intensive by its very nature and trends like the opening of data and the Internet of Things (IoT) results in the availability of even more data. Datification refers to sensing and the subsequent collecting of all kinds of data using machine-readable data formats. Datafication is rapidly becoming a mainstream activity of public organizations. In addition public organizations are opening more and more of their data to the public. Open data can be combined with all kind of data sources to infer and generate value. This can results in recommendations for improving the public sector, business model innovation and the creation of transparency. These developments are resulting in drastic changes of the public sector.

The rise of all kinds of data has resulted in the demand for new approaches for organizing, storing, processing, curation, linking and visualization result of data. Data pipelines are created in which data is combined in real- time for creating new applications. Cloud services are changing the ways of providing and processing data, based on virtualized resources meeting requirements like security, privacy and scalability. Although there is a huge potential how this should be accomplished and what the impact of public organizations is not understood. All these developments impact the operation of governments, their relationship with the public and there are changes at the technical, organizational, managerial and political level impacting the capabilities needed, the making of policies and traditional institutional structures.

This minitrack is aimed at discussing theories, methodologies, experience reports, literature and case studies in the field of Open Data, Information Processing and Datification in Government. We solicit for papers covering both organizational and technical aspects and combining theory and practice. Papers taking interdisciplinary approaches and covering a multitude of aspects are strongly encouraged. Furthermore we promote a diversity of research methods to study the challenges of this multifaceted discipline including best practices, case studies, design approaches, literature reviews and interviews.

Minitrack topics include, but are not limited to:

Open Data Practices, Technologies and Applications in Government
Impact of datification on government and society on the technical, organizational and institutional level
Big Data, open data, linked data, metadata and semantic approaches
Data analytics, processing, intelligence and visualization
Organizational strategies and policies
Data quality, privacy, trust and security
Internet of Things (IoT) in government
Changing relationship between government, private organizations and society
Methods and technologies leading to enhanced digital public services
Data-driven innovations, applications and other approaches
Interoperability and architectural standards, principles and frameworks
Technical, semantic, organizational, managerial and legal/policy aspects of interoperability
System development, implementation and agile approaches for digital public services
System, user, data and process-based integration
Co-creation using data and citizen engagement
Information and cloud infrastructures, shared services, cloud providers
Reuse and data quality and ownership
Semantic ontologies, web services and modeling for governmental infrastructures
Cloud computing, Software as service (SaaS), ICT-services, scalability, reliability, flexibility
Multi-sided platforms, interoperability, information sharing and public business models
Cross-organizational modeling and visualization ranging from the organizational to technical level
Service-oriented architectures, web services, semantic web services, orchestration and composition
Citizen-driven and entrepreneurial approaches based on open data

Minitrack Co-Chairs:

Marijn Janssen (Primary Contact)
Delft University of Technology, The Netherlands
Email: m.f.w.h.a.janssen@tudelft.nl

Yannis Charalabidis
University of the Aegean, Greece
Email: yannisx@aegean.gr

Helmut Krcmar
Technische Universität München, Germany
Email: Krcmar@in.tum.de

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Now showing 1 - 8 of 8

Understanding Datafication Effects of Open Government Information Systems – A Contemporary Systems Thinking Approach
(2017-01-04) Marjanovic, Olivera; Cecez-Kecmanovic, Dubravka
This paper contributes to an improved understanding of datafication effects of open government Information Systems (IS). We focus on a particular category of these IS that is designed to provide open performance data of a public sector (education, health, social services) in the name of accountability and transparency. While acknowledging possible positive datafication effects, in this paper we investigate the negative ones caused by propagation and reuse of open performance data. Using contemporary systems thinking as a theoretical lens, we identify three main types of datafication mechanisms, explain their underlying systemic manifestations and illustrate their societal effects. Drawing insights from a longitudinal research case study of a large-scale open government IS in Australia, we ‘unpack’ mutually-shaping relationships between technology and human behavior, reinforced by various feedback loops within a wider societal system.
The Role of Information Quality in Healthcare Organizations: A Multi-Disciplinary Literature Review
(2017-01-04) Hausvik, Geir Inge
The volume of data in healthcare repositories is growing exponentially, giving increased concerns on its organizational implications. The quality of data and information represents a considerable risk for organizations, particularly in healthcare, where consequences of poor quality may be fatal for patients. This research seeks to investigate the role of information quality in organizations, by reviewing multi-disciplinary research literature and provide a framework of the relations between IQ and its organizational implications. Findings suggest that research on information quality has focused on different aspects of organizational impact: organizational performance, process performance, process improvement, and decision-making. However, since the research is fragmented and scarce, this paper suggests a shift in research focus from defining, measuring and improving information quality, to understanding the implications and applications of information quality towards better and safer health services.
Open Government Data Ecosystems: A Closed-Loop Perspective
(2017-01-04) M. Najafabadi, Mahdi; Luna-Reyes, Luis
Open data initiatives have opened new alternatives in creating benefits for the public through secondary use governmental data. From some perspectives, benefits will come from the development of innovative applications using the data, and from the new business models enabled by these applications. From other perspectives, open data applications offer an opportunity for increased citizen participation, improved transparency and accountability. Although the number of published governmental datasets has increased in many countries, producing the expected benefits – and even measuring them – has proven difficult. Creating the expected benefits depends on the development of an ‘ecosystem’ of government actors and private stakeholders that enables multiple forms of interactions and value creation. We propose that modeling and simulation of this open data ecosystem can expand our understanding of its enablers and barriers, leading to improvements in policy making and ultimate outcome of open data initiatives.
Is Organic Labelling Enough? Information Disclosure as Policy Instrument to Empower Consumer Choices
(2017-01-04) Zhang, Jing; Boldt, Lin
Governments have been advocating for an open approach to encourage private sector disclosing relevant information in order to create more efficient market. However, it is not always clear what information are needed by consumers. Policy makers need to develop measures that help decide what information should be disclosed and whether a disclosure should be mandated. In this research, we focus on information disclosure in organic products, where consumers find the complex organic labeling hard to understand. Through two studies, we show that feed origin makes a significant difference in consumers’ choice; and sellers with feed from non-USA countries would be motivated to disguise the information on feed origin. We propose a way to implement “smart” information disclosure that can effectively distinguish USA feed from feed with undisclosed origin, which enables the feed from USA to claim a higher price premium. Our findings have policy implications for organic product disclosure.
Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development
(2017-01-04) Susha, Iryna; Janssen, Marijn; Verhulst, Stefaan
Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative.
Civic Hackers’ User Experiences and Expectations of Seattle’s Open Municipal Data Program
(2017-01-04) Young, Meg; Yan, An
This study examines the challenges and the expectations that civic hackers bring to the use of open government data, building on Gurstein’s theory of barriers to effective use. Civic hackers are hobbyists who use open government data for social good applications. Drawing on individual interviews and a focus group with fifteen total civic hackers in Seattle, Washington, we synthesize findings on their experiences using open government data, including their expectations for the kinds of data formats, metadata, API functionality, and datasets that should be provided on the city’s open data portal. Respondents report challenges using the data, including low data availability, outdated datasets, limited API functions, proprietary formats, lack of metadata, and untidy datasets. These acted as barriers to their effective use of open data. Respondents expect higher quality data and more usable data portal functionality, in part because of their professional experience in the technology sector. In our discussion, we examine the organizational structure of the open data program, and the constraints it poses for the achievement of respondent expectations. Our analysis points to a demand for an additional, third party civic institution (like a local newspaper) to host cleaned data for wider use. \
Barriers to Predictive Analytics Use for Policy Decision-Making Effectiveness in Turbulent Times: A Case Study of Fukushima Nuclear Accident
(2017-01-04) Chatfield, Akemi Takeoka; Reddick, Christopher
Predictive analytics are data-driven software tools that draw on confirmed relationships between variables to predict future outcomes. Hence they may provide government with new analytical capabilities for enhancing policy decision-making effectiveness in turbulent environments. However, predictive analytics system use research is still lacking. Therefore, this study adapts the existing model of strategic decision-making effectiveness to examine government use of predictive analytics in turbulent times and to identify barriers to using information effectively in enhancing policy decision making effectiveness. We use a case study research to address two research questions in the context of the 2011 Fukushima nuclear accident. Our study found varying levels of proactive use of SPEEDI predictive analytics system during the escalating nuclear reactor meltdowns between Japan’s central government agencies and between the central and the state government levels. Using the model, we argue that procedural rationality and political behavior can be used to explain some observed variations.
Introduction to Open Data, Information Processing, and Datification in Government Minitrack
(2017-01-04) Janssen, Marjin; Charalabidis, Yannis; Krcmar, Helmut

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