An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.@en

Data has become a core asset, as well as a “management fashion”, of our time. It brings about unprecedented opportunities for data-driven decision making and innovation in various spheres of public life. This concerns data held by governments, as well as companies, academic institutions, non-profits, and citizens. In our study we investigate a novel form of cross-sector partnership called Data Collaborative, and namely the business models employed by intermediaries in data collaboratives. Based on an analysis of six cases, we derived four generic business models based on the level of openness and added value of the data: Data Gatekeeper model, One-stop-shop model, Information-as-a-service model, and Data Controls model. Our study contributes to the literature on data partnerships and on intermediation and information sharing more broadly.@en

Data analytics for public good has become a hot topic thanks to the inviting opportunities to utilize new' sources of data, such as social media insights, call detail records, satellite imagery etc. These data are sometimes shared by the private sector as part of corporate social responsibility, especially in situations of urgency, such as in case of a natural disaster. Such partnerships can be termed as data collaboratives'. While experimentation grows, little is known about how such collaborations are formed and implemented. In this paper, we investigate the factors which are influential and contribute to a successful data collaborative using the Critical Success Factor (CSF) approach. As a result, we propose (1) a framework of CSFs which provides a holistic view of elements coming into play when a data collaborative is formed and (2) a list of Top 15 factors which highlights the elements which typically have a greater influence over the success of the partnership. We validated our findings in two case studies and discussed three broad factors which were found to be critical for the formation of data collaboratives: value proposition, trust, and public pressure. Our results can be used to help organizations prioritize and distribute resources accordingly when engaging in a data collaborative.

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The volume of data collected by multiple devices, such as mobile phones, sensors, satellites, is growing at an exponential rate. Accessing and aggregating different sources of data, including data outside the public domain, has the potential to provide insights for many societal challenges. This catalyzes new forms of partnerships between public, private, and nongovernmental actors aimed at leveraging different sources of data for positive societal impact and the public good. In practice there are different terms in use to label these partnerships but research has been lagging behind in systematically examining this trend. In this paper, we deconstruct the conceptualization and examine the characteristics of this emerging phenomenon by systematically reviewing academic and practitioner literature. To do so, we use the grounded theory literature review method. We identify several concepts which are used to describe this phenomenon and propose an integrative definition of “data driven social partnerships” based on them. We also identify a list of challenges which data driven social partnerships face and explore the most urgent and most cited ones, thereby proposing a research agenda. Finally, we discuss the main contributions of this emerging research field, in relation to the challenges, and systematize the knowledge base about this phenomenon for the research community.

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Reporting on the Sustainable Development Goals (SDGs) is complex given the wide variety of governmental and NGO actors involved in development projects as well as the increased number of targets and indicators. However, data on the wide variety of indicators must be collected regularly, in a robust manner, comparable across but also within countries and at different administrative and disaggregated levels for adequate decision making to take place. Traditional census and household survey data is not enough. The increase in Small and Big Data streams have the potential to complement official statistics. The purpose of this research is to develop and evaluate a framework to characterize a data ecosystem in a developing country in its totality and to show how this can be used to identify data, outside the official statistics realm, that enriches the reporting on SDG indicators. Our method consisted of a literature study and an interpretative case study (two workshops with 60 and 35 participants and including two questionnaires, over 20 consultations and desk research). We focused on SDG 6.1.1. (Proportion of population using safely managed drinking water services) in rural Malawi. We propose a framework with five dimensions (actors, data supply, data infrastructure, data demand and data ecosystem governance). Results showed that many governmental and NGO actors are involved in water supply projects with different funding sources and little overall governance. There is a large variety of geospatial data sharing platforms and online accessible information management systems with however a low adoption due to limited internet connectivity and low data literacy. Lots of data is still not open. All this results in an immature data ecosystem. The characterization of the data ecosystem using the framework proves useful as it unveils gaps in data at geographical level and in terms of dimensionality (attributes per water point) as well as collaboration gaps. The data supply dimension of the framework allows identification of those datasets that have the right quality and lowest cost of data extraction to enrich official statistics. Overall, our analysis of the Malawian case study illustrated the complexities involved in achieving self-regulation through interaction, feedback and networked relationships. Additional complexities, typical for developing countries, include fragmentation, divide between governmental and non-governmental data activities, complex funding relationships and a data poor context.

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This panel is dedicated to the theme of 'data collaboratives', a novel form of public private partnership to leverage data for addressing societal challenges. The panel brings together prolific researchers and practitioners to share lessons and discuss how value is created from data collaboratives for the solving of public problems. The panel will highlight prominent examples of data collaboratives at international, national, and regional/city-levels and discuss the value creation mechanisms underlying them, as well as more broadly best practices and challenges associated with data collaboratives. The panel offers an opportunity for conference attendees to engage with this emerging new theme through interactive discussions and presentations of cutting-edge research and practice.@en

The use of Open Government Data (OGD) has not kept pace with the expectations as existing OGD infrastructures mainly serve as data repositories. Many OGD infrastructures do not stimulate or support OGD use processes, and there is a lack of research regarding which functionalities can stimulate such processes. The objective of this study is to use a design science approach to evaluate whether metadata, interaction mechanisms, and data quality indicators can improve OGD use. OGD use comprises five main activities, namely searching for and finding OGD, OGD analysis, visualizing OGD, interacting about OGD, and OGD quality analysis. We expect that three OGD key infrastructure elements—metadata, interaction mechanisms, and data quality indicators—allow for improving these five OGD use activities. A prototype of an advanced OGD infrastructure was created, which implements the three OGD infrastructure elements. Three quasi-experiments with a pretest posttest control group design were conducted. The quasi-experiments showed that the prototype facilitated the usability of the novel OGD use functionalities. Our quasi-experiments supported our propositions that metadata, interaction mechanisms, and data quality indicators contribute to making OGD use easier and faster, and enhance the user experience. The infrastructure elements improved OGD use by better enabling searching, analyzing, visualizing, discussing, giving feedback on, and assessing the quality of open data. Hence, we plea for integrating metadata, interaction mechanisms, and data quality indicators in open data infrastructures to advance open data usage. @en