Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

When bureaucracy meets the crowd:Studying ‘Open Government’ in the Vienna city administration

International audienceOpen Government is en vogue, yet vague: while practitioners, policy-makers, and others praise its virtues, little is known about how Open Government relates to bureaucratic organization. This paper presents insights from a qualitative investigation into the City of Vienna, Austria. It demonstrates how the encounter between the city administration and “the open” juxtaposes the decentralizing principles of the crowd, such as transparency, participation, and distributed cognition, with the centralizing principles of bureaucracy, such as secrecy, expert knowledge, written files, and rules. The paper explores how this theoretical conundrum is played out and how senior city managers perceive Open Government in relation to the bureaucratic nature of their administration. The purpose of this paper is twofold: first, to empirically trace the complexities of the encounter between bureaucracy and Open Government; and second, to critically theorize the ongoing rationalization of public administration in spite of constant challenges to its bureaucratic principles. In so doing, the paper advances our understanding of modern bureaucratic organizations under the condition of increased openness, transparency, and interaction with their environments.<br/

Factors influencing user acceptance of public sector big open data

In recent years Government departments and public/private organizations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regards to its acceptance. To address this gap, in this paper we use an extended Technology Acceptance Model (TAM) to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions

State-of-the-art in open data research: Insights from existing literature and a research agenda

With the proliferation of mobile network, mobile devices, and Web of things, many industries, including government departments, private firms, and research communities, offer more transparency through releasing data. The resultant effort offers a new paradigm—open data—still at infancy stage. Despite the rising research initiatives explaining its benefits and challenges and demonstrating policy conception and project details, no systematic survey of extant literature on open data has been performed. Hence, there is need for studies that examine open data on a holistic canvas, assess the current status of research, and propose future directions. Here, we conduct a review of extant literature to ascertain the current state of research on open data, and present an extensive exploration for 11 types of analyses: contexts, perspectives, level of analysis, research methods, the drivers, benefits, barriers, theory/model development, the most productive journals, authors, and institutions. Additionally, we present several future research agendas. This study also explains the implications to assist researchers, policymakers, and journal editors

Stakeholder tensions in decision-making for opening government data

Various types of stakeholders are often involved in the process of deciding to open data. However, the influence of multiple–actors on the decision-making process is ill-understood. Stakeholders play different roles and have different interests in opening and analyzing datasets. The objective of this paper is to understand the influence of the stakeholder’s roles and their interests in the decision-making process to open data. The roles-interest grid method is used to determine the stakeholder’s concerns and how they influence the decision-making process to open data. In addition to stakeholder theory, we employ muddling through and bounded rationality theories to create a comprehensive analysis of the decision-making process. Stakeholders are found to be diverse, where some are proponents of opening data, and others are risk-averse and do not favor disclosing data. Stakeholder’s responsible for the actual opening of data are often focused on the risks resulting in a tension between the ambitions of politicians to open data, and the practices of administrators and decision-makers. Understanding the stakeholder’s roles and their tensions can help to ensure better decisions are made. We recommend creating incentives for generating shared objectives.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog

Towards Open Data Quality Improvements Based on Root Cause Analysis of Quality Issues

Part 2: Open Data, Linked Data, and Semantic WebInternational audienceCommercial reuse of open government data in value added services has gained a lot of interest both as practice and as a research topic over the last few years. However, utilizing open data without proper understanding of potential quality issues carries the risk of undermining the value of the service that relies on public sector information. Instead of establishing a data quality assessment framework this research considers a review of typical open data quality issues and intends to connect them to the causes leading to these various data problems. Open data specific problems are concluded from a case study and then theoretical and empirical arguments are used to connect them to root causes emerging from the peculiarities of the public sector data management process. This way both practitioners could be more conscious about appropriate cleansing methods and participants shaping the data management process could aim at eliminating root causes of data quality issues

Generating Value from Government Data Using AI: An Exploratory Study

Open government data initiatives have gained popularity around the world. Artificial Intelligence (AI) has the potential to make better use of data. Combining the OGD and AI is crucial to generate more value from data. In this paper we investigate what kind of value was generated through AI and how. A context-input-process-output/outcome (CIPO) framework is developed to describe and compare three cases. The overview of cases shows the huge potential of AI, but it also suggests that AI is hardly used by the public to create value from open data. The objectives of the three cases are efficiency, innovation and crime prevention, whereas common open government objectives like transparency, accountability and participation are given less attention. By using AI, the risks of data privacy and arriving at biased or wrong conclusions become more prominent. With the rise of data collection from Internet of Things, complying with the 5-stars of Berners-Lee becomes more important. We recommend policy makers to stimulate AI projects contributing to the open government goals and ensure that open data meets the 5-star requirements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog