Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology

AbstractPolicy-makers expect that open data will be accepted and used more and more, resulting in a range of benefits including transparency, participation and innovation. The ability to use open data partly depends on the availability of open data technologies. However, the actual use of open data technologies has shown mixed results, and there is a paucity of research on the predictors affecting the acceptance and use of open data technologies. A better understanding of these predictors can help policy-makers to determine which policy instruments they can use to increase the acceptance and use of open data technologies. A modified model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) is used to empirically determine predictors influencing the acceptance and use of open data technologies. The results show that the predictors performance expectancy, effort expectancy, social influence, facilitating conditions and voluntariness of use together account for 45% of the variability in people's behavioral intention to use open data technologies. Except for facilitating conditions, all these predictors significantly influence behavioral intention. Our analysis of the predictors that influence the acceptance and use of open data technologies can be used to stimulate the use of open data technologies. The findings suggest that policy-makers should increase the acceptance and use of open data technologies by showing the benefits of open data use, by creating awareness of users that they already use open data, by developing social strategies to encourage people to stimulate each other to use open data, by integrating open data use in daily activities, and by decreasing the effort necessary to use open data technologies

Toward Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces

Investigating meta-platforms has been a continuing concern within information system literature due to the increasingly complex constellations of platforms in ecologies of ecosystems. A meta-platform is a platform built on top of two or more platforms, hence connecting their respective ecosystems. One promising case to benefit from meta-platforms is data marketplaces: a particular type of platform that facilitates responsible (personal and non-personal) data sharing among companies. Given that business models for meta-platforms are largely unexplored in this emerging case, how they can create value for data marketplaces remain speculative. As a starting point toward business model investigations, this paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. We identify three potential value creation archetypes of a meta-platform. The discovery aggregator archetype emphasizes searching and dispatching value, while the brokerage one focuses on promoting and supporting value. Finally, the one-stop-shop archetype creates value by standardizing, regulating, sharing, and experimenting. This study is among the first that explore value creation archetypes for a meta-platform, thus identifying core value as a base for further business model investigations

Preparing Future Business Data Sharing via a Meta-Platform for Data Marketplaces: Exploring Antecedents and Consequences of Data Sovereignty

Meta-platforms have received considerable Information Systems scholarly attention in recent years. Meta-platforms enable platform-to-platform openness and are especially beneficial to amplifying network effects in highly-specialized markets. A promising emerging context for applying metaplatforms is data marketplaces—a special type of digital platform designed for business data sharing that is vastly fragmented. However, data providers have sovereignty concerns: the risk of losing control over the data that they share through metaplatforms. This research aims to explore antecedents and consequences of data sovereignty concerns in meta-platforms for data marketplaces. Based on interviews with fifteen potential data providers and five data marketplace experts, we identify data sovereignty antecedents, such as (potentially) less trustworthy data marketplace participants, unclear use cases, and data provenance difficulties. Data sovereignty concerns have many consequences, including knowledge spillovers to competitors and reputational damage. This study is among the first that empirically develops a pre-conceptualization for data sovereignty in this novel context, thus laying the groundwork for designing future data marketplace meta-platform solutions

Big and Open Linked Data (BOLD) to Create Smart Cities and Citizens: Insights from Smart Energy and Mobility Cases

Part 2: Open and Smart Government; International audience; Smart cities focus on using existing resources in a better way to improve the urban environment. At the same time Big and Open Linked Data (BOLD) can be used to better understand the use of the resources and to suggest improvements. The objective of this paper is to investigate the complementariness of the smart cities and big and Open Data research streams. Two inductive cases concerning different aspects of smart cities, energy and mobility, are investigated. The idea of using BOLD for smart cities seems initially straightforward, but the cases show that this is complex. A taxonomy for forms of collecting and opening data is derived. A major challenge is to deal with data distributed over various data sources and how to align the data push with the citizensâ needs. This paper highlights a continuous scale between open and closed data and emphasizes that not only Open Data but also closed data should be used to identify improvements. BOLD can contribute to the âsmartnessâ of cities by linking and combining data or employing data or predictive analytics to improve better use of resources. A smart city only becomes smart when there are smart citizens, businesses, civil servants and other stakeholders. Document type: Part of book or chapter of boo

From experimentation to public service delivery in social media. An analysis of institutionalization dynamics in Dutch local governments

Social media is being used by a large part of local administrations. As a highly disruptive adopted innovation, it is important to understand the process of institutionalization through empirical variables. This paper studies the Dutch case to analyze to what extent social media technologies have being institutionalized within major city councils in Netherlands. The study tries to answer the following research question: What is the level of institutionalization of social media in Dutch city councils with more than 50,000 inhabitants? Taking this into account, this work is based on two analytical levels: on the one hand, it performs a comparative analysis of Dutch city councils that responded to a survey on social media institutionalization. On the other hand, based on previous descriptive empirical results, the paper studies Utrecht as a case of success to analyze the level of social media institutionalization through Social Network Analysis and automated natural language processing with data crawled from Twitter. Overall, results show that social media institutionalization in Dutch city councils has been high, developing decentralized practices with formal commitments for social media use and with a high sense of leadership, showing interesting participatory and public service delivery logics. At the same time, the case of Utrecht confirms that a high level of institutionalization requires management capabilities and goals definition and implementation, including a conversational approach to citizens, and an emerging approach to public service delivery

Time critical requirements and technical considerations for advanced support environments for data-intensive research

Data-centric approaches play an increasing role in many scientific domains, but in turn rely increasingly heavily on advanced research support environments for coordinating research activities, providing access to research data, and choreographing complex experiments. Critical time constraints can be seen in several application scenarios e.g., event detection for disaster early warning, runtime execution steering, and failure recovery. Providing support for executing such time critical research applications is still a challenging issue in many current research support environments however. In this paper, we analyse time critical requirements in three key kinds of research support environment—Virtual Research Environments, Research Infrastructures, and e-Infrastructures—and review the current state of the art. An approach for dynamic infrastructure planning is discussed that may help to address some of these requirements. The work is based on requirements collection recently performed in three EU H2020 projects: SWITCH, ENVRIPLUS and VRE4EIC

Legal linked data ecosystems and the rule of law

This chapter introduces the notions of meta-rule of law and socio-legal ecosystems to both foster and regulate linked democracy. It explores the way of stimulating innovative regulations and building a regulatory quadrant for the rule of law. The chapter summarises briefly (i) the notions of responsive, better and smart regulation; (ii) requirements for legal interchange languages (legal interoperability); (iii) and cognitive ecology approaches. It shows how the protections of the substantive rule of law can be embedded into the semantic languages of the web of data and reflects on the conditions that make possible their enactment and implementation as a socio-legal ecosystem. The chapter suggests in the end a reusable multi-levelled meta-model and four notions of legal validity: positive, composite, formal, and ecological