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This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

The purpose of this paper is to introduce the special issue about generation of public value through smart technologies and strategies. The key argument is that smart technologies have the potential to foster co-creation of public services and the generation of public value in management processes, based on the collaborative, social and horizontal nature of these smart technologies. Understanding these processes from a public management perspective is the purpose of this paper and the rest of the special issue.

The approach to this paper is a theoretical and conceptual review, whereas practical implications both for scholars and practitioners arise from the review of the literature and the conceptual approximation to the notion of smartness in technologies and government. This approach is rooted in the potential of the latest smart technologies and strategies to transform public administrations and to better understand and cope societal problems.

The conceptual and theoretical perspective of this paper offers ideas for future developments. The content of this paper shows that new smart technologies and strategies will shape, and will be shaped by, the future of public organizations and management. This paper illustrates the process of change in public value generation over time, as a result of different public management paradigms (from traditional public administration to new public management), but also different types of technologies (from mainframes to websites and social media and beyond). The empirical evidence of the articles of this special issue supports this conclusion; that open and collaborative innovation processes developed under this emergent technological wave could become encouraging transformative practices in the public sector.

The theoretical and conceptual nature of this paper needs further empirical research to validate some of the discussed assumptions and ideas.

Although this paper is oriented to present the main contents of the special issue, it also provides an original approach to the theme of public value generation using smart technologies and strategies in public sector management.

Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use.

This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges.

Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes.

Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government.

Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.

Institutional, economic, social and technological advancements enable openness to cope with wicked public management issues. Although open innovation (OI) is becoming a new normality for public sector entities, scholarly knowledge on this topic is not fully systematized. The article fills this gap, providing a thick and integrative account of OI to inspire public management decisions.

Following the SPAR-4-SLR protocol, a domain-based literature review has been accomplished. Consistently with the study purpose, a hybrid methodology has been designed. Bibliographic coupling permitted us to discover the research streams populating the scientific debate. The core arguments addressed within and across the streams were reported through an interpretive approach.

Starting from an intellectual core of 94 contributions, 5 research streams were spotted. OI in the public sector unfolds through an evolutionary path. Public sector entities conventionally acted as “senior partners” of privately-owned companies, providing funding (yellow cluster) and data (purple cluster) to nurture OI. An advanced perspective envisages OI as a public management model purposefully enacted by public sector entities to co-create value with relevant stakeholders (red cluster). Fitting architectures (green cluster) and mechanisms (blue cluster) should be arranged to release the potential of OI in the public sector.

The role of public sector entities in enacting OI should be revised embracing a value co-creation perspective. Tailored organizational interventions and management decisions are required to make OI a reliable and dependable public value generation model.

The article originally systematizes the scholarly knowledge about OI, presenting it as a new normality for public value generation.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

This paper aims to spark a debate by presenting the need for developing data ecosystems in Europe that meet the social and public good while committing to democratic and ethical standards; suggesting a taxonomy of data infrastructures and institutions to support this need; using the case study of Barcelona as the flagship city trailblazing a critical policy agenda of smart cities to show the limitations and contradictions of the current state of affairs; and ultimately, proposing a preliminary roadmap for institutional and governance empowerment that could enable effective data ecosystems in Europe.

This paper draws on lessons learned in previous publications available in the sustainability (Calzada, 2018), regions (Calzada and Cowie, 2017; Calzada, 2019), Zenodo (Calzada and Almirall, 2019), RSA Journal (Calzada, 2019) and IJIS (Calzada, 2020) journals and ongoing and updated fieldwork about the Barcelona case study stemming from an intensive fieldwork action research that started in 2017. The methodology used in these publications was based on the mixed-method technique of triangulation via action research encompassing in-depth interviews, direct participation in policy events and desk research. The case study was identified as the most effective methodology.

This paper, drawing from lessons learned from the Barcelona case study, elucidates on the need to establish pan-European data infrastructures and institutions – collectively data ecosystems – to protect citizens’ digital rights in European cities and regions. The paper reveals three main priorities proposing a preliminary roadmap for local and regional governments, namely, advocacy, suggesting the need for city and regional networks; governance, requiring guidance and applied, neutral and non-partisan research in policy; and pan-European agencies, leading and mobilising data infrastructures and institutions at the European level.

From the very beginning, this paper acknowledges its ambition, and thus its limitations and clarifies its attempt to provide just an overview rather than a deep research analysis. This paper presents several research limitations and implications regarding the scope. The paper starts by presenting the need for data ecosystems, then structures this need through two taxonomies, all illustrated through the Barcelona case study and finally, concludes with a roadmap consisting of three priorities. The paper uses previous published and ongoing fieldwork findings in Barcelona as a way to lead, and thus encourage the proliferation of more cases through Cities Coalition for Digital Rights (CCDR).

This paper presents practical implications for local and regional authorities of the CCDR network. As such, the main three priorities of the preliminary roadmap could help those European cities and regions already part of the CCDR network to establish and build operational data ecosystems by establishing a comprehensive pan-European policy from the bottom-up that aligns with the timely policy developments advocated by the European Commission. This paper can inspire policymakers by providing guidelines to better coordinate among a diverse set of cities and regions in Europe.

The leading data governance models worldwide from China and the USA and the advent of Big Data are dramatically reshaping citizens’ relationship with data. Against this backdrop and directly influenced by the General Data Protection Regulation (GDPR), Europe has, perhaps, for the first time, spoken with its own voice by blending data and smart city research and policy formulations. Inquiries and emerging insights into the potential urban experiments on data ecosystems, consisting of data infrastructures and institutions operating in European cities and regions, become increasingly crucial. Thus, the main social implications are for those multi-stakeholder policy schemes already operating in European cities and regions.

In previous research, data ecosystems were not directly related to digital rights amidst the global digital geopolitical context and, more specifically, were not connected to the two taxonomies (on data infrastructures and institutions) that could be directly applied to a case study, like the one presented about Barcelona. Thus, this paper shows novelty and originality by also opening up (based on previous fieldwork action research) a way to take strategic action to establish a pan-European strategy among cities and regions through three specific priorities. This paper can ultimately support practice and lead to new research and policy avenues.

The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

The purpose of this paper is to survey how research data are governed at repositories in Japan by deductively establishing a governance typology based on the concept of openness in the context of knowledge commons and empirically assessing the conformity of repositories to each type.

The fuzzy-set ideal type analysis (FSITA) was adopted. For data collection, a manual assessment was conducted with all Japanese research data repositories registered on re3data.org.

The typology constructed in this paper consists of three dimensions: openness to resources (here equal to research data), openness to a community and openness to infrastructure provision. This paper found that there is no case where all dimensions are open, and there are several cases where the resources are closed despite research data repositories being positioned as a basis for open science in Japanese science and technology policy.

This is likely the first construction of the typology and application of FSITA to the study of research data governance based on knowledge commons. The findings of this paper provide practitioners insight into how to govern research data at repositories. The typology serves as a first step for future research on knowledge commons, for example, as a criterion of case selection in conducting in-depth case studies.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.