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1 – 10 of over 4000Sunday Adewale Olaleye, Emmanuel Mogaji, Friday Joseph Agbo, Dandison Ukpabi and Akwasi Gyamerah Adusei
The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.
Originality/value
This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.
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Bianca Gualandi, Luca Pareschi and Silvio Peroni
This article describes the interviews the authors conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of…
Abstract
Purpose
This article describes the interviews the authors conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word “data” in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term.
Design/methodology/approach
The authors invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into five main research areas: philology and literary criticism, language and linguistics, history of art, computer science and archival studies. The interviews were transcribed and analysed using a grounded theory approach.
Findings
A list of 13 research data types has been compiled thanks to the information collected from participants. The term “data” does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported.
Originality/value
Our findings confirm that “data” within the FAIR framework should include all types of inputs and outputs humanities research work with, including publications. Also, the participants of this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.
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Susanne Leitner-Hanetseder and Othmar M. Lehner
With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining…
Abstract
Purpose
With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.
Design/methodology/approach
Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.
Findings
Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.
Research limitations/implications
Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.
Practical implications
The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.
Originality/value
This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.
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Neema Florence Mosha and Patrick Ngulube
The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.
Abstract
Purpose
The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.
Design/methodology/approach
A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.
Findings
Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.
Originality/value
Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.
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Athanasios Ntinapogias and George Nikolaidis
Involvement of children in research on different aspects of children's rights, including research on violence against children, is continuously increasing, as is the interest in…
Abstract
Involvement of children in research on different aspects of children's rights, including research on violence against children, is continuously increasing, as is the interest in participatory approaches (European Agency for Fundamental Rights [FRA], 2014; Larsson et al., 2018; UN Committee on the Rights of the Child, 2011). Svevo-Cianci et al. (2011) noted that ‘as researchers commit to learning from community members, including children and adolescents themselves, it has become more clear that an understanding of the lived reality and definition of violence for children in their individual communities, is essential to envision and implement effective child protection’ (p. 985).
In this chapter, the legislative context regarding children's rights to be heard and participate is initially discussed; currently applied age requirements for children to acquire rights across the countries of the European Union (EU) are briefly presented; and children's potential roles and relevant provisions for their participation in social research are explored. The last part is dedicated to the presentation and discussion of the General Data Protection Regulation (GDPR; Regulation [EU] 2016/679, 2016) – specifically, children's personal data–related recitals and articles; the importance of the definition of a legal basis for personal data processing according to the GDPR, including consent; and the necessary information to be provided to children before their data are processed.
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Juliana Elisa Raffaghelli and Stefania Manca
Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains…
Abstract
Purpose
Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.
Design/methodology/approach
This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.
Findings
The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.
Research limitations/implications
Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.
Practical implications
As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.
Originality/value
Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.
Peer review
The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.
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Sari Knaapi-Junnila, Minna M. Rantanen and Jani Koskinen
Data economy is pervasively present in our everyday lives. Still, ordinary laypersons' chances to genuine communication with other stakeholders are scarce. This paper aims to…
Abstract
Purpose
Data economy is pervasively present in our everyday lives. Still, ordinary laypersons' chances to genuine communication with other stakeholders are scarce. This paper aims to raise awareness about communication patterns in the context of data economy and initiate a dialogue about laypersons' position in data economy ecosystems.
Design/methodology/approach
This conceptual paper covers theory-based critical reflection with ethical- and empirical-based remarks. It provides novel perspectives both for research and stakeholder collaboration.
Findings
The authors suggest invitational rhetoric and Habermasian discourse as instruments towards understanding partnership between all stakeholders of the data economy to enable laypersons to transfer from subjectivity to the agency.
Originality/value
The authors provide (1) theory-based critical reflection concerning communication patterns in the data economy; (2) both ethical and empirical-based remarks about laypersons' position in data economy and (3) ideas for interdisciplinary research and stakeholder collaboration practices by using invitational rhetoric and rational discourse. By that, this paper suggests taking a closer look at communication practices and ethics alike in the data economy. Moreover, it encourages clear, rational and justified arguments between stakeholders in a respectful and equal environment in the data economy ecosystems.
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Jani Koskinen, Sari Knaapi-Junnila, Ari Helin, Minna Marjaana Rantanen and Sami Hyrynsalmi
Data economy is a recent phenomenon, raised by digital transformation and platformisation, which has enabled the concentration of data that can be used in economic purposes…
Abstract
Purpose
Data economy is a recent phenomenon, raised by digital transformation and platformisation, which has enabled the concentration of data that can be used in economic purposes. However, there is a lack of clear procedures and ethical rules on how data economy ecosystems are governed. As a response to the current situation, there has been criticism and demands for the governance of data use to prevent unethical consequences that have already manifested. Thus, ethical governance of the data economy ecosystems is needed. The purpose of this paper is to introduce a new ethical governance model for data economy ecosystems. The proposed model offers a more balanced solution for the current situation where a few global large-scale enterprises dominate the data market and may use oligopolistic power over other stakeholders.
Design/methodology/approach
This is a conceptual article that covers theory-based discourse ethical reflection of data economy ecosystems governance. The study is based on the premise of the discourse ethics where inclusion of all stakeholders is needed for creating a transparent and ethical data economy.
Findings
This article offers self-regulation tool for data economy ecosystems by discourse ethical approach which is designed in the governance model. The model aims to balance data “markets” by offering more transparent, democratic and equal system than currently.
Originality/value
By offering a new ethically justified governance model, we may create a trust structure where rules are visible and all stakeholders are treated fairly.
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Jennifer L. Thoegersen and Pia Borlund
The purpose of this paper is to report a study of how research literature addresses researchers' attitudes toward data repository use. In particular, the authors are interested in…
Abstract
Purpose
The purpose of this paper is to report a study of how research literature addresses researchers' attitudes toward data repository use. In particular, the authors are interested in how the term data sharing is defined, how data repository use is reported and whether there is need for greater clarity and specificity of terminology.
Design/methodology/approach
To study how the literature addresses researcher data repository use, relevant studies were identified by searching Library Information Science and Technology Abstracts, Library and Information Science Source, Thomas Reuters' Web of Science Core Collection and Scopus. A total of 62 studies were identified for inclusion in this meta-evaluation.
Findings
The study shows a need for greater clarity and consistency in the use of the term data sharing in future studies to better understand the phenomenon and allow for cross-study comparisons. Furthermore, most studies did not address data repository use specifically. In most analyzed studies, it was not possible to segregate results relating to sharing via public data repositories from other types of sharing. When sharing in public repositories was mentioned, the prevalence of repository use varied significantly.
Originality/value
Researchers' data sharing is of great interest to library and information science research and practice to inform academic libraries that are implementing data services to support these researchers. This study explores how the literature approaches this issue, especially the use of data repositories, the use of which is strongly encouraged. This paper identifies the potential for additional study focused on this area.
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Virág Zsár and Zsuzsanna Angyal
The emergence of Research Management and Administration (RMA) is a result of the pressure on academics to secure research funding from external sources, the increasing competition…
Abstract
The emergence of Research Management and Administration (RMA) is a result of the pressure on academics to secure research funding from external sources, the increasing competition for these funds, as well as the rising requirements of research funders in terms of reporting and compliance with regulations. This is relevant in the case of the current Horizon Europe Framework Programme for Research and Innovation (HEU) funded by the European Union (EU) which requires important level of professionalisation of the research support staff on behalf of the applicant institutions. Data management, open science, research ethics and integrity, achieving impact beyond academia and the valorisation of project results can be regarded as non-research specific criteria which have to be met by applicant organisations to secure the highly competitive funding. Meeting these non-specific criteria is not always possible in countries whose performance is lagging behind compared to the Western European competitors in EU-funded programmes, such as Hungary.
Our findings reveal two things. First, research support in Hungary is in its early stage of maturity, similary to many countries in Central and Eastern Europe. In several cases, Research Managers and Administrators (RMAs) do not possess the knowledge necessary to meet the non-research specific criteria even if the knowledge is present at the institution or with other colleagues. Second, due to the continuously increasing participation in EU-funded framework programmes (FPs), the state of research support in Hungary is constantly evolving. There is also willingness to learn and improve capacities, which needs strategic planning, studying others’ examples and their adaptability. Such processes can support the capacity building and professionalisation of research offices not only in Hungary, but in countries of the Central and Eastern European region with a similar maturity level of RMA.
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