Search results

Open data and data sharing should improve transparency of research. The purpose of this paper is to investigate how different institutional and individual factors affect the data sharing behavior of authors of research articles in sociology and political science.

Desktop research analyzed attributes of sociology and political science journals (n=262) from their websites. A second data set of articles (n=1,011; published 2012-2014) was derived from ten of the main journals (five from each discipline) and stated data sharing was examined. A survey of the authors used the Theory of Planned Behavior to examine motivations, behavioral control, and perceived norms for sharing data. Statistical tests (Spearman’s ρ, χ²) examined correlations and associations.

Although many journals have a data policy for their authors (78 percent in sociology, 44 percent in political science), only around half of the empirical articles stated that the data were available, and for only 37 percent of the articles could the data be accessed. Journals with higher impact factors, those with a stated data policy, and younger journals were more likely to offer data availability. Of the authors surveyed, 446 responded (44 percent). Statistical analysis indicated that authors’ attitudes, reported past behavior, social norms, and perceived behavioral control affected their intentions to share data.

Less than 50 percent of the authors contacted provided responses to the survey. Results indicate that data sharing would improve if journals had explicit data sharing policies but authors also need support from other institutions (their universities, funding councils, and professional associations) to improve data management skills and infrastructures.

This paper builds on previous similar research in sociology and political science and explains some of the barriers to data sharing in social sciences by combining journal policies, published articles, and authors’ responses to a survey.

Data sharing is widely thought to help research quality and efficiency. Data sharing mandates are increasingly being adopted by journals and the purpose of this paper is to assess whether they work.

This study examines two evolutionary biology journals, Evolution and Heredity, that have data sharing mandates and make extensive use of Dryad. It uses a quantitative analysis of presence in Dryad, downloads and citations.

Within both journals, data sharing seems to be complete, showing that the mandates work on a technical level. Low correlations (0.15-0.18) between data downloads and article citation counts for articles published in 2012 within these journals indicate a weak relationship between data sharing and research impact. An average of 40-55 data downloads per article after a few years suggests that some use is found for shared life sciences data.

The value of shared data uses is unclear.

Data sharing mandates should be encouraged as an effective strategy.

This is the first analysis of the effectiveness of data sharing mandates.

The purpose of this paper is to highlight the problem of establishing metrics for the impact of research data when norms of behaviour have not yet become established.

The paper considers existing research into data citation and explores the citation of data journals.

The paper finds that the diversity of data and its citation precludes the drawing of any simple conclusions about how to measure the impact of data, and an over emphasis on metrics before norms of behaviour have become established may adversely affect the data ecosystem.

The paper considers multiple different types of data citation, including for the first time the citation of data journals.

Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. The purpose of this paper is to analyses the factors which influence data sharing by investigating journal data policies and the behaviour of authors in sociology.

The web sites of 140 sociology journals were consulted to check their data policy. The results are compared with similar studies from political science and economics. A broad selection of articles published in five selected journals over a period of two years are examined to determine whether authors really cite and share their data and the factors which are related to this.

Although only a few sociology journals have explicit data policies, most journals make reference to a common policy supplied by their association of publishers. Among the journals selected, relatively few articles provide data citations and even fewer make data available – this is true both for journals with and without a data policy. But authors writing for journals with higher impact factors and with data policies are more likely to cite data and to make it really accessible.

No study of journal data policies has been undertaken to date for the domain of sociology. A comparison of authors’ behaviours regarding data availability, data citation, and data accessibility for journals with or without a data policy provides useful information about the factors which improve data sharing.

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

Over the last 50 years, increased attention for personalization paved the way for one-to-one marketing efforts, but firms struggle to deliver on this promise. The purpose of this manuscript is to provide a complete picture on personalization, develop a future research agenda and put forth concrete advice on how to move the field forward from a theoretical, methodological, contextual, and practical viewpoint.

This research follows a systematic literature review process, providing an in-depth analysis of 135 articles (covering 184 studies) to distill the (1) key building blocks and components of personalization and (2) theoretical, contextual, and methodological aspects of the studies.

This manuscript uncovers six personalization components that can be linked to two personalization building blocks: (1) learning: manner, transparency, and timing and (2) tailoring: touchpoints, level, and dynamics. For each of these components, the authors propose future research avenues to stimulate personalization research that accounts for challenges in today's data-rich environments (e.g. data privacy, dealing with new data types). A theoretical, contextual, and methodological (i.e. industry, country and personalization object) review of the selected studies leads to a set of concrete recommendations for future work: account for heterogeneity, embed theoretical perspectives, infuse methodological innovation, adopt appropriate evaluation metrics, and deal with legal/ethical challenges in data-rich environments. Finally, several managerial implications are put forth to support practitioners in their personalization efforts.

This research provides an integration of personalization research beyond existing and outdated review papers. Doing so, it accounts for the impact of new technologies and Artificial Intelligence and aims to advance the next generation of knowledge development on personalization.

This article presents a novel user‐oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non‐first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content‐bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface‘s general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

Bradford distributions describe the relationship between ‘journal productivities’ and ‘journal rankings by productivity’. However, different ranking conventions exist, implying some ambiguity as to what the Bradford distribution ‘is’. A need accordingly arises for a standard ranking convention to assist comparisons between empirical data, and also comparisons between empirical data and theoretical models. Five ranking conventions are described including the one used originally by Bradford, along with suggested distinctions between ‘Bradford data set’, ‘Bradford distribution’, ‘Bradford graph’, ‘Bradford log graph’, ‘Bradford model’ and ‘Bradford’s Law‘. Constructions such as the Lotka distribution, Groos droop (generalised to accommodate growth as well as fall‐off in the Bradford log graph), Brookes hooks, and the slope and intercept of the Bradford log graph are clarified on this basis. Concepts or procedures questioned include: (1) ‘core journal’, from the Bradfordian viewpoint; (2) the use of traditional statistical inferential procedures applied to Bradford data; and (3) R(n) as a maximum (rather than median or mean) value at tied‐rank values.

This paper aims to selects 59 journals that focus on data science research in 14 disciplines from the Ulrichsweb online repository. This paper analyzes the aim and scope statement using both quantitative and qualitative methods to identify the research types and the scope of research promoted by these journals.

Multiple disciplines are involved in data science research and publishing, but there lacks an overview of what those disciplines are and how they relate to data science. In this study, this paper aims to understand the disciplinary characteristics of data science research. Two research questions are answered: What is the population of journals that focus on data science? What disciplinary landscape of data science is revealed in the aim and scope statements of these journals?

Theoretical research is mainly included in journals that belong to statistics, engineering and sciences. Almost all data science journals include applied research papers. Keywords analysis shows that data science research in computers, statistics, engineering and sciences appear to share characteristics. While in other disciplines such as biology, business and education, the keywords are indicative of the types of data to be used and the special problems in these disciplines.

This is the first study to use journals as the unit of analysis to identify the disciplines involved in data science research. The results provide an overview of how researchers and educators from different disciplinary backgrounds understand data science research.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.