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Visualisations of research and research-related activities including research data management (RDM) as a lifecycle have proliferated in the last decade. The purpose of this paper is to offer a systematic analysis and critique of such models.

A framework for analysis synthesised from the literature presented and applied to nine examples.

The strengths of the lifecycle representation are to clarify stages in research and to capture key features of project-based research. Nevertheless, their weakness is that they typically mask various aspects of the complexity of research, constructing it as highly purposive, serial, uni-directional and occurring in a somewhat closed system. Other types of models such as spiral of knowledge creation or the data journey reveal other stories about research. It is suggested that we need to develop other metaphors and visualisations around research.

The paper explores the strengths and weaknesses of the popular lifecycle model for research and RDM, and also considers alternative ways of representing them.

Librarians use lifecycle models to explain service offerings to users so the analysis will help them identify clearly the best type of representation for particular cases. The critique offered by the paper also reveals that because researchers do not necessarily identify with a lifecycle representation, alternative ways of representing research need to be developed.

The paper offers a systematic analysis of visualisations of research and RDM current in the Library and Information Studies literature revealing the strengths and weaknesses of the lifecycle metaphor.

The purpose of this paper is to document debt delinquency patterns by family lifecycle categories using multiple data sets that are nationally representative of American families.

Based on previous research, 15 lifecycle categories appropriate for American families are defined by household head's age, marital status, presence of children, and age of children. Data used are from Surveys of Consumer Finances (SCF) in the USA in 1992-2010. Multiple logistic regressions are conducted to identify probabilities of debt delinquencies of families in various lifecycle categories by controlling for income, financial assets, holdings of several types of debt, and several other demographic and socioeconomic variables.

The results show that among the 15 household lifecycle categories, the top three most likely to be delinquent are young couples with children aged seven or older, middle-aged singles with children aged 15 or older, and middle-aged singles with children under 15. Younger households are more financially distressed than their older counterparts. Presence of children increases the probability of debt delinquency.

In this study, multiple national data sets representing American families are used to document debt delinquency patterns by family lifecycle categories. Results shed light on this important topic and offer helpful information for both banking industry practitioners and consumer financial educators.

The information produced by this study can help bank managers better identify their potential clients and understand their current customers. Different marketing strategies based on the research findings can be developed to attract and retain customers with different delinquency risks.

This is the first study to examine debt delinquencies by family lifecycle categories with multiple SCF data sets in the USA. The 15 family lifecycle categories used are based on recent research that is specially designed for American families. The research findings provide straightforward implications for both bank managers and consumer educators.

This paper aims to outline principal implications for institutions, particularly universities, in supporting the increasingly complex tools which are used in the data lifecycle.

The discussion paper draws upon the experience of authors in this domain at the institutional, national and international levels.

Support for research tools by universities has high-level implications, ranging from financial, strategic and compliance through to capacity, capability and connectivity. The large number of existing tools highlights the need to evaluate them against standardised checklists to determine suitability and levels of resources required for support. Librarians and other information professionals need to expand their current support for research tools beyond the discovery phase to the entire data lifecycle.

Universities can use this paper to assess their maturity in supporting tools in the data lifecycle. Librarians, in particular, can broaden their knowledge of the various categories of tools which support specific aspects of that lifecycle.

While much attention is currently being focused on supporting researchers with their data management requirements, there is a general lack of literature on how to support tools as a critical element in enhancing research outcomes.

The purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university.

The research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation.

The article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered.

This article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management.

This article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model.

The traditional means of pursuing research by having all the parameters and processes under one roof has given way to collaborative mechanisms of performing the same task. Collaborative work increases the quality of research and it is a big contributing factor to augment the growth of the scientific knowledge. This process leads to training of new and well-informed academicians and scientists. e-Research (Electronic Research) has gained significant amount of traction as technology serves as the backbone for undertaking collaborative research. The purpose of this paper is to provide a synoptic view of existing research surrounding e-Research and suggest a data lifecycle model that can improve the outcome of collaborative research.

Systematic literature review methodology has been employed to undertake this study. Using the outcome of the literature review and the analysis of the existing data lifecycle models, an improvised version of the data lifecycle model has been suggested.

This study has brought a conceptual model for data lifecycle for collaborative research. The literature review in the domain of e-Research has shown that the focus of these papers was on the following stages of data lifecycle model: concept and design, data collection, data processing, sharing and distribution of data and data analysis.

In this paper, only journal papers have been considered and conference proceedings have not been included for literature review.

This paper suggests a conceptual model for the data lifecycle for collaborative research. This study can be useful for academic and research institutions to design their data lifecycle model.

Though Mobile Banking has raised high expectations in research and practice, it neither experienced broad adoption nor allows it banks to realize additional earnings yet. By analyzing the discourse in form of publications in research and practice as a proxy for the subsequent actual adoption, the purpose of this paper is to examine whether Mobile Banking is just a fashionable concept and whether academics or practitioners are leading the debate on Mobile Banking.

On the basis of academic and practical Mobile Banking publications from the last 13 years, discourse analysis was applied to examine patterns in the Mobile Banking literature and thus debate in research and practice. Previous patterns have been extended to examine whether the Mobile Banking discourse has fashionable aspects indicating a transient hype or whether it indicates long-term institutionalization. By differentiating between academic and practical publications, the different roles have been analyzed.

Mobile Banking discourse shows a positive trend indicating a broader adoption in nearer future which should encourage both researchers and practitioners to stay involved in the topic. Temporary developments and the emergence of technological innovations (e.g. Universal Mobile Telecommunications System, iPhone) created a fashionable hype around Mobile Banking showing that the acceptance is probably linked to developments like convenience, usefulness or availability. New phenomenon like social networks thus should be integrated in future considerations regarding Mobile Banking.

First study about the fashionable aspect in Mobile Banking literature discourse. Combination of conceptual work, literature review and methodological approach in form of regression and pattern analysis. Applies the method of a former work and extends the methodology by the characteristics of fashionable innovations.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

The purpose of this paper is to design an application profile that will enable interoperability among research management systems, support research collaboration, and facilitate the management of research information.

The approach is based on the Singapore Framework for Dublin Core Application Profile, a framework for designing metadata schemas for maximum interoperability. The application profile is built from gathering stakeholders’ requirements in research community and integrates four types of research information, i.e., information on researchers, research projects, research outputs, and research reports, which benefits researchers, research managers, and funding agencies.

The resultant application profile is evaluated against widely used similar metadata schemas and requirements; and is found to be more comprehensive than the existing schemas and meets the collected requirements. Furthermore, the application profile is deployed with a prototype of research management system and is found works appropriately.

The designed application profile has implications for further development of research management systems that would lead to the enhancement of research collaboration and the efficiency of research information management.

The proposed application profile covers information entire the research development lifecycle. Both schema and information can be represented in Resource Description Framework format for reusing purpose and linking with other information. This enables users to share research information, co-operate with others, funding agencies and the community at large, thereby allowing a research management system to increase collaboration and the efficiency of research management. Furthermore, researchers and research information can be linked by means of Linked Open Data technology.

This paper aims at understanding the current situation of research support services offered by academic libraries in world-leading universities and providing useful implications and insights for other academic libraries.

Of the top 100 universities listed in the QS World University Rankings in 2017, 76 libraries were selected as samples and a website investigation was conducted to explore the research support services. The statistical method and visualization software was used to generalize the key services, and the text analysis and case analysis were applied to reveal the corresponding implementation.

Research support service has become one of the significant services of academic libraries in the context of e-research and data-intensive research. The research support services can be generally divided into seven aspects, as follows: research data management (62, 81.58 per cent), open access (64, 84.21 per cent), scholarly publishing (59, 77.63 per cent), research impact measurement (32, 42.11 per cent), research guides (47, 61.84 per cent), research consultation (59, 77.63 per cent) and research tools recommendation (38, 50.00 per cent).

This paper makes a comprehensive investigation of research support services in academic libraries of top-ranking universities worldwide. The findings will help academic libraries improve research support services; thus, advancing the work of researchers and promoting scientific discovery.

The purpose of this paper is to provide an extensive analysis of contributions to scholarly research on decision tools.

A systematic literature review was used to collect data from 47 articles published in peer-reviewed academic journals between 1980 and 2017. Co-citation analysis was adopted to analyse recent trends in research on decision tools and recommend a framework that places such research into three categories: mature, intermediate and nascent.

The research revealed that a majority of the studies on decision tools describe decision tool implementation in a single company or setting. It also provided a clear presentation of recent trends in the decision tools literature by categorising and comparing papers according to various salient features. The study of decision tools is classified into four macro clusters: conceptualising and defining decision tools; exploring the implementation of decision tools; understanding the relationship between decision tools and other disciplines/approaches/initiatives; and discovering the outcomes of decision tools. Furthermore, the framework proposed in this paper will help scholars identify issues that merit additional theory-building and/or theory-testing research.

To the authors’ awareness, this is the first paper to have adopted both a systematic literature review and co-citation analysis to identify the dominant trends and significant gaps in the field of decision tools research.