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This paper aims to review the existing literature systematically so as to contribute towards a better understanding of methodological problems of the classical statistical techniques, artificially intelligent expert systems and theoretical approaches to solve the corporate failure syndrome.

This paper presented a systematic review of 83 articles reporting 137 prediction failure models published within 1966-2012 in scholarly reviewed journals in four main disciplines, namely, accounting, finance, banking and economics. The authors performed the systematic literature review with five main sources, namely, Science Direct, Google Scholar, Wiley Interscience, Metalib, Web of Science and Business Source Complete of the Social Sciences. The review modified the approaches used by Aziz and Dar (2006), Ravi and Ravi (2007) and Balcaen and Ooghe (2006).

The results indicate significant body of prior literature on prediction of corporate failure, but a theoretically sound, highly accurate, simple and widely used corporate failure prediction model for stakeholders has yet to be developed.

This paper contributes towards a systematic understanding of the methodological problems associated with the statistical, artificially intelligent expert systems and theoretical approaches to solve the corporate failure prediction problems faced by firms in 11 countries.

This article develops a review of the university social responsibility literature to identify the most influential countries, authors, journals, and institutions, their structure, and research lines.

The review was carried out from a bibliometric and network analysis of documents published in the Web of Science database.

In total, 192 documents were found that were scientifically mapped in this field. From the network analysis, four research perspectives were identified: strategic impact management policy, user and its stakeholders, service-learning and its contribution to user, and theories, approaches, and strategies of University Social Responsibility (USR). Finally, the agenda for future research are presented.

The present work carries out a bibliometric and network analysis that seeks to contribute to the literature on USR, identifying its current perspectives and future lines of research.

The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry.

The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature.

A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects.

The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only.

The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research.

Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies.

There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry.

The popularity of massive open online courses (MOOCs) has attracted worldwide research interest. This study aims to identify and summarize the research foci (e.g. themes, methods, contexts, etc.) and discuss the new directions and trends of MOOC research in the context of Mainland China.

A systematic review of the published MOOC research papers in Mainland China was conducted with the following inclusion criteria: (1) papers written in English; (2) context focused on Mainland China; and (3) empirical studies. Three main issues were explored with the selected 70 papers: (1) research methods (data collection and analysis); (2) the research foci; and (3) research objects.

The results found that the major MOOC research in China was quantitative, mostly using one method to collect data. Most studies collected data through the databases of MOOC platforms and survey techniques, which was consistent with the widely used descriptive statistics for data analysis. Learner-focused themes were investigated the most, aligning with the result that learners were the most popular research objects.

The findings suggest that using new technology tools, such as the Big Data approach for learning analytics, may transform traditional MOOC research into new practices. Transdisciplinary research concepts may also provide an alternative evolving model for constructing collaboratively dynamic research frameworks under the changing technologies and paradigms. Meanwhile, educational research traditions, such as qualitative methods, contribute to scaffolding MOOC research for more pragmatic applications.

Most systematic reviews on MOOCs focus on general or regional contexts other than Mainland China, and scant MOOC review is based on published English papers about Mainland China.

The purpose of this paper is to undertake a formative evaluation of growth over time that would demonstrate diverse library users’ development as they interact with mobile digital library services.

This paper incorporated a server log analysis to evaluate first, the location of users. To study the nature of diverse user development, users from unique locations were identified and tracked over several years. The type of growth that this paper analyzes is the development of a library user from the beginning stages of use into one who is more experienced. For the purposes of this paper, the authors define library experts as experienced library users. These are users who have come back to the library over multiple sessions of learning and branched out into multiple areas of library functionality and services.

The findings of modular mobile use over time suggest that, while over half of users only utilized one module, 39 per cent of all users accessed more than one module. This formative approach to assessing student library engagement suggests alternative metrics for assessing outreach and distance learning.

The underlying departure point for this study is that formative models may introduce descriptive data valuable to the learning analytics toolkit. The library research literature on learning analytics, and perhaps library service offerings that support learning, may gain additional value by attending to students’ formative development as they interact with library resources. Describing the way in which mobile app users develop can yield insights about learning over time, both on campus and at a distance.