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Purpose – The purpose of the chapter is to provide an analytical overview of information research in the United Kingdom and of the role of the Research Assessment Exercises (RAE) in shaping the form and structure of that research.

Design/methodology/approach – The approach adopted is a detailed content analysis of the submissions made to the last UK RAE. This analysis is carried out in relation to four broad subject categorisations, and specific analysis of accounts of research carried out in the departments and research groups.

Findings – The RAE have played a key role in promoting research specialisms in library and information studies (LIS) research in the United Kingdom. The former general approach to research in information studies has been replaced by more focused research activities carried out in a variety of research groups spread across a diverse range of disciplines and departments, from LIS, to business and management, information systems, and computing and engineering.

Research implications – The prospects for general LIS research departments may be increasingly limited, as research becomes concentrated in sub-groups within larger organisational structures, subverting both departmental lines and conventional subject boundaries.

Originality/value – This overview provides a novel synthesis of information research in the United Kingdom in relation to four broad categories of research in information studies and information science, information management and social informatics, information systems and information interaction, and social computing and computational informatics. The account brings together a fragmented field of research in a compact and intelligible form.

This chapter discusses the application of fuzzy cognitive map (FCM) modelling to construction management (CM) challenges and problems. It focuses on the critical issue of managing the complexity and uncertainty inherent in CM by providing a new intelligent layer that enhances classical approaches to construction modelling and management. It investigates how the myriad types of internal and external factors affecting the feasibility and performance of construction projects can be modelled using a fuzzy hybrid method that explores the complex relationships among many contributing factors and assesses and evaluates their impacts on past and future projects. This chapter proposes a hybrid modelling approach in the traditional context of cost, schedule and risk management and describes how augmenting and enhancing existing state-of-the-art tools and processes in CM can assist construction managers. This chapter provides a background on the theory of FCMs, presents foundational and current research, and explains how to apply this approach in the CM domain. This chapter also provides a detailed description of how to develop, modify and employ interactive models to specific CM challenges and problems. It includes a customisable, interactive base model and demonstrates how the model has been applied to specific CM events and issues. Examples are presented that highlight the interplay between project-specific goals and characteristics and the way these impact the interrelated and often opposing triad of cost, schedule and risk. The presented examples and practical applications make this state-of-the-art approach useful to both academic and industry practitioners.

A common problem that many universities face, especially with their specialized programs, is coordinating faculty availability and class offerings. The schedule is usually developed using paper and pencil after numerous iterations. As a result, the objectives of the program, such as course integration, length of course, and student workload, are most likely compromised in lieu of faculty availability. This chapter describes a multiple objective approach to this class assignment problem that considers the program’s objectives and faculty preferences. The results of applying this class assignment model to an Executive MBA (EMBA) program are presented.

Patrick Blessinger is the founder and Executive Director of the International Higher Education Teaching and Learning Association and a Research Fellow at the School of Education at St. John's University in Queens, New York, USA. He has taught over 150 college and university courses and he has served as a program chair at colleges and universities in the US and EU. He consults with HE institutions in the areas of technology innovation and internationalization and he serves as an academic and accreditation advisor for HE institutions. He is the co-founder and co-director of the Institute for Meaning-Centered Education. He is the founder and editor of the International HETL Review and co-editor of the Journal of Applied Research in Higher Education. He is co-editor of several volumes within the Cutting-edge Technologies in Higher Education book series (Emerald) and co-editor of the book, Meaning-Centered Education: International Perspectives and Explorations in Higher Education (Routledge: 2013). He attended Auburn University, Georgia Tech, and the University of Georgia. He is a peer-recognized expert and thought leader in the field of teaching and learning and he has received several academic awards including a Fulbright Scholarship from the US Department of State and a Governor's Teaching Fellowship from the State of Georgia, USA.

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The shift to the fourth industrial revolution presents new sustainable development challenges. The externalities of the second and third industrial revolutions were related to the degradation of the biosphere. It is suggested that the externalities of the fourth industrial revolution, underpinned by artificial intelligence, are extending the repertoire of externalities to include the degradation and manipulation of cognition and by extension the fabric of society. The implications for South Africa’s transformation within the fourth industrial revolution are highlighted. It is shown that digital nudging and choice architecture are tools that are capable of maximising profit while externalising social value through the algorithmic manipulation of our decision-making processes. The case for an alternative explanatory framework is presented because the limitations of the prevailing conception of physical reality appear unable to adequately resolve these super-wicked problems. Constructor theory is introduced as an alternative explanatory framework, and the constructor-theoretic conception is suggested as an ontological framework that will provide better explanations. Some principles of the theory are introduced.