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The Libraries are in an era where changes come with high speed and much intense issues, and this seems to be as a result of the 4th industrial revolution. These emerging issues come in the form of changes that are not supposed to be overlooked by the library managers. These are changes libraries and Librarians have to be knowledgeable about. This chapter will address the impact, issues, challenges and controversies, prospects and how libraries should adapt, embrace and redesign their services, spaces and roles to accommodate this change on the Libraries. Existing research in the 4th Industrial revolution tends to focus on the challenges without much prospects and little or no evidence that researchers have approached how the libraries should accommodate and harness this revolutionary changes for improved library services provision. Therefore, this chapter aims to provide how the 4th industrial revolution affects the library and how libraries can harness the industrial revolution for enhanced services provision.

The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education.

The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves a systematized literature review for data identification and collation adhering to defined selection criteria, and a network analysis to scrutinize data, consolidate information and unveil correlations and patterns from the literature review to produce a set of recommendations.

The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability toward future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified.

The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies.

Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies and four scenarios for policymakers and local communities to consider, enabling them to plot courses for their optimal futures.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change.

This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.

The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance.

In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).

The purpose of this paper is to gain insight into whether additive manufacturing (AM) represents incremental, radical, disruptive innovation or an industrial revolution and its implications.

This study applies a desk research strategy. Data were collected through a variety of industry sources as well as academic publications.

It was found that AM represents different innovations in different settings, while it represents incremental innovation in one industry, it has led to radical changes in other industries. There are also indications that it has a disruptive nature and some of the developments appear to be of the industrial revolutionary type, i.e. they cause fundamental shifts in society. Some explanation for the observed differences can come from different performance objectives.

The spread of AM has been limited due to initial intellectual property protection. That means that while illustrations and examples were found for the different types of innovations, the level in which AM will ultimately penetrate manufacturing industries and society overall is not (yet) known. This calls for continued research for instance to study, in-depth, the adoption characteristics of AM in very specific settings.

Manufacturing is undergoing many changes as a consequence of the AM innovation. Many manufacturing industries have already been impacted through incremental changes as well as radical changes to entire industry dynamics. Manufacturers are advised to carefully monitor the continuous innovations in the technological capabilities of AM and their competitive and strategic consequences for adoption decisions.

AM has an impact on many aspects of society because it affects many industries and enables household manufacturing. It has also affected education, i.e. the current generation of students in terms of skill requirements, and leads to legal difficulties in terms of intellectual property.

This study contributes to the understanding of the AM innovation and the widespread implications for different manufacturing industries and society at large.

The purpose of this study is to connect disruptive innovation and sharing economy by exploring the underlying mechanisms of how a disruptive innovation–based business project creates, delivers and captures value in sharing economy through analyzing the case of bike sharing in China.

An elaborate case study is used to unfold the process as well as the underlying mechanism and relationships among disruptive innovation, business model, bike-sharing business and value creation in sharing economy.

Bike sharing case fits well in disruptive innovation theory. Its low price and great convenience have led to rapid development in China. However, failures to improve their products and services and build an effective business model which can create, deliver and capture value have caused the failure of many bike-sharing companies. Other factors such as strategic decision-making, internal management problems, external conflicts as well as uncivilized consumer behaviors have also inhibited the sustainable development of bike-sharing companies.

The theoretical contributions of this study include the following: to explore how a disruptive innovation–based business creates, delivers and captures value successfully in sharing economy. This study contributes to both research and practice on disruptive innovation and sharing economy.

Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.