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This chapter presents a theoretical framework of the industrial relations (IR) system in China’s coal mining industry, combining the roles of management organizations, workers, and trade unions, as well as government agencies. It is one of the first empirical attempts to investigate the relationship between human resource (HR) practices, labor relations, and occupational safety in China’s coal mining industry over the past 60 years, based on the secondary data on coal mining accidents and case studies of two state-owned coal mines in a northern city in Anhui Province, China. The fluctuating occupational safety has been affected by government regulations over different time spans, marked by key political agendas, and by coal mining firms taking concrete measures to respond to these regulations, while exhibiting differing safety performance in state-owned versus township-and-village-owned mines. The field studies compared a safety-oriented to a cost-control-oriented HR and labor relations system, and their influences on safety performance. Coal mining firms and practitioners are advised to shift the traditional personnel management paradigm to a modern HR management system. In addition, although workers are often blamed directly for accidents, it is suggested that workers’ participation and voice in various processes of decision-making and policy implementation, and trade unions’ active involvement in protecting workers from occupational hazards, be encouraged.

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

This chapter presents an overarching overview of how the rather recent technological phenomena, like data mining, machine learning, and artificial intelligence, are applied in the field of education. The author provides examples of how technological developments associated with the so-called Fourth Industrial Revolution are applied in education and considers the benefits and challenges they may bring regarding the economic system, as education (at least in the higher education sector) tends to be monetized and commercialized. The framework for education is perceived in the context of the economic intelligence of states, which is instrumental in ensuring their economic security. It is further expanded to the global scale, as Digital Education is crossing national borders and is being implemented in broader national processes.

How does leadership empower seasoned staff to relinquish historical practices without compromising self-image with new staff? Libraries are rife with legacy practices; those processes and procedures that were valid and important yet are no longer useful. Relinquishing those practices can be challenging for some staff members. In many cases it is simply, “we’ve always done it that way.” In other cases it has to do with ownership, self-image, or perceptions of job security. The authors examine literature on organizational change exploring the implications of legacy practices and procedures through the lens of Generational Theory. A targeted literature review establishes the link between theories and practices. Specific examples of workflow transitions are examined in order to understand how generational and change theories inform staff behaviors. Legacy practices may be perceived as a barrier that disenfranchises younger staff while at the same time be perceived as a barrier that isolates and devalues older staff. Literature informs us that intra-generational stereotypes prevail and add tensions to discussions of workflow changes. Times of change can be emotionally charged and these stereotypes often lead to misunderstandings, hurt feelings, and conflict. Leadership strategies emerging from literature on organizational change must be applied with careful attention to characteristics identified by generational theory. Communication is a prevalent and recurring theme for successful change initiatives. It is also a moment when generational theory awareness will inform good practice and avoid emotional pitfalls. A careful step-by-step examination of specific workflows that have changed in libraries during recent decades will provide examples in order to inform leaders’ planning for future changes.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

This chapter discussed the role of business intelligence (BI) in healthcare twofold strategic decision making of the organization and the stakeholders. The visualization techniques of data mining are applied for the early and correct diagnosis of the disease, patient’s satisfaction quotient and also helpful for the hospital to know their best commanders.

In this chapter, the usefulness of BI is shown at two levels: at doctor level and at hospital level. As a case study, a hospital is taken which deals with three different kinds of diseases: Breast Cancer, Diabetes, and Liver disorder. BI can be applied for taking better strategic decisions in the context of hospital and its department’s growth. At the doctor level, on the basis of various symptoms of the disease, the doctor can advise the suitable treatment to the patients. At the hospital level, the best department among all can be identified. Also, a patient’s type of admission, continued their treatments with the hospital, patient’s satisfaction quotient, etc., can be calculated. The authors have used different methods like Correlation matrix, decision tree, mosaic plots, etc., to conduct this analysis.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

In this chapter, I argue, contrary to some current views, that workflow process mapping can be an important and relevant tool for assessing and improving the effectiveness and efficiency of library Technical Services departments. I also propose that linking workflow process mapping to the “High Performance” style of organizational management of W. Edwards Deming underlines both the value of process mapping and how the latter can obviate the need for hierarchical managerial control, by building a cohesive and efficient technical services team. First, I describe the “High Performance” management style of Deming, focusing in particular on what is generally called the “Deming Cycle.” Second, I describe the process of mapping workflows and emphasize its value for highlighting waste, improving existing processes, and maintaining sustainability. Third, I argue that linking workflow mapping to this larger understanding of management style results in several positive consequences for technical services departments, such as a team-based rather than hierarchical style of management, increased departmental and interdepartmental effectiveness and efficiency, and a better return on investment. I illustrate these points by looking directly at an example of an acquisitions department.