HR managers have been trained to think of themselves as cost centers, challenged at every budget cycle to cut, pare, and shrink expenses. Their staff brandish benchmarks and statistics to say how engagement affects business success, how the ratio of HR staff to employees is low, and how our turnover is too high. But with all these data, executives still roll their eyes and ask, "But what impact do you have on our bottom line?" By breaking down organizational silos and using a process to generate and foster collective thinking, HR can shift the paradigm from developing programs, policies, and processes to improving the performance and productivity of the workforce. Repurposing HR presents a RoadMap for a new way to look at human resources in terms of the multiple roles that HR plays on the business scene. It will walk through each "StopOver" on the journey, including the * purpose, objectives, knowledge, and skills required * work products * tools and resources that are useful to the practitioner. This is a compelling process for using the skills, competencies, and attributes of the HR team in a systematic and holistic way.

The importance of people in organizations has been growing in the organizational environment over the last decades. Digital transformation, artificial intelligence, and sustainability have already reinforced the role of people as a differentiating element for the success and survival of organizations. These phenomena alone are already challenging for people. There is a consensus that the world of work as we knew before the pandemic will not return. Human resource management (HRM) practices must prepare organizations for the future of work. The Handbook of Research on Challenges for Human Resource Management in the COVID-19 Era analyzes state-of-the-art HRM in a digital transformation context and investigates the factors that promote more learning and development dynamics in organizational contexts. It also evaluates the impact of HRM policies on individuals, organizations, and societies. Covering topics such as corporate social responsibility, job satisfaction, and electronic performance monitoring, this major reference work is a crucial resource for leaders from public and private companies, human resource professionals, specialists, students and educators of higher education, researchers, and academicians.

The Emerald Handbook of Work, Workplaces, and Disruptive Issues in HRM considers the way work, employment and people is being managed across the globe, using a multidisciplinary range of voices to illustrate just how fundamental recent developments will be in reshaping work and employment.

Learn step-by-step how to create employees who are committed, passionate, energetic, take initiative, put high levels of effort into their jobs, and exceed expectations. Fostering Employee Engagement has the answers along with practical tools and specific actions you can easily implement to engage your workforce.

This insightful Research Handbook delivers a comprehensive analysis of the significant contemporary trends and issues affecting human resource management (HRM) for health care, and their subsequent impact on individuals, organisations and national health services. This title contains one or more Open Access chapters.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.