Sustainable development sets the agenda for the 21st century. Human technological capability and needs mean that nature is and will be challenged and damaged in many ways. Whilst many social and technological innovations are being made to improve our survival prospects, they are likely to be insufficient to avoid continued social and ecological stress and the prospect of global tension if significant changes do not come about.The ideas in this book offer a new solution to sustainable development problems. They are concerned not with what we know but how we know, or rather how we order knowledge and create understanding in the human world.This book shows that some of the fundamental practices that shape modern society, especially in the business world, are the unwitting cause of unsustainable development. By extrapolating the epistemic analysis of Michel Foucault, a major social scientist, this book identifies a new episteme. It outlines a new way of ordering knowledge that better serves sustainable development.This pioneering book synthesizes the sciences of human and natural worlds and applies the findings to the creation of sustainable business models and equitable lifestyles for all.

The book considers how to make the methodology of business ethics more scientific, especially its normative branch. Storchevoy explores the attempts of economic theory to contribute to the scientific normative analysis of economic behavior, particularly the welfare economics of 1910-1950 and methodological discussions of economics and ethics from 1980-2015. He then examines the development of the methodological structure of business ethics in general since the 1980s and the scientific validity of normative business ethics, including stakeholder theory, the separation thesis, integral social contract theory, corporate social responsibility, virtue ethics and other frameworks. He concludes by suggesting an additional step to make business ethics a more systematic discipline by developing a typology of moral issues and dilemmas. Business Ethics as a Science will be a thought-provoking resource for students and practitioners of business ethics and economists alike.

Most books on the biotechnology industry focus on scientific and technological challenges, ignoring the entrepreneurial and managerial complexities faced bio-entrepreneurs. The Business Models for Life Science Firms aims to fill this gap by offering managers in this rapid growth industry the tools needed to design and implement an effective business model customized for the unique needs of research intensive organizations. Onetti and Zucchella begin by unpacking the often-used ‘business model’ term, examining key elements of business model conceptualization and offering a three tier approach with a clear separation between the business model and strategy: focus, exploring the different activities carried out by the organization; locus, evaluating where organizational activities are centered; and modus, testing the execution of the organization’s activities. The business model thus defines the unique way in which a company delivers on its promise to its customers. The theory and applications adopt a global approach, offering business cases from a variety of biotech companies around the world.

Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics. Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.

Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments

The recent financial crisis has sparked debates surrounding the nature and role of accounting in informing capital markets and regulatory bodies about the financial performance and position of a firm. These debates have drawn attention to the broader implications of accounting for the economy and society. Accounting and Business Economics brings together leading international scholars to examine the current state of accounting theory and its fundamental connection with the economics and finance of firms, viewing the business entity from not only accounting, but also national, economic, social, political, juridical, anthropological, and moral points of view.

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.