As the world population is expanding and resources are shrinking it creates a challenging environment for people in low income and transitioning economies as well as developed countries. Hence, researchers are proposing different solutions and entrepreneurship is one of them. Academic institutions need to update their study programs and knowledge, modernize the curriculum and integrate research activities in degree programs about entrepreneurship. Economic challenges are getting very difficult to handle for every country and entrepreneurship can play a key role in dealing with these new realities. Aspects of academic entrepreneurship and entrepreneurial education unlock the discussion about the concepts, risks, actions and approaches towards the entrepreneurial university. Academic entrepreneurship focuses on the educated population who are learning and working in the universities and how they can create an entrepreneurial ecosystem so that academia can play forth the role of entrepreneurship after teaching and research. This book will contribute to the knowledge on developing an entrepreneurial ecosystem through academia. The target audience are university teachers, students, and researchers. This book will be appropriate for universities and policy makers to adopt and apply entrepreneurial approaches to develop academic entrepreneurship and an entrepreneurship ecosystem.

AI-Based Data Analytics: Applications for Business Management covers various topics related to marketing and business analytics. It explores how organizations can increase their profits by making better decisions in a timely manner through the use of data analytics. This book is meant for students, practitioners, industry professionals, researchers, and academics working in the field of commerce and marketing, big data analytics, and organizational decision-making. Highlights of the book include: The role of Explainable AI in improving customer experiences in e-commerce Sentiment analysis of social media Data analytics in business intelligence Federated learning for business intelligence AI-based planning of business management An AI-based business model innovation in new technologies An analysis of social media marketing and online impulse buying behaviour The book has two primary focuses. The first is on analytics for decision-making and covers big data analytics for market intelligence, data analytics and consumer behavior, and the role of big data analytics in organizational decision-making. The book’s second focus is on digital marketing and includes the prediction of marketing by consumer analytics, web analytics for digital marketing, smart retailing, and leveraging web analytics for optimizing digital marketing strategies.

Presents concepts of data for business decision making as well as algorithms and models used to analyze data used to solve business problems. Use data analytics to inform decisions related to product price and possession utilities Market products on the basis of consumer analytics

Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.

Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.

Economic challenges are becoming very difficult to manage throughout the world, and entrepreneurship can play a key role in handling these new realities. Due to this, academic institutions must update their study programs and knowledge, modernize their curricula, and integrate research activities in their degree programs that encompass topics about and related to entrepreneurship. Developing Entrepreneurial Ecosystems in Academia provides implications, best practices, and approaches for countries to improve their economic systems using entrepreneurship and increasing entrepreneurial education. As the world population is expanding and resources are shrinking, it creates a challenging environment for people in low-income and transition economies, as well as developed countries. This book discusses entrepreneurship and entrepreneurial education as a potential solution and critical concept. Covering a range of topics such as financial education and entrepreneurial management, it is ideal for instructors, academicians, researchers, practitioners, business professionals, policymakers, and students.