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Today’s most precious raw material is not gold, but Big Data: Each one of us generates a huge amount of information every single day, rendering thus both ourselves and our choices transparent. But in addition to that, Big Data helps companies to improve their decision-making. Since managers have to address highly complex issues in an ever more complicated world, they cannot do without Big Data and Artificial Intelligence, as Carolin Nothof explains. By taking into account various external factors, their algorithms predict right entrepreneurial choices. These choices can be made in areas such as retail, Human Resources, the Internet of Things, and marketing. Nothof’s publication is not only rich in theoretical explanations, but also gives examples of the practical use of Big Data in various industries. Machines are a man’s best co-workers. In this book: - Big Data; - decision-making; - AI; - Behavorial Economics; - Machine Learning; - algorithms
Today's most precious raw material is not gold, but Big Data: Each one of us generates a huge amount of information every single day, rendering thus both ourselves and our choices transparent. But in addition to that, Big Data helps companies to improve their decision-making. Since managers have to address highly complex issues in an ever more complicated world, they cannot do without Big Data and Artificial Intelligence, as Carolin Nothof explains. By taking into account various external factors, their algorithms predict right entrepreneurial choices. These choices can be made in areas such as retail, Human Resources, the Internet of Things, and marketing. Nothof's publication is not only rich in theoretical explanations, but also gives examples of the practical use of Big Data in various industries. Machines are a man's best co-workers. In this book: - Big Data; - decision-making; - AI; - Behavorial Economics; - Machine Learning; - algorithms
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.
Managers are facing unprecedented complexity, volatility, and ambiguity, quickly adapting their decision-making, leadership, vision, and strategies. Megatrends and forces of change have profound implications for business models, processes, and organizational structures, calling into question current paradigms and designing future change. Additionally, unprecedented disruptions, unforecastable in their nature, have increased the need for resilience and strategic flexibility. The book aims at tackling the potential interrelations among environmental transformations, strategic decisions, and leadership to better understand the role of external and internal factors on the effectiveness of managers. The book defines “change”: its extent, nature, and characteristics. Then, it focuses on decision-making, the role of potential cognitive biases, and how the interaction with the perception of determined environmental events affects the way in which decision-makers decide to implement specific strategies. Finally, in the light of waves of strategic change, it reviews theories on leadership and transformation by looking at the role and traits of leaders. Since environmental transformations have the potential to “disrupt” not only strategies but also decision-making processes and leadership, the book provides a review on the issue and propose an integrative framework which can be useful for both scholars and managers, especially in the fields of decision-making and strategy.
This book explores disruption and artificial intelligence in an organisational context to inform and prepare those that are in management positions now and into the future.
In just a few years, Web 2.0 communities have demonstrated astonishing levels of innovation, knowledge accumulation, collaboration, and collective intelligence. Now, leading organizations are bringing the Web's novel tools and philosophies inside, creating Enterprise 2.0.
Customers are treated badly. Not all customers. Not always. But many are and often. Some customers are bad. They treat firms badly. Firms have to react. Employees and customers endure the consequences. Such bad behaviours, by firms and customers, have consequences for perceptions of trust and fairness, for endorsements and referrals, for repeat purchasing and loyalty, and ultimately for a firm’s profitability and RoI. The management of customer relationships is core to the success and even survival of the firm. As The Dark Side of CRM explores, this is an area fraught with difficulties, duplicitous practice and undesirable behaviours. These need acknowledging, mitigating and controlling. This book is the first of its kind to define these dark sides, exploring also how firms and policy-makers might address such behaviours and manage them successfully. With contributions from many of the leading exponents globally of CRM and understanding customers, The Dark Side of CRM is essential reading for students, researchers and practitioners interested in managing customers, relationship marketing and CRM, as well as social media and marketing strategy.
This e-ISBN collection of 34 chapters draws on the diverse insights of the opportunities and emerging challenges, changes in the smart technologies and artificial intelligence{AI} paving path towards interdisciplinary research in the fields of Engineering, Arts, Humanities, Commerce, Economics, Social Sciences, Law and Management. It offers decision-makers a comprehensive picture of the impact of Smart technologies and Artificial Intelligence (AI) expected in the long-term changes, and inspiration to leverage the opportunities that offer to improve the state of education. Academicians must find and establish a new equilibrium and a new normal for learning amid the present challenges.
This book covers cutting-edge and advanced research on data processing techniques and applications for cyber-physical systems, gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2020), held in Laibin City, Guangxi Province, China, on December 11–12, 2020. It examines a wide range of topics, including distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; machine learning algorithms for CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers, and professionals alike, while also providing a useful reference guide for newcomers to the field.