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Sustainable Business Model Innovation - - Bog - De Gruyter - Plusbog.dk

Transformation of the Electric Utility Business Model - John Manshreck - Bog - De Gruyter - Plusbog.dk

Transformation of the Electric Utility Business Model - John Manshreck - Bog - De Gruyter - Plusbog.dk

This book examines business model transformation through the study of electrical utilities, an industry at the center of today''s efforts to combat climate change. When change comes to the business model of such a mature industry, the pattern is often recognizable. The foundational elements of the industry shift, allowing the innovation of business models by new competitors, while established firms face the threat of disruption. The utility sector, after decades of relative stability, is in the midst of such a transformation today. After providing a historical summary of the dominant business models of the utility sector, Transformation of the Electric Utility Business Model looks at the factors currently impacting the industry. Utilities and policy makers today are facing two long-term issues that will dominate their agendas in the coming decades: rebuilding utility infrastructure to enable the decarbonization of the economy, and managing the risk of catastrophic events that can leave large areas without power for extended periods. Fortunately, with proper planning, many utility investments in decarbonization will also support risk management. However, these investments are often not compatible with current utility business models, requiring creativity and new regulatory frameworks to successfully implement. This book considers the impact of these factors, and then discusses the future. This well-researched, extremely insightful book is essential reading for all those with an interest in business strategy, energy studies and sustainability.

DKK 722.00
1

Successful Business Model Transformations in Disruptive Times - Thomas Rudolph - Bog - De Gruyter - Plusbog.dk

Ambassadors of Beauty - Matilde Cartolari - Bog - De Gruyter - Plusbog.dk

Family Business Transformation - - Bog - De Gruyter - Plusbog.dk

The CMO of People - David Creelman - Bog - De Gruyter - Plusbog.dk

ALM Modeling and Balance Sheet Optimization - Diogo Gobira - Bog - De Gruyter - Plusbog.dk

The Oligarchs' Grip - Valentina Rodriguez Guerra - Bog - De Gruyter - Plusbog.dk

Machine Learning with Python - Tarkeshwar Barua - Bog - De Gruyter - Plusbog.dk

Memory as Power - - Bog - De Gruyter - Plusbog.dk

Understanding the Future - Ronald Bradfield - Bog - De Gruyter - Plusbog.dk

Understanding the Future - Ronald Bradfield - Bog - De Gruyter - Plusbog.dk

Organizations today face an increasingly complex contextual environment. The intensity of what is recognized as a VUCA world has changed how they view the world, interact with each other, and respond to this environment. Understanding the Future shows individuals and organizations how to develop scenario planning, using the Intuitive Logics (IL) model, to perceive what is happening in the business environment and how to improve strategic decision-making to plan for uncertainty. Ronald Bradfield, a renowned scenario planning practitioner, traces the origins of scenario planning from its evolution to associated techniques and details the IL development process from Stage 1 to Stage 5. He includes an insightful chapter on how people think, describing the role of heuristics and biases, reviewing some of the commonly known ones, and concludes with the pros and cons of the IL model. This book includes extensive reference material: appendices, a list of Foresight and Scenario organizations, Futures journals and magazines, published scenarios, select readings and guides, and the author’s unique case material directly from his world-leading consulting work of the past 30 years. Understanding the Future is an exceptional, comprehensive guide for postgrads, practitioners, leaders, policymakers and anyone involved in organizational development or management risk who needs to understand the IL scenario framework and its value in addressing organizational challenges amidst complexity.

DKK 361.00
1

School Buildings - - Bog - De Gruyter - Plusbog.dk

The CMO of People - Peter Navin - Bog - De Gruyter - Plusbog.dk

The CMO of People - Peter Navin - Bog - De Gruyter - Plusbog.dk

The extremely positive response to the first edition of The CMO of People from both practitioners and educators spoke of the value of fresh ideas along with specific steps on how to execute them. This second edition of Peter Navin and David Creelman’s pathbreaking book, with new sections including industry leaders’ insights from Nike, UKG, and DocuSign, corroborates the approach that sees the CMO of People as a business focused people function that utilizes the proven tools of the marketing function and creates a predictable and immersive employee experience that drives productivity and performance. If the human resources function in your talent-centric organization is not bringing the excitement and business impact it should, you need a new mental model that approaches getting the best from people with the same mindset marketing uses to get the best results with customers. Just as the Chief Marketing Officer curates an experience to get the best lifetime value from customers, the head of HR, the CMO of People, can curate an experience to get the best lifetime value from employees. This unique book discusses: What it takes to change the character and intensity of an organization How to run HR so that it has impact Why we need to structure the HR department differently How to find unconventional people to staff this unconventional model How to create a predictable and immersive end-to-end experience for employees How a CMO of People can overcome barriers and drive performance

DKK 190.00
1

Industry 4.0 - - Bog - De Gruyter - Plusbog.dk

Machine Learning under Resource Constraints - Applications - - Bog - De Gruyter - Plusbog.dk

Machine Learning under Resource Constraints - Applications - - Bog - De Gruyter - Plusbog.dk

Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems'' sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous source...

DKK 1323.00
1

Machine Learning under Resource Constraints - Fundamentals - - Bog - De Gruyter - Plusbog.dk

Machine Learning under Resource Constraints - Fundamentals - - Bog - De Gruyter - Plusbog.dk

Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems'' sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous source...

DKK 1323.00
1

Corporate Governance in Russia - - Bog - De Gruyter - Plusbog.dk

Corporate Governance in Russia - - Bog - De Gruyter - Plusbog.dk

This book explores discussions and practice around corporate governance in Russia from the early 1990s until 2018. It covers three major aspects of corporate governance theory and practice: a vision of corporate governance in Russia in the context of global trends and challenges, the general perception of corporate governance in Russia, and the real nature of Russia''s corporate community from the viewpoint of its corporate governance practices. It provides a unique complex analysis and detailed description of how corporate governance has been perceived by both Russian regulators and the business community, and how it has been applied in Russian companies. This analysis covers the period of over 25 years: from early attempts at directing transfer and implanting the Western model of corporate governance to the nascent Russian big private business, up to the period of resurgence of the state as the dominant player both in Russian society and its economy at large. It gives an understanding of what corporate governance is in Russia in the days of "sovereign democracy" and confrontation with the West. It explains how cultural, political, economic and institutional factors have shaped corporate governance in Russia. The authors provide insights into such aspects of Russian corporate governance framework and practices as regulatory philosophy and enforcement, ownership structure, the role of the state, the impact of unfriendly domestic business climate, how the value of corporate governance is perceived in Russian context, etc. Predominantly, the book paints an interesting picture of how the "sovereign corporate governance" model has been shaped in Russia. This book will be useful not just for experts in corporate governance and investors, but also for those who have an interest in modern Russia at large.

DKK 644.00
1

Risk-Sharing Finance - Abbas Mirakhor - Bog - De Gruyter - Plusbog.dk

Risk-Sharing Finance - Abbas Mirakhor - Bog - De Gruyter - Plusbog.dk

The contemporary finance deals mainly with multilateral and multi-counterparty transactions. Islamic Jurisprudence (Fiqh) has yet to develop its conceptualization of this modality of financing. Thus far, it has become a norm for large financing projects to rely on a complex structure of interconnected bilateral contracts that in totality becomes opaque, complex and costly. An unfortunate result of the unavailability of an efficient Fiqhi model applicable to modern multilateral and multi-counterparty contracts has been the fact that the present Islamic finance has been forced to replicate conventional risk-transfer (interest rate based) debt contracts thus drawing severe criticisms of duplicating conventional finance. In 2012, a gathering of some of the Muslim world''s most prominent experts in Jurisprudence (Fuqaha) and economists issued the Kuala Lumpur Declaration (Fatwa) in which they identified risk sharing as the essence of Islamic finance. The Declaration opened the door for a new Fiqh approach to take the lead in developing the jurisprudence of multilateral and multi-counterparty transactions. This Declaration (Fatwa) provides a prime motivation to search for a comprehensive model of risk sharing that can serve as an archetypal contract encompassing all potential contemporary financial transactions. From the perspective of Islamic Jurisprudence (Fiqh), the technicalities of the concept of risk sharing in contemporary finance have yet to be defined in Islamic literature. This book attempts to clarify and shed light on these technicalities from the perspective of Fiqh. It is a comprehensive study that relies on the fundamental Islamic sources to establish a theoretical and practical perspective of Fiqh encompassing risk-sharing Islamic finance as envisioned in the Kuala Lumpur Declaration of 2012. This new paradigm should lead to a more efficient approach to multilateral and multi-counterparty Islamic contracts which, here-to-fore has been lacking in the current configuration of Islamic finance.

DKK 970.00
1

Zionism and Cosmopolitanism - Dekel Peretz - Bog - De Gruyter - Plusbog.dk

Fintech Business Models - Matthias Fischer - Bog - De Gruyter - Plusbog.dk

Fintech Business Models - Matthias Fischer - Bog - De Gruyter - Plusbog.dk

This book on fintechs shows an international comparison on a global level. It is the first book where 10 years of financing rounds for fintechs have been analyzed for 10 different fintech segments. It is the first book to show the Canvas business model for fintechs. Professionals and students get a global understanding of fintechs. The case examples in the book cover Europe, the U.S. and China. About the author:Matthias Fischer is professor of finance and banking at the Institute of Technology Nuremberg Georg-Simon-Ohm in Germany. His research has focused on strategy and M&A in the banking sector, value-based management, robo-advisory and fintechs. Dr. Fischer also serves as a member of the Groupe de Recherche en Management at the IAE Nice Graduate School of Management, Université Côte d''Azur in France. He is internationally active as a strategy and financial advisor. Reviews of the book: FinTech is not the next ''big thing.'' It is the big thing now! FinTech is the new business model for the global financial sector, offering clear and enormous potential for vast economies of scale and scope, massive cost savings and efficiency gains, significant risk reduction, and opening the door to banking for literally billions of currently unbanked people. Professor Fischer has done a masterful job of expertly and informatively taking us through all aspects of the revolutionary new FinTech business models. Using state-of-the-art research techniques, he insightfully shows us how FinTech firms are financed and how they aspire to create value. His in-depth case studies unlock the keys to success in the FinTech sector. His fascinating book is a ''must read'' for all financial professionals.Dr. Stephen Morrell, Professor of Economics and Finance, Andreas School of Business, Barry University, Miami, USA Matthias Fischer''s latest book offers a comprehensive overview of Fintech business models around the world. With a very pedagogical approach, and in a particularly fluid style, the author takes us into the strategic logics of these new entrants to finance, who are carriers of innovation and sometimes of disruption, and whose strategies are focused on the need to always meet the emerging expectations of their customers. This precise and well-documented analysis should enable banks to reposition themselves in their ecosystem by studying these new business models, which will enable them to boost their growth.Professor Dr. Nadine Tournois, Dean of IAE Nice Graduate School of Management, Université Côte d''Azur, France, Chevalier de la Légion d''honneur Fintech Business Models is a must-have book to understand the rapid and intense changes occurring in the financial sector. New technologies have allowed the birth of new financial species, such as Fintech, more adapted to the new digital economy. The content dedicated to the application of blockchain technology helps to understand its opportunities in the financial sector, not only in the means of payment and cryptoactives, but also in how blockchain can make multiple internal processes improve, allowing to optimize the management, efficiency and even security of operations. Without any doubt, this book offers an extraordinary vision of how the fintech sector has become a catalyst for change in banking in the context of the current Digital Society.Phd. Ricardo Palomo, Full Professor of Finance, Deputy Chancellor for Digital Transformation at Universidad CEU San Pablo, Madrid, Spain and member of the Board of Alastria Blockchain Ecosytem This book provides a detailed and original overview of the most important fintech business models in the major global markets. Through a savvy use of the well-known Business Model Canvas methodology, the author explores the unique ecosystem, business model''s components, and sources of competitive advantage of successful fintech firms. The book, in particular, offers an insightful and comprehensive analysis of the winning and losing strategies and performa

DKK 452.00
1

Corporate Governance in a Nordic Setting - Peter Beusch - Bog - De Gruyter - Plusbog.dk

The Tragedy of Ukraine - Nicolai N. Petro - Bog - De Gruyter - Plusbog.dk