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Aaron C.T. Smith, James Skinner and Daniel Read

Chapter 1 notes that the need for change features prominently in organizational ambitions, as its success or failure can lead to decisive consequences, from transformational improvements in productivity to catastrophic plunges towards insolvency. Research also reveals a discord between organizations’ change ambitions and their tangible effects on organizational performance. Perhaps most concerning, the evidence informing organizational change is scant and tends to rely on unvalidated theories, models, cases and commentaries. The chapter subsequently outlines how this book’s second edition aims to inventory and explain the diverse and pluralistic organizational change approaches that have attracted research and practitioner interest. It reveals the ‘philosophies’ that guide change theories and models on the presupposition that a better understanding of these underpinning perspectives provides valuable insight for the research and practice of change. The approach assumes that organizational change can be best studied and applied when the philosophies that structure an approach are clearly exposed.

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Peter Edlund

In Chapter 1, I introduce the ERC and its remarkable development into the most important science funding organization that Europe has witnessed. Entering its second decade of existence, the ERC’s funding is currently regarded as a benchmark of scientific quality, which researchers, departments, universities, and governments across large swaths of the continent regularly use to compare their performance in science. In many respects, however, the ERC has become more than a research funder. By extension, I suggest that the ERC’s development can be approached as the emergence of an outright status intermediary in the field of European science. While previous status literature has enhanced our understanding of the pervasive consequences that authoritative intermediaries generate, we know much less about the processes through which such intermediaries are constructed in the very first place. I thus study the ERC to produce new theory that will augment our knowledge about the construction of authoritative status intermediaries in fields.

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Peter Edlund

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Paul D. Reynolds

While both forms contribute to economic growth, a small number of dramatic Wall Street favorites gain much of the attention, while the cumulative contributions of millions of Main Street efforts are often overlooked. Business creation is a critical intermediate variable in mechanisms leading to economic growth.

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Albert N. Link

This book is about inventions, and the genesis of the specific inventions that I will discuss have taken place in U.S. federal laboratories. The inventions discussed in this book are defined by the technology transfer mechanism known by the term invention disclosures that refers to an aspect of the tangible results from inventive ideas in a federal laboratory. In Chapter 2, the term Inventive Ideas, which are measured in terms of the number of new invention disclosures in a federal laboratory, is described over time. In Chapter 3, empirical evidence is presented that suggests that an Experiences _ Inventive Ideas paradigm has construct validity across federal agencies. In Chapter 4, a conceptual as well as empirical explanation is offered for why some agencies are more efficient in the process of creating new inventive ideas. In Chapter 5, an enhanced knowledge production function is proposed, and evidence is offered in support of the statistical significance of the relationship: Patent Applications = G (Inventive Ideas). In Chapter 6, a case study of the Patent Applications = G (Inventive Ideas) relationship using invention disclosures information from the National Institute of Standards and Technology (NIST) is discussed. In Chapter 7, CRADA (cooperative research and development agreement) activities are examined. And, in Chapter 8, a brief summary of the findings presented in this book is given.

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Paul D. Reynolds

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Albert N. Link

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Paul D. Reynolds

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Andrzej Wodecki

In Chapter 1, we present the essential concepts underlying intelligent, autonomous, and self-learning systems. We start by indicating the key features of such solutions, in particular the essence of intelligence, and the most important machine learning models. In the next step, we focus on reinforcement learning (RL) as the basis for many of the autonomous systems discussed later. We present the key RL concepts, the idea of learning by interaction, and the most important algorithms. Finally, we analyze the role of autonomous systems in the modern world, which is an introduction to the topic of their applications presented in the following chapters.

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Edited by Lukas Parker and Linda Brennan