Economists examine the genesis of technological change and the ways we commercialize and diffuse it. The economics of property rights and patents, in addition to industry applications, are also surveyed through literature reviews and predictions about fruitful research directions. Two volumes, available as a set or sold separately - Expert articles consider the best ways to establish optimal incentives in technological progress - Science and innovation, both their theories and applications, are examined at the intersections of the marketplace, policy, and social welfare - Economists are only part of an audience that includes attorneys, educators, and anyone involved in new technologies
This book presents unique insights and advice on defining and managing the innovation transformation journey. Using novel ideas, examples and best practices, it empowers management executives at all levels to drive cultural, technological and organizational changes toward innovation. Covering modern innovation techniques, tools, programs and strategies, it focuses on the role of the latest technologies (e.g., artificial intelligence to discover, handle and manage ideas), methodologies (including Agile Engineering and Rapid Prototyping) and combinations of these (like hackathons or gamification). At the same time, it highlights the importance of culture and provides suggestions on how to build it. In the era of AI and the unprecedented pace of technology evolution, companies need to become truly innovative in order to survive. The transformation toward an innovation-led company is difficult – it requires a strong leadership and culture, advanced technologies and well-designed programs. The book is based on the author’s long-term experience and novel ideas, and reflects two decades of startup, consulting and corporate leadership experience. It is intended for business, technology, and innovation leaders.
Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage. But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects. Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully. You'll learn: What machine learning is, how it compares to "big data" and "artificial intelligence," and why it's suddenly so important What machine learning can do for you: solutions for computer vision, natural language processing, prediction, and more How to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new products Separating hype from reality: identifying pitfalls, limitations, and misconceptions upfront Knowing enough about the technology to work effectively with your technical team Getting the data right: sourcing, collection, governance, security, and culture Solving harder problems: exploring deep learning and other advanced techniques Understanding today's machine learning software and hardware ecosystem Evaluating potential projects, and addressing workforce concerns Staffing your project, acquiring the right tools, and building a workable project plan Interpreting results -- and building an organization that can increasingly learn from data Using machine learning responsibly and ethically Preparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications. For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way.
Technology management as a field came together during the 1980s in response to the question of how society could deliberately create new technology and exploit it in economic development. This updated edition introduces technology management, covers the importance of managing information technologies, and compares them to existing physical technologies.
This book of case histories is devoted solely to service industries and the technologies that drive them, as told by those who have developed segments of these industries. The chapters cover innovations such as Federal Express's advanced system for package tracking, Citicorp's development of the Automated Teller Machine, AT&T's experience with mobile telephones, Bell & Howell's introduction of an automated automotive parts catalog, and the New York Stock Exchange's development of electronic trading. Some broader analyses discuss the interfaces between services technologies and manufacturing, operations research in services, and technology in professional services.
While science and technology research, sources of funding, performance, incentives, and motivations for technology innovation activities are reasonably well understood by academics and policy makers, the complex process by which scientific results are exploited and transformed into new technologies through an innovation process is poorly documented and studied little. Technology Dynamics is dedicated to the complex activity of technology innovation, with the aim of describing how innovative ideas are generated and their transformation into new technologies. It is based on the idea that technology evolves continuously with time, is changed by innovations, and is characterized by a dynamic that is constituted by technological processes occurring in organizational structures, as well as during the use of technologies. The five chapters Discuss technological processes for innovation; Describe innovation within organizational structures; Offer information on interfacing of science and economic factors with technology; Suggest new statistical studies for innovation and new approaches for innovation policies; and Examine the contribution of technology dynamics to statistical studies and promotion of technology innovation. This book is aimed at managers developing strategies for technology innovation, researchers interested in exploiting scientific results for innovative ideas and new technologies, scholars and students studying the economics of innovation. The book would also of interest to private or public financiers of innovation and policy makers involved in economic growth strategy.
Scarpetta and Tressel present empirical evidence on the determinants of industry-level multifactor productivity growth. They focus on "traditional factors," including the process of technological catch up, human capital, and research and development (R & D), as well as institutional factors affecting labor adjustment costs. Their analysis is based on harmonized data for 17 manufacturing industries in 18 industrial economies over the past two decades. The disaggregated analysis reveals that the process of technological convergence takes place mainly in low-tech industries, while in high-tech industries, country leaders tend to pull ahead of the others. The link between R & D activity and productivity also depends on technological characteristics of the industries: while there is no evidence of R & D boosting productivity in low-tech industries, the effect is strong in high-tech industries, but the technology leaders tend to enjoy higher returns on R & D expenditure compared with followers. There is also evidence in the data that high labor adjustment costs (proxied by the strictness of employment protection legislation) can have a strong negative impact on productivity. In particular, when institutional settings do not allow wages or internal training to offset high hiring and firing costs, the latter reduce incentives for innovation and adoption of new technologies, and lead to lower productivity performance. Albeit drawn from the experience of industrial countries, this result may have relevant implications for many developing economies characterized by low relative wage flexibility and high labor adjustment costs. This paper--a joint product of the Social Protection Team, Human Development Network, World Bank, and the International Monetary Fund--is part of a larger effort to understand what drives productivity growth.
Innovation has shaped society since civilization began. Imperial China was the most innovative society on earth, but it failed to join the 19th century industrial revolution. In the 20th century, the Communist Party of China addressed that failure. Today China boasts an internationally compliant, rapidly developing IP system. State planning continues to be critical as the case of the largest, single, technology acquisition and infrastructure project in world history, high speed rail, demonstrates. But most of the innovation in China comes from the private sector: government incubators are among the government stimuli of private initiative, both local and global. And as the case on Cisco shows, foreign MNCs management of innovation in China is attractive but must involve co-ordination with government policy.This book presents cases where managers determine policy in China's increasingly innovative society. Readers take the roles of decision-makers to make strategy decisions. The cases in this volume showcase China's traditional three teachings, socialist market institutions, and modern management using studies on current Chinese companies and their leaders, among them big names such as Haier and Huawei. Each case stands alone as teaching material for instructors. Taken together, the book presents evolving models of innovation. Their subtle differences from western constructs critically impact the development of our global society.
