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Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

This chapter presents the many premises of this book. It first discusses the book’s central questions and lays out the design of the large multi-national and multi-method study, carried out across Northern Europe. It also places the book at the interdisciplinary space between contemporary innovation economics and cultural and social theory. It then discusses the complex set of social processes that have conditioned the phenomena that the book studies – how and why are the contemporary audiovisual media industries co-innovating and converging with other sectors including education, tourism and health care? Within this framework, it discusses the effects of the broader individualisation and mediatisation processes, of media convergence, of the emergence of cross-media or transmedia strategies, of the evolution of the service and experience economies and of the emergence of creative industries policy frameworks.

This chapter provides the background and relevant policy context information necessary to understand the approach to development and implementation of tailored gender equality plans provided by the TARGET project. It describes the development of European gender equality polices in research and innovation (R&I) since the 1980s and experiences with early structural change projects. TARGET refers to these experiences and aims at providing an innovative approach to overcome challenges towards gender equality, especially for research organisations located in countries that have been classified as rather inactive when it comes to gender equality policies in R&I. The chapter closes with an outline of the structure of the book and its individual chapters.