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This chapter reviews diffusion of innovations theory which has been the theory that has used network principles and perspectives most extensively. An introduction to the theory and a review of its principles is provided. The chapter then reviewed the 4 major classes of diffusion models (1) integration/opinion leadership, (2) structural models, (3) critical levels, and (4) dynamic models. All four models explicitly account for network diffusion dynamics, but vary in their mathematical rigor and complexity. The chapter also introduced the calculation of infectiousness and susceptibility which dynamically account for adoption behavior and in-degree and out-degree, respectively. Empirical data illustrating network exposure effects are presented as well as the calculation and interpretation of network thresholds. The chapter closes with a brief critique of the theory.

This chapter continues the exploration of justice issues, focusing on the problem of inadequate diffusion—the worry that valuable biomedical enhancements may not be available or may only be available after an extended period of time, to the world's poorest people. In keeping with Chapter 1's emphasis on the fact that enhancement is not new and that biomedical enhancement is not distinctively morally problematic, the chapter situates this problem as one aspect of a the larger problem of inadequate diffusion of beneficial technologies, and offers a global institutional response to this larger problem. Thus, the book concludes with an example of how to move beyond the exchange of pros and cons to a constructive, practical, institutional response to one of the major challenges of biomedical enhancement.

In this chapter, the authors examine the past, present, and future of police BWCs. The spread of BWCs in policing has been extraordinary, especially given the cost and degree of difficulty in implementing and managing a BWC program. In this chapter, the authors use two different, complementary lenses to explain the rapid spread of BWCs. The first is the diffusion of innovations framework. The second lens is the evidence-based policing framework. Both the diffusion of innovation and evidence-based policing frameworks provide insights on the “how and why” questions regarding current rates of BWC adoption, and just as important, they provide an informed position to consider the prospects for BWCs in the future.

This chapter provides a brief historical background to social learning research. The history of research into social learning and imitation dates back to Aristotle, who explicitly made the claim that animals acquire behavior through imitation and other forms of social learning. Aristotle was particularly impressed with the human imitative tendency. The three insights made in the fourth century BC—that humans are uncharacteristically reliant on imitative learning compared to other animals, that young children in particular acquire important aspects of their behavioral repertoire through copying, and that imitation appears intrinsically rewarding to children—are remarkably relevant to contemporary social learning research. The chapter examines how investigations of social learning have been central to research into the evolution of mind, the mechanisms of social learning, animal culture, the diffusion of innovations, child development, and cultural evolution.

This introductory chapter introduces the idea of hot spots policing by tracing its origins to two key studies in the development of modern police strategies. In 1974, the Kansas City Preventive Patrol Experiment challenged the ability of police patrol as a crime prevention approach. In 1995, the Minneapolis Hot Spots Experiment showed that police could prevent crime if they focused on high crime hot spots. This chapter also introduces a key objection to hot spots policing, that it will simply displace crime to other areas. Focusing on the Jersey City Drug Market Analysis Experiment and the Jersey City Displacement and Diffusion study, the chapter reveals why this argument has not hindered the development of hot spots approaches. Indeed, there is much stronger evidence that hot spots policing will lead to a diffusion of crime prevention benefits than that it will lead to crime displacement. Adding to the argument for hot spots policing, the chapter also shows that crime hot spots evidence considerable stability over time, making them an efficient focus for crime prevention. Finally, the diffusion of hot spots policing as a concept in American policing is examined.

This chapter identifies an important inbuilt difference between the two paradigms with respect to innovation diffusion. The difference springs from the fact that, unlike producers, free innovators do not protect their innovations from free adoption, and they do not sell them. As a result, benefits that free-riding adopters may gain are not systematically shared with free innovators—there is no market link between these parties. This chapter thus presents evidence for the systematic shortfall in free innovators' incentives to invest in the diffusion of free innovations, and then argues that it is caused by the absence of a market link between free innovators and free-riding adopters. The chapter then concludes with suggested ways for addressing this situation.

This chapter askes how innovations spread – both over space and through time. It attempts to bridge the distance between the individual-scale sharing of knowledge or skills and the regional scale patterning visible archaeologically. The case study explored in this chapter is the spread of Lapita material, people, and practices in the prehistoric Pacific. As the study makes clear, especially when small-scale and pre-modern societies are concerned, kin networks are crucial vectors for the dissemination of new ideas, technologies, and practices particularly through teaching and learning. This observation leads to an extended discussion of craft learning that explores the ways various training models identified ethnographically and historically encourage or discourage innovative practices. Evolutionary models of knowledge transfer are contrasted with more embedded approaches, such as models of situated learning and communities of practice. The discussion then broadens to explore the geographies of innovation dissemination. While archaeologists fixate on narratives of diffusion and migration, research in the contemporary world focuses on the development of regional innovation systems. The chapter argues that these dominant approaches overlook the role of peripheral populations and the creativity of marginal communities.

This chapter describes variation in diffusion and rates of cultural change. It addresses the possible role of social-influence processes and of economic inequalities in identifying the rate of growth in sales of new products of this kind. It analyzes the differences and similarities of the Bass model of innovation diffusion and the dual-inheritance (DI) model in terms of adoption dynamics. This chapter suggests that economic inequality in a population's income distribution is likely to be an important factor in explaining the time course of new product diffusion. It shows that both the Bass and the DI models assume that agents are homogeneous with respect to the economic capacity to adopt.

This chapter describes how network data can be used to accelerate behavior change and/or improving organizational performance. Network interventions consist of behavior change programs that use social network data to identify specific people or groups to deliver and/or receive the behavior change program. Network interventions can take many different forms and have many different implementation considerations. Six classes of methods were reviewed: opinion leaders, groups, leaders matched to groups, snowball methods, re-wiring networks, and crossing network data with attributes. Many cautions are expressed about the importance of theory and ethnography to ensure the research team has sufficient knowledge of the community. The potential for network interventions to accelerate behavior change is great, but the application of these methods is likely to be challenging at first as both researchers and communities learn how best to apply networks and networking in the most effective ways. Examples from empirical studies are presented throughout.

This chapter proposes an international regulating body for the purposes of eliminating the problem of diffusion of innovation. Patent laws, intellectual property rights, monopolies, among other things, exacerbate political and economic inequalities by withholding access of innovations to those who need it. A major branch of concern is the distribution of pharmaceuticals. In order to maintain distributive justice and reduce the global burden of disease, a Global Institute for Justice in Innovation can be established to encourage rapid diffusion of innovations and discourage firms that restrict access to them.

Chapter 3 is the second chapter dedicated to the technological imperative stage of the political communication cycle (PCC). It focuses on the technological component of political communication revolutions (PCRs) and addresses how the cost, rate of diffusion, and perceived benefits of each new information and communication technology (ICT) affects its political utility. In other words, chapter 3 evaluates how new ICTs become politically viable. A politically viable ICT does not enter American politics without active choices made on the part of political actors who try to use these new tools in innovative ways. All widely diffused ICTs do not share wide-scale political utility. As a result, some ICTs—like mass-marketed newspapers, radio, television, and the internet—have had a major impact on communication practices broadly and political communication innovations specifically, while others like the telephone and telegraph have transformed social communication but not political communication.

Chapter 4 explains the concept of political choice, the second and most important phase of the political communication cycle (PCC). The political choice phase is the process in which political actors choose if and when to incorporate new information and communications technologies (ICTs) into their communication strategies. This chapter details the process that political actors or organizations go through when determining whether to innovate and helps to identify characteristics of those parties that are more likely to innovate earlier than others, known as innovativeness. Political choice is the behavioral component of the political communication cycle. These innovation decisions are the primary determinants regarding if and how ICT innovations are used to change political communication activity. Therefore, political choice is the most important phase of the PCC, differentiating political communication change from social and societal communication change more broadly.

This chapter examines why the development and adoption of combinatorial technology was so slow after the first industrial revolution sparked by the invention of hafting. It first considers where hafting was invented and the evidence of hundreds of thousands of years of technological development stretching from the later Acheulean to the Middle Palaeolithic. It then looks at the intellectual school of the Sociology of Invention, with special reference to the argument of W. F. Ogburn and his cohorts that invention was a cumulative process founded on existing traditions, before turning to the set of integrative technologies shared by the late Acheulean populations in parts of Africa, Eurasia, and south Asia. Furthermore, the chapter describes two basic models that explain the diffusion of innovation: the first invokes the movement of people (demic diffusion) and the second emphasises the interaction between neighbouring communities (cultural diffusion). Finally, it analyses early projectile machines in terms of their working properties and the archaeological evidence for their innovation, namely, the spear-thrower, bow and arrow, and traps.

The successful shift away from chemicals of concern requires the development of new and safer alternatives. This involves the synthesis of new chemicals and chemical processes. Nanotechnology and synthetic biology both offer novel routes to such alternatives if they are effectively guided by a drive for safety and ecological compatibility. Green engineering and nonchemical alternatives also offer safer substitutes. These innovations require a clear definition of the term “safer” and tools for assisting in such searches. However, there is now a growing market of safer chemical and nonchemical alternatives that could use either government of private investment assistance to get to the scale and competitive prices that are needed for effective market conversions.

Integral to the translational science framework is analysis of what happens to the knowledge generated through research. There exists a burgeoning science that addresses how research-based knowledge and innovations inform and are adopted into practice and how practice informs the development of the research agenda. The body of research concerned with the dissemination and implementation of knowledge and technology identifies factors that determine the crucial processes of translating research results into routine practice and policy. The goal is to increase the extent to which “best practices” are implemented. This chapter examines approaches that address these science-to-practice translational research issues for social work investigators engaged in substance use research.

Chapter 4 initially provides data about the growth of ITA as well as offering a series of frames against which to understand its evolution, including a sociological perspective analyzing changes in ITA’s caseload against Professor Everett’s theory of diffusion of innovations. It next explores other elements of time, namely case length and deliberative delays, by offering descriptive data and testing whether case length has reliably increased over time or whether resolution times vary on the basis of institutions resolving disputes. It then turns to exploring temporal elements related to bifurcation and separate opinions. After providing an explanation of the fiscal costs data particularly related to parties’ legal costs as well as tribunal costs and expenses (and conducting a variety of tests to explore the potential impact of missing data), the last section identifies the reliable relationship between the costs of dispute resolution and the time required to resolve the dispute.

The same basic differential equation model has been adapted for time-dependent conversions of members of a population among different states. The conversion model has been applied in different contexts such as epidemiological infections, the Bass model for the diffusion of innovations, and the ideodynamic model for public opinion. For example, the ideodynamic version of the model predicts changes in public opinions in response to persuasive messages extending back to an indefinite past. All messages are measured with error, and this chapter discusses how errors in message measurements disappear with time so that predicted opinion values gradually become unaffected by past measurement errors. Prediction uncertainty is discussed using formal statistics, sensitivity analysis, and bootstrap variance calculations. This chapter presents ideodynamic predictions for opinion time series about the Toyota car manufacturer calculated from daily Twitter scores over two-and-a-half years. During this time, there was a sudden onslaught of bad news for Toyota, and the model was able to accurately predict the accompanying drop in favourable public opinion towards Toyota and rise in unfavourable opinion.