Diffusion of innovations

About: Diffusion of innovations is a research topic. Over the lifetime, 2139 publications have been published within this topic receiving 191397 citations. The topic is also known as: diffusion of innovation & diffusion of innovations theory.

Technological progress in enterprises and diffusion of innovations : theoretical reflections and empirical evidence

Abstract: ion in the realm of control processes may obscure some important features of technical change. The development and use of technology is to a large extent a control process: it is getting to know how to make or do something. This type of control activity is different from decision making by calculating. It is a process that works with technical information, beside price information, which has rather different characteristics. First of all, technical information is not readily accessible. Secondly, it is complex and cannot be reduced to a single dimension. Thirdly, it requires research and training to be able to put technical information to use. Thus control processes, when technological information is involved, are not limited to computing and deciding. There is also gathering and researching of information, comparing and evaluating, studying and mastering, training and developing skills and building up routines. These are all processes that require an investment of time and effort. Thus cognitive activities acquire the characteristics of a production process: inputs are transformed into outputs, which are, beside decisions, knowledge and skills, inventions and innovations. In this context, it is worthwhile to refer to an alternative to the usual rationality concept, the concept of bounded rationality. Simon, who first introduced this term, argues that, due to cognitive constraints, rationality in economics is restricted: "In any realistic description of the environment of a human decision maker, the variables and information to which he might attend (and to which he must attend to satisfy the strict requirements of rationality) arc innumerable. The hypothesis of bounded rationality claims that human beings handle this difficulty by attending to only a small part of the complexity about them. They make a highly simplified model of the world, and they make their decisions in terms of that model and the subset of variables that enter into it. Now this approach may work very well (...) if the number of very important variables is small at any given lime, and if this list of important variables does not change from time to time without the change being noticed." [Simon (1986) p. 33-34]. Simon 3 Mueller argues that our understanding of (be functioning of the business corporation has suffered, not so much from lack of recognition of constraints, but from the unwillingness of economists to adopt a more realistic set of behavioural assumptions about managerial motivation. He supports bis attack on the assumptions of profit and shareholder wealth maximization and his plea for broader models of managerial motivation with empirical evidence from principal/agent and industrial organization literature [Mueller (1992)].

Diffusion of innovations over multiplex social networks

Abstract: The ways in which an innovation (e.g., new behaviour, idea, technology, product) diffuses among people can determine its success or failure. In this paper, we address the problem of diffusion of innovations over multiplex social networks where the neighbours of a person belong to one or multiple networks (or layers) such as friends, families, or colleagues. To this end, we generalise one of the basic game-theoretic diffusion models, called networked coordination game, for multiplex networks. We present analytical results for this extended model and validate them through a simulation study, finding among other properties a lower bound for the success of an innovation. While simple and leading to intuitively understandable results, to the best of our knowledge this is the first extension of a game-theoretic innovation diffusion model for multiplex networks and as such it provides a basic framework to study more sophisticated innovation dynamics.

Multistep Flow of Communication: Network Effects

Abstract: The multistep flow paradigm describes the way media and interpersonal influence shape public opinion. Works in that tradition explore the diffusion of media messages and the complex patterns of behavioral contagion in social networks. The flows of influence and information are amplified by opinion leaders: key individuals who can change the beliefs and actions of others in their community. Sociometric approaches provide a way to identify influencers based on their structural position or ability to trigger information cascades. Advances in network methodology allow us to model diffusion processes and study the interplay between interpersonal ties and individual behavior. Multistep Flow of Communication: Network Effects The multistep paradigm emerged as an extension of the two-step flow theory (Katz & Lazarsfeld, 1955), an important research tradition exploring the interplay of interpersonal and mass communication. According to the two-step model, direct media effects are hampered by social interactions and audience selectivity in exposure, perception and retention. Rather than reaching the public directly, ideas broadcasted by news outlets are channeled through a particularly active audience segment known as the opinion leaders. Those key individuals would receive, interpret, and disseminate media messages among the larger public. In the decades after its inception, the two-step flow theory went through a number of modifications. The framework was criticized for underestimating the direct effect of mass communication (Robinson, 1976), especially as the Internet and mobile devices provided ubiquitous direct access to media content (Bennett & Manheim, 2006). Scholars have also suggested that the linear topdown model proposed by the Lazarsfeld and Katz oversimplified patterns of interpersonal influence. The flow of ideas from mass media to individuals was found to be more complex than the theory predicted. Key dynamics missing from the original model included the information exchange among opinion leaders, as well as that among the less engaged audience members (Weimann, 1982). The effects also did not disappear after two steps – the opinion leaders could convey ideas to followers who in turn would spread those ideas to other individuals. New models featured complex patterns of multidirectional flow allowing for bottom-up impact of audiences on media coverage (Brosius & Weimann, 1996). Diffusion and social contagion Research exploring the flow of ideas in social groups requires a holistic analytical perspective that incorporates individual characteristics, dyadic relations, and system-level processes. Network models of influence and diffusion provide a natural extension to the multistep flow paradigm. Diffusion studies track the spread of ideas, conventions, or technologies through a social network. Works in this area explore the factors that promote or hinder the propagation of information, practices, and products. This research tradition does sometimes examine the role of influencers, but it is more concerned with the characteristics of innovators and early adopters (Rogers, 1995). Although those categories have been conflated by some authors, in a network context they are quite distinct. Opinion leaders are typically well-embedded in the social structure, highly connected, and very visible. In order to retain their central position, they have to follow community conventions and cannot deviate too much from the accepted norms. Trying new things, however, is easier when social control is weaker. Innovation comes from the edges, which is why early adopters are often found at the periphery of social systems. The propagation of information and opinions can be presented analytically using threshold and cascade models. In threshold models, an individual adopts a behavior or opinion only after a certain proportion of their social ties have already adopted it. In a cascade model, each time a person is "infected" with a new opinion or information, there is a certain probability that the infection will spread to their connections. Adapting models from epidemiology, researchers have used the SIR (Susceptible Infected Recovered) cycle to describe social contagion. Individuals are considered susceptible when they are exposed to certain information (for instance a newspaper story), infectious while they talk about it to others, and recovered once they stop propagating it. Network interpretations of the multistep flow Network research in the multistep flow tradition falls into one of two categories (Ognyanova & Monge, 2013). The first investigates social structure as a conduit for the spread of ideas and information. The focus in that context is on individuals and the connections among them (including, among others, ties of friendship, kinship, collaboration, discussion, advice – as well as their online equivalents). Media outlets are not seen as part of this network, though they do produce the content that propagates through it. The work of Menzel and Katz (1955) provides one canonical example of this type. Their research mapping the social ties of health professionals finds a multistep influence of medical journals and interpersonal relations on drug adoption. In the second and more recent type of study, both individuals and media outlets are seen as embedded in a multidimensional network. As above, this model examines interpersonal ties, but it also incorporates connections to (and potentially among) specific media sources. Friemel (2015) for instance models the social networks of high-school students along with their connections to various TV programs. Interestingly, his analysis finds no evidence of opinion leadership in that context. Many works in this second category allow for the possibility that individuals as well as media outlets can generate, selectively filter, and disseminate messages. Various network metrics (described in the next section) have been used to evaluate the relative influence of people and news sources in the system. This line of research has generated a number of studies exploring online influence patterns among news organizations and audiences, including research on opinion leadership on social media platforms (Xu, Sang, Blasiola, & Park, 2014). Figure 1. The two-step and the networked multistep flow models. The sociometric profile of opinion leaders Social influence refers to the notion that the people we know can affect our actions and attitudes. Substantive explanations for the emergence of interpersonal influence include patterns of persuasion, coercion, authority, identification, competition and expertise (Friedkin, 1998). The opinion leaders are individuals who influence the attitudes, beliefs and actions of others. Their impact is domain-specific – the friends who can sway our political decisions may not be the same ones we go to for fashion advice or stock market tips. People in that position can inform, persuade, and manipulate others, as well as serve as role models and provide cues that signal the expected, acceptable or desirable behavior in a social group. Since opinion leaders (also referred to as influentials) emerge in a variety of groups and contexts, they are not known to have specific socio-demographic characteristics. One thing that they do have in common, according to the classic two-step flow theory, is their high exposure to media content. Elaborating on the qualities of opinion leaders, Katz (1957) lists three major dimensions that differentiate influentials from the rest of the social group they belong to: (1) Who one is – the individual characteristics and values of a person (2) What one knows – the level of competence or expertise of a person (3) Whom one knows – the person’s accessibility and the connections they can mobilize. Broadly speaking, this reflects a person’s position in their social network. Research efforts seeking to identify opinion leaders have used a variety of measures and techniques that tap into one or more of these three dimensions. Some approaches rely on self-reported data collected by asking people to rate their own influence, or administering more sophisticated survey scales. Other techniques are based on nominations from domain experts or community members. Influencers can be selected because of their prominent role in a group (as formal leaders, elected officials, media representatives, etc.), or identified by trained researchers through observational methods. Taking a network perspective to the theoretical framework, researchers have also evaluated opinion leadership through sociometric techniques. Depending on the goals and the resources of the study, one of several standard data collection strategies may be used: • Full networks: used when the researchers have access to a whole population forming a well-defined community (e.g. doctors in a hospital, students in a school, employees in an organization). Every individual is asked to describe their social ties with others in this group. Combining the individual reports allows researchers to map the entire network of the focal community. • Personal (ego) networks: used when the population under study is too large to feasibly map with the available resources (e.g. all citizens of a country), or when it is difficult to identify all members of the group. In those cases, a random sample of respondents can be selected. Each participant (here called ego) is asked to describe their social ties with others (called alters) who may not be in the sample. The combined reports do not form one global network for the sample, but describe the local social structure of each respondent. • Snowball networks: used when the full population is unknown, especially with difficult to reach or at-risk groups (e.g. drug users, HIV positive people). A sample of respondents is selected and each is asked to identify their social ties. At the next step, researchers contact those connections and collect information about their

New Innovation Identification Approach Development Matrix

Abstract: Innovations have become one of the contemporary economics key driving forces behind competitive advantages and have become socioeconomic category of their own. Diffusion of innovations, which is an integral part of the scientific and technological base of national economies, promotes structural changes in economy. Companies have to pay close attention to market trends and have to be able to identify and create new innovations, thus increasing their competitiveness and meeting consumer demands and expectations. The objective of this research is to evaluate the concept of different innovations in organizations and propose a complex approach to identifying innovations in the form of a matrix that would aid in creating new and competitive innovations, based on the study of scientific literature and expert surveys results theoretical evaluation of different innovation approaches in organizations and the scientific and practical approaches to identifying innovative ideas at the initial stage of the innovation process.