The purpose of this essay is to explain the main elements in the diffusion of innovations model, and to apply them to the special case of the diffusion of new telecommunications technologies like fax, E-mail, mobile telephones, INTERNET, and others.
Since the first edition of this landmark book was published in 1962, Everett Rogers's name has become "virtually synonymous with the study of diffusion of innovations," according to Choice. The second and third editions of Diffusion of Innovations became the standard textbook and reference on diffusion studies. Now, in the fourth edition, Rogers presents the culmination of more than thirty years of research that will set a new standard for analysis and inquiry.The fourth edition is (1) a revision of the theoretical framework and the research evidence supporting this model of diffusion, and (2) a new intellectual venture, in that new concepts and new theoretical viewpoints are introduced. This edition differs from its predecessors in that it takes a much more critical stance in its review and synthesis of 5,000 diffusion publications. During the past thirty years or so, diffusion research has grown to be widely recognized, applied and admired, but it has also been subjected to both constructive and destructive criticism. This criticism is due in large part to the stereotyped and limited ways in which many diffusion scholars have defined the scope and method of their field of study. Rogers analyzes the limitations of previous diffusion studies, showing, for example, that the convergence model, by which participants create and share information to reach a mutual understanding, more accurately describes diffusion in most cases than the linear model. Rogers provides an entirely new set of case examples, from the Balinese Water Temple to Nintendo videogames, that beautifully illustrate his expansive research, as well as a completely revised bibliography covering all relevant diffusion scholarship in the past decade. Most important, he discusses recent research and current topics, including social marketing, forecasting the rate of adoption, technology transfer, and more. This all-inclusive work will be essential reading for scholars and students in the fields of communications, marketing, geography, economic development, political science, sociology, and other related fields for generations to come.
Dr. Everett M. Rogers is Distinguished Professor in the Department of Communication and Journalism at the University of New Mexico (UNM), where he teaches and conducts research on the diffusion of innovations.
Choice The name of Everett Rogers...is virtually synonymous with the study of the diffusion of innovations.... His coverage is comprehensive, ranging from the elements of diffusion and the history of diffusion research to generators of innovation, change agents, and the consequences of innovations. Among the many features that make this an exemplary interdisciplinary effort are Rogers's clear, literate style and his ability to stay in touch with social realities. He sets a high standard for social theorists.
In the persuasion stage, Rogers (1983) proposes five perceived characteristics of the innovation as a means to describe innovations as well as their likelihood and speed of diffusion (see Table I). These five stages guided our research questions and will be the focus of the findings section outlined below.
There has been a stream of research dedicated to studying product adoption from a dynamic perspective, i.e., if a product will be adopted over time, stemming from the diffusion of innovations paradigm. The diffusion of innovations (DoI) paradigm has been established as a common way of researching the spread of a new product in the marketplace . In the 1960s, DoI was used to develop the mathematical Bass diffusion model, which was employed to analytically study new product adoption on the market [16, 17]. The original Bass diffusion model consisted of a limited number of variables, and the decades following its development saw a rise of literature dedicated to expanding it (e.g., ). However, the Bass diffusion model requires sufficient sales data (e.g., of the existing or analogous product) to estimate the model parameters . Obtaining data of processed foods can be particularly challenging, especially in situations when a new to the world food product (e.g., radical food such as edible insects) is being developed and no similar products exist on the market that could be used to estimate the parameters of the analytical Bass diffusion model [20, 21]. In such situations, simulation modeling can be useful to study complex problems, such as product adoption over time .
The adoption process is a problem that has commonly been approached by a simulation modelling approach called system dynamics (SD). System dynamics simulation models focus on dynamic complexity, wherein one investigates causal relationships between variables, which lead to different types of behaviour over time. Instead of using only sales data to estimate model parameters, other appropriate data can be used to formalize the model, such as data on customer choices (e.g., ). SD modelling is a way to simplify reality, making it easier to comprehend, with the purpose of testing possible consequences of various strategies , such as promotion intensity or product quality level. SD models are not predictive, but descriptive models [16, 24, 25]. One chooses to build an SD model when the aim is to improve general understanding of a dynamic problem by studying the patterns of change (i.e. the shapes of the curves over time that result from many different model simulations), and to identify knowledge gaps and guide future research efforts . System dynamics, coupled with the diffusion of innovations paradigm, has been used to study a wide range of adoption problems (e.g., improved maize seed , alternative fuel vehicles , cell-phones , renewable energy , golf clubs , application of a product adoption model for pricing strategy , medical technologies ). However, to the best of our knowledge, the SD approach has not been adopted to study the adoption of radical new foods by consumers.
While Nathan Rosenberg in 1972 asserted that the diffusion process is slow and the variations in the rates of acceptance of different innovations are wide, Hall et al., op. cit. posited that although the ultimate decision is made on the demand side, the benefits and costs can be influenced by decisions made by suppliers of the new technology, confirming the other studies such as  . The resulting diffusion rate is then determined by summing over these individual decisions. Hall, et al., op. cit., further noted that the most important thing to observe about this kind of decision is that, at any point in time the choice being made is not a choice between adopting and not adopting but a choice between adopting now or deferring the decision until later and that the reason it is important to look at the decision in this way is because of the nature of the benefits and costs.
The theory underpinning the model tested in this paper is grounded in the classical diffusion of innovations theory      introduced by Rogers and the works of Frambach & Schillewaert op. cit.; that the process nature of innovation should be investigated through a research-based type of data gathering and analysis seeking to determine the ordered sequence of a set of events.
Rogers, op. cit., had argued in 1995 that communication is the process by which participants create and share information with one another in order to reach a mutual understanding. To Rogers, what underlies the diffusion process is information exchange. The process he added involves: 1) an innovation (here the Smart ID technology), 2) an individual or unit of adoption that has knowledge or experience with using an innovation, 3) another individual or unit of adoption, without knowledge of the innovation and 4) a communication channel connecting the two units (2 and 3 above). He had in the same publication noted that, in connection with the categories of adopters, there are four main elements in the diffusion of innovations, which are 1) the innovation 2) communication channels 3) time and 4) a social system.
Second, it is instructive to note that a good understanding of user segment needs is crucial to diffusion of new technologies. Ziemer  investigated diffusion models for forecasting technological innovations and suggested that further insights into diffusion models can be obtained by examining trends in both the noncumulative adopter distribution and its rate of change, in a bid to answer the question of how the Bass adopter distribution can be used for developing adopter categories. It is a commonly known concept among diffusion scholars that the various user segments are static and cannot be changed. Therefore, innovation managers should first, track down Innovators and make them their first followers, if possible invite them to be partners in the improvement or re-design of the innovation, where need be. Early adopters will leap in once Innovators have been captured and the benefits of the innovation have become apparent. It is imperative to offer strong support for a limited number of early adopters to try a new idea while maintaining relations with regular feedback. A set of design strategies (endorse, curate, integrate, economize, play and refresh) have been formulated for technology adoption  with a view to promoting adoption of new technologies. Dickerson et al.  reported that adopters of personal computers were older and had higher income, more education and higher status occupations, consistent with most empirical studies in the diffusion theory literature  . This could not be confirmed in this study. However, only 14.1% of the respondents lack university education, confirming high education levels, while the age of the respondents rather showed a relatively young population (between 18 and 45 years). Studies of technical ideas such as innovations indicate that the key attributes of technical innovations differ markedly from those that influence the diffusion of other innovations  and studies of policy ideas as innovations indicate that their socio-political aspects are critical diffusion drivers   . 2b1af7f3a8