Diffusion of Innovations Model (DOI)

Melkote (1991) indicated that “the coexistence of traditional and modern life styles casts doubts on the widespread belief among certain Western sociologists that traditional beliefs and practices are always obstacles to modernization” (p. 112). Similarly, Hung, Lee, and Lim (2012) affirmed that educational programs designed from a top-down perspective are not sustainable in the long-term. Top-down innovations rarely acknowledge the importance of tacit knowledge (defined as “funds of knowledge,” or prior knowledge or what Melkote’s refers to as traditional beliefs and practices), in the educational process, yet they continue to be used in the educational

field (Hung et al., 2012). The Diffusion of Innovations (DOI) model is the current and traditional model used in understanding the development and adoption of many educational technologies.

Everett M. Rogers, who originally theorized the model in 1964, argued that DOI should be understood as the process by which an innovation is communicated through certain channels over time among the members of a social system (Rogers, 1983). In the process of adopting a given innovation, each individual passes through five distinct phases: (1) the phase of knowledge or awareness, in which the individual becomes aware of the innovation and has a general idea of its use; (2) the persuasion period, in which that individual develops an attitude toward the new concept; (3) the decision phase, during which the individual chooses to accept or reject the innovation; (4) the phase in which the adopter implements or uses the innovation to his or her advantage; and (5) the evaluation phase, in which the individual reflects upon the results of using the innovation. Although the DOI model is described in terms of an individual undertaking, it is important to recognize that other members of the social system weigh heavily on its application.

Rogers (1983) also categorized members of a given population as belonging to one of five possible groups, who demonstrate varying amounts of influence in the adoption process. Based on their level of “innovativeness,” or degree to which they are able and willing to adopt new ideas in relation to the rest of the population, each member is classified in one of the five categories: (1) innovators; (2) early adopters; (3) early majority; (4) late majority; and (5) laggards. According to the model, innovators represent only about 2% of the population and are the first group to try out new ideas, processes, goods, and services. They are generally affluent, with a high level of education and an appreciation for change and new experiences. They constitute a well-informed and visionary group when it comes to deciding their next “purchase” or innovation assimilation. Early adopters are strongly influenced by innovators. This group

accounts for 14% of the distribution and is important for the opinion leaders that arise from within its ranks. Since the remaining population cannot be part of these first two groups, they place their trust on the lived experiences of those members in a higher group. The early majority, which comprises roughly 34% of the population, generally waits to see whether the innovation is successful, and only when its success is proven do they adopt it. Members of the early majority tend to be less educated than those in the first two groups and wait for cues to choose what they think is best for them. The next group, the late majority, also represents roughly 34% of the population, and generally represents a group that is older, less educated, and from a lower socioeconomic status. They sometimes adopt innovations but more often are forced to do so by the larger trend of adoption in the rest of the population. The “laggards,” as Rogers dubbed them, is a group that accounts for 16% of the population and tend to be either very conservative or very isolated within the social system. The social isolation of low-income Latino populations, for example, most often results in their being portrayed as laggards. Examining this fifth category in finer detail, however, one encounters a vast degree of variation based on factors like class, income, or specific ethnic cultural background. In other words, variability within the laggards group requires that particularities be considered at a more granular level. Within this already very specific low-income Latino demographic, for instance, a foreign origin further exacerbates the factors underlying this isolation and makes this sub-group an even clearer expression of the laggard profile.

Rogers’s model assumed that the DOI process is universal and that cultural differences do not play a part in its operation; this ideality or insularity is closely related to the model’s broader theoretical underpinning: the modernization theory of development. Armer and Katsillis (2001 p. 1885)identified four principles either explicitly or implicitly present in the application

of modernization theory within the different social disciplines: that development in societies takes place in discrete evolutionary stages; that these stages are based on degrees of social differentiation and reintegration of the structural/cultural components necessary for the maintenance of society; that developing societies remain at a “pre-modern” stage and will eventually take on the socio-political and economic expressions of the Western societies that have reached the highest stage of development; and finally, that this modernization in the developing world will be prompted by the importation of Western technology and the overcoming of traditional structural-cultural features incompatible with development.

Technology in this context represents the motive force behind not only economic growth, but behind structural and cultural transformations that eventually manifest—both in institutional structures and individual activities—the increased socio-political and economic specialization, differentiation, and integration characteristic of Western societies.

Critiques of the DOI model. Modernization theory’s linear and rigid conception of development has been contested on several different fronts, but the alternatives tend to share in common the notion that modern “underdeveloped” countries face a unique set of circumstances and that they therefore cannot and should not follow the same path as developed countries. One such counter-theory that has arisen amongst theorists of the Global South is “dependency theory” (Cardoso, 1979; Casanova, 1983). Whereas modernization theory postulates that

“underdevelopment” is an endogenous process within the societies of the Global South, advocates of dependency theory argue that developmental problems in this global region are rather the result of the global capitalist order (propagated by “developed” countries) and the unequal distribution of resources fostered by a world market economy.

Even today the DOI model and its underlying modernizing logic are often presented in a fairly positive light. I follow Jucker (2002), however, in the belief that society always remains blissfully ignorant of the institutional, political, and social consequences that a given

technological innovation will bring about. Ulrich (1992) detailed how in the aftermath of World War II faith in scientific and technological progress spread across the globe like a universal law. The mighty forces of science and technology, Ulrich pointed out, were thought to hold out hope for the progress of the “underdeveloped,” or so-called Third-World, countries. In the adoption of innovations, however, the resulting transformations have often been overlooked. Ulrich (1992) narrated the story of the introduction of the automobile, and how “underdeveloped” governments were not aware of all the technical, social, and psychological costs that would be introduced along with it—street networks, petrol stations, refineries, insurance, safety, driving lessons, training for children crossing streets, and so on. To describe the effects of innovation researchers have commonly invoked the metaphor of an invasive foreign species colonizing a particular ecosystem (Zhao & Frank, 2003). The timbre of the metaphor makes clear why the development of more sustainable, organic, and ecological innovations is becoming a growing area of

theoretical interest, especially in the field of education.

McAnany (1984) suggested that the DOI process and its consequences are unequal among different social classes. In the third edition of Diffusion of Innovations, Rogers conceded that the uneven consequences of innovation adoption represent a topic of interest. According to McAnany (1984), however, Rogers only brought the weakest of evidence to bear on an

explanation of how the inequalities within the DOI model could be avoided. The third edition of Rogers’ DOI provided a useful insight into one of the prevailing DOI criticisms by highlighting the problem of pro-innovation bias, or the assumption that innovations are always good. Rogers

(1983) defined pro-innovation bias as “the implication of most diffusion research that an

innovation should be diffused and adopted by all members in the social system, that it should be diffused more rapidly, and that innovation should be neither re-invented nor rejected” (Rogers, 1983, p. 92).

Rogers (1983) also described multiple causes for the pro-innovation bias in diffusion research. The first is that much innovation research is funded by change agencies who support innovation in itself as part of their own business strategies. Secondly, because a visible trace is easier to document and analyze, “successful” innovations—that can more clearly demonstrate the rate of adoption—are more often selected as objects of study. Furthermore, due to a similar bias resulting from the imperatives of the research process itself, we know more about the adoption of rapid diffusion technologies than slower diffusion technologies. In other words, it is easier to study successes than failures. Rogers (1983) concluded by conceding the importance of

innovation researchers with an anti-innovation bias whose work could balance the overwhelming pro-innovation bias of early studies.

Taking this critique even further, Melkote and Steeves (2001) argued that change agencies decided what innovations were best for their clients and implemented marketing campaigns to convince the users of the potential of the change agency’s choice. The problem, according to the authors, is that the innovation was not created and implemented by local users, resulting in a problem of ownership.

Another fundamental problem is that the DOI approach does not take into account the cultural context in which the diffusion process takes place. To address this issue, a more participatory approach to development communication emerged as an alternative to the DOI model. Advocates of the participatory approach held the primary goal of empowering local

communities to manage their own development. This emphasis on empowerment aligns with the underlying precepts of Paulo Freire’s critical pedagogy, which I review below as a critical complement to the participatory approach and a viable alternative to the DOI model as a tool for the empowerment of Latino students at the classroom level.

Diffusion of innovations in educational research. According to Dooley (1999), the past four decades in the U.S. have been characterized by extreme social, political, economic and technological revolutions. Despite these mainstream ideological transformations, however, the basic organizational structure of public schools remains unchanged. In response to this

institutional rigidity, Dooley (1999) has developed a model to test the adoption of technologies in different American schools. This model seeks to take into account the unique context of each school that could impact the rate of diffusion. Dooley took into account both the DOI stages and contextual factors, and determines that educational innovation research should begin by

determining where school personnel are in the innovation-decision process and what their concerns are with regard to technology in the school. Only with this information can we begin to design appropriate professional development programs and an environment where impediments are minimized. (p. 43)

Dooley concluded that teachers have the greatest impact on the use of technology in the classroom and her findings highlight the crucial role of the teachers in the reception and

appropriate implementation of educational technologies. The study failed to take into account, however, the role of the student or the role of classroom diversity (i.e., gender, race, age, and background) in the use of technology in the classroom. The lack of acknowledgement of the student’s perspective in an application of the DOI model to educational innovation is the reason why many FBL children encounter educational programs not tailored or designed to meet their actual needs (Warschauer, 2004). The negative results of this disconnect reinforce the need to

demonstrate the need for an alternative and more critical approach to educational innovation than the DOI model permits.

Hung et al. (2012) offered an alternative and more sustainable model to the DOI model. After researching 250 educational projects in Singapore, they propose the notion of Communities of Practice (CoP). These groups of people with shared interests and/or professions were

conceived as a resource for diffusion and a mechanism to encourage dialogue among students, teachers, principals, and policy makers throughout the school system. This alternative model stresses the importance of meaningful dialogue and contextualized activities, and is therefore heavily influenced by critical pedagogy. The authors explained that while in the past innovations have been created in laboratories and then spread to the masses, innovations today either grow or should grow out of a more ecological approach. In other words, innovation should be driven in an organic or natural way by the target community or users rather than being manipulated by a small group of people and then thrust into a broader context. School and education policymakers typically scale up and spread established practices, which results in standardization and

minimizes each learner’s potential to experience a unique learning process. Finally, it is necessary to pay close attention to an innovation’s development and evolution in order to properly assess whether the technology is in fact beneficial to the target community.

To ensure this, Hung et al. (2012) emphasized that researchers working with organic educational technology in the classroom should strengthen the teacher-researcher relationship in order to “walk the journey” together and allow a sustainable interventions (p. 36).