Breaking the Mold: An Educational Perspective on Diffusion of Innovation/Rogers’ Diffusion of Innovations

By Ritushree Chatterjee

When discussing Roger’s Diffusion of Innovations, many have wondered how Roger’s model would apply in today’s information age. What relevance does the Diffusion of Innovation model have in today’s information age? The information age is signified by the instant access to information, more stress on self-reliance, not bound by any geographic boundaries. Do we still have a strong sense of belonging to a community, the origin of the Diffusion of Innovation? Are we bothered by sanctions by the community? Is our value system influenced by the community? Do we associate a community with a physical boundaries? How does all this affect the process of Diffusion and the Innovation to begin with? Chatterjee's chapter reviews Roger's Diffusion of Innovations, discusses the limitations and applications of this model, and examines the model applicability to today's changing world.

=Rogers' Diffusion of Innovations= Innovation and adoption have become mundane words in our fast paced technologically savvy lives. We take these two words for granted often, not trying to delve deeper into them to find their true meaning and their relevance in our day-to-day lives. We come across many new innovations on a daily basis. The iPhone 5 or the iPad mini, commercials of new medicines, articles about social uprisings, news about social campaigns in developing countries. We see them, read about them, form an opinion occasionally and sometimes even adopt an innovation. All of these without any concrete deep thought about the entire process of innovation, diffusion and adoption. More often than not, we do not realize that these two words constitute a whole academic discipline, with researchers and studies being done for the past 30 years and still going strong. Not to mention, diffusion research has helped make some big moolah for a few very persevering and enthusiastic people or entrepreneurs. In this chapter, one of the forerunners in Diffusion of Innovation research, Everest.M.Rogers’ diffusion model, is discussed. He put forth a model to understand and comprehend this complex process along with the various factors affecting and contributing towards it, which is explained in his book, Diffusion of Innovations. His model has been applied to many disciplines, ranging from political science, economics, communications, public health, and education (Dooley, 1999; Stuart, 2000), and is considered the most widely used framework for understanding the innovation-diffusion-adoption process.

Diffusion
Rogers (Rogers, 2005, p. 5) defines diffusion as "the process in which an innovation is communicated though certain channels over time among the members of a social system." There are four key elements that make up this definition. These interacting factors include ‘innovation’, ‘communication’, ‘time’ and ‘social system’. Diffusion of Innovation includes both spontaneous spread of new ideas and a planned method of propagating a new idea (Rogers, 2005, p. 6). Rogers says that it is not an all–encompassing theory, but rather a culmination of several theoretical perspectives, all of which relate to the concept of diffusion. These processes involve the innovation-decision process, the individual innovativeness, and the rate of adoption of an innovation.

Innovation
What is innovation? Whenever we come across the word ‘innovation,’ the first thing that comes to mind of an educated individual is a scientific discovery. But this may not always be the case. An innovation may or may not have any immediate practical applications or any revolutionary effects on mankind. It means ‘new,’ and the idea of ‘newness’ can differ from person to person depending on their educational, socio-economic background and prior knowledge of the subject matter. Rogers (2005, p. 12) defines Innovation as "an idea, practice or object that has perceived as new by an individual or other unit of adoption." First time knowledge about a well-established practice can be perceived as ‘new,’ and an innovation, for that particular group of individuals. As Rogers said, "newness can be expressed in terms of knowledge, persuasion or a decision to adopt." It is incorrect to assume that all innovations are beneficial and that all innovations are equally adopted. The main characteristics of an innovation that significantly affect its adoption (or rejection) are (1) relative advantage (2) compatibility (3) complexity (4) trialability (5) observability. These factors will be discussed later in the chapter.

Communication Channels
Communication plays a significant role in the spread of ideas and exchange of information. From the quintessential imparting of knowledge by a teacher to her students, to the global spread of a social messages, unless communicated, the knowledge will remain in a black box. As defined by Rogers (2005, p. 18), communication is the "process by which participants create and share information with one another to reach a mutual understanding." By means of a communication channel, messages are transmitted from one individual to another. The two most powerful communication channels are the mass media and interpersonal communication. The former helps in creating awareness and spreading knowledge about an innovation, whereas, the latter is effective in creating an opinion and possible adoption or rejection of the innovation. Most effective communication takes place between individuals who have similar backgrounds such as education, socio-economic status, so forth. Such a communication is called homophilic. But, often, in diffusion of innovation, heterophilic communication occurs between an individual who has better knowledge and understanding of the innovation to an individual with lesser awareness.

Time
Time is an important factor in studying diffusion research. As described by Brown (2009, p. 129-149), time constraint helps translate ideas into decisions. Time is involved in various phases of the diffusion process, namely (1) the innovation decision process; (2 )the individual innovativeness, i.e., the time taken for an individual to accept/reject an innovation as compared to others; and (3) the rate of adoption of the innovation. These intermediate time bound steps are explained later in the chapter.

Social System
It is a well-established fact that man is a social animal. We cannot survive in isolation (the western socialist) and need a healthy interaction with fellow beings. Guided by this notion, the diffusion process, which deals with humans, can be said to be a social process and occurs within a social system. A social system has a definite structure, defined as the patterned arrangements of the units in a system (Rogers, 2005, p. 25) and a set of norms. The latter is the established behavior patterns for the members of the social system (Rogers, 2005, p. 37). Hence, it is clear diffusion and adoption of innovation are greatly affected by the social system and the characteristics of the individual units of that system.

Attributes of Innovation and Rate of Adoption
What characterizes an innovation? Do characteristics matter? When any new idea is brought to our attention, the foremost tendency as humans is to put it under the microscope and dissect it. This helps us carefully understand the related features, advantages and disadvantages. It helps us make a mental picture and comprehend the innovation better. This is where the importance of understanding the attributes of an innovation comes to picture, which then affects its rate of adoption. Rogers (2005) defined the rate of adoption as “the relative speed with which an innovation is adopted by members of a social system” (p. 221). This implies that the number of individuals who adopted the innovation for a period of time can be measured as the rate of adoption of the innovation. These perceived attributes of an innovation act as predictors of the rate of adoption. Most of the difference in the rate of adoption of any innovation can be explained by these attributes. In addition to these attributes, the innovation-decision type (optional, collective, or authority), communication channels (mass media or interpersonal channels), social system (norms or network interconnectedness), and change agents impact the diffusion process and may increase the predictability of the rate of adoption of innovations. For instance, personal and optional innovations usually are adopted faster than the innovations involving an organizational or collective innovation-decision. However, for Rogers, relative advantage is the most important contributor in predicting the rate of adoption of an innovation. Moreover, the cumulative function of the rate of adoption of an innovation is an S-Shaped curve. The S-shaped curve rises very slowly in the beginning, which implies only a few adopters. It gradually increases and then shoots up to a maximum when more than half of the adopters have adopted. It continues to rise gradually, but slowly, signifying the group of people left to adopt the innovation.

Coming back to the perceived attributes of innovation, the understanding of the same mitigates to some extent, the uncertainties associated with any new innovation. These attributes of innovation are relative advantage, compatibility, complexity, trialability, and observability (Rogers, 2005, p. 16).

Relative Advantage
Rogers (2005, p. 219) defines relative advantage as "the degree to which an innovation is perceived as being better than the idea it supersedes." In layman’s terms, this implies how beneficial a possible adopter perceives an innovation to be. The other contributing factors are economic and social. For early adopters, innovators, and early majority social status is a highly motivating factor. The greater the relative advantages of an innovation, the greater its rate of adoption. For example, to integrate technology into education, teachers should first see its usefulness and that it betters their instruction for them to use technology (Finley, 2003). Once the adopter sees the relative advantages of an innovation, the adopter generally perceives how compatible the innovation is to their current situation. This brings us to the next attribute.

Compatibility
Compatibility is defined as "the degree to which an innovation is perceived to be consistent with the existing value system, past experiences, and needs of potential adopters." This implies that the more the innovation is in line with the current value system and way of life of possible adopters, the more acceptable and accommodating the adopters are. If an innovation requires the adopters to change or adopt a completely new value system or way or life, this increases uncertainty in their minds, and generally, will scare them away. Positioning an innovation, as well as naming it, contributes to the perceived compatibility of an innovation. The more compatible an innovation is, the greater the rate of adoption. A recent study describes how teacherss opinions, beliefs, values and opinions about teaching are influenced by each and every innovation (Hoerup, 2001).

Complexity
Some innovations are easy to understand and use while others are more difficult to comprehend. In general, the more complex an innovation, the lower the chance of it being adopted. Complexity is defined as the "degree to which an innovation is perceived as relatively difficult to understand and use." For example, in a study by Parisot (1995), a technological innovation may have different levels of complexity. Teachers may have to change their method of instruction in order to integrate technology into teaching and instruction.

Trialability
Trialability is the "degree to which an innovation can be experimented on with a limited basis." When an innovation can be tried, it increases its chances of adoption. The exception is where the undesirable consequences of an innovation appear to outweigh the desirable characteristics. Trialability helps clear many uncertainties associated with the same, as well as to know more about its usefulness and purpose. Not all innovations can be tried and tested. Many times, an innovation is changed according to the user feedback during the trial phase. For example, we have umpteen number of laundry products that come out, and often we see a trial pack being advertised before the launch of the actual product. Similarly, most of the pharmaceutical drugs have to cross a mandatory trial phase before their actual market launch. In general, adopters wish to benefit from the functional effects of an innovation, but avoid any dysfunctional effects. However, where it is difficult or impossible to separate the desirable from the undesirable consequences, trialability may reduce the rate of adoption.

Observability
The last characteristic of an innovation is observability, defined as "the degree to which the results of an innovation are visible to others." This is positively related to the rate of adoption. When we see our peers using a new technological gizmo, we are more likely to buy and try it out on our own. This shows that ideas easily observed and communicated are more likely to be adopted.

Innovation-Decision Process
The innovation-decision is a phase in the diffusion process when a decision making body (a group or an individual) moves from the initial knowledge about an innovation, to forming an attitude towards it, to making a decision whether to adopt or reject it, to implementing the innovation, and finally to the confirmation of the same. This is a process that happens over time where all pros and cons of an innovation are examined and a decision is gradually reached upon either accepting or rejecting the innovation. This process mainly focuses on the newness and the perceived uncertainties associated with any innovation. It consists of 5 stages: knowledge, persuasion, decision, implementation, and confirmation.

Knowledge Stage
This is the stage when the users or possible adopters first hear about the existence of the innovation, and then gain knowledge and understanding about its various functions. The primary questions addressed regarding an innovation are ‘what,’ ‘why,’ and ‘how.’ There are three types of knowledge associated with these questions: (a)	Awareness-Knowledge: When a possible adopter seeks information regarding what the innovation is all about, has inquisitiveness that falls under the first category of knowledge. This may also motivate other adopters to seek similar information about the innovation and also lead them to ask further questions. (b)	How-to-Knowledge: This type of knowledge enlightens the users about how to correctly use an innovation. This is an important variable in the innovation-decision process and gains even more importance in cases of complex innovations. If the user has proper and correct how-to knowledge before the trial and adoption of the innovation, it increases the likelihood of its adoption. (c)	Principle-Knowledge: This knowledge includes information regarding the underlying principles of the innovations and addresses the questions of how and why the innovation works. Lack of principle knowledge may lead to misuse of an innovation and subsequent discontinuance of the same. By all this know-how knowledge, individuals are in a better position to judge the effectiveness of any innovation. But being equipped with all this knowledge does not guarantee the adoption of the innovation as it also depends on the attitude of the individual towards it.

Persuasion Stage
In the persuasion stage, an individual forms a favorable or an unfavorable attitude towards an innovation, but this attitude does not necessarily lead to adoption or rejection of the innovation. Since one can form an opinion after complete knowledge about what the innovation is all about, the knowledge stage is followed by the persuasion stage. Moreover, Rogers states that the former is more cognitive or knowing, whereas the latter is more affective or feeling. It is the integral step where the user starts forming a perception about the innovation and hence, more intricately and psychologically involved. There are other factors that also influence the opinions and beliefs of the individual such as the perceived attributes of the innovation. Compatibility, relative advantage, and complexity become significant contributing factors towards the attitude formation as the individual tries to uncover the different facets of the innovations and its applicability to his or her current situation or future situation. The uncertainties associated with the functioning of the innovations along with social reinforcement from peers and colleagues also have an influence in the persuasion stage where the individual seeks social support for his thinking and opinion. The user continues to seek information about the innovation.

Decision Stage
In the decision stage, the individual puts his knowledge and opinion into practice and decides whether to adopt or reject an innovation. Adoption is the decision "to make full use of the innovation as the best course of action available" and rejection implies not to adopt an innovation. One of the important aspects of the decision to adopt an innovation is to try it out for a limited time to judge its usefulness. Though not all innovations can be tried and tested, those who can be have an advantage of being adopted. At times, trial by peers can also provide vicarious trial by an individual. The innovation-decision process is faced with the possibility of rejection at every step. There are two types of rejection: (a) decision to reject after considering the innovation and trying it out, called active rejection; and (b) passive rejection, which consists of not considering the innovation at all. Sometimes, discontinuance may also occur, which implies the decision to stop using the innovation after being fully adopted. It is generally assumed that knowledge, persuasion and decision stages occur sequentially in the innovation-decision process, but it may be culturally bound, and knowledge-decision-persuasion may occur instead, for some innovations.

Implementation Stage
Implementation occurs when the innovation is put into practice. In the prior stages, a mental picture is created about the innovation. A person gathers all necessary information regarding the innovation and comes to a decision of adopting (or rejecting) it. Finally, in this stage it is put into practice. The decision stage is immediately followed by the implementation stage unless any logistic issue crops up, such as lack of supply of the innovation. The role of change agents is significant here as technical advisors as they answer various questions regarding the innovation. Implementation is a more challenging process when an organization is involved as the users are different sets of people, and often, different than the deciders. When an innovation becomes a part of the daily work of the users and gradually its uniqueness fades away, at that point, it is said that the implementation process is complete. Often times, during the implementation process, an innovation may be changed or modified to meet the needs of the adopters better. This is known as reinvention.

Confirmation Stage
The Confirmation stage involves the reinforcement of the adoption decision and integration of the new innovation within the framework of existing practices. It is also possible that the individual also decides to reverse his decision of adoption, if he comes across any conflicting views about the innovation. However, one tends to avoid such conflicting messages and always looks for support for the adoption decision already made. This is also called dissonance reduction. Adopters of complex, controversial technologies, therefore, look for signals that their decision was the correct one. If we buy an expensive car, we will always look for reasons or support of people to accentuate our decision and reinforce it. At times a discontinuance may also occur in two ways: (1) replacement discontinuance where an innovation is replaced by another one which supersedes the former in terms of its usability and advantages. For instance, mail via post has almost been replaced by e-mail correspondence; and (2) Disenchantment discontinuance is one where an innovation is rejected due to lack of performance.

=Limitations of Rogers' Diffusion of Innovation Model= Rogers’ model has been widely accepted and used over the years. It is widely used to explain the adoption and use of technology in higher education (Medlin, 2001) in many disciplines. Scholars can understand the entire process by which adoption (or rejection) of an innovation occurs. The model can also be statistically tested in a fairly simple way.

One of the foremost criticisms of the diffusion model is the pro-innovation bias. This implies that the innovation, if adopted, will be beneficial to all the possible adopters equally. Hence, the underlying drawback is the assumption that adoption of the innovation is the right choice.

Over-adoption comes into the picture when experts suggest rejection or fewer adoptions of an innovation. For example, the over-use of pesticides has harmful effects on the soil, and over-development of housing complexes can have detrimental effects on soil, ecology, and the environment.

Another limitation of the diffusion model is its linearity wherein it is assumed that one stage will be followed by the other in the innovation-decision process. However, Beal and Rogers (1960), in one of their studies of Iowa farmers, found that such is not the case and often farmers would skip or change the order of the four stages, namely, knowledge, persuasion, implementation and confirmation. This implies that either the diffusion of innovation model is not applicable to all fields, or that the model does not always follow the linear path.

The individual-blame bias is another criticism of the diffusion model where individuals are blamed for their non-adoption of the innovation. Quite often people, namely laggards, resort to non-adoption, which need not be due to any individualistic reason. It could be possible that some characteristics of the innovation force people to be laggards. For example, non-availability of the innovation in one's vicinity or financial constraints, or perhaps the innovation is not in tune with an individual's values and culture.

Rogers' diffusion model has been used as a basic framework for a variety of diffusion studies, but not many scholars have examined it critically. Hence, there exists a lack of criticism of the model in literature.

=Applications= As stated earlier, Rogers' diffusion model has been widely applied to various fields. A few studies are discussed here that show the multidisciplinary nature of the model and its wide applicability.

Isleem (2003) studied the level of computer use by the teachers of Ohio public school for instructional purposes. This study is based on the theoretical framework of Rogers’ diffusion of innovation. The studies selected the following factors: expertise, access, attitude, support and teacher characteristics and their relationships with the level of use. A quantitative study was undertaken where a questionnaire was distributed to all technology education teachers in the state of Ohio in the school year 2002-2003. The return survey rate was 66%. A survey-correlation research design was used. The study indicated that technology education teachers use computers for more mainstream applications rather than specialized applications. The level of use is significantly affected by the teachers' perceived attitude, expertise and access to computers. It also says that proper training, both in-service and pre-service, can increase the level of usage of computers.

A qualitative study was undertaken by the nursing department (2004) at the National Taipei College of Nursing where nurses’ perceptions towards a computerized care system was examined. Twelve nurses from the respiratory intensive care unit were interviewed and the data was comparatively analyzed. The findings were compared to the five attributes of an innovation as perceived by users and as described by Rogers. It was found that the model can accurately describe the nurses’ behavior during the process of adoption of the new system.

A study by Spiering and Erickson (2006) demonstrated the application of Rogers’ model to international education. They studied United States undergraduate college students who attended the information session regarding study abroad opportunities but do not translate them into actions. Surveys were sent to two groups of students who had studied abroad and those who did not and ranked their answers based on the five attributes of innovation as described by Rogers. The results indicated that relative advantage and triability were the most important factors for deciding to opt for the study abroad program, where complexity and compatibility were the main reasons for deciding against it. The main recommendations were to change the role of the study abroad advisor to that of a change agent who can then help and influence the students, as well as make the faculty aware of the benefits of such a program so that they can, in turn, encourage students to do so.

In a study by Haider and Kreps (2004), the contribution of the model in the field of public health was examined. Using effective messages and their dissemination is a critical component of public health programs, which deal with the maintenance of health and welfare of a community or population. The diffusion of innovation model serves as an excellent tool to help spread such messages. For example, dividing the target population according to adopter categories can help increase the efficiency of the diffusion process. Secondly, identifying the advantages and disadvantages of the innovation based on the perceived attributes of the same and designing the message campaign accordingly can also enhance the chances of adoption of the innovation. Identification of societal norms, which is another feature of the model, is an extremely important factor that workers must keep in mind while designing the messages. Taking into account the religious and cultural values while disseminating the message can greatly enhance the chances of the innovation receiving a positive response from the target population.

=The Information-Age Implications on Diffusion of Innovations=

This section is based on Peter Korsching's personal communication on November 29, 2012.

What relevance does the diffusion of innovation model have in today’s information age? The information age is signified by the instant access to information, more stress on self-reliance, not bound by any geographic boundaries. Do we still have a strong sense of belonging to a community, the origin of the diffusion of innovation? Are we bothered by sanctions by the community? Is our value system influenced by the community? Do we associate a community with a physical boundary? How does all this affect the process of diffusion and the innovation to begin with?

These are some of the questions that hover over the diffusion of innovation model in today’s information age. Rogers' model has a very sturdy and broad theoretical framework. It covers a gamut of factors that affect or may affect the diffusion process, starting from the characteristics of the innovation, to the various stages in the innovation-decision process, the innovation-development process, factors affecting the diffusion in general to the consequences of innovation. But the origin of all of these factors is based on one’s ties to a community bound by geographical boundary. A community which has a strong value system, a culture and strong personal interactions and sanctions for the outliers. With the internet, mobile technologies and advanced communication systems, the world is gradually heading towards becoming a global village. The role of opinion leaders, change agents, and the demarcation of adopter categories will radically change. The communication will be more heterophilous between groups on a level platform. Will this model create a diffusion divide between developing and developed countries? Does the model hold more significance in developing countries where telecommunication, digital devices and the spread of the internet is very gradual? Entrepreneurs are another possible category of adopters that may be added to the list and a possible change in the diffusion curve. We need to ask ourselves, will this model stand the test of time? More importantly, will this model be suffice to explain the diffusion taking place in a non-traditional fashion, over the virtual world of the world wide web?

=References= Brown, T. (2009). Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. New York: Harper, 129-149.

Beal, G. M., & Rogers, E. M. (1960). The adoption of two farm practices in a central Iowa community. Agricultural and Home Economics Experiment Station, Iowa State University of Science and Technology.

Dooley, K.E. (1999). Towards a holistic model for the diffusion of educational technologies: An integrative review of educational innovation studies. Educational Technology & Society 2(4), 35-45.

Finley, T.R. (2003). A descriptive study of utilization of technology from a perspective of full-time faculty in Virginia’s higher education teacher-education programs (Doctoral dissertation, The George Washington University, 2003). ProQuest DigitalDissertations. (UMI No. AAT 3083800).

MUHIUDDIN, H & KREPS, B. (2004). Forty Years of Diffusion of Innovations: Utility and Value in Public Health, ''Journal of Health Communication. International Perspectives'', 9, Supplement 1, 3-11

Hoerup, S.L. (2001). Diffusion of an innovation: computer technology integration and the role of collaboration (Doctoral dissertation, Virginia Polytechnic Institute and State University, 2001). ProQuest Digital Dissertations. (UMI No. AAT 3031436).

Isleem, M I. (2003). Relationships of selected factors and the level of computer use for instructional purposes by technology education teachers in Ohio public schools: a statewide survey (Doctoral dissertation, The Ohio State University, 2003). ProQuest DigitalDissertations. (UMI No. AAT 3124087).

Medlin, B.D. (2001). The factors that may influence a faculty member's decision to adopt electronic technologies in instruction (Doctoral dissertation, Virginia Polytechnic Institute and State University, 2001). ProQuest Digital Dissertations. (UMI No. AAT 3095210).

Mitra, A., Steefensmeier, T., Lendmeier, S. & Massoni, A. (1999). Changes in attitudes towards computers and use of computers by university faculty. Journal of Research on Computing in Education, 32(1), 189-202.

Nurses' adoption of technology: application of Rogers' innovation-diffusion model. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15573331 on November 28, 2012.

Parisot, A.H. (1995). Technology and teaching: The adoption and diffusion of technological innovations by a community college faculty (Doctoral dissertation, Montana State University, 1995). ProQuest Digital Dissertations. (UMI No. AAT 9542260).

Relationships of selected factors and the level of computer use for instructional purposes by technology education teachers in Ohio public schools: a statewide survey. Retrieved from http://etd.ohiolink.edu/view.cgi?acc_num=osu1059507787 on November 28 2012.

Ryan, B. & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, 8, 15-24.

Rogers, E.M. (2005). Diffusion of Innovations (5th edition). Elements of Diffusion (p 23-27). New York: Free Press.

Study abroad as innovation: Applying the diffusion model to international education. Retrieved from http://ehlt.flinders.edu.au/education/iej/articles/v7n3/Spiering/paper.pdf 28 November 2012.

The Diffusion of Innovation: Part 2.(n.d.) Retrieved from http://www.soc.iastate.edu/sapp/soc415Diffusion2.html on November 28, 2012.

The Western Socialist. (1962). 29(226): 20-21.