Diffusion of Innovations
Much research from a broad variety of disciplines has used the model as a framework. Dooley (1999) and Stuart (2000) mentioned several of these disciplines as political science, public health, communications, history, economics, technology, and education, and defined Rogers’ theory as a widely used theoretical framework in the area of technology diffusion and adoption.
Rogers’ diffusion of innovations theory is the most appropriate for investigating the adoption of technology in higher education and educational environments (Medlin, 2001; Parisot, 1995).In fact, much diffusion research involves technological innovations so Rogers (2003) usually used the word “technology” and “innovation” as synonyms. For Rogers, “a technology is a design for instrumental action that reduces the uncertainty in the cause-effect relationships involved in achieving a desired outcome” (p. 13). It is composed of two parts: hardware and software. While hardware is “the tool that embodies the technology in the form of a material or physical object,” software is “the information base for the tool” (Rogers, 2003, p. 259).
Diffusion of Innovations Essay Example
Since software (as a technological innovation) has a low level of observability, its rate of adoption is quite slow. For Rogers (2003), adoption is a decision of “full use of an innovation as the best course of action available” and rejection is a decision “not to adopt an innovation” (p. 177). Rogers defines diffusion as “the process in which an innovation is communicated thorough certain channels over time among the members of a social system” (p. 5). As expressed in this definition, innovation, communication channels, time, and social system are the four key components of the diffusion of innovations.Four Main Elements in the Diffusion of Innovations Innovation Rogers offered the following description of an innovation: “An innovation is an idea, practice, or project that is perceived as new by an individual or other unit of adoption” (Rogers, 2003, p.
12). An innovation may have been invented a long time ago, but if individuals perceive it as new, then it may still be an innovation for them. The newness characteristic of an adoption is more related to the three steps (knowledge, persuasion, and decision) of the innovation-decision process that will be discussed later.In addition, Rogers claimed there is a lack of diffusion research on technology clusters. For Rogers (2003), “a technology cluster consists of one or more distinguishable elements of technology that are perceived as being closely interrelated” (p. 14). Uncertainty is an important obstacle to the adoption of innovations.
An innovation’s consequences may create uncertainty: “Consequences are the changes that occur in an individual or a social system as a result of the adoption or rejection of an innovation” (Rogers, 2003, p. 436).To reduce the uncertainty of adopting the innovation, individuals should be informed about its advantages and disadvantages to make them aware of all its consequences. Moreover, Rogers claimed that consequences can be classified as desirable versus undesirable (functional or dysfunctional), direct versus indirect (immediate result or result of the immediate result), and anticipated versus unanticipated (recognized and intended or not). Communication Channels The second element of the diffusion of innovations process is communication channels.For Rogers (2003), communication is “a process in which participants create and share information with one another in order to reach a mutual understanding” (p. 5).
This communication occurs through channels between sources. Rogers states that “a source is an individual or an institution that originates a message. A channel is the means by which a message gets from the source to the receiver” (p. 204). Rogers states that diffusion is a specific kind of communication and includes these communication elements: an innovation, two individuals or other units of adoption, and a communication channel.Mass media and interpersonal communication are two communication channels. While mass media channels include a mass medium such as TV, radio, or newspaper, interpersonal channels consist of a two-way communication between two or more individuals.
On the other hand, “diffusion is a very social process that involves interpersonal communication relationships” (Rogers, 2003, p. 19). Thus, interpersonal channels are more powerful to create or change strong attitudes held by an individual. In interpersonal channels, the communication may have a characteristic of homophily, that is, “the degree to which 14The Turkish Online Journal of Educational Technology – TOJET April 2006 ISSN: 1303-6521 volume 5 Issue 2 Article 3 two or more individuals who interact are similar in certain attributes, such as beliefs, education, socioeconomic status, and the like,” but the diffusion of innovations requires at least some degree of heterophily, which is “the degree to which two or more individuals who interact are different in certain attributes. ” In fact, “one of the most distinctive problems in the diffusion of innovations is that the participants are usually quite heterophilous” (Rogers, 2003, p. 9). Communication channels also can be categorized as localite channels and cosmopolite channels that communicate between an individual of the social system and outside sources.
While interpersonal channels can be local or cosmopolite, almost all mass media channels are cosmopolite. Because of these communication channels’ characteristics, mass media channels and cosmopolite channels are more significant at the knowledge stage and localite channels and interpersonal channels are more important at the persuasion stage of the innovation-decision process (Rogers, 2003).Time According to Rogers (2003), the time aspect is ignored in most behavioral research. He argues that including the time dimension in diffusion research illustrates one of its strengths. The innovation-diffusion process, adopter categorization, and rate of adoptions all include a time dimension. These aspects of Rogers’ theory will be discussed later in more detail. Social System The social system is the last element in the diffusion process.
Rogers (2003) defined the social system as “a set of interrelated units engaged in joint problem solving to accomplish a common goal” (p. 23).She used both qualitative and quantitative methods to analyze the characteristics of early adopters and the difference between early adopters and mainstream faculty. The selected factors investigated were patterns of computer use, computer expertise, generalized self-efficacy, participant information, teaching and learning changes, motivators to integrate technology for teaching and learning, impediments to integrating technology for teaching and learning, earning about technology, methods for using and integrating technology in teaching and learning, and evaluating the outcomes of using technology for teaching and learning. Less’ (2003) quantitative research study used Rogers’ (1995) diffusion of innovations theory to investigate faculty adoption of computer technology for instruction in the North Carolina Community College System. She classified the faculty members based on Rogers’ five categories of innovation adoption and compared them on the demographic variables of age, gender, race/ethnicity, teaching experience, and highest degree attained.While a significant relationship emerged between Rogers’ adopter categories and their years of teaching experience and highest degree attained, the results did not show an important difference between faculty adopter categories and age, gender, and race/ethnicity.
Less further classified the faculty as users in any of Rogers’ five categories and non-users of computer technology in instruction. No significant difference existed between users and non-users in demographic characteristics of age, gender, race/ethnicity, teaching experience and highest degree attained.Using Rogers’ diffusion theory, Blankenship (1998) employed both qualitative and quantitative research methods in studying the factors that were related to computer use by instructors in teaching. In his study, the variables were attitude toward computers, access to computers, training in computer use, support for computer use, age, grade level taught, curriculum area, gender, and teaching expertise. All these factors were used to predict computer use by teachers in classroom instruction. One of the major findings of the study was that grade level and curriculum area must be considered for successful training.Also, attitude, support, access, and age were statistically significant predictors of computer use in classroom instruction.
Finally, Blankenship suggested the following strategies to increase computer use in classroom instruction: grade and curriculum targeted computer training, technical support, and computer labs in every building. Using quantitative research methods, Surendra (2001) examined the diffusion factors proposed by Rogers (1995) and other sources to predict the acceptance of Web technology by professors and administrators of a college. He reviewed the training factor among the types of access.Access in general and training in particular were found to be the best predictors in the diffusion process of Web technology-based educational innovation. Moreover, he found that the diffusion factors, Rogers’ attributes of innovations, are useful predictors of the adoption of innovation. Also, a relationship was found between computer knowledge and the adoption of innovation. Carter (1998) conducted a computer survey and in-depth interviews to determine computer-based technologies that were being used by the faculty members and the factors that affect their use of these technologies.
Faculty attitudes toward using computer-based technology, support, resources, and training were the selected factors needed to use these technologies effectively. Also, Carter found that word processing software, e-mail, and Internet resources were the most frequently used computer-based technologies. Another study was conducted by Zakaria (2001) on factors related to IT implementation in the curriculum. The selected factors in the study were the Malaysian Ministry of Education Polytechnic faculty members’ attitudes toward IT, their IT use in teaching, and the availability of IT.Despite a lack of IT use in general, faculty members usually had a very positive attitude toward IT use in their teaching. Most faculty members reported barriers to IT use in their teaching. Furthermore, Zakaria argued there was a gender difference in terms of IT use.
No significant difference existed between the faculty members’ department membership and IT use in general. Also, he found that the highest level of education was negatively correlated with IT use and other demographic variables, and the level of education was correlated with email and World Wide Web use.While age was positively correlated with teaching experience, teaching load was significantly correlated with online discussion use. Finally, the highest level of education and adoption willingness were found to be the most significant predictors of IT use in teaching. Analyzing the data quantitatively and qualitatively, Anderson et al. (1998) studied the attitudes, skills, and behaviors of the faculty members related to their IT use at a large Canadian research university.Based on Roger’s (1995) two major adopter categories, they defined the faculty members as “earlier adopters” and “mainstream faculty” and provided strategies for reducing the gap between these two groups.
Although mainstream faculty used information technologies for research and professional communication applications, their adoption of these applications in teaching was very low. To increase their adoption of computer 21 The Turkish Online Journal of Educational Technology – TOJET April 2006 ISSN: 1303-6521 volume 5 Issue 2 Article 3 echnologies for instructional purposes, the incentives, training programs, and barriers should be taken into account in comprehensive adoption strategies.