Running head: DIFFUSION FRAMEWORKS
Research and Theoretical/Conceptual Frameworks of Diffusion Theory Applied to the
Development of Coordinated Community Response Teams in Oklahoma
Tara Roberson-Moore
Oklahoma State University
1
DIFFUSION FRAMEWORKS
2
Abstract
Diffusion of Innovation Theory states: “When the number of individuals adopting a new idea is
plotted on a cumulative frequency basis over time, the resulting distribution is an S-shaped
curve” (Rogers, 2003, p. 23). This paper presents a research framework and a
theoretical/conceptual framework that uses the Diffusion of Innovations Theory to study the
development of Coordinated Community Response (CCR) Teams in Oklahoma. The research
framework presents the independent and dependent variables that would be used in a research
study to determine the rate of adoption of the CCR Team concept in counties across the state of
Oklahoma and the eventual assignment of each team to the appropriate adopter category.
DIFFUSION FRAMEWORKS
3
Research and Theoretical/Conceptual Frameworks of Diffusion Theory Applied to the
Development of Coordinated Community Response Teams in Oklahoma
The District Attorneys Council started the Coordinated Community Response (CCR)
Team project with funding from a discretionary grant from the Department of Justice, Office on
Violence Against Women in 2008. CCR Teams are multi-disciplinary teams made up of victim
services providers, law enforcement officers, prosecutors, and other criminal justice
professionals who come together to find community-level solutions to address domestic violence
(Malik, Ward, and Janczewski, 2008, p. 933). Since the beginning of the project, 28 CCR Teams
have been organized in Oklahoma. The long-term goal of the project is to establish teams in all
77 counties in Oklahoma. By studying the variables involved in determining the rate of adoption
of current participants, the researcher will identify successful diffusion practices. These
successful practices can then be recommended for use in the future diffusion of the innovation to
the rest of the state.
Diffusion of Innovation Theory is so versatile there have been thousands of research
projects in almost every discipline from anthropology, to marketing, to general sociology
(Gouws & van Rheede van Oudtshoorn, 2011; Dearing, 2009; Harting, Rutten, Rutten, &
Kremers, 2009; Knuth, 1997; Rogers, 2003; Ryan and Gross, 1943; and Surry, 1997). This
versatility is why it was selected for this assignment. Despite the empirical popularity it has
now, Diffusion of Innovation Theory was not always so well known. The theoretical roots of
diffusion can be traced back to Frenchman Gabriel Tarde in 1900, when he referred to diffusion
as “the laws of imitation” (Rogers, 2003, p. 41). He was considered far ahead of his time when
he recognized that diffusion of an innovation, when plotted on a graph, would form an S-shaped
curve. According to Rogers (2003), “For Tarde, the diffusion of innovations was a basic and
DIFFUSION FRAMEWORKS
4
fundamental explanation of human behavior change.” Despite his insights, his theory was not
widely adopted until two researchers in Iowa conducted a study more than 40 years later. In
1943, one of the most influential studies using the Diffusion of Innovations Theory took place in
rural Iowa when two researchers, Ryan and Gross, investigated the adoption of hybrid corn
among farmers in two small Iowa communities (Rogers, 2003, p. 31; Ryan and Gross, 1943). By
investigating the four main elements of the theory: the innovation, communication channels,
time, and the social system, Ryan and Gross “played key roles in forming the classical diffusion
paradigm. The hybrid corn study has left an indelible stamp on the history of all diffusion
research” (Rogers, 2003, p. 35).
Figure 1. Theoretical Framework for the Diffusion of Innovations Theory as it is applied to the
development of CCR Teams in Oklahoma.
DIFFUSION FRAMEWORKS
5
DIFFUSION RESEARCH FRAMEWORK
Independent Variables
Perceived
Attributes
Advantages
Compatibility
Complexity
Trialability
Observability
Dependent Variable
Interpersonal
Mass Media
Communication
Channels
Nature of
Social
System
Extent of
Change Agent
Promotion
RATE OF
ADOPTION
Norms
Network
Proximity
Homophily
Types
Frequency
ASSIGNMENT TO
ADOPTER CATEGORY
Innovators
Early Adopters
Early Majority
Late Majority
Laggards
Figure 2. Research Framework for the Diffusion of Innovations Theory as it is applied to the
development of CCR Teams in Oklahoma. Adapted from Diffusions of Innovations, by E.M.
Rogers, 2003, p. 222. Copyright 2003 by Everett M. Rogers.
Research framework. According to Rogers (2003), the rate of adoption is the “relative
speed” that an innovation or idea is adopted by the people in a social system and is typically
measured by the number of people who adopt the new innovation idea in a specific period of
DIFFUSION FRAMEWORKS
6
time. The research will use mixed methods in the study, depending on the variable. The first
variable, perceived attributes, are based on whether or not the people involved in the adoption of
the innovation see its adoption as advantageous; compatible with their belief systems and current
practices; easy to adopt and use; whether or not they can test it before formal adoptions; and if
the positive results can be easily observed. A mixed methods instrument will be used or
developed to obtain information on perceptions of the new innovation. The second variable,
communication channels, measures which types of communications were most instrumental in
the Rate of Adoption, like mass media, social networks, or interpersonal communications; and
how much exposure the participants experienced. Quantitative data will be gathered for this
variable. Nature of the social system is the third variable used to determine Rate of Adoption. It
uses items like social norms in the community, the level of networking that took place before the
introduction of the innovation, and the homophily of the community as measures. This will be a
mixed methods or a qualitative method, depending on what measurement tools are already
available. The final independent variable in the research framework is the extent of change agent
promotion. A change agent is someone who is charged with promoting, training, or educating
people about the innovation or idea. This can be salesmen, advocates, or, as in the case of the
CCR Team project in Oklahoma, the efforts of the CCR Coordinator employed by the District
Attorneys Council. Quantitative method will gather the needed information.
All data collected about each of these variables can then be used to determine the rate of
adoption, and more importantly, identify diffusion practices that the fastest adopters, or
innovators, found appealing. These would then be repeated to use with those who have not
adopted the innovation. Information from the late majority could also be beneficial in identifying
what finally convinced them to adopt the innovation.
DIFFUSION FRAMEWORKS
7
Once the Rate of Adoption is determined and charted on a graph, which should be in an
S-curve, a timeline can be determined to assign each of the CCR Teams to one of five adopter
categories: innovators, early adopters, early majority, late majority, and laggards.
Future research. This framework offers several opportunities for several future
individual studies. Any of the independent variables of the Diffusion of Innovations theory could
be coupled with any one of the many decision-making theories to determine further impacts of
specific variables on when the CCR Teams were finally formed. It could be a measure of each of
the individual members of the teams or a theory surrounding group decision making. Another
possible research study could use the homophily theory to possibly compare the innovator teams
to the laggard or late majority teams. Rogers (2003) stated that studying why people or groups
refuse to accept a new innovation is not common. It would be interesting to survey groups of
criminal justice professionals in counties that have some knowledge of CCR Teams, but have not
adopted, to see why the innovation has diffused for them. This could be done using some of the
key independent variables from diffusion theory coupled with theories on resistance to change or
new ideas.
DIFFUSION FRAMEWORKS
8
References
Dearing, J.W. (2009). Applying Diffusion of Innovation Theory to Intervention Development.
Research on Social Work Practice, 19(5), 503-518. doi: 10.1177/1049731509335569
Gouws, T. & van Rheede van Oudtshoorn, G.P. (2011). Correlation between brand longevity
and the diffusion of innovation theory. Journal of Public Affairs, 11(4), 236-242. doi:
10.1002/pa.416
Harting, J., Rutten, G.M.J., Rutten, S.T.J., & Kremers, S.P. (2009). A qualitative application of
the diffusion of innovations theory to examine determinants of guideline adherence
among physical therapists. Physical Therapy, 89(3), 221-232. Retrieved from
http://argo.library.okstate.edu/login?url=http://search.proquest.com.argo.library.okstate.e
du/docview/223119806?accountid=4117
Knuth, R. (1997). Innovation diffusion: Proposal of an organizing theory on which to base
research into school. Library & Information Science Research, 19(3), 301-313. Retrieved
from http://dx.doi.org.argo.library.okstate.edu/10.1016/S0740-8188(97)90017-7
Malik, N.M., Ward, K., & Janczewski, C. (2008). Coordinated Community Response to Family
Violence: The Role of Domestic Violence Service Organizations. Journal of
Interpersonal Violence, 23(7), 933-955. doi: 10.1177/0886260508315121
Rogers, E. M. (2003). Diffusion of Innovations, (5th ed.). New York, NY: Free Press.
Ryan, B. & Gross, N. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural
Sociology, 8(1), 15-24. Retrieved from http://chla.library.cornell.edu/cgi/t/text/textidx?c=chla;idno=5075626_4294_001
Surry, D.W. (1997, February). Diffusion theory and instructional technology. Paper presented at
the Annual Conference of the Association for Educational Communications and
Technology (AECT), Albuquerque, NM.