Diffusion Theory and Instructional Technology
Daniel W. Surry
University of Southern Mississippi
Paper presented at the Annual
Conference of the
Association for Educational Communications and Technology (AECT),
Albuquerque, New Mexico February 12 - 15,
© 1997, DAN SURRY
This paper discusses how the theories of innovation
diffusion have been incorporated into the field of instructional technology.
The paper begins with a brief description of general diffusion theory
that includes mention of the four most commonly discussed diffusion theories.
Following the discussion of general diffusion theory, the author describes
how general diffusion theories have been used to form diffusion theories
specific to the field of instructional technology. The paper states that
the two major categories of IT-related diffusion theory are Systemic Change
Theories and Product Utilization Theories. Examples of each category are
provided. The paper identifies and describes two opposing philosophical
views of technology: Determinism and Instrumentalism. The author uses
the two philosophies of technology to create two subcategories of IT-related
diffusion theory: Developer Based Theories and Adopter Based Theories.
Examples of both subcategories are provided.The author contends that Developer
àBased Theories are flawed in that they overstate the role of technological
superiority in the diffusion process.
DIFFUSION THEORY AND INSTRUCTIONAL
The purpose of this paper is to describe how the theory of innovation
diffusion has been incorporated into the field of instructional technology.
Professionals in a number of disciplines, from agriculture to marketing,
have used the theory of innovation diffusion to increase the adoption
of innovative products and practices. Instructional technologists, faced
with a growing realization that innovative instructional products and
practices have suffered from a lack of utilization, are beginning to turn
toà diffusion theory in an effort to increase the adoption of instructional
Diffusion is defined as the process by which an innovation
is adopted and gains acceptance by members of a certain community. A number
of factors interact to influence the diffusion of an innovation. The four
major factors that influence the diffusion process are the innovation
itself, how information about the innovation is communicated, time, and
the nature of the social system into which the innovation is being introduced
(Rogers, 1995). Diffusion research, in its simplest form, investigates
how theàse major factors, and a multitude of other factors, interact
to facilitate or impede the adoption of a specific product or practice
among members of a particular adopter group.
The study of diffusion theory is potentially valuable
to the field of instructional technology for three reasons. First, most
instructional technologist do not understand why their products are, or
are not, adopted. In a very real sense, the underlying causes of instructional
technology's diffusion problem remain a mystery to the field. There appear
to be as many reasons for instructional technology's lack of utilization
as there are instructional technologists. Some instructional technologists
blame teachers and an intrinsic resistance to change as the primary causes
of instructional technology's diffusion problem, others cite entrenched
bureaucracies and inadequate funding (Schneberger and Jost, 1994). By
better understanding the multitude of factors that influence adoption
of innovations, instructional technologist will be better able to explain,
predict and account for the factors that impede or facilitate the diffusion
of their products.
Second, instructional technology is inherently an innovation-based
discipline. Many of the products produced by instructional technologists
represent radical innovations in the form, organization, sequence, and
delivery of instruction. An instructional technologist who understands
the innovation process and theories of innovation diffusion will be more
fully prepared to work effectively with clients and potential adopters
Third, the study of diffusion theory could lead to the
development of a systematic, prescriptive model of adoption and diffusion.
Instructional technologists have long used systematic models to guide
the process of instructional development (ID). These systematic ID models
have resulted in the design and development of effective and pedagogically
sound innovations. A systematic model of diffusion could help guide the
process of adoption and diffusion in a similar manner and, perhaps, with
similarly effàective results.
General Diffusion Theory
Before discussing how diffusion theory has been
incorporated into instructional technology, I will provide a brief background
and overview of general diffusion theory. The most important fact to consider
in discussing diffusion theory is that it is not one, well-defined, unified,
and comprehensive theory. A large number of theories, from a wide variety
of disciplines, each focusing on a different element of the innovation
process, combine to create a meta-theory of diffusion.
The most likely reason why there is not a unified theory
of diffusion is that the study of innovation diffusion is a fairly recent
field. Rogers (1995) points out that a 1943 study by Ryan and Gross at
Iowa State University provided the genesis of modern diffusion research.
The Ryan and Gross (1943) study, from the field of rural sociology, used
interviews with adopters of an innovation to examine a number of factors
related to adoption. The interview-based methodology used in the Ryan
and Gross study àhas remained the predominant diffusion research
methodology ever since (Rogers, 1995). A number of researchers from rural
sociology (e.g., Fliegel and Kivlin, 1962) and other disciplines (e.g.,
Weinstein, 1986) have built on the Ryan and Gross' work to conduct studies
and develop theories related to the diffusion of innovations.
The researcher who has done the most to synthesize all
of the most significant findings and compelling theories related to diffusion
is Everett M. Rogers. Rogers' book Diffusion of Innovations, first published
in 1960, and now in its fourth edition (Rogers, 1995) is the closest any
researcher has come to presenting a unified theory of diffusion.. Four
of the theories discussed by Rogers are among the most widely-used theories
of diffusion: Innovation Decision Process; Individual Innovativeness;
Rate ofà Adoption; and Perceived Attributes.
Innovation Decision Process
Decision Process theory (Rogers, 1995) states that diffusion is a process
that occurs over time and can be seen as having five distinct stages. The
stages in the process are Knowledge, Persuasion, Decision, Implementation,
and Confirmation. According to this theory, potential adopters of an innovation
must learn about the innovation, be persuaded as to the merits of the innovation,
decide to adopt, implement the innovation, and confirm (reaffirm or reject)
the decision to adopt the innovaàion. This theory has been so widely
cited in the instructional technology literature that Sachs (1993) writes,
somewhat derisively, "after looking at [the literature] in our field, one
might get the impression that the only important thing we need to know about
how to encourage the adoption of innovations or how to be better change
agents is that there are five stages to the innovation adoption process
(p. 1)". While Sachs correctly concludes that many other important theories
of innovation diffusion areà overlooked, the Innovation Decision
Process theory remains among the most useful and well known.
The Individual Innovativeness theory (Rogers, 1995) states individuals
who are predisposed to being innovative will adopt an innovation earlier
than those who are less predisposed. Figure 1 shows the bell shaped distribution
of Individual Innovativeness and the percentage of potential adapters
theorized to fall into each category. On one extreme of the distribution
are the Innovators. Innovators are the risk takers and pioneers who adopt
an innovation very early in the diffusion process. On the other extreme
are the Laggards who resist adopting an innovation until rather late in
the diffusion process, if ever.
Rate of Adoption
The third widely-used
diffusion theory discussed by Rogers (1995) is the theory of Rate of Adoption.
Rate of Adoption theory states that innovations are diffused over time in
a pattern that resembles an s-shaped curve. Rate of Adoption theorizes that
an innovation goes through a period of slow, gradual growth before experiencing
a period of relatively dramatic and rapid growth. An example of how rate
of adoption might typically be represented by an s-curve is shown in Figure
2. The theory also states that following the period of rapid growth, the
innovation's rate of adoption will gradually stabilize and eventually decline.
The Theory of Perceived Attributes (Rogers, 1995)
states that potential adopters judge an innovation based on their perceptions
in regard to five attributes of the innovation. These attributes are:Trialability;
Observability; Relative Advantage; Complexity; and Compatibility. The
theory holds that an innovation will experience an increased rate of diffusion
if potential adopters perceive that the innovation: 1) Can be tried on
a limited basis before adoption; 2) Offers observable results; 3) Has
an adàvantage relative to other innovations (or the status quo);
4) is not overly complex; and 5) Is compatible with existing practices
and values. The Theory of Perceived Attributes
has been used as the theoretical basis for several studies relevant to
the field of instructional technology. Perceptions of compatibility, complexity,
and relative advantage have been found to play a significant role in several
IT-related adoption studies. Wyner (1974) and Holloway (1977) each found
relative advantage and compatibility to be significant perceptions among
potential adopters of instructional technology in high schools. Eads (1984)
found compatibility àwas the most important attribute among students
and school administrators. Surry (1993) studied the perceptions of weather
forecasters in regard to innovative computer based training and found
relative advantage, complexity and compatibility were important adoption
Instructional Technology Diffusion Theory
A number of researchers have attempted to use the general
theories of innovation diffusion to develop diffusion theories specific
to the field of instructional technology. It would be impossible for one
paper to adequately discuss in detail the techniques and purposes of all
of these attempts at theory building. Even providing a brief synopsis
of each major application of diffusion theory to IT would result in a
lengthy discussion far beyond the scope of any one paper. I will limit
the present paper to àa discussion of the broad goals and major
philosophical premises of instructional technology diffusion theory.
Macro and Micro Theories
of diffusion theory to instructional technology can be grouped into two
major, categories with distinctly separate goals. The first major category
focuses on the reform and restructuring of educational institutions. The
goal of this category of IT diffusion research is to develop theories
of organizational change, most commonly school change, in which technology
plays a major role. Examples of this category include Reigeluth's (1987)
Third Wave Educational System, The Schoolyear 2000 modàel (Center
for Educational Technology, 1989), and the New American Schools Development
Corporation (NASDC) (Mehlinger, 1995). These theories, often referred
to as systemic change theories, typically involve the adoption a wide
range of innovative technologies and and practices. Because of their broad
scope, systemic change theories can be thought of as macro-level IT diffusion
The second major category of IT diffusion research focuses
on increasing the adoption and utilization of specific instructional products.
The goal of this category of research is to develop theories of technology
adoption that will lead to a more widespread use of instructional innovations.
Examples of product adoption and utilization theories include Burkman's
(1987) User-Oriented Instructional Development process, Environmental
Analysis (Tessmer, 1990), Adoption Analysis (Farquhar and Surry, 1994),
àand the Technological Imperative Model (Schneberger and Jost,
1994). Theories in this category are not concerned with large scale, systemic
change, but focus on the adoption of a specific innovation by a specific
set of potential adopters. Because of their focus on specific innovations
and specifics environments, these theories are, in effect, micro-level
IT diffusion theories.
The two major categories of IT-related diffusion research,
which I will call Macro, or Systemic Change Theories, and Micro, or Product
Utilization Theories, can each be divided into two subcategories. These
subcategories represent the two predominant philosophies of technology
and technological change: Technological Determinism and Technological
Instrumentalism. Before discussing the subcategories, which I will call
"Developer (Determinist)" and "Adopter (Instrumentalist)", I will provide
a brief overàview of the two predominant philosophies.
Determinist versus Instrumentalist
a theoretical standpoint, views of technology range on a continuum from
technological determinism to technological instrumentalism. Autonomy and
continuity are the key issues in the philosophical debate between determinists
and instrumentalists. Technological determinists view technology as an
autonomous force, beyond direct human control, and see technology as the
prime cause of social change (Chandler, 1995). Determinists view the expansion
of technology as discontinuous. They see technologicalà growth
not as a gradual, evolutionary process, but as a series of revolutionary
leaps forward (McCormack, 1994).
Among the most widely-cited deterministic works is Toffler's
(1971) book Future Shock. Toffler concisely outlines the determinist's
philosophy when, after citing several examples of accelerated economic
growth, he writes "behind such prodigious economic facts lies that great,
growling engine of change - technology" (p. 25). While acknowledging that
technology is not the only force in social change, Toffler adds, "technology
is indisputably a major force behind this accelerative thrust" (p. 25)
and "by noàw the accelerative thrust triggered by man has become
the key to the entire evolutionary process of the planet" (p.485).
Technological determinists, united in their belief that
technology is an autonomous and revolutionary force, often differ in their
opinion of the morality of technology. Determinists commonly have either
a radically utopian or radically dystopian opinion on technology (Kaplan,
1996). Figure 3 provides an outline of the respective positions. Utopian
determinists believe that technology is a positive and uplifting force
that will, over time, mitigate or eliminate most or all of the ills that
afflict humaànity. They believe technology is leading society towards
an ever more utopian existence. Karl Marx is the most often cited example
of a utopian determinist philosopher, although the exact nature of his
philosophy is a hotly debated question (Misa, 1994). Other well known
utopian determinists include Marshall McLuhan and Alvin Toffler.
Dystopian determinists believe that technology is an inherently
evil, or dehumanizing, force that will lead, inevitably, to the moral.
intellectual, or physical destruction of humankind. Jacques Ellul's (1964)
work The Technological Society is the seminal writing in technological
determinism and provides a classic outline of the dystopian position.
Another well-known dystopian determinist is George Orwell (1949) who provides
a fictional account of the dehumanizing effects of technology in his càlassic
Opposed to the determinist philosophers are the instrumentalist
philosophers. Human control over technology is the issue that most dramatically
divides instrumental philosophers and determinist philosophers. Technological
instrumentalists, as their name may imply, view technology as a tool.
The instrumentalists often cite the knife as an example of their philosophy
(Levinson, 1996). A knife is a tool and can be used for either good or
evil, depending upon the intentions of the person employing the toolà
Extrapolating from that simple example, instrumentalists believe that
all technology is a tool, largely under human control, that can be used
for either positive or negative purposes. While determinists see technology
as the most powerful force for change, instrumentalists see social conditions
and human aspiration as the primary causes of change. The other major
difference between the two philosophies is that instrumentalists view
the growth of technology as an evolutionary process, not as a series of
àrevolutions or technological leaps (Levinson, 1996). They see
technological growth as the ultimate culmination of a long history of
slow, gradual expansion.
As mentioned above, the two major categories of IT-related
diffusion research can be sub-divided into two subcategories. The result
is a breakdown of IT-related diffusion theory into four areas. The areas
are shown in Figure 4. I will now describe the two subcategories, Developer
Based and Adopter Based, in more detail
Developer Based (Determinist) Theory
The goal of developer based theory is to increase diffusion by maximizing
the efficiency, effectiveness and elegance of an innovation. The developer,
or architect, of superior technology is seen as the primary force for change.
The underlying assumption of developer based theories is deterministic in
its belief that superior technological products and systems will, by virtue
of their superiority alone, replace inferior products and systems. Developer
based theories of diffusion see change as following dàirectly from
a technological revolution.
Developer based theories in instructional technology assume
that the best way to bring about educational change is to create a system
or product that is significantly superior to exiting products or systems.
Potential adopters are viewed as being predisposed to adopt innovations
that are quantifiably superior. Top down school reform efforts such as
the Goals 2000 initiative (Mehlinger, 1995) are excellent examples of
developer based diffusion theories. These top down reform efforts seek
to diffuse eduàcational change by proposing educational systems
that are superior to existing systems. By specifying goals, organizational
structures, managerial philosophies, instructional products, and fiscal
strategies that have been proven to be, or at are least theorized to be,
superior to existing practice, top down school reformers are counting
on technological superiority to bring about change.
Instructional development (ID) models are another example
of developer based theories of diffusion. Diffusion is not an element
overtly described in a typical ID model (Andrews and Goodson, 1991),
but the adoption of an innovation does have an implied place in the
ID process. Diffusion through technological superiority is the implicit
goal of the process. Andrews and Goodson (1991) list four purposes of
systematic instructional design: Improved learning; improved management
(of the ID process), improveàd evaluation (of products); and
theory building. Three of the four purposes center on the creation of
technologically superior products. The instructional development process
assumes that technological superiority is a sufficient condition that
will lead directly to the adoption and diffusion of innovative products
Limitations of Developer Based (Deterministic)
Instructional development is a
process based on the research, development, and diffusion (RDD) paradigm
(Burkman, 1987). Saettler, in the first edition of is classic work A
History of Instructional Technology (1968) provides an insight into
the thinking of those who were early advocates of the RDD approach when
"In the education sector, it is becoming increasingly
apparent to scientifically oriented educators that education must discard
the folklore approach to instruction and move forward to new frontiers,
this includes the development of instructional systems based on behavioral
science theory, research, and development." (p. 270).
As Saettler describes, one of the hallmarks of the RDD
approach is to abandon "folklore" approaches to education and, in their
place, to develop systematic, scientific alternatives. Saettler writes
that the systems engineering approach has been the foundation of industrial
engineering since the beginning of the industrial revolution and that
"one of the most successful applications of the systems concept . . .
was the development of the atomic bomb" (p. 269).
While there can be ethical debate as to whether the same
process used to develop the atomic bomb should be used to develop human
minds, there can be little argument that the continuing refinement and
wider use of the RDD paradigm have resulted in the creation of instructional
products that are pedagogically sound and technically advanced. Instructional
technologies greatest challenge is not developing effective products,
but developing effective products that people want to use. As Dalton (1989)
writesà, "although w can fill instructional gaps with fervor, we
never seem to examine our solutions in light of the wants of the implementors"
(p. 22). Hall and Hord (1987) point to the failure of many large-scale
curriculum reform projects in the 1960s as evidence that instructional
technology has failed to meet the challenge of utilization.
The primary limitation of instructional development theory,
and the RDD paradigm upon which it is based, is their inherent deterministic
bias. There is a general consensus in the diffusion and adoption literature
that technological superiority alone is not enough to guarantee the adoption
of an innovation. In fact, some would argue whether technological superiority
is even a necessary condition, at least at the beginning of the adoption
process (MacKenzie, 1996). If technological superiority is not suffàicient
to increase adoption, where does that leave us? Several instructional
technologists suggest that the ultimate answer to this important question
can be found in a more instrumentalist approach to diffusion.
Adopter Based (Instrumentalist) Theory
Adopter based theories focus on the human and interpersonal
aspects of innovation diffusion. Adopter based theories are inherently instrumental
in philosophy because they view the end user -- the individual who will
ultimately implement the innovation in a practical setting, as the primary
force for change. These theories reject the assumption that superior products
and practices will automatically be attractive to potential adopters.
Segal (1994) states the importance of adopter based theories
when he writes "all structures and machines, primitive or sophisticated,
exist in a social context and, unless designed for the sake of design
itself, serve a social function" (p.2). Adopter based theories seek to
understand the social context in which the innovation will be used. Tenner
(1996) describes the concept of revenge effects which is central to many
adopter based theories. Revenge effects occur when "new structures, devices,
and orgaànisms react with real people in real situations in ways
we could not foresee" (p.9). Predicting and account for probable revenge
effects caused by an innovation is a defining component of many adopter
based diffusion theories.
Adopter based theorists (e.g., Tessmer, 1990) argue that
a variety of factors, most unrelated to technical superiority, influence
the decision to adopt or reject an innovation. Adopter based theorists
such as Burkman (1987) often site the QWERTY and Dvorak keyboard example.
The Dvorak keyboard configuration is superior and allows for more efficient
and faster typing. However, since most typists learned to type using the
QWERTY configuration and are comfortable with that configuration, there
is great ràeluctance to adopt the Dvorak configuration, despite
its superiority. This is a classic example of how human, interpersonal,
and social factors often play a more significant role in adoption than
Examples of adopter based theories can be found in both
the Macro and Micro categories of IT diffusion research. Ernest Burkman
(1987) was the first major author in the field to suggest a Micro (Product
Utilization) theory based on an instrumentalist view of instructional
technology. Burkman's theory of a user-oriented instructional development
(UOID) rejects the idea that technological superiority is a sufficient
condition for the adoption of an instructional product. In UOID, the opinions,
needs, andà perceptions of the potential adopters are seen as the
primary forces that influence adoption.
Burkman's User Oriented Instructional Development process
consists of 5 steps:
Burkman's UOID is representative of instrumentalist philosophy
because UOID assumes the end user is the most important force in the adoption
of a new product. Other adopter-based theories of product utilization include
Stockdill and Morehouse's (1992) adoption checklist and Farquhar and Surry's
(1994) Adoption Analysis.
- Identify the potential adopter
- Measure relevant potential adopter perceptions
- Design and develop a user-friendly product
- Inform the potential adopter (of the product's user-friendliness)
- Provide Post Adoption Support
Hall and Hord's (1987) Concerns Based Adoption Model (CBAM)
is a notable example of a Macro (Systemic Change) theory of diffusion
that is instrumentalist, rather than determinist, in philosophy. Hall
and Hord describe a process in which change facilitators understand change
from the point of view of the people who will be affected by change. The
idea of CBAM is to bring about systemic restructuring by understanding
the social, political, and interpersonal aspects of the school. The Coalition
of essentiaàl Schools, and many other Bottom Up reform strategies
(Mehlinger, 1995), are other examples of adopter based, systemic change
In this section, we have seen that diffusion theory has
been incorporated in the field of instructional technology in a number
of ways, both subtle and overt. We have seen that diffusion theories can
have as their goal the total restructuring of an entire instructional
system or the adoption of a specific instructional product. We have also
seen that theories of adoption and diffusion can represent either a determinist
or instrumental philosophy. Figure 5 shows examples of instructional technology
diffusàion theories in each of the four resulting areas.
of instructional technology is a broad and diverse field. Instructional
technologists routinely incorporate theories from communication, cognitive
psychology, management, computer science, behavioral psychology and many
other fields into the development of instructional products and systems.
In this paper, I have discussed several important ways that instructional
technologists have begun to incorporate the theories of innovation diffusion.
The increased awareness of diffusion's importance anàd expanded
use of diffusion theories are of potentially great benefit to instructional
In order to maximize the potential benefit of diffusion
theory, instructional technologists should adopt a more instrumentalist
philosophy of technology. No reasonable diffusion theorist would suggest
that technological superiority is the only necessary condition for diffusion.
Instructional technologists have been seduced by the simplicity and basic
logic of technological determinism. The decision to adopt an innovation,
however, defies simple logic. The best products are not always the ones
people wàant to use. As MacKenzie (1996) writes: "Technologies
. . . may be best because they have triumphed, rather than triumphing
because they are best" (p. 7).
Superior technology does not always steam roll inferior
technology, as the determinists believe. Nor does a superior technology
explode onto the scene in a glorious, perfect form -- it creeps along
in fits and starts. Technology's advance may be inevitable, but it is
gradual. Instructional technologists should, therefore, look to the potential
adopters to show us ways to gradually introduce our innovations into their
Of course, while a less determinist philosophy would be
beneficial to instructional technology, a totally instrumentalist philosophy
would be disastrous. Turning out technically inferior and pedagogically
weak products that people want to use is not the answer. Every technologist
is inherently a determinist. There is no danger in being driven to improve
society by improving instructional technology. The danger is to ignore
the society we are attempting to improve.
Note: I would like to thank Dr. John D. Farquhar
of Pennsylvania State University, Harrisburg for his assistance in preparing
this paper and for his invaluable assistance in several past projects
related to the diffusion of innovations. Please address any correspondence
in regard to this paper to: Dan Surry, University of Southern Mississippi,
Department of Technology Education, Box 5036, Hattiesburg MS, 39406. Phone:
601-266-4446 Email: firstname.lastname@example.org
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© 1997, DAN SURRY
Cite this document as:
Surry, Daniel W. Diffusion Theory and Instructional Technology.
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