Undoubtedly the
second oldest concept in the study of innovation, next to that of
"innovation" itself, is "adoption". The use of this word is
so much a part of the common language that we all feel that we understand its meaning intuitively. Because of this assumption, the
term has come to be used with rather less precision than the norms of social or
policy analysis should allow. Nor have the various uses of the term been
subjected to much systematic analysis. In fact, the problems with this concept
have contributed to perpetuating some major misconceptions about the innovation
process generally, and the techniques for "transferring technology"
in particular. This paper explores some of these problems with the concept of
"adoption", and suggests some ways of overcoming them.
Adoption is the
original dependent variable in innovation research -- the criterion which is
sought. It is the desirable property of innovative systems which change agents
seek to enhance. "Innovation" is broadly defined as any change in
structure, design, products, or processes in which there is a definable new
element introduced into the sys tem; the process is essentially the same for
technologies of all degrees of "hardness" al though the specifics may
vary. By far the largest body of knowledge relating to innovation is the
so-called "correlates of innovativeness" research the information about which characteristics of
people or organizations are associated with higher levels of adoption. The
voiced or unvoiced assumption underlying the examination of correlates of
innovativeness is causal: If we manipulate the characteristics of organizations
or individuals so that they more closely resemble those of the highly
innovative, we will make the organizations or individuals themselves more
innovative (that is, more prone to "adopt" our innovations).
There is little
doubt that innovation research, and the technology-transfer programs based on it,
have consistently exhibited what Rogers (1975) called the "pro-innovation
bias" -- a tendency to assume that adoption of the innovation should be
carried out by all possible adopters. In accounting for this slant, it is
useful to remember that innovation research began as market research – that is,
studies aimed at identifying ways to get people to buy more goods and services,
particularly in agriculture. Its transition into “diffusion research”, studying
the spread of new ideas or practices through a population, was a response to
the observation that the existence of the innovation alone was not sufficient
in most cases to produce adoption. The development of implementation research
in recent years is a further reaction to the failure of diffusion or communication
alone to promote adoption, leading in turn to an examination of the internal
dynamics of innovative individuals or organizations (Giacquinta,
1978). In this transition, the vocabulary and guiding metaphors have also
changed. The market research approach is essentially derived from economics;
the diffusion approach, from a combination of cognitive psychology and
political science. Innovation research has both benefited and suffered from not
being the property of a single academic discipline.
The picture is
further complicated by the tendency to transfer to the analysis of
organizations the models of innovativeness originally developed for
individuals, often without careful analysis of the ways in which the two types
of "systems" were alike or unlike (Katz, 1962). Most of this paper is
concerned with organizational innovation, and we will generally use the term
"organization" as a referent. However, most of the same points might
be usefully made in other contexts about the analysis of individual adoption
There is nothing
inherently wrong with market research or even with a pro-innovation value
system. Many innovations currently on the market are good ideas in terms of
almost any value system, and encouraging their spread can be viewed as
virtually a public duty. But any organizational innovation process involves many
people and many possible sets of values. It is certainly proper for the
analyst, or the innovator, to define a set of predominant values, such as
"ultimate interests of the client", to take precedence over more
local values. But one should not forget that others may not share the same
whole hearted commitment, and that they tend to act and make decisions based on
their own values not necessarily on those of the analyst. Evaluation of organizations
and innovations requires a single value set; accounting for their behavior, on
the other hand, must admit the operation of different value sets.
The classic
definition of the term "adoption" is found in
If these assumptions cannot be made, it is very hard to justify the use
of "adoption" as a dependent variable interesting in any policy
relevant sense at least. The first two assumptions constitute the basis for
generalizing the analysis; the third constitutes the value judgment on which
the analysis is based. Generalizability of findings and clarity of values and
purposes are essential to useful policy research.
It
is the appropriate function and responsibility of the analyst to define the
criteria in terms of which he judges the innovation to be a desirable or
undesirable result for society. These criteria may describe outcomes (future configurations of
desirable relationships) or processes
(suitable methods of achieving outcomes). These are approximately equivalent to
Rokeach's (1973) elaboration of Dewey's
"terminal" and "instrumental" values. When such criteria
serve to define the desirability of "adoption", the implicit assumption
is that the criteria hold in settings other than the one under immediate
consideration. Thus, generalizability of the value setting is as important as
that of the circumstances involved.
To say that one must clarify the value context in which an adoption
decision is made is not to say that one must accept those values personally. It
is to say that one must recognize those values as genuine for those who hold
them. For Yin (1976) to speak of "bureaucratic self-interest" or
Feller (1977) to describe "conspicuous production" in local
governments is not to endorse these value systems as appropriate terminal values
for public policy. But adoption modeling is an explicit or implicit decision
making approach, and as such must embody values as the basis for such
decisions. If the values of the potential adopters are not analyzed explicitly,
the values used are likely to be by default those of the analyst. Whether they
will provide information useful in helping predict the choices of innovation
actors is likely to be a matter of chance.
We
described "adoption" earlier as the basic dependent variable in
innovativeness research. Its major value is to distinguish between the
result-states of innovation and non-innovation. Innovators adopt;
non-innovators do not adopt. It is a convenient dependent variable in
social-science terms. Not only does it neatly embody a crucial value choice, as
we noted earlier; it has the major analytical virtue of being coded dichotomously. Both its interpretation and its
analysis are thus considerably simplified. Unfortunately, the counterpart
concept of "rejection of innovation" is not so easily dealt with (and
is, in fact, not nearly so frequently analyzed). It is used in the literature
in two rather distinct senses, usually without clarification of the
differences:
·
Active
rejection: Consideration, perhaps even trial of the innovation, followed by
passing it over in favor of some other course of action.
·
Passive
rejection: Lack of attention to the innovation, leading to its never being
really considered for incorporation into the system.
These two situations describe
widely different sets of actions. Interpreting them as cases of the same
behavior (non-innovation), as most adoption analyses do, says
that these behavioral differences are insignificant in terms of the value
choices being made. This assumption may be justified. However, it should be
made explicitly rather than implicitly.
The issue of whether or not
there is a definable "adoption point" in the innovation
process should be carefully distinguished from the issue of the degree of
imitation, or the similarity of the innovation in different settings. The
question of similarity has received useful attention in recent years (e.g.. Hall and Loucks, 1978); it has
too long been assumed that "adoptions" in different settings were
identical without testing this assumption. There is no question that imitation
does take place.
If one is convinced that one
has a good or "validated" innovation (in the sense of the National
Diffusion Network's model for transferring educational technology between local
school districts), then one is justified in concern over adaptations or
modifications which threaten its essential character or its ability to achieve
the purposes which led one to de fine it as "good" in the first
place.
Social science research is
accustomed to create and manipulate "concepts", or "abstractions
from observed events...to simplify thinking by subsuming a number of events
under one general heading". (Selltiz
and others, 1965). "Adoption of innovation" is such a concept.
One studies adoption, as we noted earlier, because one believes that one can
then usefully generalize to settings other than the original. Thus, studying
how farmers adopt hybrid seed corn can presumably help us learn how to
introduce new reading programs into the public schools. Whatever value the
concept of "adoption" has lies in its generalizability, since, as we
will see, it means many different things in practice. It is not a unique and
consistent act which simply happens to occur in many different settings.
It
is a besetting sin of social science research, and of administrative practice
based on that research, that the use and definition of key concepts are not subjected
to periodic and rigorous criticism. The value of using any concept lies in its
ability to generate useful understanding of human behavior, not in its inherent
inner beauty. We have a tendency to use concepts again and again, to
"reify" them, without carefully thinking about what their real
behavioral referents are, or to what degree their meaning to participants is
situation-specific. Meyer (1979) notes that interpretation of
most measures of organizational phenomena depends heavily on the history and
institutional contexts of the particular organization under study.
Increasingly, analysts studying the transfer of technology are coming to the
same conclusion: a technology in one setting frequently means and looks very
different from the "same" technology in another (Feller, 1979), and
implicit equation of the two will be misleading.
Closely
related to the problem of clear conceptual definition is the problem of
operational measurement of the concept in a form which can be qualitatively or
numerically analyzed. The process of creating operational measurements is
generally uncomfortable for most social scientists to talk about -- at least,
very few studies spend much time discussing just why their measures are in fact
valid reflections of the concepts under discussion. Technology-transfer
practitioners are less ambiguous than researchers about defining what
constitutes "success" but their measures are likely to be quite
situation-specific, as we noted earlier, and even less generalizable. No
operational definition can be any more satisfactory than the concept which
underlies it. But even a clearly defined, bounded, and generally valid concept
can be spoiled by a bad measurement, and its generalizability thus called into
question.
The
more the process of innovation is studied as a whole, the more difficult it
becomes to define "adoption" unambiguously. "Adoption" as a
concept refers to some definable act of decision (conscious or subconscious) on
someone's part. The problem is locating such actions and interpreting them.
Organizational innovation research has suffered in this area from its origins
in individual market research. Where individual "adoptions" are
considered, defining such a key act is usually relatively easy. It is usually
the purchase or acquisition act which is centrally valued by the analyst. One
can reasonably easily determine if a farmer has bought hybrid seed corn, or if
a woman is using birth-control pills. A single act (or a limited set of acts) serves
as the criterion for judging the outcomes of the process, and the process itself is usually unexplored.
But analyses of organizational
innovation, particularly in relation to technology, are usually concerned (or
should be) with more than a single act -- variables relating to use of
technology are probably more important, as well as
more complex, in getting at the impact of the transfer process. Once we admit
more than one dependent variable, we are obligated to examine the processes that
interrelate them, and to consider the decision making
underlying those processes.
Recent studies of complex
organizational processes which have tried to explore the dynamics of innovation
have almost uniformly found that organizational innovation is not a matter of
one or even a few decisions (Lambright
and others, 1977; Eveland and others, 1977; Yin and others, 1978).
Rather, it is typically a complex set of interlocking decisions or defaults:
Some large in scope, some small, some made at the top of the hierarchy, some
made lower down. Many of these decisions are not "optimal", in the
sense of employing decision criteria relevant to the whole system under
consideration; frequently, they tend to be sub-optimizations made in the
interests of particular individuals or groups at particular points in time.
Reconciling these different suboptimal activities is usually a
"political" process. This should have come as no surprise; the
general out lines of this analysis go back at least as far as March and Simon
(1958), even though innovation analysts are just rediscovering them.
But if the innovation process
is, as these studies suggest, a series of complex and contingent decisions,
then the logical question is just which one of these decisions is in fact the
crucial decision -- the one appropriately naming the point in time at which the
organization moved from the category of not having the innovation to the
category of having it. Reference to the concept of "adoption"
requires conceptually that one be able to make this distinction.
In research practice,
"adoption" in organizational cases is usually assessed largely in
retrospect. That is, we look at an organization, determine by "the weight
of the evidence” that the innovation in question is or is not present in it,
and conclude that there must be some point in the past at which the
organization "adopted" it. But it is usually almost impossible to
define just what that past "critical decision" really was. In the
absence of such a clear behavioral referent for the concept, "adoption"
becomes frequently more of an analytical construct than an action description.
This does not usually prevent analysts from proceeding to generalize to other
behavioral situations" -- usually without clarifying their assumed basis
for that generalization.
Measurement strategies and
methods tend to reflect the problems of concept definition. When one is
measuring individual "adoption", where the basis for the definition
is a de finable and retrievable decision-act, one has a choice of looking at
"hard data" sources such as purchase records, or using recall data of
various sorts. Each strategy has its costs and benefits, but either can be
relied on within certain limits to produce reliable information about the
concept. But in a complex organizational innovation sequence, how should this
problem be addressed?
The first problem is to find a
decision at all. As we noted, innovation-process research suggests that the
sequence is shaped as a series of decisions and non-decisions -- a set of acts
of individuals and groups which affect all stages of the process. The adoption
analyst has two choices:
·
Identify
one decision (or a few) which one believes to be critical in some sense, and
use the occurrence of that decision as a test of adoption.
·
Use a
"commitment" measurement, assessing the degree to which a series of
decisions aggregate toward the same conclusion; when the degree of commitment
reaches a certain point, either quantitatively of qualitatively, assume
adoption.
Either of these approaches can be
employed with either a "hard" or "soft" data strategy.
Organizational records can be searched for the key decision's occurrence
(Gordon and others, 1974). The presumably crucial decision-maker (usually the
top executive or line official) can be queried about the organization's
adoptions (Becker, 1970). Aggregate in dices of commitment can be assembled
from organizational records (Palumbo, 1969; Mohr, 1969), or from observation of
the innovation as it is practiced (Hall and Loucks,
1978).
Again, each strategy has costs
and benefits. Records are more precise, but tend to exclude certain items
systematically -- items which may be crucial to the
research aims (Garfinkel, 1967). Impressionistic data
is easy to collect, but may be limited by the choice of people to supply it;
how much a top manager, for example, knows about what his organization is
really doing is a systematic function of the nature of the organization itself.
No strategy for definition or measurement of adoption produces unambiguous
results which make it possible to generalize "adoption of
innovations" entirely.
When one defines
an operational measure of a concept one implicitly assumes that the measure is
sufficiently representative as to permit generalization to situations in which
other measures might be selected. The concept of "adoption" in
organizations has been subject to an extremely wide range of operational
measurements, and it is questionable as to whether all of these are in fact
appropriate representations of the concept in question. It is not within our
present scope to provide a complete tabulation of possible measures, as Steers
(1975) did with the idea of "organizational effectiveness". However,
let us catalogue a number of specific actions which have been used in reputable
innovation studies as embodiments of the general act of "adoption":
Many
others could be cited. It is not our intention to criticize any of these
operational definitions (although some seem more intuitively generalizable than
others. Each makes sense within the value system of the research project that employs
it. But the key problem is that each describes very different actions on the
part of the organization and its members.
The
ultimate contribution of social science research is not to explore
relationships among abstract concepts. It is to help people understand the
actions of individuals and organizations in the social context. Concepts are
tools; we use them only because they help us to translate our understanding of
one set of actions into an understanding of an- other set of actions. In any
research, therefore, the burden must be on the analyst to define just why his
chosen set of actions tells us about some other sets of actions. In short, he
must define the common component of the different actions. In innovation
research, this is seldom done. It is left to the reader to assume that
conclusions drawn from, say, analysis of purchasing decisions, can be used to
draw conclusions about policy acquiescence issues. This is unfair to both the
reader and the field, and makes the development of theory more difficult.
The
increasing attention to the questions of innovation process -- the sequence of
decision-making and other acts which shape the eventual outcome -- has led to
the development of a number of stage models of innovation. It is now common to
trace the evolution of an innovation from its inception as a general idea to
its "routinization" or full acceptance as
an integral part of the organization's behavioral repertoire. While these
models vary considerably in sophistication and complexity, they generally share
a distinction between two general phases of the process -- what Zaltman and others (1973) called the "initiation"
and "implementation" phases. Thus, most innovation process models
require some act of "adoption" as a criterion for determining passage
from one stage to the other.
This
distinction has served effectively to preserve the utility of the familiar
concept of "adoption" while at the same time recognizing that the innovation
process is highly variable and contingent. The term "implementation"
has come to be used as a code word for all things which can go wrong in the
process -- and by implication, "adoption" refers to the original,
correct, starting point. This approach is essentially an adaptation of the
political or legislative-behavior model, which separates the act of legislating
(adoption) from the execution of the law (implementation). This analysis is used
explicitly in analyses such as that of Pressman and Wildavsky
(1973) and Bardach (1977), and strongly questioned by Elmore (1979). The crucial
assumption in this mode of analysis, of course is that there is some form of
correct implementation which, if it were to be achieved, would
"truly" carry out the law or adopted policy. Clearly, the more
complex the technology which underlies the prescription, the more closely the
correct implementation can be specified.
As we noted in the original definition of
the term "adoption, both the idea and its uses are elements to be
accounted for. The duality of this definition has until recently been less than
fully explored. At first, any variation in either element was "noise"
in the analysis. Then, implementation analysts highlighted that variation in
uses was a fact of life. Recently, attention has been given to the possibility
of variation in the idea and its meaning as well. Not only do different people
perhaps hold different views about the innovation and its characteristics; they
may even change the idea itself in the course of working with it. This concept
of "reinvention" (Berman and McLaughlin, 1974; Larsen and Agarwala-Rogers, 1977; Rogers, 1977) calls into question
the basis for any real adoption analysis. If people define the innovation
differently, and create new elements for it during the innovation process, to
what degree do they meaningfully "adopt" the same t thing? At a bare
minimum, any reasonable definition of "adoption" ought to include distinct
measurements of the two aspects which Eveland (1977) called "tool” and “use",
and Pelz and Munson (1979) call "technological
content" and "embedding content". Given that each of these
elements is in itself the product of a number of decisions, made at different
times by different people in different ways, finding specific unambiguous
operational measures for them may be difficult.
A related question is the problem of
accounting for variation of opinion and activity within the organization under
study. Most of the studies noted earlier have considered "adoption"
to be a variable property of the organization as a whole, analogous to its
"size" or "complexity". Thus, they have sought a single
measure to categorize the status of the organization as a whole. But
intra-organizational process analysis reveals clearly that behavior relating to
innovations is not uniformly distributed through the organization in most
cases. Some parts are likely to be highly committed to the idea, while others
have never heard of it. If one interviews only one person in the system,
gathering data on "adoption" is likely to be easy. Interpreting that
data is not so easy, since it is extremely risky to project that opinion over
the whole system. As Roberts and others (1978) note in their review of recent
findings on data aggregation, the use of aggregate measures frequently conceals
as much interesting behavior as it reveals.
Given the theoretical and methodological
difficulties with using the concept of adoption which have been outlined here,
what can we do? At the very least, our observations should suggest that a
healthy degree of skepticism should greet any statements about "adoption
of innovations". Whenever we encounter findings which refer to
"adoption" and “adopters", we should ask certain questions:
In the long run,
perhaps the field of innovation research would be better served
by an indefinite moratorium on the use of the word "adoption". If
researchers were to describe the actions they choose to study not as versions
of some abstraction called "adoption" but rather as straightforward
behavior, in the context of other organizational behavior, it would probably be
easier to develop a body of knowledge about innovation as a process. The
problem of generalizability is no greater when speaking about actions than when
using concepts which do not have clear behavioral referents.
Coombs (1964) outlined
a "theory of data" in which there were three general stages:
Most innovation research
has been conducted at Stage 3. In this paper, I have suggested that perhaps
some attention to a central Stage 2 topic is needed -- that is, how do we
decide that particular observations which we make in an organization constitute
a datum called "adoption"?
In a policy sense,
the issue is "what constitutes the behavior which our policies ought to
encourage". If one is in the business of transferring technology from one
group to another, how does one know when one succeeds? The present contribution
of innovation process research is largely to call into question many of the
casual assumptions of past studies (and programs) that this criterion behavior
was easily and clearly defined. Without a simple dependent variable such as
"adoption" -- and this paper has tried to show that it is neither
simple nor even usable -- program designers and researchers alike will be
forced to specify criteria explicitly. The effective utilization of technology
cannot help but be served by this development.
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