Diffusion, Technology Transfer, and
Implementation: Thinking and Talking About Change
J. D. EVELAND
Cognos Associates
Knowledge:
Creation. Diffusion. Utilization, Vol. 8 No. 2,
December 1986 303 -- 322
There is today an increasing consciousness
that our technology has, in enough cases to worry us, outstripped the ability
of many organizations and individuals to make productive use of it. In almost
any scientific field one cares to mention -- from agriculture to robotics,
computing to genetic engineering -- the refrain of practitioners is the same:
"We know so much -- why can't we get people to use it right?" The
degree of frustration and uncertainty surrounding the effects of technology on
society generally has reached serious proportions for both technology
developers and users. And yet it is also clear that there is a substantial body
of knowledge, both theoretical and practical, that bears on precisely this set
of issues -- namely, how ideas move and become modified in the course of being
used by people and groups to accomplish purposes. Why then the frustration with
the applications of these ideas in the day-to-day world? Does it matter? And
what might be done about it through social action? Are there generic mechanisms
whereby knowledge might be moved from place to place more effectively? And
would it be a good thing if there were such mechanisms? And given what we have
learned in 50 years of systematic analysis, what might we as "knowledge
workers" do about the problem?
It is my
contention that the problem of making productive use of technology -- generically
called "implementation" -- is essentially a phenomenological issue -- that is, one of understanding how people
think about technology in relation to their lives and interests, and how
thoughts lead to human action (Cochran, 1980). It is, after all, basically
fruitless to look at technology outside of the context of human systems.
Technology application is a problem only for people -- it does not bother a
machine at all not to be used, or to be used as a fancy doorstop; it matters
only to those who paid for it and do not get a return on their investment.
In this article
I will outline first what I see as some of the basic dimensions of the
technology transfer issue generally, then look at some
of the implications of those dimensions for action. The theme throughout is the
centrality of the problem of meaning
in technology utilization, and how we can use the phenomenological viewpoint to
organize commonly recognized problems in diffusion, technology transfer, and
implementation. There is a long and rich tradition of analysis revolving around
these social issues, and my purpose is less to add entirely new insights to
this tradition than to suggest how certain recurrent themes in the literature
-- both theoretical and "wisdom" -- can be used to scientific and
practical advantage. Helping others to think and talk creatively about change
requires that we think as creatively ourselves, and find the appropriate
organizing vision for our knowledge. It is to these points that my final
conclusions are addressed.
Defining
"Technology Transfer"
If we are to
understand how technology transfer should be conceived and understood, we have
to begin with the words themselves. First, technology. The concept of
technology has to be used in the broadest possible sense if it is to make any
sense at all. Technology is not simply hardware or physical objects; rather, it
is knowledge about the physical world and how to manipulate it for human
purposes. This point is absolutely critical -- technology is essentially
information. The physical objects
usually regarded as "technology" are important only insofar they
embody and convey this information. At a
minimum, they must encompass both the tools (sometimes physical, sometimes
procedural) and the uses -- the purposes to which that tool is put (Eveland
Rogers and Klepper, 1977). All technology is
essentially behavioral; tools cannot be understood aside from the things they
are used to do -- the purposes of the individuals and groups that use them.
This is essence of the "sociotechnical system" concept (Taylor, 1975;
Cherns 1976).
Both tools and
uses are defined at varying levels of abstraction -- "hammers,"
"computers," "hybrid corn," and "flexible
manufacturing systems" all can refer to extremely generic concepts, or to
highly specific objects and procedures, or to a vast range in between. Choosing
an appropriate degree of specificity is critical to the technology
implementation process. Over time, uses help define tools and tools help define
uses, iteratively (Pelz, 1982).
The critical
dimension is forming an appropriate level of specificity which to define facilitation -- that is, how does this
tool help me something valuable? Technology transfer depends critically on
facilitation -- if what you seek to transfer does not facilitate the
achievement of goals, you are not likely to succeed (Bikson et al., 1985). As
we note later, goals for technology may be defined in many different ways and
from the point of view of many diverse groups and individuals. A consequence
valued by one person may be a disaster from the viewpoint of another.
Understanding how the utilities of technology as seen many diverse points of
view interact with implementation process one of the major advantages of a phenomenological
approach to technology transfer.
The term transfer is also problematical. Since
technology is essentially information, "transfer" is essentially communication of information -- both
within individuals and groups and between them -- and the use of that
information in the recipient system (Bikson et al., 1984).1
Technology transfer, accordingly, cannot be understood out of the context of
technological change or innovation (Eveland, 1979). The term transfer tends to encourage a focus on
physical relocation. But the movement of
physical objects from one place to another is meaningless unless the recipient
does something with that object and information it embodies;
"utilization" is both the target and the test of the process (Larsen,
1980). Concentrating on the transaction itself rather than on what happens as a
result of the transaction is a notable shortcoming in technology transfer as it
is currently practiced (if not in the conceptual literature itself).
Technology
transfer is in large measure an exercise in the use of language to communicate,
and an appreciation of the role that language plays in leading to individual,
organizational, and social action is essential (McHugh, 1968; Blumer, 1969). Looking at how language functions in
technology transfer requires looking at how people, either individuals or
groups, understand new things. Such understanding is substantially a process of
metaphor formation -- that is,
understanding how the new thing is both like and unlike things already familiar
(Bandler and Grinder, 1975; Lakoff
and Johnson, 1980). Each metaphor carries with it a set of affective and
substantive associations that for good or ill carry over to the new thing
(Meyer, 1982).
Different
metaphors create different responses. Consider the personal computer during its
introduction to an organization that has not had such tools before. Three
commonly used metaphors for such computers are "typewriter,"
"calculator," and "terminal." Seeing PCs as typewriters
implies one-to-one access, usually by secretaries, on desks or in typing pools
("WP Centers") with relatively little consultation by system
engineers with those who use them except about aesthetics or ergonomics. The
"calculator" metaphor implies that the tools will be used one-on-one
in professional offices, with choices about both equipment and usage left
largely to the individuals. Others see PCs as "terminals", an
approach that implies they should be scattered around, spaced roughly equally
apart, for open use by anyone who wanders by. None of these metaphors is
precisely wrong -- but each tends to limit the choices of users in critical
ways (Englebart, 1982).
"Myths"
are sets of metaphors used for explanation in circumstances where empirical
evidence is lacking. They help with sense making while such experience is
accumulating. Eventually, myths are gradually confirmed or disconfirmed. Thus,
metaphors are continually changing, usually in the direction of more
specificity -- for instance, what kind of typewriter, or calculator, or
whatever. Once you have decided what something is, it is often difficult to go
back and decide it is really something else. This is true for both physical
tools and social roles. Eventually objects and practices become their own
things, and serve as the basis for subsequent metaphors for new ideas and
objects (while, of course, retaining their own metaphors) -- that is, they
become familiar constructs whose meaning is generally assumed to be shared and
not generally discussed. For many organizations now, a PC is just a PC -- and
we compare, for example, shared -- logic systems to PCs in an attempt to
understand their meaning.2
Sharing
information among people (and organizations) requires that all be operating on
somewhat the same general level of abstraction, and be using something like the
same variety of metaphors. It does not require perfect information, or precise
specificity, to be effective -- sometimes ambiguity and generality can
be very effective, particularly one does not know just what sorts of metaphors
an information recipient is applying. This is a lesson known to all good
salesmen, but only latterly has it been understood equally well by the research
community (Havelock, 1973).
In some
critical ways, therefore, the term 'technology transfer" is an unfortunate
one -- almost as unfortunate as 'diffusion," which also gets applied to
these phenomena. Both terms have the disadvantage of erroneous metaphorical
connotations. Speaking of "technology" tends to lead us to focus on
the hardware, the physical object involved, which is, as I noted earlier, almost
the smallest part of the question. The term focuses our attention away from the
behavioral dimensions of tools and their interactions with human purposes.
"Transfer" emphasizes the movement of physical objects from one place
to another, with the implication that the object moved is the same at the
beginning and at the end. "Diffusion" is even worse; it implies some
sort of anonymous at inexorable physical process of spreading something across
the landscape, rather like a disease.3 If we as
analysts persist in using terms whose connotations are directly opposite from
what we wish to convey, cannot really blame an audience of practitioners trying
to apply the concepts for drawing the wrong conclusions.
Our
understanding of technology transfer systems was shaped in this awkward way
through a perfectly reasonable and logical chain of events. Like many parts of
behavioral science, the "diffusion of innovations" started out as a
real-world problem, and only later turned into a field of study (Rogers, 1983).
The original problem was simple market research, in this case how to sell
hybrid seed corn. In the course of finding out that what farmers thought about
corn really did affect what they decided to do about it, Ryan and Gross (1943)
and their followers also formulated a set of categories and models that soon
came to be seen as generalizable. Generalizing came first from individuals to
organizations, then to a lot of other situations -- first fluoridation and
health practices (Becker, 1970), then school programs, public works, and social
policies (Feller and Menzel, 1976: Bingham, 1976;
Berman and McLaughlin, 1977; Lambright, 1980), recently computers and related
tools (Johnson et al., 1985). The number of such studies is now incalculable,
and there is a well-established "literature" in the field (Doctors
and Stubbart, 1979). What is less clear is how deeply
the best ideas in this field have penetrated into the applications literature,
still less into field practice in transfer.
Likewise, the
practice of innovation diffusion was critically shaped by marketing. It is impossible
for anyone to speak ten words about diffusion without two of them being
"agricultural extension." Expectations about what technology transfer
systems should and should not do and look like have, for good or ill, been
critically shaped by our understanding of that program, its practices and its
effects (Rogers, et al., 1976; Feller et al., 1985). In many ways, it
constitutes the defining metaphor for all technology transfer efforts. I will
not attempt here to define or describe all its features -- only to note that
what extension really is is virtually impossible to untangle from
all the things people think it is or
should be. Untangling extension-as-an-organization from extension-as-a-concept
is more readily accomplished in the literature than in the field.
This point is
evident when one looks at how agricultural extension served as the basis for a
large number of Federal programs in the 1970s aimed at replicating extension's
success in other technical areas (Roessner, 1975;
FCCSET, 1977). Agencies such as NASA (Chakrabarti and
Rubenstein 1976), the Department of Defense (Hetzner
and Rubenstein, 1971), the Office of Education, and the National Institute of
Justice (Blakely et al., 1983), among others, all started "diffusion"
programs aimed at industry or governmental users of technology. There are ebbs
and flows in these movements; lately, direct transfer efforts appear to have
been overshadowed by an emphasis on "university-industry cooperative
relationships" and the various approaches to this goal (Eveland and Hetzner, 1982). While transfer remains a significant
political symbol, it is clear that its content has and will continue to shift
considerably.
In summary,
each phase of the development of the field of technology transfer -- both
conceptual and practical -- has contributed new insights and complexities that
have enriched subsequent, developments. But there has been a consistent
tendency to focus on the content of the change rather than on the meaning of
the change for those who changed. If one's research is being sponsored by seed
companies, it is reasonable to concentrate on the seed as the central focus -- but
fundamentally limiting to let the meaning of the seed expected from users to be
defined entirely by the meaning as perceived by developer/sellers. Only by
taking an oblique look at the problem -- from the point of view of the
recipient systems -- are we likely to be able to take our understanding of
technological innovation to its next productive stages (Havelock and Eveland,
1985).
Generic
Problems with Understanding Technology Transfer
Having gone to
great lengths to define what I think "technology" and "technology
transfer" really are, it is now appropriate to consider what we might do
with this formulation. In the remainder of this article I sketch some of the
generic factors of the context of technology transfer that make the formulation
and application of generic models of process rather problematical, and then
suggest some principles might guide us to a new and more effective formulation
of the the issues involved.
Let us begin with
two main sets of problems/issues that complicate understanding of technology
transfer – cross-sectional problems (not
posed by context and organization) and dynamic
problems (those posed by processes evolving over time).
Problems of context/organization. The first problem is that of
deciding what the technology really is. A great deal of the conceptual history
of diffusion research was focused on the development of lists of
"innovation characteristics," aimed at defining
"adaptability" of different technologies (Tornatzky
and Klein, 1982. We only understood
enough things about technologies, it was felt, if we could predict efficiently
where and by whom they would (or at should) be used.
By the mid -1970s,
we had come to see that this approach was terminally complicated by differences
in perceptions, or, in the language used earlier here, by varying metaphors for
the new ideas (Downs and Mohr, 1977). This became particularly apparent when
the innovations under study were "social technologies" such as
educational or social programs (Larsen, 1982). One way around this was to
conceive of innovations as sets of specific "elements," bundled in
various ways -- like a car that can be bought in any number of different
configurations (Hall and Loucks, 1977). The more
specific these elements are, the better chance they stand of being
"transferred" in some form recognizable to their original definer
(Blakely et al., 1983). While this approach makes the job easier for the
analyst, it does little to resolve problems for the user.
The second set
of issues revolves around the fact that most technological innovations of any
interest are embedded in organizational contexts (Chakrabarti,
1973). Each change has repercussions for the whole system, "ripple
effects" across both space and time moderated by the degree of
"coupling" of the system but always present to varying degrees. Understanding
how different parts of the system are interdependent can help a lot in
accounting for unplanned and unanticipated effects, which can be both positive
and negative. Often when we fail to understand such interdependencies, we sub-optimize
a system, making one part work a lot better and others work a lot worse. The
degree to which this is satisfactory depends on whether you are talking to one
in charge of the first part of the system, or to one in charge of the others, or
to one who has to balance the interests of the whole system.
As noted
earlier, the choice of an appropriate level of aggregation to look at
organizational behavior is a key issue both analytically and practically
(Roberts et al., 1978). Organizations are at bottom made up of individuals, who
are at best "partially included" in the organizational system -- that
is, they participate in many other systems as well, and must relate what they
do in one system to what they do in another to maintain some degree of personal
integration. When we speak of "organization's behavior" we sometimes
lose sight of the fact that such behavior -- however useful as an analytical
construct -- is a composite average of the behavior of lots of individuals each
acting out of their own context and responding to their own imperatives and
interests. Ultimately, technology transfer is a function of what individuals
think -- because what they do depends on those thoughts, feelings, and
interests. Choosing a higher level of aggregation to look at transfer phenomena
can sometimes obscure this key concern.
Understanding the interplays of individual and collective judgments
'about costs, benefits, and behavior is essentially the dimension of perceived
characteristics of organizational politics. I use this word here in its
relatively strict sense to refer to the interactions of interests among parties
in a relationship (Weiss, 1973; Benson, 1975). Commitment to goals in social
group always relative -- people embrace goals with positive consequences to
themselves with considerably more fervor than they do goals, the payoff which
are more personally tenuous. The problem is complicated by culturally induced
embarrassment in talking about values and value conflicts; but such issues do not
go away simply because we avoid them.
Any
technological change -- indeed, any technology at all -- involves an unequal
distribution of these costs and benefits in the system; some people must pay
the costs, and others receive the benefits. If all the costs are to be paid by
the lower hierarchical levels of the system and all benefits appropriated by
the upper levels, "resistance to change" is merely understandable but
positively rational (Mechanic, 1962). The problem should rather be phrased as a
question of why should some make a
change -- that is, what is in it for them. As I noted earlier, research tends
to confirm that functionality is a critical determinant the acceptance of new
technology; people do things that reward them. Any analysis of technological change
that does not address explicitly cost/benefit distribution or allows the costs
and benefits to be defined according to the perspective of only a limited part
of the participants will be fundamentally misleading.
Since all
organizations have a range of purposes, they also have reasons why those
purposes have not been reached -- the set of things they define as
"problems" (Walker, 1974). This agenda is a constantly shifting set,
redefined as circumstances change. Innovation, as a part of the general system
demand for adaptability, is only one of the system problems to be addressed -- others
include integration, coordination, and the achievement of output. In fact, most
organizational decisions have very little to do with technology as such, but
with things like finance, personnel, scheduling, and resource management. That
is sometimes hard for a change agent (or even someone researching change) to
appreciate. No one else takes your changes as seriously as you do. On the other
hand, you do not take the organization's problems as seriously as it does.
Eventually, the interaction balances out.
Culture has recently become a word in
organizational analysis with many diverse meanings. The term is more an
umbrella concept for looking anew at a range of social phenomena that have
previously been looked at atomistically than it is a brand-new insight, but it
is none the less valuable for that. Essentially, the concept is a way of
stating that the shared meanings, remembrances, patterns of activity, and
particularly expectations about what other people will do in organizations
really matter to what takes place. The idea is a somewhat broader system
understanding of the concept of "role" -- if you will,
an anthropologist's view of the relationships rather than a sociologist's or a
psychologist's.
Technology
affects culture dynamically. For example, as we noted earlier, personal
computers have a wide variety of potential meanings to those who use them,
meanings that change over time. These meanings are part of organizational culture,
and both are shaped by it and shape it in turn as they evolve through
experience. Consider a hierarchical, controlled organization introducing PCs --
a potential anarchic," power-to-the-user" situation. Such
organizations often respond with elaborate control systems, sets of passwords,
procedures for controlling access to disks, and the like. Results are often
circumvention of the rules, frustration on the part of managers, and general
failure to achieve the promised benefits of the technology. Sometimes this just
produces paralysis; sometimes it can lead to a new culture more adapted to
being able to use the technology, as, for example, professionals begin to
keyboard their own work and clerical personnel are freed for more valuable and
productive tasks. Sometimes there is a "synthesis" in which old
patterns are reinterpreted in light of new conditions, such as is evident in
the recent trend for data-processing managers to reassert control over stand-alone
computing equipment. The point is that lots different outcomes are possible,
but no one outcome is necessary.
Over time,
cultures and patterns of technology usage both change; new information based on
experience is incorporated into the mental sets of the participants in the
culture. The process almost always involves friction and costs; the degree to
which those costs are worth the positive consequences of the change is a
function of the change process itself as well as of the inherent features of
the technology and the context. Appreciating the role that culture plays in
organizations, and how culture can be dynamically shaped by the organization's
own intelligent sociotechnical choices, can vastly improve the efficiency
innovation utilization (Johnson, 1985).
Problems of Process. Issues related to the staging and dynamics of implementation
have intrigued researchers for a long time. It is self-evident that putting
technology into place in an organization is not matter of a single decision,
but rather of a series -- usually a long one – of linked decisions and non-decisions.
People make these choices, and these choices condition future choices. While
the researcher may identify one particular choice as a focal point of
"adoption," he only fools himself he believes that choice has the
same meaning to the user as it does to him A concept of the leverage exerted by
some decisions over other decision is critical to making intelligent choices
about where one might intervene creatively in the process to enhance the
likelihood of consequences o desires (Hall and Nord, 1984).
Researchers
have developed the idea of innovation "stages" as a way of
categorizing decisions and defining how this leverage operates -- that Is, seeing how some decisions of necessity precede and shape
those later on. There are many different
formulations of such stages; the question is not which one (if any) is
"true," but what the relative utility of a particular formulation
might be to you (Tornatzky et al., 1983). One basic
difference in frameworks relates to whether you prefer to focus on the content of decisions (such as the
technology itself) or on the nature of the action
being taken by the system. These different approaches lead somewhat different
ways of categorizing behavior. While the
same general phenomena are under discussion in each mod the categories tend to
highlight rather different focal issues.
Two Views of
Innovation
The action-centered
approach essentially considers change as process of gradually shaping a general idea, which can mean lots of
different things to different people, into a specific idea that most people understand to mean more or less the
same thing. Five general stages or categories of decisions to be made in
sequence can be distinguished:
|
·
agenda setting, |
|
|
·
matching, |
|
|
·
redefining, |
|
|
·
structuring, |
|
|
·
interconnecting. |
The first stage
is one of establishing the "agenda of problems and solutions," a set
of ideas known to the system but that do not necessarily provoke the system to
action directly. When a problem and a solution come together in the mind of a
person or persons in the system, a "match" is made and organizational
action commences. Rather than a defined "adoption" point, this model
emphasizes a more or less gradual “redefinition" in which both the
proposed innovation and its potential uses come to be understood in
sequentially greater detail. When both "tool and use" are defined
clearly enough to be communicated to others, a process of creating the organizational
structure to embody the innovation can begin. When the structure is generally
understood, it can be interconnected to other parts of the system as its
relationships to them become clear. The whole process, in these terms, is one
of defining the innovation in successively greater detail, distinguishing both
what it is and what it is not.
Regardless of
what focus is taken for defining stages, they cannot be stretched too far out
of shape; nor can they be anticipated in great de before they take place. The
principal value of stage models of any sort lies in helping the analyst and the
change agent to understand that he or she can encompass or affect only a
relatively small part of the process any given time. Analytical humility is
generally to be encouraged.
A second dynamic
problem relates to the assessment of effects of technological change. First,
whose criteria are to shape decisions? As I have noted, organizations are made
up of multiple people (and aggregates of people), and therefore multiple
criteria -- ways of evaluating outcomes based on goals are the rule in complex
decision sequences (Mintzberg, Raisinghani, and Theoret, 1976; Nutt, 1984). Multiple criteria can affect
even individual decisions aimed at a similar purpose. Sometimes such complex
decision criteria are compatible with allowing "win-win" solutions to
be formulated; sometimes situations are truly zero-sum, and someone has to lose
(Quinn and Rohrbaugh, 1981). Moreover, criteria
change in salience and applicability over time (Prien,
1966; Kimberly and Miles, 1980). In any event, the problem of multiple criteria
of assessment is the dynamic problem posed by the political nature of
organizations described above.
A related time-based
issue is that of horizons -- when do
you choose to make your valuation of outcomes, given that there is never any if
defined end-point to a change process? Short-term and long-term criteria are
both appropriate (and used) depending on the perspective of the analyst and his
or her interests (Hayes and Abernathy, 1985). Reinterpretation of past results is a constant
phenomenon, as new information about decision consequences remote in space and
becomes available. Again, there is no single answer about what "true"
outcomes are, only the need to remember that the issue cannot be unequivocally
resolved, either by the participants in the process or of the analyst. This
does not mean that perceptions about consequences do or should not shape
decision making, only that such perceptions should not be "reified"
beyond their limits.
The Bottom Line
Where does all
this leave us in our quest for efficient and effective ways to increase the
utility of knowledge transfer research for organizational and social
management? In some ways, it is easy to feel that we almost know
less than we did 30 years ago; at least, we are probably a good deal less
certain of what we do know than we used to be. A more realistic assessment is
that we are a good deal more conscious now of just what are the limitations on
the utility or prescribabilty of any particular analytical paradigm or
organizational model. The more we study the technological innovation processes
that underlie technology transfer, the more complex and contingent they seem,
and the less clear it is that any model, regardless of its sophistication, can
adequately represent more than a small part of the whole range of processes of
interest to us. Even agricultural extension has proved singularly inapplicable
to most other situations -- and perhaps even, today, to agriculture.
What I would
like to suggest here is a set of propositions that must underlie any effective
approach to understanding technological change, regardless of context or
content. Any administrative system that we create to distribute and apply
knowledge must take these principles into account or fail.
First, technological change is a process without beginning or end. Individual people and tools and
purposes come and go, but the sequence is iterative and evolutionary, and
linear patterns are always artificial constructs generated by the analyst
(Eveland, 1979). If the working model for organizational research is the novel, a model with a clear starting
point, defined characters (variables), a plot (the model), and an ending
(dependent variables), the working model of organizational life must be the soap opera, where characters come and
go, their roles are constantly changing and being reinterpreted, and what seems
good today is bad tomorrow and good again day after tomorrow.
Like the
characters, the technology is constantly subject to modification and reinterpretation.
"Routinization" of technology takes places only in the sense that one
tends after time to forget that one ever thought of a particular tool as
"new," given all the other new things that have come along in the
meantime. As we noted earlier, over time even a technology as unusual and even
shocking as personal computers becomes accepted and even ignored; the keyboard
is today as ubiquitous and unremarkable as the telephone, and this is in barely
five years. But technology is never "routine" to the point that it is
not subject to change and modification. If we aim our efforts at routinization,
we are likely to damn ourselves with success. Organizations that carefully
implement state-of-the-art computer systems tend to have a great deal of
difficulty taking advantage of changing technology; they have too many
"sunk costs" in the old systems (Bikson et al., 1985). It is well to
remember that every old, outdated, ossified tool or practice in any
organization was once an innovation" that got "routinized" all
too well. We would do well to remember this in our zeal to fasten new things on
organizations.
Second, the context of change is vitally important. Because organizations are systems,
any action or choice has repercussions across both space and time, and even
across the borders of systems we are trying to affect. Members are aware
(sometimes) of these -- a change agent/sales person must be equally so. The
organization's culture and its connections with the rest of the world provide
the context within which all external messages -- including those dealing with
technological change -- get filtered and interpreted. Meaning must of necessity
be generated internally by people; only in the most general terms can it be
supplied by an external source.
The one thing
we have rather conclusively demonstrated in the course of 20 years of public
programs intended to promote technological change -- in fact, through the long
years of agricultural extension as well -- is that one cannot pay people
enough, long enough, to get them to do things or use tools that do not have
intrinsic worth and value to them. "Incentives" that do not
institutionalize a clear long-term yield have only short-term effects. While
one can through "demonstration programs" or other subsidy mechanisms
induce the temporary use of a technique or policy, it will not outlast the
subsidy unless it become structured as part of the system and interconnected to
it in multiple ways, because it provides such value. External sources cannot
provide that value; it must be the value to those who practice it. This is one
of the hardest lessons all change agents must come to terms with. It implies
that change agents must concentrate far more attention on how people think
about the change than what actually changes.
Third, what matters most to organizations, whether they realize it or
not, is process, not technological content. From the point of view of; given organization, the
key problem should be less choosing an implementing the "right"
technology than it is developing and putting into place a procedural set for
making technology choices intelligently. Computers are today perhaps the most extreme
of a technological area where no single choice remains valid indefinitely;
those organization that cope well with computer
technology are those where the system has the capacity to remain experimental
(Johnson et al., 1985). What organizations need is to encourage is continuous
learning about technology and sociotechnical interactions on the part of
members, and to maintain and use that learning without being paralyzed by it.
Remembering too much, after all, can create so many metaphors that the system
can never work through to an understanding of the change itself.
An organization
that understands the strategic nature of innovation choices, and can approach
the process systematically rather than as series of individual and discrete
decisions, will always have an advantage. A technology transfer system that can
facilitate change processes rather than sell specific technologies is one that
will have long-term success.
Finally, the purpose of innovation/diffusion research is not to
prescribe but to raise consciousness. To the extent that research can help organizations
understand that they have the power to make good choices, and help them
understand the implications of those choices, it will contribute to social
goods. To the extent that research creates new and better ways to manipulate
individuals and organizations into adopting other people's views of what is a
"good thing," it will contribute by contrast to a
dissolution of social progress. I realize that this may be a difficult
point to swallow for those who legitimately believe they have a "good
thing" other people really need -- a group that includes most of the
"true believers" in technological and social innovation. On balance,
however, we are all likely to be better off by encouraging the development of
the capacity for effective and purposive internalized self-directed evolution
and control than by relying on any "diffusion system" to overcome the
shortcomings of organizational and individual change processes. As Peters and
Waterman (1983) tell us, one of the key lessons their "excellent
companies" have all learned is to appreciate the validity of their customers'
needs and understanding of those needs. Surely public mechanisms for
"technology transfer" can do as much.
Notes
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