I’m studying for my Computer Science class and need an explanation.
Question: How would social media affect the early adoption of eco-farming in the 1980’s if it was available in that time period? What are the different ways that it could be applied? Who would be the stakeholders?See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/225616414
Farmers and researchers: How can collaborative advantages be created in
participatory research and technology development?
Article in Agriculture and Human Values · July 2007
DOI: 10.1007/s10460-007-9072-2
CITATIONS
READS
133
73,918
3 authors:
Volker Hoffmann
University of Hohenheim
Kirsten Probst
8 PUBLICATIONS 341 CITATIONS
81 PUBLICATIONS 694 CITATIONS
SEE PROFILE
SEE PROFILE
Anja Christinck
German Institute for Tropical and Subtropical Agriculture (DITSL); seed4change Rese…
36 PUBLICATIONS 375 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Collaborative research and multi-stakeholder approaches in food and farming systems View project
Upland Program View project
All content following this page was uploaded by Anja Christinck on 24 June 2014.
The user has requested enhancement of the downloaded file.
1
Fachgebiet Landwirtschaftliche Kommunikations- und
Beratungslehre
Prof. Dr. Volker Hoffmann
Module Reader
M 4301-410
Knowledge and Innovation Management
(KIM)
WS 2011/12
A 7020
2
Modul: Knowledge and Innovation Management (4301-410)
Modulverantwortliche/r: Prof. Dr. Volker Hoffmann
Studiengang: Agrarwissenschaften – Agricultural Economics (Master, 2004-03-22),
Sem.,semi-elecitve
Organic Food Chain Management (Master, 2005-07-01), 1. Sem.,
elective
Good completion to Rural Communication and Extension (M5121) or
Bezug zu anderen Modulen:
Beratungslehre (B0031) and/or Fachkommunikation (B0030).
Teilnahmevoraussetzungen: none
Sprache: englisch
ECTS gesamt: 6 credits
Angebotshäufigkeit: each WS
Dauer des Moduls: 3,5 weeks (B04)
Studienleistung:
Modulprüfung: oral
Arbeitsaufwand: 140-180 h
Lern- und Qualifikationsziele: To understand the dynamics of continuity and change, and how to
manage them it in different fields like firms, administrations,
universities, unions or farms. Understand the problem solving,
knowledge generation and knowledge systems and the adoption and
diffusion of innovations. Ability to apply innovation and diffusion
concepts to complex cases.
Lehrveranstaltung: Knowledge and Innovation Management (4301-411)
LV-Verantwortliche/r: Prof. Dr. Volker Hoffmann
Lehrform: Lecture with exercises
Verbindlichkeit: compulsary
SWS: 4
ECTS: – credits
Prüfung:
Inhalt: Information and knowledge systems. Science and everyday life. Learning,
problem solving, researching and exploring. Theories of innovation, adoption
and diffusion. Economics of research and development. Protection of
knowledge properties. Strategies of innovation and change. Change
management. Examples of technological revolutions worldwide.
Literatur: Module reader – available at ASTA and under ILIAS.
Veranstaltungsort: Hohenheim
Anmerkungen: Lecture with exercises, homework assignments, data-projector.
Open to external participants.
HOFFMANN, Volker (ed.) 2011:Knowledge and Innovation Management. Module Reader. Hohenheim
University.
3
Universität Hohenheim
Fachgebiet:
Landwirtschaftliche Kommunikations- und Beratungslehre (430A)
KIM-00
August 2011
Hoffmann
4301-410: Knowledge and Innovation Management
Indications on exam volume, content and literature
The Module Knowledge and Innovation Management consists of 56 contact hours (4 SWS)
and is offered for students in the M.Sc. Studies of Agricultural Economics and as an elective for
all other Master Courses.
The Module aims to provide students with a basic knowledge about the development and diffusion of innovations and related questions of information and knowledge systems, in order to gain
more insight into the social aspects of agriculture.
The Module is more than just the script and the presentations, which present basic concepts, selected examples, and cases. The Module starts with epistemology, looking at the relationship between humans, reality, and the developments in the theories of knowledge, and the implications
for modern science and philosophy. The Renaissance was a tremendous period of change in
Europe. It shows interaction between the parts (personalities) and the whole (political, cultural
and technological change). It shows that history changes, and that much of today is owed to the
past. Students get personalities from the renaissance period to do homework with and to see
changes from individual, to social and technological changes. Students should gain an appreciation for the fact that the individual can never make an impact without being a part of a network,
which depends a lot on the context of time and space.
The chapter on knowledge perspectives presents the emergence and development of a new field
of study that links theory and practice in a systems approach to conceptualize the entire knowledge process. Based on this , the Module proceeds to deal with its main parts: knowledge management and innovation processes. Case studies are presented to illustrate the purpose and practices of knowledge management in an organization or a system. Through the case study exercises,
students will understand the interactions between individuals, organizations and social networks
and processes. The diffusion of innovations as a social process is covered at length with examples
and case studies. The Module provides a summary of the classical approach to the diffusion of
innovations (ROGERS 2003) and to the criticisms of diffusion research. Book reviews are used to
illustrate developments and gaps in diffusion research. The Module presents the Hohenheim concept of the diffusion of innovations and its practical implications for advisory work or bringing
about change. The consequences of innovations are a prevalent theme throughout the Module.
The Module ends with a final exam. Students are given 2 hours (120 minutes) to answer 6 questions. Students must answer all six questions, but only the top five answers will count toward a
student’s final exam score. The highest score is a 4.0. A score of 1.0 is required in order to pass.
The reader together with the powerpoint-files contains all of the information and material necessary to be able to answer the questions. The reader is available as printout at the ASTA or can be
downloaded from ILIAS as well as all powerpoint-presentations.
4
Objectives:
Students should
•
•
•
•
understand the dynamics and continuity of change
understand how to manage change at different levels
gain insight into the knowledge creation and utilization processes and system dynamics
understand the development of the adoption and diffusion of innovations research
Exam volume
•
•
•
•
•
•
•
•
General definitions, concepts and approaches of knowledge management
Knowledge management in firms and organizations
The generation of knowledge through science and the history of the European University
Basic definitions and concepts of ROGERS’ classical innovation theory
The main critics on E. ROGERS
The Hohenheim concept of adoption and diffusion
The comparison of farmers’ and researchers’ knowledge systems
The interrelationship between technical innovation and social change
The various parts of the reader
Number
page
KIM-01
The Allegory of the Cave
6
KIM-02
Creativity in Science and Technology
11
KIM-03
Knowledge Management
17
KIM-04
Models of knowledge transfer: critical perspectives
23
KIM-05
Diffusion of Innovations-Summary
37
KIM-06
Diffusion of Hybrid Corn in Iowa
51
KIM-07
Acceptance of the Salk Polio Vaccine – an example of the situational
approach to the diffusion of innovations
57
KIM-08
Book Review: Rogers & Shoemaker, 1971
59
KIM-09
Book Review: Five editions (1962-2003) of Everett ROGERS: Diffusion
of Innovations
64
KIM-10
Basic concepts for understanding adoption and diffusion
75
KIM-11
The diffusion of innovations – the Hohenheim concept
87
KIM-12
Nondiffusion of the Dvorak Keyboard
97
KIM-13
Learning selection
99
KIM-14
Farmers and researchers
120
KIM-15
The diffusion of eco-farming in Germany
138
KIM-16
Farmer innovation in Africa
154
KIM-17
As Hegel once examined students
163
5
PowerPoint presentations
Title
KIM-P01
The allegory of the cave
KIM-P02
Knowledge management: The case of the Tea Company, Part 1
KIM-P03
How knowledge management works: The case of the Tea Company,
Part 2
KIM-P04
Knowledge management: Basic understanding and definitions
KIM-P05
Intellectual property rights
KIM-P06
Renaissance
KIM-P07
Carl Hirnbein
KIM-P08
The history of the University
KIM-P09
Adoption and diffusion theory
KIM-P10
Case: Bull fattening with corn silage
KIM-P11
Mapping knowledge systems
KIM-P12
Farmers and researchers compared
Further reading material / literature 1
The following literature can be helpful in gaining additional insights and orientation for the exam.
These books will be help deepen the various topics covered during the module.
BEAL, George, M., DISSANAYAKE, Wimal, KONOSHIMA, Sumiye (Eds.) 1986: Knowledge Generation,
Exchange and Utilization. Westview Press, Boulder, Colorado. FGB: 3631
HOFFMANN, VOLKER. et al. (2009): Rural Extension. Volume 1: Basic Issues and Concepts (ed.) FGB:
Hdb 362,3.
LEEUWIS, C., 2003: Communication for Rural Innovations. Rethinking Agricultural Extension. Third
Edition. Blackwell. FGB: 5304.
NOTEBOOM, Bart, 2000: Learning and innovation in organizations and economies. Oxford University
Press, Oxford, New York. FGB: 5288
ROGERS, Everett, 2003: The Diffusion of Innovations. Fifth Edition. The Free Press, New York. FGB:
4437,4
REIJ, Chris, WATERS-BAYER, Ann, 2001: Farmer Innovation in Africa. Earthscan, London. 362 S. FGB
5066
1
For easy finding we indicate the library signatures: FGB = Department library, BB =Economic and
Social Sciences Library, UB = Central Library of the University.
3
6
Universität Hohenheim 430
Fachgebiet:
Landwirtschaftliche Kommunikations- und Beratungslehre
KIM-01
January 2007
Hoffmann
The Allegory of the Cave
In short after PLATON
1. Plato realizes that the general run of humankind can think, and speak, etc., without (so far as
they acknowledge) any awareness of his realm of forms.
2. The allegory of the cave is supposed to explain this.
3. In the allegory, Plato compares people untutored in the Theory of Forms to prisoners chained
in a cave, unable to turn their heads. All they can see is the wall of the cave. Behind them burns a
fire. Between the fire and the prisoners there is a parapet, along which puppeteers can walk. The
puppeteers, who are behind the prisoners, hold up puppets that cast shadows on the wall of the
cave. The prisoners are unable to see these puppets, the real objects, that pass behind them. What
the prisoners see and hear are shadows and echoes cast by objects that they do not see.
4. Such prisoners would mistake appearance for reality. They would think the things they see
on the wall (the shadows) were real; they would know nothing of the real causes of the shadows.
5. So when the prisoners talk, what are they talking about? If an object (a book, let us say) is
carried past behind them, and it casts a shadow on the wall, and a prisoner says “I see a book,”
what is he talking about? He thinks he is talking about a book, but he is really talking about a
shadow. But he uses the word “book.” What does that refer to?
6. Plato gives his answer at line (515b2). The text here has puzzled many editors, and it has
been frequently emended. The translation in GRUBE/REEVE states the point correctly: “And if they
could talk to one another, don’t you think they’d suppose that the names they used applied to the
things they see passing before them?”
7. Plato’s point is that the prisoners are mistaken. they are using the terms in their language to
refer to the shadows that pass before their eyes, rather than (as is correct, in Plato’s view) to the
real things that cast the shadows. If a prisoner says “That’s a book” he thinks that the word “book”
refers to the very thing he is looking at. But he would be wrong. He’s only looking at a shadow.
He cannot see the real referent of the word “book”. To see it, he would have to turn his head
around.
8. Plato’s point: the general terms of our language are not “names” of the physical objects that
we can see. They are actually names of things that we cannot see, things that we can only grasp
with the mind.
9. When the prisoners are released, they can turn their heads and see the real objects. Then they
realize their error. What can we do that is analogous to turning our heads and seeing the causes of
the shadows? We can come to grasp the Forms with our minds.
10. Plato’s aim in the Republic is to describe what is necessary for us to achieve this reflective
understanding. But even without it, it remains true that our very ability to think and to speak depends on the Forms. For the terms of the language we usually get their meaning by “naming” the
Forms that the objects we perceive participate in.
1
7
11. The prisoners may learn what a book is by their experience with shadows of books. But they
would be mistaken if they thought that the word “book” refers to something that any of them has
ever seen. Likewise, we may acquire concepts by our perceptual experience of physical objects.
But we would be mistaken if we thought that the concepts that we grasp were on the same level
as the things we perceive.
And here the “original text”: Plato: The Allegory of the Cave,
from The Republic
Plato, the most creative and influential of Socrates’ disciples, wrote dialogues, in which he frequently used the figure of Socrates to espouse his own (Plato’s) full-fledged philosophy. In “The
Republic,” Plato sums up his views in an image of ignorant humanity, trapped in the depths and
not even aware of its own limited perspective. The rare individual escapes the limitations of that
cave and, through a long, tortuous intellectual journey, discovers a higher realm, a true reality,
with a final, almost mystical awareness of Goodness as the origin of everything that exists. Such
a person is then the best equipped to govern in society, having a knowledge of what is ultimately
most worthwhile in life and not just a knowledge of techniques; but that person will frequently be
misunderstood by those ordinary folks back in the cave who haven’t shared in the intellectual insight. If he were living today, Plato might replace his rather awkward cave metaphor with a
movie theatre, with the projector replacing the fire, the film replacing the objects which cast
shadows, the shadows on the cave wall with the projected movie on the screen, and the echo with
the loudspeakers behind the screen. The essential point is that the prisoners in the cave are not
seeing reality, but only a shadowy representation of it. The importance of the allegory lies in
Plato’s belief that there are invisible truths lying under the apparent surface of things which only
the most enlightened can grasp. Used to the world of illusion in the cave, the prisoners at first resist enlightenment, as students resist education. But those who can achieve enlightenment deserve
to be the leaders and rulers of all the rest. At the end of the passage, Plato expresses another of his
favorite ideas: that education is not a process of putting knowledge into empty minds, but of making people realize that which they already know. This notion that truth is somehow embedded in
our minds was also powerfully influential for many centuries.
Judging by this passage, why do you think many people in the democracy of Athens might have
been antagonistic to Plato’s ideas? What does the sun symbolize in the allegory?
Is a resident of the cave (a prisoner, as it were) likely to want to make the ascent to the outer
world? Why or why not? What does the sun symbolize in the allegory? And now, I said, let me
show in a figure how far our nature is enlightened or unenlightened:–Behold! human beings living in an underground den, which has a mouth open towards the light and reaching all along the
den; here they have been from their childhood, and have their legs and necks chained so that they
cannot move, and can only see before them, being prevented by the chains from turning round
their heads. Above and behind them a fire is blazing at a distance, and between the fire and the
prisoners there is a raised way; and you will see, if you look, a low wall built along the way, like
the screen which marionette players have in front of them, over which they show the puppets.
I see.
And do you see, I said, men passing along the wall carrying all sorts of vessels, and statues and
figures of animals made of wood and stone and various materials, which appear over the wall?
Some of them are talking, others silent.
You have shown me a strange image, and they are strange prisoners.
2
8
Like ourselves, I replied; and they see only their own shadows, or the shadows of one another,
which the fire throws on the opposite wall of the cave?
True, he said; how could they see anything but the shadows if they were never allowed to move
their heads?
And of the objects which are being carried in like manner they would only see the shadows?
Yes, he said.
And if they were able to converse with one another, would they not suppose that they were naming what was actually before them?
Very true.
And suppose further that the prison had an echo which came from the other side, would they not
be sure to fancy when one of the passers-by spoke that the voice which they heard came from the
passing shadow?
No question, he replied.
To them, I said, the truth would be literally nothing but the shadows of the images.
That is certain.
And now look again, and see what will naturally follow if the prisoners are released and disabused of their error. At first, when any of them is liberated and compelled suddenly to stand up
and turn his neck round and walk and look towards the light, he will suffer sharp pains; the glare
will distress him, and he will be unable to see the realities of which in his former state he had
seen the shadows; and then conceive some one saying to him, that what he saw before was an illusion, but that now, when he is approaching nearer to being and his eye is turned towards more
real existence, he has a clearer vision,–what will be his reply? And you may further imagine that
his instructor is pointing to the objects as they pass and requiring him to name them,–will he not
be perplexed? Will he not fancy that the shadows which he formerly saw are truer than the objects which are now shown to him?
Far truer.
And if he is compelled to look straight at the light, will he not have a pain in his eyes which will
make him turn away to take refuge in the objects of vision which he can see, and which he will
conceive to be in reality clearer than the things which are now being shown to him?
True, he said.
And suppose once more, that he is reluctantly dragged up a steep and rugged ascent, and held fast
until he is forced into the presence of the sun himself, is he not likely to be pained and irritated?
When he approaches the light his eyes will be dazzled, and he will not be able to see anything at
all of what are now called realities.
Not all in a moment, he said.
He will require to grow accustomed to the sight of the upper world. And first he will see the
shadows best, next the reflections of men and other objects in the water, and then the objects
themselves; then he will gaze upon the light of the moon and the stars and the spangled heaven;
and he will see the sky and the stars by night better than the sun or the light of the sun by day?
3
9
Certainly.
Last of all he will be able to see the sun, and not mere reflections of him in the water, but he will
see him in his own proper place, and not in another; and he will contemplate him as he is.
Certainly.
He will then proceed to argue that this is he who gives the season and the years, and is the guardian of all that is in the visible world, and in a certain way the cause of all things which he and his
fellows have been accustomed to behold?
Clearly, he said, he would first see the sun and then reason about him.
And when he remembered his old habitation, and the wisdom of the den and his fellow-prisoners,
do you not suppose that he would felicitate himself on the change, and pity them?
Certainly, he would.
And if they were in the habit of conferring honors among themselves on those who were quickest
to observe the passing shadows and to remark which of them went before, and which followed after, and which were together; and who were therefore best able to draw conclusions as to the future, do you think that he would care for such honors and glories, or envy the possessors of them?
Would he not say with Homer,
Better to be the poor servant of a poor master, and to endure anything, rather than think as they
do and live after their manner? 1
Yes, he said, I think that he would rather suffer anything than entertain these false notions and
live in this miserable manner.
Imagine once more, I said, such a one coming suddenly out of the sun to be replaced in his old
situation; would he not be certain to have his eyes full of darkness?
To be sure, he said.
And if there were a contest, and he had to compete in measuring the shadows with the prisoners
who had never moved out of the den, while his sight was still weak, and before his eyes had become steady (and the time which would be needed to acquire this new habit of sight might be
very considerable), would he not be ridiculous? Men would say of him that up he went and down
he came without his eyes; 2 and that it was better not even to think of ascending; and if any one
tried to loose another and lead him up to the light, let them only catch the offender, and they
would put him to death. 3
No question, he said.
1
This refers to a famous passage in Homer’s Odyssey in which the ghost of the great hero Achilles,
when asked if he is not proud of the fame his deeds have spread throughout the world, answers that
he would rather be a slave on a worn-out farm than king over all of the famous dead. Interestingly,
Plato quotes the same passage elsewhere as disapprovingly as depicting life after death in such a
negative manner that it may undermine the willingness of soldiers to die in war.
2
The comic playwright Aristophanes had mocked Socrates by portraying Plato’s master, Socrates,
as a foolish intellectual with his head in the clouds.
3
Plato undoubtedly had in mind the fact that the Athenians had condemned to death his master Socrates, who Plato considered supremely enlightened.
4
10
This entire allegory, I said, you may now append, dear Glaucon, to the previous argument; the
prison-house is the world of sight, the light of the fire is the sun, and you will not misapprehend
me if you interpret the journey upwards to be the ascent of the soul into the intellectual world according to my poor belief, which, at your desire, I have expressed–whether rightly or wrongly
God knows. But whether true or false, my opinion is that in the world of knowledge the idea of
good appears last of all, and is seen only with an effort; and, when seen, is also inferred to be the
universal author of all things beautiful and right, parent of light and of the lord of light in this
visible world, Here Plato describes his notion of God in a way that was influence profoundly
Christian theologians. and the immediate source of reason and truth in the intellectual; and that
this is the power upon which he would act rationally either in public or private life must have his
eye fixed.
I agree, he said, as far as I am able to understand you.
Moreover, I said, you must not wonder that those who attain to this beatific vision are unwilling
to descend to human affairs; for their souls are ever hastening into the upper world where they
desire to dwell; which desire of theirs is very natural, if our allegory may be trusted.
Yes, very natural.
And is there anything surprising in one who passes from divine contemplations to the evil state of
man, misbehaving himself in a ridiculous manner; if, while his eyes are blinking and before he
has become accustomed to the surrounding darkness, he is compelled to fight in courts of law, or
in other places, about the images or the shadows of images of justice, and is endeavoring to meet
the conception of those who have never yet seen absolute justice?
Anything but surprising, he replied.
Any one who has common sense will remember that the bewilderments of the eyes are of two
kinds, and arise from two causes, either from coming out of the light or from going into the light,
which is true of the mind’s eye; and he who remembers this when he sees any one whose vision is
perplexed and weak, will not be too ready to laugh; he will first ask whether that soul of man has
come out of the brighter life, and is unable to see because unaccustomed to the dark, or having
turned from darkness to the day is dazzled by excess of light. And he will count the one happy in
his condition and state of being, and he will pity the other; or, if he have a mind to laugh at the
soul which comes from below into the light, there will be more reason in this than in the laugh
which greets him who returns from above out of the light into the den.
That, he said, is a very just distinction.
But then, if I am right, certain professors of education must be wrong when they say that they can
put a knowledge into the soul which was not there before, like sight into blind eyes.
They undoubtedly say this, he replied.
Whereas our argument shows that the power and capacity of learning exists in the soul already;
and that just as the eye was unable to turn from darkness to light without the whole body, so too
the instrument of knowledge can only by the movement of the whole soul be turned from the
world of becoming into that of being, and learn by degrees to endure the sight of being and of the
brightest and best of being, or in other words, of the good.
Source: Translated by Benjamin Jowett. Available at
http://www.wsu.edu:8080/~wldciv/world_civ_reader/world_civ_reader_1/plato.html, accessed on
4. 1. 2006
5
11
Universität Hohenheim 430
Fachgebiet:
Landwirtschaftliche Kommunikations- und Beratungslehre
KIM-02
January 2007
Hoffmann
Creativity in Science and Technology 1
Steven H. KIM
The advance of science is not comparable to the changes of a city, where old edifices
are pitilessly torn down to give place to new, but to the continuous evolution of zoologic types which develop ceaselessly and end by becoming unrecognizable to the
common sight, but where an expert eye finds always traces of the prior work of the centuries past. One must not think then that the old-fashioned theories have been sterile
and vain.
Jules Henri Poincaré 2
The scientific enterprise is one of generating new knowledge for its own sake. The technological
process, in contrast, refers to the application of knowledge to satisfy human needs aside from curiosity. The spectrum of technological activities, from science to engineering and marketing, is
depicted in Figure A.l.
Types of Discovery
If the fields of science and technology are to progress in an orderly fashion, it is important to develop a systematic theory for the nature of results attained in these domains. To this end, a
framework is presented for the types of discoveries as well as their methods of derivation.
The process of scientific discovery is depicted in Figure A.2. An investigator, working individually, or in concert with colleagues, envisions a result based on her knowledge of the universe.
This knowledge may result from direct personal observation or indirect knowledge through the
work of others.
The result is generated through some methodology. The specific method may rely on some cognitive mechanism that we do not yet comprehend, such as the realization that sunrise and sunset are
due to the rotation of the Earth, rather than the motion of the sun. We call these yet-unknown
mechanisms “intuition” or “inspiration.”
On the other hand, certain methods are more straightforward, as exemplified by the technique of
proof by contradiction or refutation. To illustrate this, we may show that a specific statement in
predicate logic must be derivable from an initial set of hypotheses, by showing that the negation
of the statement would imply some inconsistency in the overall set of statements. In fact, this
refutation procedure is routinely used as the basis for the programming language of Prolog.
The results of scientific research may be classified into the following four categories: alignment,
possibility, impossibility, and trade-off. These groups of results may be obtained by methods of
construction or contradiction.
1
From: KIM, Steven H., 1990: Essence of Creativity. A Guide to Tackling Difficult Problems. Oxford
University Press, Oxford, 88-92
2
POINCARE 1904: Valeur de la Science, as given in POINCARE 1946, 208.
1
12
Table A. l shows examples of results by category and method of proof. These classifications are
discussed further below.
Alignment
Alignment refers to the fit between our models and the world around us, or among the models
themselves. In attaining such harmony, our perception of the universe takes a simpler form. The
issue of alignment may be further classified into two types: paradigm and unification.
2
13
A paradigm is defined by our perception of the universe. Hence a paradigmatic result refers to a
shift in our views 3 . For example, the replacement of the geocentric view of the solar system by
the heliocentric paradigm represented a major advance, and was instrumental for further advances
in astronomy.
On the other hand, unification refers to the alignment among our models or views of the world. A
unifying structure provides a general framework for organizing results that previously seemed unrelated. The structure may take the form of a framework, model, theory, or some combination of
the three.
An example of a unifying structure is the development of the periodic table, and the classification
of elements into related groups based on their electron configurations. Another example is found
in the theory of electromagnetism, which unifies the seemingly unrelated phenomena of electricity and magnetism. This integrative model also accounts for many types of radiation, from
gamma rays at one end of the spectrum to radio waves at the other. We now recognize ultraviolet
emissions, as well as light and heat, as variations on the single theme of electromagnetic radiation.
Another unifying structure is found in the laws of thermodynamics, relating the conservation of
energy and the tendency of systems toward increasing disorder. These two laws encapsulate observations that arise in all realms of natural science and engineering.
A subcategory of unification relates to the laws of invariance. Invariance refers to the constancy
among objects that appear to be different at first sight. Such principles assert, for example, the
immutable nature of certain objects despite transformations.
The conservation laws of physics typify the category of invariance principles. For example, the
principle of matter-energy conservation states that matter and energy may change from one form
into another, but the total energy of a system remains the same.
The single most important result in the field of statistics is the Central Limit Theorem. This theorem states that a particular probability law called the Gaussian distribution is pervasive. The
3
KUHN 1962, Ch. 1
3
14
Gaussian function, familiar to many people as the “bell-shaped” curve, serves as a good model in
many practical and theoretical applications 4 . A common example is intelligence scores, whose
bell curve peaks at 100 and trails off toward either end to reflect the fact that decreasing numbers
of individuals possess either very high or very low scores. Engineers often rely on dimensional
analysis to show the validity of their reasoning. This approach is based on the idea that the physical properties of systems may depend only on the combinations of certain characteristics, rather
than their individual values. The field of fluid mechanics, for example, uses the Reynolds number:
R = pvd l µ
where p is the density of a fluid having viscosity µ. and flowing with velocity v in a conduit diameter d. The units of these parameters are such that R is a dimensionless number. The fluid
travels in orderly, laminar flow for low values of R, and becomes turbulent for high values.
Crude oil flowing in a transcontinental pipeline has a different character from water running
through a garden hose. Their densities differ as well as their viscosity or internal resistance to
flow; the diameters of the conduits will be dissimilar, and the speeds of flow may also vary. In
other words, the values of the density p, the velocity v, the diameter d, and the viscosity µ. are
distinct for each fluid. But as long as the compound factor pvd l µ is much less than the threshold
of about 1000, the flow will be laminar. On the other hand, if the number is much higher than this
threshold, the flow will be turbulent. The identity of the fluid itself is of little consequence in this
determination. Only the composite parameter in the form of the Reynolds number is a reliable indicator of turbulence, short of actually observing the fluid under the stated conditions of flow.
The field of automata theory uses a model of computation called the Turing Machine. This model
depicts computational procedures as a set of simple operations. Much of the work in automata
theory deals with the invariance of computational power among different versions of the Turing
Machine. One such result is the equivalence of all existing computers to the Turing Machine and therefore to each other – in the range of problems they can resolve.
Einstein’s Theory of Relativity may also be regarded as an invariance result. In particular, the
laws of physics are unchanged by the choice of a particular frame of reference.
Possibility
Much of the work in the sciences and in engineering deals with showing what is possible. One of
the most convincing ways is proof by construction. The most cogent means of showing that humans can attain powered flight is to build a flying machine. The seminal experiment by the
American chemist Stanley Lloyd Miller in the mid-1950s showed that amino acids – the basic
components of life – can be formed from a broth of lifeless chemicals when exposed to a flux of
energy. 5
A good deal of the theoretical work in the sciences is also one of construction. This relates to the
development of general models, frameworks, or theories that can accommodate diverse empirical
observations.
4
The mathematically inclined reader may be aware that according to the Central Limit Theorem, the
Gaussian distribution is pervasive in the following sense. Given any set of random variables that are
mutually independent and have an finite variance, their sum tends toward a Gaussian distribution
when the set is large. This result is unchanged by the fact that the independent variables may have
similar or dissimilar probability distributions, any of which (or none) might be Gaussian.
5
MILLER, 1957.
4
15
Analytic studies in engineering are often dedicated to the determination of what is possible. For
example, the hypothesis that manned spacecraft can visit Mars and return to Earth can be confirmed from our knowledge of the chemical properties of propellants, the mechanical properties
of materials, and the physics of interplanetary flight.
Impossibility
A negative result, if proven, is as useful as a positive result. In fact, most of the major scientific
advances in the 20th century are of the negative kind: Einstein’s Theory of Relativity says that
there are no absolutes; Heisenberg’s Uncertainty Principle states that position and momentum
cannot both be determined simultaneously with arbitrary precision; Gödel’s Incompleteness
Theorem says that there is no decisive algorithm to prove invalidity in predicate logic; according
to Arrow’s Impossibility Theorem, a particular set of reasonable assumptions will admit no consistent economic welfare function. These discoveries of the impossible actually serve to define
the limits of the possible.
Trade-offs
An important class of results relates to interdimensional trade-offs 6 . These may relate to the relationships between performance and efficiency, or time versus space, etc..
The student of economics quickly learns that life involves the pursuit of happiness under resource
constraints. A simplified economy may have sufficient resources each year to produce exactly
one of the following baskets of goods: 5 million muffins; or 800 videos; or 2 million muffins and
400 videos. These three alternatives define the “production possibility frontier” for the economy.
The actual choice among the three alternatives will depend on the collective disposition of the
consumers and producers. The nature of the choice is largely a subjective matter; but the tradeoffs between muffins and videos is an objective phenomenon whose understanding facilitates the
subjective decision. Happiness may be a subjective subject, but its pursuit can be supported by rational decision making.
In the realm of computer science, the area known as complexity theory deals with the consequences of differing algorithms for computational efficiency, and the trade-offs between requirements for memory versus computational time in solving a specific problem. These results support
the design of computer systems, just as production possibility frontiers assist in formulating economic policy.
Nature of the Categories
The classification of scientific results into a set of categories provides a convenient framework
for exposition and discussion. However, the various categories are not intended to be independent
or mutually exclusive.
For example, the Theory of Relativity stipulates the lack of absolute frames of reference. This result may be classified as an example of the impossibility of determining an absolute reference as
well as one of invariance of physical laws across reference frames.
Moreover, a result that falls into one category may engender results in other categories. As discussed previously, the laws of thermodynamics represent a unifying structure. The first law, how6
Kim, 1990, Ch. 2.
5
16
ever, stipulates the invariance of the total amount of energy in any insulated system. In addition,
the second law can be used to deduce the impossibility of a perpetual motion machine.
The four categories of results highlight the different types of contributions. This framework can
help to promote creativity by providing a platform for the orientation of research efforts and outlining the nature of results that may be achieved.
Bibliography:
KIM, S. H. 1990: Designing Intelligence: A Framework for Smart Systems. Oxford University Press,
New York.
KUHN, T. S., 1962: The Structure of Scientific Revolutions. 2nd ed. University of Chicago Press, Chicago, 1970.
MILLER, Stanley L., 1957: The Mechanisms of Synthesis of Amino Acids by Electrical Discharges. In:
Biochimica and Biophysica Acta, v.23(3), 480-489.
POINCARÉ, Henri, 1946: The foundations of Science: Science and Hypothesis; The Value of Science;
Science and Method: transl. By G.B. Halsted. Science Press, Lancaster PA.
6
17
KIM-03
Universitat Hohenheim 430
Fachgebiet:
Landwirtschaftliche Kommunikations- und Beratungslehre
November 2008
Hoffmann
Knowledge management 1
A combination of the two disciplines, structuring learning processes and organization and
management, has recently become popular in the field of organizational development: knowledge management. We start with clarifying some basic terms and concepts and then go to the
implications for learning of individuals and organizations.
Figure 1: The knowledge pyramid (following AAMODT & NYGARD, 1955)
action
knowledge
information
data
signs
management
knowledge
management
information
management
data
management
Symbol competence
(reading and writing)
Ö Figure 1 shows that knowledge links information with action. Information is built on data,
which is composed of signs. Signs stand for something else. Their meaning is fixed by convention and must be learned. Data are signs to be analyzed, and this is also possible when they
are electronically stored as bits and bytes. The handling of the elements of the pyramid can be
called management. Going up and down the knowledge pyramid requires certain competences,
which are normally acquired in literate societies.
Can knowledge be stored? No, at least not directly, not outside of living human brains. But it
can be stored indirectly as information. Knowledge can be acquired through learning and internalizing information, and it can be passed on by teaching and externalizing it into information. That means information can be regarded as externalized knowledge and also as processed
data serving a purpose or reducing uncertainty. Computers and other machines can process data, but not information. Semantic interpretation, i.e. the assessment of meaning, is a capacity
requiring human intelligence, often paired with creativity and no machine can be taught this,
even if the term artificial intelligence suggests it. This extends our knowledge pyramid as
shown in Ö Figure 2.
1
Parts of this chapter are based on HOFFMANN, 2007.
1
18
Figure 2: The extended knowledge pyramid
To learn, internalize
To teach, externalize
Action
Pragmatics
Conceptualized information, interpreted,
evaluated, interlinked
Knowledge
Semantics
Information
Syntax
Reduces uncertainty,
purposeful
Meaning, related to facts
Data
Semiotics
Signs
Without meaning, no
context
The knowledge pyramid only depicts a part of all knowledge processes, the explicit part. This
can be used to pass information based on signs and data. However, there is also implicit or tacit knowledge. POLANYI (1985) said that “we know more than we can tell”.
Implicit knowledge is knowledge from experience, is the part of knowledge that is difficult to
describe, is used subconsciously and intuitively, is a special feeling, a talent, e.g. “green fingers” when dealing with plants, and cannot be copied or programmed for robots, and is a basis
for sustainable competitive advantage of enterprises.
The special trick is to find ways of rendering implicit knowledge explicit, of converting it into
information that can be handed on to others. This is where the distinction arises between
knowledge and capability. To acquire skills, reading is not very helpful. Progress only comes
through repeated practice, by doing something again and again, and by training. Skills are implicit. Training that is successful in guiding and motivating is another teaching skill. Not every
expert is also a good trainer or teacher.
Ö Figure 3 goes more into details of individual learning. The bigger circle represents the
learner explaining learning as information processing. Signals are perceived and interpreted
under the influence of arising emotions and occurring cognitions, which are then stored and
lead to action. Observing the own action transforms this into data and information that again
enters perception. That is what we call learning by doing or experiential learning (KOLB, 1984)
and what improves skills by manifold repetition called training. Instead of observing the own
actions, we can also learn from the behavior of others called model learning or imitative learning. Apart from behavior, we inherit the products of previous learning processes, like cultural
landscapes, tools and domestic plants and animals, which we go on using. The same happens,
when we just buy products or services; there we also profit from the learning results
2
19
Figure 3: The knowledge cycle: How individuals learn
Codes
Information-Processing
Objets
Environment
Tools
Plants
Animals…
Feeling
Selective
Projektive
Potential
Sources
of Information
Behaviour
Own Behaviour
Beh. of Others
Symbols
linguistic
gestural, mimical
iconic
logical
musical
Data
Bits and
Bytes
Information
Uncertainty
Reduction
Learning
Individuum
Memory
Perception
Action
Ordering
Shaping
ordering
shaping
Learning
Thinking
Recognizing
Individual Knowledge
Acqaintance
Capacities
„between the ears“
of former times and other persons. The most frequent source of learning nowadays is symbolic
learning, accessing information by symbolic communication, mainly in school like settings,
but also in individual work in libraries or distance learning. The lion share of such information
is carried by text, spoken or written (linguistic), often illustrated by different forms of pictures
(iconic), sometimes combined with numbers or formulas (logical), and also body language or
music can help to ease the information uptake und understanding or memorizing, e.g. by accompanying spoken words, and by creating additional emotional effects.
NANOKA and TAKEUCHI (1997, 85) were the first internationally recognized academics to
point out the importance of converting knowledge between implicit and explicit forms, which
they depicted with their famous spiral, shown in Ö Figure 4.
If knowledge is available in explicit form, then acquiring it means internalizing it. Once internalized, it is implicit, and to acquire it one must go through socialization in this field of knowledge and action. You grow into it and acquire it by helping and assisting, by imitation and
training, and often to a great degree by trial and error. When you want to pass it on to others
without time-consuming socialization, you should externalize it and make it explicit. If different contents of explicit knowledge arise, new knowledge may emerge through a process of
combination that has to prove its usefulness in actions. Therefore, it has to be internalized first.
3
20
Figure 4: Transforming knowledge (NANOKA & TAKEUCHI, 1997, 85)
implicit
implicit
Socialization
explicit
Externalization
explicit
Internalization
Combination
Because this process between humans is never static, the spiral is an appropriate symbol for
representing it. The authors describe how to externalize knowledge, even against all resistance,
in their story of the development of the bread-baking machine. When the leader of the
development department was near to despair because the bread made in the new automatic
machine did not taste good, she became an apprentice to a famous baker in a five-star hotel in
Tokyo. Observing what he was doing did not help, so she started to pound dough herself, and
practice makes her perfect. After some weeks, her bread was as good as his. But in addition,
she could explain in technical terms (although he could not) what she was doing. Pounding
dough means pulling, pushing and twisting it. The machine was only twisting the dough. To
pull and push, some side bands had to be added. The rest was routine work. So by systematic
variation her engineers were able to optimize the machine. In the first six months 500,000
small automatic household units were sold.
With this example they point out that the implicit knowledge of the co-workers is the decisive
comparative advantage in international competition. When it is possible to externalize it, it can
be patented, computerized, automatized, used in robots but also sold for license fees.
Organizations, as well valid for extension organizations must constantly adapt to changing
conditions and co-evolve with their environments. This again is learning. But we stated that
knowledge only exists in living brains. If “knowledge is between the ears” (RÖLING, 1994),
how can organizations learn, not having ears? Ö Figure 5 gives a first answer. In an organization, files and people interact. The persons can learn and can improve the information in the
files, insofar their learning results can be made explicit. And in creating the outputs, the company performance, the members interact by using their implicit as well as their explicit knowledge. By interlinking files and people, implicit and explicit knowledge, the organizations
learns and creates synergies.
But it would never be sufficient to withstand international competition, if an organization
would depend solely on own information sources. Information is nowadays a commodity,
which is partly free of charge in public domain and partly commercialized as indicated on the
right side of Figure 6, under external acquisition. To “make or buy” is the general question
dealt with in the figure, and often buying is cheaper.
Figure 5: How organizations learn I
4
21
File
Explicit
Person
Explicit
Implicit
Firm
Explicit
Implicit
Interlinked
Information
Knowing
Mastering
Wanting
Learning
Organization
The whole is more than the sum of it‘s parts !
To make the picture more complete, Ö Figure 6 stresses on two additional points: learning
by feedback through monitoring results and when exchanging information, services, goods and
assets with outside partners or customers. In striving for constantly improved reach of objectives, not only the members learn and the files improve, but also the structures of the organization are constantly adapted and the standard procedures are improved Ö Figure 7.
In this perspective, we can understand organizational development also as a process of improving communication and knowledge management.
Figure 6: Knowledge acquisition
Externally
Internally
Try, train
Investigate, look up
Buy information/ license
Buy product/service
Consult expert/advisor
Train/educate co-workers
Hire expert
Figure 7: How organisations learn II
5
22
By information processing
Services
Goods
Members
Files
Information
Structures,
Procedures
Assets, Rights
Bibliography
AAMODT, A., NYGARD, M., 1955: Different roles and mutual dependencies of data, Information and
Knowledge. In: Data & Knowledge Engineering, 16, 191-222.
HOFFMANN, Volker, 2007: Knowledge management: what are we talking about? In: GTZ Services for
Rural Development, No 15, 6-8.
KOLB, D.A., 1984: Experiential learning. Learning experience as a source of learning and development.
Prentice Hall, New York.
NONAKA, I., TAKEUCHI, H., 1997: Die Organisation des Wissens. Campus, Frankfurt am Main, 299 pp.
POLANYI, M. 1985: Implizites Wissen. Suhrkamp, Frankfurt am Main.
RÖLING, Niels, 1994: Agricultural Knowledge and Information Systems. In: BLACKBURN (ed.): Extension
Handbook, 2nd edition. Thompson Educational Publishing, Toronto. 57-68.
6
23
Universität Hohenheim 430
Fachgebiet:
Landwirtschaftliche Kommunikations- und Beratungslehre
KIM-04
January 2007
Hoffmann
Models of knowledge transfer: critical perspectives1
Everett M. ROGERS
The field of knowledge transfer today has much reason to view its past accomplishments with
considerable pride. In the last couple of decades this field of scholarly activity has attracted a
growing number of dedicated researchers and theorists, several outstanding books have appeared
that synthesize work on this topic, and a number of university-level courses and programs of
graduate-level have been launched. On the pragmatic side, most government agencies (both in
developing and in industrialized nations) recognize their responsibility for conducting knowledge-transfer activities. In fact, many agencies see knowledge transfer as one of their main activities.
Critical perspectives
Recently, I met with representatives of a dozen U.S. government agencies (in health, mental
health, education, public transportation, etc.) to review their knowledge-transfer strategies. Each
was allocating a portion (albeit small) of its total budget for knowledge transfer, and each had an
office or division established to carry out knowledge-transfer functions. Significantly, each of
these U.S. government agencies was, in its activities, questioning certain aspects of the conventional wisdom about knowledge transfer.
I regard this as a healthy sign. Scholarship and practice on knowledge transfer have advanced to
the point where we should be questioning our past models, and searching for improved alternatives, rather than just “doing more of the same.” It is in the light of such critical perspectives that
the present chapter is written. A theme of this chapter is that a fundamental shift may have occurred in recent years as we have realized gradually that centralized knowledge-transfer systems
are not “the only wheel in town.” While such centralized approaches have advantages under
many conditions, in certain cases a more decentralized model of knowledge transfer may be more
appropriate.
Every field of scholarly activity makes certain simplifying assumptions about the complex reality
that it studies. Such assumptions are built into the intellectual paradigm that guides every field.
Often these assumptions are not very fully recognized, even as they affect such important matters
as what is studied and what is ignored and which research methods are favored and which are rejected. So, when a scholar follows a theoretical paradigm, he or she puts on a set of intellectual
blinders that help the researcher avoid seeing much of reality. “The prejudice of training is always a certain ‘trained incapacity’: The more we know about how to do something, the harder it is
to learn to do it differently” (KAPLAN 1964,31). Such trained incapacity is, to a certain extent, necessary; without it, a scholar could not cope with the vast uncertainties of the research process in
his field. Every research worker, and every field of science, has many blind spots.
1
Source: Everett ROGERS, 1986: Models of Knowledge Transfer: Critical Perspectives. In: BEAL,
George, M., DISSANAYAKE, Wimal, KONOSHIMA, Sumiye (Eds.): Knowledge Generation, Exchange
and Utilization. Westview Press, Boulder, Colorado, 37-60
1
24
The growth and development of a research field is a gradual puzzle-solving process by which important research questions are identified and eventually answered. The progress of a scholarly
field is helped by realization of its assumptions, biases, and weaknesses. Such self-realization is
greatly assisted by intellectual criticism. Unfortunately, the field of knowledge transfer has not
been subjected to much critical review, a deficiency that we hope to remedy in this chapter.
What is knowledge transfer?
Past scholarship on issues of knowledge generation, exchange, and utilization grew out of several
different disciplines, each of which favored certain theoretical viewpoints, research approaches,
and terminologies. While the general trend is toward integration of this intellectual diversity, such
academic unity is yet far from being accomplished. Perhaps a certain degree of difference in approach is a good thing, at least up to a certain point, but one area in which diversity still causes
troublesome problems is in terminology. What I loosely refer to in this chapter as “knowledge
transfer” is also known as knowledge, utilization, technology, transfer, and the diffusion of innovations (although these concepts are not exact synonyms).
We often use “innovation,” “technology,” and “knowledge” as synonyms, but in fact they are not
the same. An innovation is an idea perceived as new. A technology is a design for instrumental
action that reduces the uncertainty in the cause-effect relationships involved in achieving a desired outcome (ROGERS 1983,12). A technology usually has two components: (1) a hardware aspect, consisting of the tool that embodies the technology as a material or physical object, and (2)
a software aspect, consisting of the information base for the tool. Both the hardware and software
dimensions of a technology encompass knowledge, but of course there are many other kinds of
knowledge besides the new knowledge that is involved in an innovation or a technology. Nevertheless, most of the past studies of knowledge transfer have actually been researches focusing on
innovation or technology transfer.
The scope of knowledge transfer that has been studied in the past has been the process through
which technological information resulting from an R & D system is transferred by a linking system (e.g., an agricultural extension system) to a user system (e.g., farmers). This conception of
knowledge transfer implies that it is mainly a one-way process: in actuality (or at least ideally),
the R & D may have been initiated at the request of the user system, or at least in order to meet
certain of their needs, further, once the users have received the knowledge and put it into use,
feedback (as to how well the knowledge meets the preexisting needs) may be conveyed back to
the R & D system. So it is an oversimplification to think of knowledge transfer as a one-way, topdown process. 2
The agricultural extension model
Any discussion of models of knowledge transfer must begin with agriculture extension, both for
historical reasons and because the agriculture extension model has so influenced all of our thinking about this topic. While our intellectual dependence upon this model was mainly functional in
the past, we have also been unfortunately limited in the scope of our conceptualizations about
2
This discussion of the concept of knowledge transfer raises the issues of what should be the main
dependent variable(s) (1) in research on knowledge transfer, and (2) in practice, as an indicator of
performance. In past studies of the diffusion of innovations, the usual dependent variable has been
adoption versus rejection of a technological innovation. But there are many other possible dependent
variables in knowledge transfer research and/or practice: Awareness-knowledge of a technological
innovation or of another idea, development of a favorable or unfavorable attitude toward the innovation or another idea, or beneficial consequences of adoption or rejection of the idea (to meet the original needs).
2
25
knowledge transfer. The first step in breaking outside the bounds of our prior thinking is to realize that certain alternative models may be possible. Of course, it may be advantageous to combine certain elements of a relatively centralized model like agricultural extension with parts of a
decentralized model to formulate a knowledge transfer system that is especially suited to a set of
particular conditions. This contingency approach to knowledge transfer is more academically
sound than the numerous descriptions of a knowledge transfer system in the past, which stated or
implied that that system was the best alternative for a wide range of conditions. For example, it
has been claimed that the agricultural extension model could be effectively applied to solve
knowledge transfer problems in education, family planning, vocational rehabilitation, and so
forth. An examination of these “extensions” of the agricultural extension model, however, has
shown them to be relatively unsuccessful unless major modifications were made (ROGERS, EVELAND, and BEAN 1984).
The agricultural extension model is a set of assumptions, principles, and organizational structures for diffusing the results of agricultural research to farm audiences in the United States. This
“model” is based directly on the experience of the U.S. government agency responsible for diffusing agricultural innovations; it closely parallels the conventional conceptions of a research and
development/diffusion/utilization process.
Eight main elements constitute the agricultural extension model:
1. A critical mass of new technology, so that the diffusion system has a body of innovations
with potential usefulness to practitioners.
2. A research subsystem oriented to utilization, as a result of incentives and rewards for researchers, research funding policies, and the personal ideologies of the agricultural researchers.
3. A high degree of user control over the knowledge transfer/research utilization process, as
evidenced through client participation in policy determination, attention to user needs in guiding
research and diffusion decisions, and the importance accorded feedback from clients on the system’s effectiveness. 3
4. Structural linkages among the research utilization system’s components, as provided by a
shared conception of the system, use of a common “language” by members of the system, and by
a common sense of mission.
5 A high degree of client contact by the linking subsystem, which is facilitated by reasonable
agent/client ratios and by a relatively homogenous client audience.
6. A spannable social distance across each interface between components in the system
(where social distance might occur in levels of professionalism, formal education, technical expertise, and specialization). Generally, these variables decrease as one moves from the research
subsystem (where Ph.D. ‘s are usually employed), through linkers, to the client subsystem.
7. Evolution as a complete system, rather than the knowledge transfer system having been
grafted on as an additional component to an existing research system.
8. A high degree of control by the system over its environment, thus enabling the system to
shape the environment rather than passively reacting to changes. Such a system is less likely to
face unexpected crises or competitors, and is able to obtain adequate resources. The degree of
3
While much rhetoric is given to this feedback about needed research from farmers through the extension service to agricultural scientists, it is actually a fairly rare occurrence.
3
26
control is expressed through the system’s power base, its perceived legitimacy, and its policiallegal influence.
The following generalizations are offered about the agricultural extension model:
1. In response to alterations in the environment, the agricultural extension model has changed
considerably since its origin in the United States in 1911. To a large extent, these adjustments are
a reason for its relative success.
2. The agricultural extension model is based on client participation in identifying local needs,
program planning, and evaluation and feedback.
3. Agricultural research activities are oriented toward potential utilization of research results. This
pro-utilization policy facilitates the linking function of the extension workers.
4. State-level extension specialists are in close social and spatial contact with agricultural researchers in their specialty, which facilitates their performance in linking research-based knowledge to farmer problems.
5. The agricultural extension model was more effective in diffusing agricultural production technology to farmers (such as in crop and livestock production) than in its latter-day extensions to
farmers on other subjects and to non-farm audiences.
6. The agricultural extension model recognizes the importance of communication as a basic
process-skill for extension change agents and provides communication training on an in-service
basis.
7. The agricultural extension model includes not only a systematic procedure for the transfer of
innovations from researchers to farmers but also an institutionalized means for orienting research
activities toward users’ needs.
Thus, the land-grant college/agricultural experiment station/extension service complex is a total
knowledge utilization system, which includes innovation-diffusion as only one of its components.
The federal investment in agricultural extension represents a heavy commitment, compared to
that in agricultural research. Federally funded extension activities represent about 40 to 60 percent of the annual federal investment in agricultural R & D. For example, the USDA recently allotted $423 million for R & D. This figure would be considerably higher (over $600 million) if
state funding were also included. 4 The annual federal budget for extension was $200 million:
with state and county government contributions, the total annual budget for the extension services
was about $500 million. Thus the total extension budget almost approaches the total public agricultural R & D budget. Even if only the federal investment is considered, extension receives
about half the funding of R & D. Comparable figures for federal extension-type activities as a
proportion of federally supported R & D are much, much smaller in other fields:
1.
2.
3.
4.
5.
4
Law Enforcement Assistance Administration
National Institute of Education
U. S. Department of Labor
National Institute of Mental Health
National Aeronautics and Space Administration (NASA)
14 %
10 %
3 %
2 %
0.17%
The activities of the extension services over the years have focused somewhat narrowly on immediate technical problems in agriculture, rather than on the longer range social, political, economic,
and ecological consequences of technological change in U.S. agriculture (HIGHTOWER 1972).
4
27
Undoubtedly one of the reasons for the success of the agricultural extension services is their relatively high, stable budget. The financial success is, in turn, aided by the support given to the agricultural extension services by the powerful American Farm Bureau Federation.
Extending the agricultural extension model
What factors drawn from the agricultural extension model can be applied to other knowledge
transfer systems, and which are unique to the agricultural extension services? In other words, can
the agricultural extension model be extended to other situations? ROGERS, EVELAND, and BEAN
(1984) compared seven selected attempts to extend the agricultural extension model on the eight
main elements of the model (that we stated previously). The seven “extensions” occurred during
the 1955 to 1975 period and represent cases with which the analysts were personally acquainted.
These seven “extensions” have (in most cases) been extensively evaluated, and so rather definite
conclusions are possible.
The general pattern of extension system development in the agricultural case, and the relative
successes and failures evidenced in the other seven cases, suggest some broad conclusions about
knowledge transfer. The historical development of the agricultural extension system stretches
over about 100 years. Comparatively speaking, knowledge transfer efforts in education, vocational rehabilitation, and other fields appear woefully underfunded and to have been treated like unwanted children of over-expectant parents. Two experiences (agricultural extension and family
planning) in the developing countries of Latin America, Africa, and Asia show a lack of understanding of the importance of cultural adaptation of elements of the agricultural extension model
(even when the model is applied to agricultural problems).
The county extension agent in the United States was a product of commercial agriculture, not
subsistence farming. Until American agriculture began to modernize, there was not much need
for an extension service. Subsistence (precommercial) farming in developing countries has not
embraced the agricultural extension model with much success, a fact that suggests that the successful introduction of a knowledge transfer system must be carefully timed so that a feeling of
need for its services exists or can soon be developed.
Attempts to introduce one or two elements of the agricultural extension model to non-agricultural
settings should not be undertaken without adequate appreciation of the difficulties involved. The
time and resources required to permit these knowledge transfer elements to prove their utility and
to become assimilated into the culture of the host system can be easily underestimated. The failure of modestly funded efforts to transplant specific elements of the agricultural extension model
into other sectors suggests that an extension system approach needs to be taken. When only certain elements of the agricultural extension model were introduced without support from the other
elements, they usually failed.
Knowledge cannot be transferred effectively unless the goals of such transfer are very clear. The
goals of the agricultural extension services were fairly direct and unambiguous: to produce more
food and to raise farm incomes. In education and in rehabilitation, for example, the goal situation
is much more complicated, with multiple, conflicting goals for knowledge transfer.
The agricultural extension services begin with users’ needs and problems, and the system operates
to find useful information to meet these needs, while many other, less effective knowledge transfer systems take an opposite approach of conducting research largely in answer to researchers’
needs, and then attempting to find some use for the results. Naturally, the research topics usually
do not match with users’ needs. An effective knowledge transfer system must begin with users’
needs.
5
28
Decentralized diffusion systems
We have already implied that there is considerable flexibility in the way the eight elements of the
agricultural extension model can be adapted in selected knowledge transfer systems. During very
recent years, a diffusion system in marked contrast to the centralized diffusion system of the agricultural extension model has been identified: decentralized diffusion.
Centralized Diffusion System
In 1971, Professor Donald SCHON of MIT wrote that “theories of diffusion have characteristically
lagged behind the reality of emerging systems.” SCHON particularly singled out classical diffusion theory for criticism: he termed classical diffusion a “center-periphery model.” This model,
SCHON (1971, 81) said, rests on the basic assumption that “An innovation to be diffused exists fully realized in its essentials, prior to its diffusion” and that the diffusion process can be centrally
managed. The best-known example of a centralized diffusion system is the agricultural extension
services.
In this classical diffusion model, an innovation originated from some expert source (often an R &
D organization). This source then diffuses the innovation as a uniform package to potential adopters, who accept or reject the innovation. The role of the adopter of the innovation is that of a relatively passive accepter. This classical model owes much of its popularity to the success of the
agricultural extension services and to the fact that the basic paradigm for diffusion research grew
out of the RYAN and GROSS (1943) hybrid corn study. Much agricultural diffusion in the United
States is relatively centralized, in that key decisions about which innovations to diffuse, how to
diffuse them, and to whom are made by a small number of technically expert officials near the
top of a diffusion system. While SCHON noted that it fails to capture the complexity of relatively
decentralized diffusion systems in which innovations originate in numerous sources and evolve
as they diffuse via horizontal networks.
During the late 1970s, I gradually became aware of diffusion systems that did not operate at all
like the relatively centralized diffusion system that I had described in previous publications. Instead of coming out of formal R & D systems, innovations often bubbled up from the operational
levels of the system, with the inventing done by users. Then the new ideas spread horizontally via
peer networks, with a high degree of re-inventing occurring as the innovations were modified by
users to fit their particular conditions. Such decentralized diffusion systems usually are not run by
a small set of technical experts. Instead, decision making in the diffusion system is widely shared
with adopters making many decisions. In many cases, adopters served as their own change
agents.
Gradually, I begun to realize that the centralized diffusion model was not the only wheel in town.
Comparing centralized versus decentralized diffusion systems
How does a decentralized diffusion system differ from its centralized counterpart? Table 1 shows
six of the main differences between centralized and decentralized diffusion systems. This distinction is somewhat oversimplified because it suggests a dichotomy, rather than a continuum, of
centralized/decentralized diffusion systems. In reality, an actual diffusion system is usually some
combination of the elements of a centralized and a decentralized diffusion system. For example,
the agricultural extension services in the United States are nearer the more centralized end of the
centralized/decentralized continuum, although they have certain characteristics of a decentralized
system.
In general, centralized diffusion systems are based on a linear, one-way model of communication.
Decentralized diffusion systems more closely follow a convergence model of communication, in
6
29
Table 1: Characteristics of centralized and decentralized diffusion systems
Characteristics of diffusion
Centralized diffusion systems
Decentralized diffusion systems
The degree of centralization Overall control of decisions by
in decision-making and
national government adminispower.
trators and technical subjectmatter experts.
Wide sharing of power and control among the members of the
diffusion system; client control by
local officials/leaders.
Direction of diffusion.
Top-down diffusion from experts to local users of innovations.
Peer diffusion of innovations innovations through horizontal
networks.
Sources of innovations.
Innovations come from formal R Innovations come from local ex& D conducted by technical ex- perimentation by nonexperts,
perts.
who often are users.
Who decides which innovations to diffuse?
Decisions about which innova- Local units decide innovations
should the basis of their evaluations should be diffused are
made by top administrators and tions of the innovations.
technical subject-matter specialists.
How important are clients’
needs in driving the diffusion process?
An innovation-centered approach; technology-push, emphasizing needs created by the
availability of the innovation.
A problem-centered approach;
technology-pull, created by locally perceived needs and problems.
Amount of re-invention ?
A low degree of local adaptation and re-invention of the innovations as they diffuse
among adopters.
A high degree of local adaptation
and re-invention of the innovations as they diffuse among
adopters.
Source: ROGERS 1983,335
which participants create and share information with one another in order to reach a mutual understanding (ROGERS and KINCAID 1981). A fundamental assumption of decentralized diffusion
systems is that members of the user system have the ability to make sound decisions about how
the diffusion process is managed. This capacity of the users to run their own diffusion system
makes the most sense (1) when the users are highly educated and technically competent practitioners (for example, cardiovascular surgeons), so that all the users are experts, or (2) when the innovations being diffused are not at a high level of technology (for example, home energy conservation or organic gardening versus building a nuclear power plant), so that intelligent laymen
have sufficient technical expertise.
The fact that relatively decentralized diffusion systems exist in a wide variety of fields and locations suggests that in the past we may have severely underestimated the degree to which the user
system was capable of managing its own knowledge transfer process. Our understanding of decentralized diffusion systems is still limited, owing to the general lack of investigations of such
user-dominated diffusion. However, it seems apparent that certain elements of decentralized diffusion systems might be combined with certain aspects of the centralized model to fit a particular
situation uniquely. In other words, the classical diffusion model is being questioned in certain
very important ways.
7
30
Advantages and Disadvantages of Decentralized Diffusion
Decentralized diffusion systems have both advantages and disadvantages. Compared to centralized systems, the innovations that decentralized systems diffuse are likely to fit with users’ needs
and problems more closely. Users feel a sense of control over a decentralized diffusion system, as
they participate in making many of the key decisions, such as which of their perceived problems
need most attention, which innovations best meet these needs, how to seek information about
each innovation and from what source, and how much to modify an innovation as they adopt and
implement it to their particular setting. The high degree of user control over these key decisions
means that a decentralized diffusion system is geared closely to local needs. Problems of change
agent/client heterophily are minimized. It is mainly user motivations to seek innovations that
drive a decentralized diffusion process, and this may be more cost-efficient than situations in
which professional change agents manage the diffusion process. User self-reliance is encouraged
in a decentralized system, finally, decentralized diffusion is publicly popular: users generally like
such systems. Several disadvantages, however, often characterize decentralized diffusion systems:
1. Technical expertise is sometimes difficult to bring to bear on decisions about which innovations to diffuse and to adopt, and it is possible for “bad innovations” to diffuse through a decentralized system because of this lack of “quality control.” So when a diffusion system is disseminating innovations that involve a high level of technical expertise, a decentralized diffusion system may be less appropriate than a more centralized diffusion system.
2. Furthermore, extremely decentralized diffusion systems lack a coordinating role (that is, the
“big picture” of the system, where problems exist and which innovations might be used to solve
them). For example, a local user may not know which other users he or she could visit to learn
about an innovation. Thus, completely decentralized diffusion systems suffer from the fact that
local users, who control the system, lack certain aspects of the big picture about users’ problems
and about available innovations to meet these problems.
3. A highly decentralized system will not be appropriate for innovation for which potential users
do not feel a need. An example is family planning in developing nations, which a government
may regard as a high priority but which people may not want. There are very few decentralized
diffusion systems for contraception in Latin America, Africa, and Asia.
Thus, our present discussion suggests that:
1. Decentralized diffusion systems are most appropriate for certain conditions, such as for diffusing innovations that do not involve a high level of technical expertise, among a set of users with
relatively heterogeneous conditions. When these conditions are homogeneous, a relatively more
centralized diffusion system may be most appropriate.
2. Certain elements of centralized and decentralized diffusion systems can be combined to form a
diffusion system that uniquely fits a particular situation. For example, a diffusion system may
combine a central-type coordinating role, with decentralized decisions being made about which
innovations should be diffused and which users others should site-visit. Technical evaluations of
promising innovations can be made in an otherwise decentralized diffusion system.
Biases in knowledge transfer
The constructive criticisms that have been made of knowledge transfer models in very recent
years help us identify several biases in such work, and they also suggest ways of overcoming
such biases.
8
31
The pro-innovation bias
The pro-innovation bias is the implication that an innovation should be diffused and adopted by
all members of a social system, that it should be diffused more rapidly, and that the innovation
should be neither re-invented nor rejected. Seldom is the pro-innovation bias straightforwardly
stated in scholarly publications. Rather, the bias is assumed and implied. This lack of recognition
of the pro-innovation bias makes it especially troublesome and potentially dangerous in an intellectual sense. The bias leads researchers to ignore the study of ignorance about innovations, to
underemphasize the rejection or discontinuance of innovations, to overlook re-invention, and to
fail to study anti-diffusion programs designed to prevent the diffusion of “bad” innovations (like
marijuana or drugs or cigarettes, for example). The net result of the pro-innovation bias is that we
have failed to learn about certain very important aspects of the diffusion of innovations. What we
do know about diffusion (and other aspects of knowledge transfer) is unnecessarily rather limited.
But it need not be so.
How did the pro-innovation bias originally occur? Part of the reason is historical. Undoubtedly,
hybrid corn was profitable for each of the Iowa farmers in the early RYAN and GROSS (1943) diffusion study, but most other innovations that have been investigated do not have this extremely
high degree of relative advantage. Many individuals, for their own good, should not adopt them.
Perhaps if the field of diffusion research had not begun with highly profitable agricultural innovations in the 1940s and the 1950s, the pro-innovation bias would have been avoided, or at least
recognized and dealt with properly.
During the 1970s, several critics of diffusion research recognized the pro-innovation bias. For example, DOWNS and MOHR (1976,700) stated; “The act of innovating is still heavily laden with
positive value. Innovativeness, like efficiency, is a characteristic we want social organisms to
possess. Unlike the ideas of progress and growth, which have long since been casualties of a new
consciousness, innovation, especially when seen as more than purely technological change, is
still associated with improvement.”
What causes the pro-innovation bias in diffusion research?
1. Much diffusion research is funded by change agencies; they have a pro-innovation bias (understandably so, since they are in the business of promoting innovations), and this viewpoint has
often been accepted by many of the diffusion researchers whose work they sponsor, whom they
call upon for consultation about their diffusion problems, and whose students they may hire.
2. “Successful” diffusions leave a rate of adoption that can be retrospectively investigated by diffusion researchers, while an unsuccessful diffusion does not leave visible traces that can be very
easily studied. For instance, a rejected and/or a discontinued innovation is not so easily identified
and investigated by a researcher by interrogating the rejectors and/or discontinuers.
As a general result of the pro-innovation bias, we know much more (1) about the diffusion of rapidly diffusing innovations, (2) about adoption than about rejection, and (3) about continued use
than about discontinuance. The pro-innovation bias in diffusion research is understandable from
the viewpoint of financial, logistical, methodological, and practical policy considerations. The
problem is that the pro-innovation bias is limiting in an intellectual sense: we know too much
about innovation successes and not enough about innovation failures. While we have largely discussed the pro-innovation bias here in terms of the diffusion of innovations, it also permeates all
other aspects of the knowledge transfer process.
How might the pro-innovation bias be overcome?
9
32
1. Alternative research approaches to post hoc data-gathering about how an innovation has diffused should be explored in knowledge-transfer research. Diffusion research does not necessarily
have to be conducted after an innovation has diffused completely to the members of a system.
Such a rearward orientation to most diffusion studies helps lead them to a concentration on successful innovations. It is also possible to investigate the diffusion of an innovation while the diffusion process is still underway, or, in fact, before it even begins.
2. Researchers should become much more questioning of, and careful about, how they select their
innovations of study. Even if a successful innovation is selected for investigation, a scholar might
also investigate an unsuccessful innovation that failed to diffuse widely among members of the
same system. Such a comparative analysis would help illuminate the seriousness of the proinnovation bias. In general, a much wider range of innovations should be studied in knowledgetransfer research.
3. Researchers should investigate the broader context in which an innovation diffuses, such as
how the initial decision is made that the innovation should be diffused to members of a system,
how public policies affect the rate of diffusion, how the innovation of study is related to other innovations and to the existing practice (s) that it replaces, and how it was decided to conduct the R
& D that led to the innovation in the first place. This wider scope to research studies would help
illuminate the broader system in which the knowledge-transfer process occurs.
4. We should increase our understanding of the motivations for adopting an innovation. Strangely, such “why” questions about adopting an innovation have only seldom been probed by diffusion researchers; undoubtedly, motivations for adoption are a difficult issue to investigate. Some
adopters may not be able to tell a researcher why they decided to use a new idea. Other adopters
may be unwilling to do so. Seldom are simple, direct questions in a survey interview adequate to
uncover an adopter’s reasons for using an innovation. But we should not give up on trying to find
out the “why” of adoption just because valuable data about adoption motivations are difficult to
obtain by the usual methods of diffusion research data-gathering.
It is often assumed that an economic motivation is the main thrust for adopting an innovation, especially if the new idea is expensive. Economic factors are undoubtedly very important for certain types of innovations and their adopters, such as the use of agricultural innovations by U. S.
farmers, but the prestige secured from adopting an innovation before one’s peers may also be an
important factor. Certainly the first and most important step in shedding a pro-innovation bias in
knowledge-transfer research is to recognize that it may exist.
The Individual-Blame Bias in Knowledge Transfer
In addition to a pro-innovation bias in much past diffusion research, there has also been a sourcebias, a tendency for diffusion research to side with the change agencies that promote innovations
rather than with the audience of potential adopters. This source-bias is perhaps even suggested by
the words that we employ to describe this field of research: “Diffusion” research might have been
called something like “problem-solving,” “innovation-seeking,” or the “evaluation of innovations” had the audience originally had a stronger influence on this research. One cannot help but
wonder bow the diffusion research approach might have been different if the RYAN and GROSS
(1943) hybrid corn study had been sponsored by the Iowa Farm Bureau Federation (a farmer’s organization) rather than by an agricultural research center like the Iowa Agricultural Experiment
Station. And what if the Columbia University drug study (COLEMAN, KATZ, and MENZEL 1966)
had been sponsored by the American Medical Association, rather than by the Pfizer Drug Company? The source-sponsorship of early diffusion studies may have given these investigations not
only a pro-innovation bias but may have also structured the nature of diffusion research toward
individual-blame.
10
33
Individual-blame is the tendency to hold an individual responsible for his or her problems, rather than the system of which the individual is a part (CAPLAN and NELSON 1973). In other
words, an individual-blame orientation implies that “if the shoe doesn’t fit, there’s something
wrong with your foot.” An opposite point of view would blame the system, not the individual: it
might imply that the shoe manufacturer or the marketing system could be at fault for a shoe that
does not fit.
Of course it is likely that some of the factors underlying a particular social problem may indeed
be individual in nature, and that any effective solution to the problem may have to deal with
changing these individual factors. However in many cases the causes of the social problem lie in
the system of which the individual is a part. Ameliorative social policies that are limited to individual interventions will not be very effective in solving system-level problems. How a social
problem is defined is an important determinant of how we go about solving it, and therefore of
the effectiveness of the attempted solution. A frequent error in defining a social problem is to
overstress individual-blame and to underestimate system-blame.
System-blame may be defined as the tendency to hold a system responsible for the problems of
individual members of the system. How else can the person-blame bias be overcome?
1. Researchers must attempt to keep an open mind about the causes of a social problem, at least
until exploratory data are gathered, and guard against accepting others’ definitions of knowledgetransfer problems, which often tend to be in terms of individual-blame.
2. All the participants should be involved, including potential adopters, in the definition of a research problem, rather than just those individuals who are seeking amelioration of a problem.
3. Social and communication structural variables, as well as intra-individual variables, should be
considered in knowledge-transfer research. Past diffusion studies largely consisted of audience
research, while seriously neglecting source research. The broader issues of who owns and controls (1) the R & D system that produces innovations and (2) the communication systems that diffuses them, and to whose benefit, also need attention in future knowledge-transfer investigations.
As in the case of the pro-innovation bias in diffusion research, perhaps one of the first and
most important ways to guard against the individual-blame bias is to be aware that it exists. To
what extent does knowledge-transfer research have an individual-blame bias? It is difficult to assess the degree of individual-blame in past researches accurately, but, on careful reading, there
seems to be a certain flavor of individual-blame in many of the resulting publications. An individual-blame orientation is not, in and of itself, always inappropriate. Perhaps individual-level
variables are the most appropriate to investigate in a particular study. By no means do we advocate the complete discarding of all individual-level, psychological variables in knowledgetransfer research, but in almost all cases, such a psychological approach centering on individuallevel variables is not a complete explanation of the behavior being investigated.
The generation of innovations
Knowledge transfer consists of much more than just diffusion. Past investigations have overlooked the fact that a great deal of relevant activities and decisions usually occurred long before
the diffusion process began: A perceived problem, funding decisions about R & D activities that
led to research work, invention of the innovation and then its development and commercialization, a decision that it should be diffused, transfer of the innovation to a diffusion agency, and its
communication to an audience of potential adopters. Then the first adoption occurs.
This entire pre-diffusion series of activities and decisions is certainly an important part of the innovation-development process, of which the diffusion phase is but one component. The impor11
34
tance of what happens prior to the beginning of an innovation’s diffusion (especially those events
that affect the nature of diffusion later on) has been almost entirely ignored in past research.
The innovation-development process consists of all of the decisions, activities, and their impacts that occur from recognition of a need or problem, through research, development, and
commercialization of an innovation, through diffusion and adoption of the innovation by users, to
its consequences. Here we take up each of the main steps in the innovation-development process,
which corresponds roughly to the process of knowledge transfer.
1. Recognizing a Problem or Need. One of the ways in which the innovation-development
process begins is by recognition of a problem or need, which stimulates research and development activities designed to create an innovation to solve the problem/need. In certain cases, a
scientist may perceive a forthcoming problem and launch research to find a solution. An example
is the agricultural scientist at the University of California at Davis who foresaw a severe labor
shortage for California tomato farmers when the bracero program ended and initiated an R & D
program to breed hard tomato varieties that could be machine-picked.
In other cases, a problem/need may rise to high priority on a system’s agenda of social problems
through a political process. Research and development to develop safer cars and highways had
been conducted and accumulated for several years, but the results were not put into practice until
the mid-1960s when a series of highly publicized legislative hearings and Ralph NADER’s (1965)
book, Unsafe at Any Speed, called national attention to the high rate of traffic fatalities. The social problem of auto safety rose to a high national priority owing to higher fatality rates in the early 1960s, when the annual death rate reached 50,000. But the interpretation of this dangerous
trend was in large part a political activity.
2. Basic and Applied Research. Most innovations that have been investigated in diffusion researches have been technological innovations. Most such innovations are created by scientific research activities, although they often result from an interplay of scientific method and practical
operations. The knowledge base for a technology usually derives from basic research, defined as
original investigations for the advancement of scientific knowledge that do not have the specific
objective of applying this knowledge to practical problems. In contrast, applied research consists of scientific investigations that are intended to solve practical problems. Scientific knowledge is put into practice in order to design an innovation that will solve a perceived need or problem. Applied researchers are the main users of basic research. Thus, an invention may result from
a sequence of (1) basic research followed by (2) applied research leading to (3) development.
3. Development. The abbreviation R & D corresponds closely to the concept that it represents:
“R” always appears together with “D” and, moreover, always precedes “D”; development is always based on research. In fact, it is usually difficult or impossible to separate research and development, which is why the term “R & D” is so often used.
Development of an innovation is the process of putting a new idea in a form that is expected to
meet the needs of an audience of potential adopters. This phase normally occurs after research but
prior to the innovation that stems from research.
4. Diffusion and Adoption. Perhaps the most crucial decision in the entire innovation-development process is the decision to begin diffusion of an innovation to potential adopters. On the one
hand, there is usually pressure to approve an innovation for diffusion as soon as possible, as the
social problem/need that it seeks to solve may have been given a high priority. Public funds may
have been used to sponsor the research, and such financial support is an unrealized public investment until the innovation is adopted by users. On the other hand, the change agency’s reputation and credibility in the eyes of its clients rests on only recommending innovations that will
12
35
have beneficial consequences for their adopters. Scientists tend to be cautious when it comes time
to translate their scientific findings into practice.
A novel approach to gatekeeping medical innovations is followed by the National Institutes of
Health through the conduct of “consensus development conferences.” Consensus development is
a process that brings together biomedical research scientists, practicing physicians, consumers,
and others in an effort to reach general agreement on whether a given medical technology is safe
and effective (LOWE 1980). The technology may be a device, a drug, or a medical or surgical
procedure. A consensus conference differs from the usual state-of-the-art scientific meeting in
that a broadly based panel is constituted to address a set of predetermined questions regarding the
particular medical innovation under review. A three-day consensus conference typically begins
with a series of research synthesis papers that are discussed by the expert investigators, users of
the technology, and their consumers, A consensus statement is prepared by the panel and read on
the final day of the conference to the audience, who then react to it. The final consensus statement is then published by the U.S. Government Printing Office and widely disseminated to physicians, the mass media, medical journals, and the public.
Consensus conferences were begun in 1978 in recognition of the fact that the medical field lacked
a formal process to assure that medical research discoveries were identified and scientifically
evaluated to determine if they were ready to be used by doctors and other health-care workers. It
was feared that some new technologies might have been disseminated without an adequate scientific test, while other well-validated medical technologies might be diffusing too slowly. The consensus panels have, in fact, occasionally recommended against using a given medical or surgical
procedure, device, or drug under certain conditions. So, they serve an important function in gatekeeping the flow of medical innovations from research into practice.
Some other fields also utilize a formal procedure for deciding when an innovation should be diffused. Most knowledge transfer systems however, do not evaluate innovations for diffusion in
such a rigorous way. Here, perhaps, we see an example of how one knowledge-transfer system
can learn and adapt useful lessons from another such system. Such transfer of knowledge-transfer
methodologies can be greatly facilitated by the world of scholars of the knowledge-transfer
process, as they engage in comparative analyses and evaluations of knowledge transfer systems.
The entrepreneurial transfer of knowledge
Previously in this chapter we argued that past research on the knowledge transfer process has
been unduly limited in scope. There are many types of knowledge transfer that have been ignored
by scholars. One of these is technology transfer that occurs between private firms and that is driven by market forces, rather than by public policies enacted through activities of a government
agency. We should not forget that most of what we now understand about the nature of knowledge transfer is based, rather narrowly, upon the transfer of innovations from a national government agency to individuals; here again we see the considerable influence of the agricultural extension model upon our thinking about knowledge transfer. Yet a great deal of knowledge transfer obviously must take place in the context of for-profit firms that are competitively seeking to
market innovative products to consumers. One spectacular illustration is provided by Silicon Valley, the high-technology complex in Northern California that is the world center of the microelectronics industry. Silicon Valley produces the semiconductor chips, microcomputers, video games,
and lasers that are transforming industrialized nations into information societies.
At the heart of Silicon Valley is severe competition in continuous technological innovation; each
company tries to …
Purchase answer to see full
attachment
Why Choose Us
- 100% non-plagiarized Papers
- 24/7 /365 Service Available
- Affordable Prices
- Any Paper, Urgency, and Subject
- Will complete your papers in 6 hours
- On-time Delivery
- Money-back and Privacy guarantees
- Unlimited Amendments upon request
- Satisfaction guarantee
How it Works
- Click on the “Place Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
- Fill in your paper’s requirements in the "PAPER DETAILS" section.
- Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
- Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
- From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.