LEARNING

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LEARNING INNOVATION DIFFUSION
Organizational learning occurs when one
organization actor causes change in capacities
of another, either thru experience sharing, or by
somehow stimulating innovation (Ingram 2002).

• How does orgl learning resemble/differ from
  individual learning?
• How are newcomers socialized to acquire orgl
  norms, procedures?
• What orgl mechanisms to store, recall, apply
  collective memories?
• How to create and institutionalize new
  organizational routines? the forms, rules,
  procedures, conventions, strategies,
  technologies around which orgs are constructed
  (Levitt March 1988)

Two basic types of orgl learning (James G. March
1999) (1) Exploit existing knowledge and
routines to gain competitive advantages (Japanese
firms after WW2) (2) Explore new knowledge via
basic science recombinant technologies (RD
joint ventures)
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TRANSFERING KNOWLEDGE
What network mechanisms affect the transfer of
knowledge? Ray Reagans and Bill McEvily studied
RD firm to determine effects of cohesion range
on perceived ease of knowledge transfer.
Knowledge transfer is costly to
sender/source Tacit knowledge isnt codifiable in
documents Expertise overlap reduces need to know
more Tie strength emotional closeness communic
Controlling for codifiability and expertise
overlap, both higher network cohesion broader
range facilitate the transfer of knowledge
it is easier to transfer all kinds of knowledge
in a strong tie tacit knowledge was more
difficult to transfer than codified knowledge.
it is more efficient to use strong ties to
transfer tacit knowledge and weak ties to
transfer codified knowledge. (p. 262)
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POPULATION-LEVEL LEARNING
Chris Argyris Donald Schön (1978) proposed two
orgl learning loops Single-loop Firm uses data
to improve performance by adjusting routines,
taking-for-granted its goals values Double-loop
Firm changes its core assumptions about
mission, underlying values beliefs (transform
culture)
Chris Argyris

• Population-level learning systematic change in
  the nature and mix of organizational routines in
  a population arising from experience. (Miner
  Haunschild 1995)
• - Mimetic orgl interaction copy anothers
  routines
• Broadcast transmission a peak source diffuses a
  new practice to the population via mass media
• Population learning of routines through
  cooperative collaborative interactions e.g.,
  using an industry association, standards board,
  RD consortium

Anne Miner
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VARIETIES of INNOVATIONS
INNOVATION Any departure from existing
technologies or management practices changes in
orgl routines
Most innovations are small competence-enhancing
changes that orgs easily fit into existing
routines and capabilties. Such adaptations
incrementally improve worker and orgl
productivity without disruptively transforming
organizational populations (Tushman Anderson
1986) EX PowerPoint Google new, improved
Tide
But, competence-enhancing innovations cannot
unleash the gales of creative destruction Add
as many mail-coaches as you please, you will
never get a railroad by so doing (Joseph
Schumpeter 1926)
Much rarer competence-destroying breakthroughs by
new orgl entrants threaten the status quo, force
all orgs to restructure their skills routines
radically to survive the inevitable shake-out EX
airplanes computers -- but Internet, genetic
modification?
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INNOVATION DIFFUSION
Transferring new knowledge from creators to users
(from innovators to imitators) involves network
linkages that diffuse information in two-step
flow from opinion leaders to laggards
Diffusion is a kind of social change, defined as
the process by which an innovation is
communicated through certain channels over time
among the members of a social system. It is a
special type of communication, in that the
messages are concerned with new ideas. (Everett
M. Rogers 19955)
Interpersonal diffusion involves peer pressures
reassurance Iowa farmers adopt hybrid corn
Korean villagers family planning Interorgl
adoption of technical equipment and social
skills CAT-scan machines in hospitals social
movement tactics Cross-level knowledge flows
from new employees into orgs Professionals hired
by bureaucracies import noncorporate norms
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BASS DIFFUSION MODEL
Frank Bass (1969) equation to forecast size of
durable goods market by modeling the influence of
innovators on imitators
Innovation Effect Imitation Effect
Qt of adopters during time t
Q ultimate of adopters (market
size) Nt-1 cumulative number of adopters at
the beginning of time t r effect of each
adopter on each nonadopter (coefficient of
imitation) p individual conversion rate
absent adopters influence (coefficient of
innovation)
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NETWORK FORMS of DIFFUSION
Relational network diffusion involves opinion
leaders direct ties
? Opinion leaders with low-density ego-nets boost
the early-adoption rate ? Leaders increase
diffusion rates of high-potential innovations
(with large percentage finally adopting), but
slow the rates for low-potential innovations
Structural diffusion involves complete-network
dynamics
? Centralized networks favors rapid spread of
nonrisky innovations, but slows diffusion of
innovations seen as risky or irrelevant (Valente
1995)
Plotting the cumulative adoption of an innovation
typically reveals S-shaped curve, reflecting
dynamics among heterogeneous consumers network
thresholds, their risk-benefit ratios, resistance
to adoption, and rates of critical mass formation
contagion.
Saturation
PERCENT
Take-off
TIME
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Diffusion of Household Goods
SOURCE Bronwyn H. Hall. 2004. Innovation and
Diffusion. Oxford Handbook of Innovation, edited
by Jan Fagerberg, David C. Mowery Richard
D.Nelson. New York Oxford University Press.
ltemlab.berkeley.edu/users/bhhall/
papers/Diffusion_Ch18_BHHfinal.pdfgt (Downloaded
10/22/04)
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SOURCE D.S. Ironmonger, C.W. Lloyd-Smith and F.
Soupourmas. New Products of the 80s and 90s The
Diffusion of Household Technology in the Decade
1985-1995. University of Melbourne.
ltwww.economics.unimelb.edu.au/ research/workingpap
ers/wp00_01/744.pdfgt (Downloaded 10/22/04)
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COHESION or STRUCTURAL EQUIVALENCE?
Burt (1987) reanalyzed Coleman et al.s Medical
Innovation (1966) study of tetracycline diffusion
among doctors in four towns
? Network contagion wasnt the dominant diffusion
factor a physicians personal preference
strongly determined whether he prescribed the
drug ? The date when a doctor began prescribing
was strongly predicted by the time when
structurally equivalent people (peer
models/competitors) started to write tetracycline
prescriptions ? No social cohesion effects
from a doctors discussion partners ? But, Burt
did not examine the possibility of adoption via
mass media
Van den Bulte and Lilien (2001) applied a network
threshold model with adoption probability as a
logit function. A doctors exposure to
pharmaceutical company marketing effort was the
most important predictor of tetracycline
adoption, while interpersonal contagion through
networks was negligible.
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References
Argyris, Chris Donald Schön. 1978.
Organizational Learning A Theory of Action
Perspective. Reading, MA Addison-Wesley. Bass,
Frank M. 1969. A New Product Growth Model for
Consumer Durables. Management Science
15215-227. Burt, Ronald S. 1987. Social
Contagion and Innovation Cohesion Versus
Structural Equivalent. American Journal of
Sociology 921287-1335. Levitt, Barbara and James
G. March. 1988. Organizational Learning. Annual
Review of Sociology 14319-340. March, James G.
1999. The Pursuit of Organizational Intelligence.
Malden, MA Blackwell. Miner, Anne S. and Pamela
R. Haunschild. 1995. Population Level Learning.
Research in Organizational Behavior
17115-166. Rogers, Everett M.. 1995. Diffusion
of Innovation, 4th Ed.. NY Free
Press. Schumpeter, Joseph A. 1926. Theorie der
wirtschaftlichen Entwicklung. 2nd ed. München und
Leipzig Duncker Humblot. Tushman, Michael and
Philip Anderson. 1986. Technological
Discontinuities and Organizational Environments.
Administrative Science Quarterly
31439-465. Valente, Thomas. 1995. Network Models
of the Diffusion of Innovations. Cresskill, NJ
Hampton Press. Van de Bulte, Christophe and Gary
Lilien. 2001. Medical Innovation Revisited
Social Contagion versus Marketing Effort.
American Journal of Sociology 1061409-1435.