Transcript Document 7473865

TECHNOLOGY & LEARNING
American myth of heroic lone inventors: Edison, Burbank, Ford
During the 20th century, the creation of new scientific knowledge and
technologies grew increasingly systematized by organizations and
promoted by governmental policies, especially military needs.
Theoretical Explanations
Different org’l theories explain diverse aspects of technological
innovations, from learning processes to organizational survival rates
INNOVATION: Any departure from existing technologies
or management practices; changes in routines
Org’l innovations are sources of population variation for
evolutionary selection & retention
Institutional isomorphism induces conformity in corporate R&D
practices
Resource dependence inhibits organizations from investing in
innovation investigations that yield competitive advantages
Org’l cultures may foster or inhibit innovative orientation (3M)
Inter- & intraorg networks shape innovation dynamics and diffusion
rates
National Innovation Systems
In US, “communism and cancer” (military priorities of the Cold War
and biomedical demands by 1970s) spurred the emergence of an
intricate national innovation system
“Hierarchical, multidimensional network
of public and private institutions
interacting non-linearly in a given
historical context” (Leoncini 1998:75).
R&D expenditures as key indicator of national capacities to solve
complex sociotechnical problems
R&D = 2.9% of US and Japan GDP; 1.8% of European GDP
European Union's Sixth Framework Programme (2002-06) sets funding
priorities for the EU, but 83% of public funds are still decided by each nation
<http://europa.eu.int/comm/research/faq.html>
NSF Research Classification
NSF’s triadic classification tracks annual spending patterns:
•Basic research on comprehensive knowledge w/o specific
applications: subatomic particles, human genome, global climate
change
•Applied research to meet specific needs: improved battlefield
communications, genetically modified crops
•Development to apply knowledge “directed toward the production
of useful materials, devices, systems, or methods,” including
prototypes: demonstrate zero-pollution engine; universal verballanguage translation machine
Decreasing US public sector R&D funding after fall of USSR; similar
trends in other capitalist democracies
US has 44% of world R&D, far ahead of Japan (1995); but other Group
of Seven nondefense R&D is 118% of US spending
Shifting R&D Trends
Components of US National System
(1) increased reliance by US firms on sources of R&D outside their
organizational boundaries, through such mechanisms as
consortia, collaboration with US universities and federal
laboratories, and strategic alliance with other US and foreign
firms;
(2) expanded performance of R&D offshore by US firms and
increased performance by non-US firms of industrial R&D within
the United States [especially by Japanese firms];
(3) increased reliance by US universities on US and foreign industry
for research funding and expanded efforts by US universities to
license and otherwise realize commercial returns from the results
of academic research.
Mowery (1998)
The Triple Helix
A “Triple Helix” interdependent clusters of collaborative networks
among universities, businesses, and local-regional governments in
applied & development projects to transfer knowledge into
commercial products & services
Etzkowitz & Leydesdorff. (2000)
Institutionalized conflicts erupt between:
A: university openness norms of peer-reviewed basic research
grants & journal publication (payoff in disciplinary prestige)
B: business secrecy norms of patent-protection for commercial
developments (payoff in startup firms’ IPO stock options)
Is this system transforming & deforming the universalistic
principles on which US & European universities are based?
Org’l & Population Learning
Innovation is closely tied to organizational learning processes
involving routines, collective memory storage mechanisms, and the
socialization of newcomers (individual learning). (Levitt & March 1999)
Argyris & Schön (1978) proposed two org’l learning loops:
Single-loop: Firm uses data to improve performance by
adjusting its routines, taking for granted its goals & values
Double-loop: Firm changes core assumptions about its
mission, underlying values & beliefs (transform culture)
Chris Argyris
Population-level learning: “systematic change in nature and mix
of org’l routines in a population…arising from experience.”
(Miner & Haunschild 1995)
- Mimetic org’l interaction: copy other’s routines
Anne S. Miner
- Broadcast transmission: peak source diffuses a new practice
- Population learning of routines: cooperative interaction, e.g.,
industry assn, standards board, R&D consortium
Innovator Organizations
Organizational and population learning processes imply an
innovation continuum from a tiny minority of innovator
organizations to the large majority reproducer orgs
Most orgs’ resource limitations prevent adequate
R&D investments necessary to generate new
technological knowledge and “killer apps.” They
rely on exploitation of existing technology to
wring small competitive advantages (March 1999)
James G. March
Innovator orgs engage in exploration of new science & technology
(1) small startup entrepreneurs use investors’ capital to pursue highrisk, unproven technologies
(2) large deep-pocket corporations (3M, H-P, Microsoft) maintain large
subunits for routinized R&D
EX New biotech firms & pharmaceuticals
Increments vs Breakthroughs
Most innovations are incremental “competence enhancing”
improvements that organizations can easily fit into their
existing routines & capabilties. These small adaptations
modestly increase worker performance and org’l productivity
without disruptively transforming organizational populations.
EX: typewriters; PowerPoint; Google; “new, improved Tide”
Much rarer “competence destroying” breakthroughs by new
entrants threaten status quo, forcing all orgs to restructure their
skills & routines radically to survive an inevitable shake-out
EX: railways, airplanes -- but Internet, genetic modification?
Innovation Journey
Case studies of organizational innovation
processes inside firms, at the Minnesota
Innovation Research Program led by Andrew Van
de Ven, refute the notion that discoveries are a
simple linear pathway extending from initial ideas
through research & development resulting in
commercialized products.
Innovation journey is chaotic process that increases
organization’s learning capacity: “nonlinear cycle of divergent
and convergent activities that may repeat over time and at
different organizational levels if resources are obtained to
renew the cycle” Van de Ven et al. (1999)
Three Phases
Appearances are deceiving: Innovation journey’s three activity
phases resemble a linear sequence
But actual org’l innovation experiences alternate between:
• unpredictable discoveries made under divergent-chaotic
conditions
• systematic testing under more stable-convergent conditions
THE INITIATION PHASE
1.
Gestation in extended period lasting several years
2.
Shocks from internal & external sources trigger concentrated efforts
3.
Plans developed and submitted to controllers for resources to launch development
Three Phases, cont.
THE DEVELOPMENTAL PHASE
4. Proliferation of numerous ideas and activities on divergent, parallel, convergent paths
5. Setbacks and mistakes cause resource and development time lines to diverge
6. Shifting success-failure criteria trigger power struggles between project managers and
resource controllers
7. Innovation personnel participate in highly fluid, emotionally charged ways
8. Investors and top management frequently involved as checks-and-balances
9. Relationships with other organizations lock innovation units into specific action
courses with unintended consequences
10. Involvement with competitors, trade associations, government agencies to create
supportive infrastructure
THE IMPLEMENTATION/TERMINATION PHASE
11. Adoption of innovation by linking, integrating new and old or fitting innovation to
local situation
12. Termination by implementing innovations or when resources run out
References
Anderson, Philip. 1999. “Collective Interpretation and Collective Action in Population-Level
Learning: Technological Choice in the American Cement Industry.” Advances in Strategic
Management 16:277-307.
Argyris, Chris & Donald Schön. 1978. Organizational Learning: A Theory of Action
Perspective. Reading, MA: Addison-Wesley.
Etzkowitz, Henry and Loet Leydesdorff. 2000. “The Dynamics of Innovation: From
National Systems and ‘Mode 2’ to a Triple Helix of University-Industry-Government
Relations.” Research Policy 29:109-123.
Levitt, Barbara and James G. March. 1988. “Organizational Learning.” Annual Review of
Sociology 14:319-340.
March, James G. 1999. The Pursuit of Organizational Intelligence. Malden, MA: Blackwell.
Mowery, David C. 1998. “The Changing Structure of the US National Innovation System:
Implications for International Conflict and Cooperation in R&D Policy.” Research Policy
27:639-654.
Van de Ven, Andrew H., Raghu Garud, Douglas E. Polley and Sankaran Venkataraman.
1999. The Innovation Journey. New York: Oxford University Press.
Tushman, Michael and Philip Anderson. 1986. “Technological Discontinuities and
Organizational Environments.” Administrative Science Quarterly 31:439-465.