TECHNOLOGY & LEARNING

1. 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.

2. 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 culturesmay foster or inhibit innovative orientation (3M) Inter- & intraorg networks shape innovation dynamicsand diffusion rates

3. 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>

4. 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 verbal-language 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

5. Shifting R&D Trends

6. Components of US National System • 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)

7. 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?

8. 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 • Broadcast transmission: peak source diffuses a new practice • Population learning of routines: cooperative interaction, e.g., industry assn, standards board, R&D consortium Anne S. Miner

9. 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 high-risk, unproven technologies • (2) large deep-pocket corporations (3M, H-P, Microsoft) maintain large subunits for routinized R&D • EX New biotech firms & pharmaceuticals

10. 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?

11. 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)

12. 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 • Gestation in extended period lasting several years • Shocks from internal & external sources trigger concentrated efforts • Plans developed and submitted to controllers for resources to launch development

13. 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

14. 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.