LEARNING: MIMESIS, CONTAGION, DIFFUSION

1. LEARNING: MIMESIS, CONTAGION, 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 org’l learning resemble/differ from individual learning? • How are newcomers socialized to acquire org’l norms, procedures? • What org’l 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 org’l learning (James G. March 1999): (1) Exploit existing knowledge and routines to gain competitive advantages (Japanese firms after WW2) (2) Explorenew knowledge via basic science & recombinant technologies (R&D joint ventures)

2. Transferring Knowledge What network mechanisms affect the transfer of knowledge? Ray Reagans and Bill McEvily studied R&D firm to determine effects of cohesion and range on perceived ease of knowledge transfer. Knowledge transfer is costly to sender/source Tacit knowledge isn’t codifiable in documents Expertise overlap reduces need to know more Tie strength = emotional closeness * communic Controlling for codifiability & expertise overlap, both higher network cohesion and 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)

3. Population-Level Learning Chris Argyris & Donald Schön (1978) proposed two org’l 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 org’l interaction: copy another’s 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, R&D consortium Anne Miner

4. Varieties of Innovations INNOVATION: Any departure from existing technologies or management practices; changes in org’l routines • Most innovations are small competence-enhancing changes that orgs easily fit into existing routines and capabilities. Such adaptations incrementally improve worker and org’l productivity without disruptively transforming organizational populations (Tushman & Anderson 1986) • PowerPoint; Google; “new, improved Tide” But, competence-enhancing innovations can never 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 org’l entrants threaten the status quo, force all orgs to restructure their skills & routines radically to survive the inevitable shake-out • Airplanes; computers; -- but Internet, genetic modification?

5. Bass’ Innovation Diffusion Equation Frank Bass (1969) forecast size of durable goods market, modeling gross innovator & imitators effects, without knowledge of their network relations: 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)

6. Innovation Diffusion via Networks Transferring new knowledge from creators to users involves their network connections, which diffuse information in two-step flows from opinion leaders to early & later adopters, then 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 1995:5) • Interpersonal diffusion involves peer pressures & reassurance • Iowa farmers adopt hybrid corn; Korean villagers & family planning • Interorg’l 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

7. 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

8. 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. <emlab.berkeley.edu/users/bhhall/ papers/Diffusion_Ch18_BHHfinal.pdf>

9. 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. <www.economics.unimelb.edu.au/ research/workingpapers/wp00_01/744.pdf>

10. A Network Threshold Model Tom Valente’s (1996) network threshold diffusion model involves micro-macro effects & nonadopters’ influence on adopter decisions. It assumes “behavioral contagion through direct network ties” (p.85) An adoption threshold is measured as ego’s direct communication ties to others, not as the collective behavioral threshold of the entire social system. Degree of exposure = (N adopters)/(N ego-net size) Adoption threshold is exposure at time-of-adoption Applied to tetracycline diffusion data, Doctor #20’s five alter-physicians each prescribed the drug before he did, so his exposure at the time of adoption in 8th period was 100%. The community’s adoption rate has no effect because doctors vary in their personal adoption thresholds.

11. Cohesion or Structural Equivalence? Burt (1987) also reanalyzed Coleman et al.’s Medical Innovation (1966) study of tetracycline diffusion among doctors in four towns ►Network contagionwasn’t the dominant diffusion factor; a physician’s 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 doctor’s 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 doctor’s exposure to pharmaceutical company marketing effort was the most important predictor of tetracycline adoption, while interpersonal contagion through networks was negligible.

12. 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 15:215-227. Burt, Ronald S. 1987. “Social Contagion and Innovation: Cohesion Versus Structural Equivalent.” American Journal of Sociology 92:1287-1335. 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. Miner, Anne S. and Pamela R. Haunschild. 1995. “Population Level Learning.” Research in Organizational Behavior 17:115-166. 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 31:439-465. Valente, Thomas W. 1995. Network Models of the Diffusion of Innovations. Cresskill, NJ: Hampton Press. Valente, Thomas W. 1996. “Social Network Thresholds in the Diffusion of Innovations.” Social Networks 18:69-89. Van de Bulte, Christophe and Gary Lilien. 2001. “Medical Innovation Revisited: Social Contagion versus Marketing Effort.” American Journal of Sociology 106:1409-1435.