SOC 8311 Basic Social Statistics

Transcript SOC 8311 Basic Social Statistics

• • • •

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)

Explore

new knowledge via basic science & recombinant technologies (R&D joint ventures)

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)

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:

and mix of organizational routines in a population …arising from experience.” “systematic change in the nature (Miner & Haunschild 1995) Mimetic org’l interaction: copy another’s routines Anne Miner - 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

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?

Bass’ Innovation Diffusion Equation

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

Q t







N t

 1  

  

N Q t

 1    



Innovation Effect Imitation Effect

 1     



r N t

 1

   



N t

 1  Q t = # of adopters

during

time

Q = ultimate # of adopters ( market size N t-1 = cumulative number of adopters at the

beginning

of time

r = effect of each adopter on each nonadopter ( coefficient of imitation ) p = individual conversion rate absent adopters’ influence ( coefficient of innovation ) )

Generations of M ainframe Computers (Performance Units) 1974-1992

120000 100000 80000 60000 40000 20000 0 1 2 3 4 Gen1 Actual Gen3 Actual 5 6 7 8 9 10

Year

11 12 13 14 15 16 17 18 19 Gen1 Fit and Forecast Gen3 Fit and Forecast Gen2 Actual Gen4 Actual Gen2 Fit and Forecast Gen4 Fit and Forecast

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

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.

Take-off Saturation

TIME

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.

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.

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 8 th period was 100%.

The community’s adoption rate has no effect because doctors vary in their personal adoption thresholds.

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 contagion wasn’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.

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. “