Transcript Diffusion of Innovation

Diffusion of Innovation
Theories, models, and future
directions
Innovation Diffusion Models
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General vs. Domain specific
Conceptual vs. Mathematical
Focus on innovation vs. adopters
Organizational vs. Individual
Process vs. Outcome
Proximity vs. Network
Rate-oriented vs. Threshold
Original Theorists
• Gabriel Tarde (1903)
– S-shaped curve for diffusion processes
• Ryan and Gross (1943): adopter categories
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Innovators
Early adopters
Early/Late Majorities
Laggards
Original Theorists
• Katz (1957) :
– media  opinion leaders  opinion followers
• Everett M. Rogers
Diffusion of Innovations (1962-95)
– the process by which an innovation is
communicated through certain channels over
time among the members of a social system
Rogers’ (1995)
Diffusion of Innovation
Stages of adoption:
Awareness - the individual is exposed to the
innovation but lacks complete information about
it
Interest - the individual becomes interested in
the new idea and seeks additional information
about it
Evaluation - individual mentally applies the
innovation to his present and anticipated future
situation, and then decides whether or not to try
it
Trial - the individual makes full use of the
innovation
More Theorists
• Hagerstrand (1965) studied diffusion of
hybrid corn in farmers. Model based on
proximity.
• Bass (1969) developed differential
equations borrowed from physics to model
diffusion of innovation
More Theorists
• Midgley & Dowling
(1978):
– Contingency model.
• Mahajan & Peterson
(1985):
– Extension and
simplification of Bass
model (has 2
parameters, internal &
external influence)
Abrahamson & Rosenkopf (1990):
Bandwagons & Thresholds
Rational efficiency vs. Fad theories
• Rational Efficiency: The more organizations adopt
an innovation, the more knowledge about the
innovation’s true efficiency is disseminated
• Fad theories: The sheer number of adopters creates
“bandwagon pressures”
– Institutional pressures: Adoption of innovation can
become a social norm
– Competitive pressures: Fear that not adopting will lead to
loss of competitive advantage
Valente (1996)
Social network thresholds
• Personal network thresholds: number of
members within personal network that must have
adopted before one will adopt
– Accounts for some variation in overall adoption
time
– “Opinion leaders” have lower thresholds and
influence individuals with higher thresholds
Factors affecting diffusion
• Innovation characteristics
• Individual characteristics
• Social network characteristics
• Others…
Innovation characteristics
• Observability
– The degree to which the results of an innovation are visible to
potential adopters
• Relative Advantage
– The degree to which the innovation is perceived to be superior to
current practice
• Compatibility
– The degree to which the innovation is perceived to be consistent
with socio-cultural values, previous ideas, and/or perceived needs
• Trialability
– The degree to which the innovation can be experienced on a
limited basis
• Complexity
– The degree to which an innovation is difficult to use or understand.
Individual characteristics
• Innovativeness
– Originally defined by Rogers: the degree to which
an individual is relatively earlier in adopting an
innovation than other members of his social system
– Modified & extended by Hirschman (1980):
• Inherent / actualized novelty seeking
• Creative consumer
• Adoptive / vicarious innovativeness
Other individual characteristics
• Reliance on others as source of information
(Midgley & Dowling)
• Adopter threshold (e.g. Valente)
• Need-for-change / Need-for-cognition
(Wood & Swait, 2002)
Network characteristics
• Opinion leadership: number of nominations
as source of information
• Number of contacts within each adopter
category (Valente)
• Complex structure
Other possible factors:
• Lyytinen & Damsgaard (2001)
– Social environment of diffusion of innovation
– Marketing strategies employed
– Institutional structures (e.g., government)
Cellular Automata &
Diffusion of Innovation
• Boccara & Fuks (1998)
– CA model of diffusion based on contact theory.
(Not heavily based in innovation diffusion theory)
• Strang & Macy (2001)
– Used decision rule: if current practice is
unsatisfactory, evaluate “best practices”. Fadlike behavior emerged
Cellular Automata &
Diffusion of Innovation
• Goldenberg, Libai, & Muller (working
paper)
– Used CA to model Bass parameters in
individuals and observed aggregate-level
behavior (no focus on fad-like behavior)