Diffusion of Innovation
Theories, models, and future
directions
Innovation Diffusion Models
1.
2.
3.
4.
5.
6.
7.
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
–
–
–
–
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)