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					Diffusion of Innovation

 Theories, models, and future
Innovation Diffusion Models

1.   General vs. Domain specific
2.   Conceptual vs. Mathematical
3.   Focus on innovation vs. adopters
4.   Organizational vs. Individual
5.   Process vs. Outcome
6.   Proximity vs. Network
7.   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

   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

   Trial - the individual makes full use of the
            More Theorists
• Hagerstrand (1965) studied diffusion of
  hybrid corn in farmers. Model based on

• Bass (1969) developed differential
  equations borrowed from physics to model
  diffusion of innovation
                More Theorists
• Midgley & Dowling
  – Contingency model.

• Mahajan & Peterson
  – 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

  – “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”. Fad-
    like behavior emerged
         Cellular Automata &
        Diffusion of Innovation
• Goldenberg, Libai, & Muller (working
  – Used CA to model Bass parameters in
    individuals and observed aggregate-level
    behavior (no focus on fad-like behavior)

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