Diffusion of Innovation

Theories, models, and future directions

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

• Gabriel Tarde (1903)– S-shaped curve for diffusion processes

• Ryan and Gross (1943): adopter categories– Innovators

– Early adopters

– Early/Late Majorities

– Laggards

Original Theorists

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

Adoption - the individual decides to continue the 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”. Fad-like 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)