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== '''Diffusion of innovations''' ==

=='''Diffusion of innovations'''==

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== Acronym ==

==Acronym==

DOI

DOI

== Alternate name(s)==

==Alternate name(s)==

Innovation Diffusion Theory (IDT)

Innovation Diffusion Theory (IDT)

== Main dependent construct(s)/factor(s)==

==Main dependent construct(s)/factor(s)==

Implementation Success or Technology Adoption

Implementation Success or Technology Adoption

== Main independent construct(s)/factor(s) ==

==Main independent construct(s)/factor(s)==

Compatibility of Technology, Complexity of Technology, Relative Advantage (Perceived Need for Technology)

Compatibility of Technology, Complexity of Technology, Relative Advantage (Perceived Need for Technology)

== Concise description of theory ==

==Concise description of theory==

DOI theory sees innovations as being communicated through certain channels over time and within a particular social system (Rogers, 1995).  Individuals are seen as possessing different degrees of willingness to adopt innovations and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time (Rogers, 1995).  Breaking this normal distribution into segments leads to the segregation of individuals into the following five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, laggards (Rogers, 1995).  Members of each category typically possess certain distinguishing characteristics as shown below:

DOI theory sees innovations as being communicated through certain channels over time and within a particular social system (Rogers, 1995).  Individuals are seen as possessing different degrees of willingness to adopt innovations and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time (Rogers, 1995).  Breaking this normal distribution into segments leads to the segregation of individuals into the following five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, laggards (Rogers, 1995).  Members of each category typically possess certain distinguishing characteristics as shown below:

* innovators - venturesome, educated, multiple info sources

 

* early adopters - social leaders, popular, educated

*innovators - venturesome, educated, multiple info sources

* early majority - deliberate, many informal social contacts

*early adopters - social leaders, popular, educated

* late majority - skeptical, traditional, lower socio-economic status

*early majority - deliberate, many informal social contacts

* laggards - neighbours and friends are main info sources, fear of debt

*late majority - skeptical, traditional, lower socio-economic status

*laggards - neighbours and friends are main info sources, fear of debt

When the adoption curve is converted to a cumulative percent curve a characteristic S curve (as shown in the first figure below) is generated that represents the rate of adoption of the innovation within the population (Rogers, 1995).  The rate of adoption of innovations is impacted by five factors: relative advantage, compatibility, trialability, observability, and complexity (Rogers, 1995).  The first four factors are generally positively correlated with rate of adoption while the last factor, complexity, is generally negatively correlated with rate of adoption (Rogers, 1995).  The actual rate of adoption is governed by both the rate at which an innovation takes off and the rate of later growth.  Low cost innovations may have a rapid take-off while innovations whose value increases with widespread adoption (network effects) may have faster late stage growth.  Innovation adoption rates can, however, be impacted by other phenomena.  For instance, the adaptation of technology to individual needs can change the nature of the innovation over time.  In addition, a new innovation can impact the adoption rate of an existing innovation and path dependence may lock potentially inferior technologies in place.

When the adoption curve is converted to a cumulative percent curve a characteristic S curve (as shown in the first figure below) is generated that represents the rate of adoption of the innovation within the population (Rogers, 1995).  The rate of adoption of innovations is impacted by five factors: relative advantage, compatibility, trialability, observability, and complexity (Rogers, 1995).  The first four factors are generally positively correlated with rate of adoption while the last factor, complexity, is generally negatively correlated with rate of adoption (Rogers, 1995).  The actual rate of adoption is governed by both the rate at which an innovation takes off and the rate of later growth.  Low cost innovations may have a rapid take-off while innovations whose value increases with widespread adoption (network effects) may have faster late stage growth.  Innovation adoption rates can, however, be impacted by other phenomena.  For instance, the adaptation of technology to individual needs can change the nature of the innovation over time.  In addition, a new innovation can impact the adoption rate of an existing innovation and path dependence may lock potentially inferior technologies in place.

Sources: http://en.wikipedia.org/wiki/Diffusion_of_innovation

Sources: http://en.wikipedia.org/wiki/Diffusion_of_innovation

Since the early applications of DOI to IS research the theory has been applied and adapted in numerous ways.  Research has, however, consistently found that technical compatibility, technical complexity, and relative advantage (perceived need) are important antecedents to the adoption of innovations (Bradford and Florin, 2003; Crum et. al., 1996) leading to the generalized model presented below (see second figure below).

Since the early applications of DOI to IS research the theory has been applied and adapted in numerous ways.  Research has, however, consistently found that technical compatibility, technical complexity, and relative advantage (perceived need) are important antecedents to the adoption of innovations (Bradford and Florin, 2003; Crum et. al., 1996) leading to the generalized model presented below (see second figure below).

== Diagram/schematic of theory ==

==Diagram/schematic of theory==

[[Image:Doi1.JPG]]

[[Image:Doi1.JPG]]

[[Image:Doi2.JPG]]

[[Image:Doi2.JPG]]

== Originating author(s) ==

==Originating author(s)==

Lazarsfeld et. al. (1949); Rogers (1962); Rogers and Shoemaker (1971); Rogers (1995)

Lazarsfeld et. al. (1949); Rogers (1962); Rogers and Shoemaker (1971); Rogers (1995)

== Seminal articles ==

==Seminal articles==

Lazarsfeld, P.F., Berelson, B. & Gaudet, H. (1949). The people’s choice: How the voter makes up his mind in a presidential campaign. New York: Columbia University Press.  

Lazarsfeld, P.F., Berelson, B. & Gaudet, H. (1949). The people’s choice: How the voter makes up his mind in a presidential campaign. New York: Columbia University Press.  

Rogers, Everett M. Diffusion of Innovations. 5thed. New York: Free Press, 2003.

Rogers, Everett M. Diffusion of Innovations. 5thed. New York: Free Press, 2003.

== Originating area ==

==Originating area==

Anthropology/Sociology/Education/Communication/Marketing and Management/Geography/Economics

Anthropology/Sociology/Education/Communication/Marketing and Management/Geography/Economics

== Level of analysis ==

==Level of analysis==

Group, Firm, Industry, Society

Group, Firm, Industry, Society

== IS articles that use the theory ==

==IS articles that use the theory==

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.  

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.  

Zmud, R. W. (1982). Diffusion of modern software practices: Influence of centralization and formalization. Management Science, 28(12), 1421-1431.

Zmud, R. W. (1982). Diffusion of modern software practices: Influence of centralization and formalization. Management Science, 28(12), 1421-1431.

== Links from this theory to other theories ==

==Links from this theory to other theories==

[[Technology acceptance model]], [[Theory of planned behavior]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]], [[Evolutionary theory]], [[Technology-organization-environment framework]]

[[Technology acceptance model]], [[Theory of planned behavior]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]], [[Evolutionary theory]], [[Technology-organization-environment framework]]

== External links ==

==External links==

http://en.wikipedia.org/wiki/Diffusion_of_innovations, Wikipedia provides a brief synopsis of DOI theory

http://en.wikipedia.org/wiki/Diffusion_of_innovations, Wikipedia provides a brief synopsis of DOI theory

http://www.personal.psu.edu/staff/c/a/cam240/litreview.htm, A number of additional web links on DOI

http://www.personal.psu.edu/staff/c/a/cam240/litreview.htm, A number of additional web links on DOI

== Original Contributor(s) ==

==Original Contributor(s)==

Brent Furneaux

Brent Furneaux

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