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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.  

Beatty, R. C., Shim, J. P., & Jones, M. C. (2001). Factors influencing corporate web site adoption: A time-based assessment. Information & Management, 38(6), 337-354.  

Beatty, R. C., Shim, J. P., & Jones, M. C. (2001). Factors influencing corporate web site adoption: A time-based assessment. Information & Management, 38(6), 337-354.  

Bharati, P. and Chaudhury, A. (2006), “Current Status of Technology Adoption: Micro, Small and Medium Manufacturing Firms in Boston”, Communications of the ACM, Vol. 49, No. 10, pp. 88-93.

Bharati, P. and Chaudhury, A. (2006), “Current Status of Technology Adoption: Micro, Small and Medium Manufacturing Firms in Boston”, Communications of the ACM, Vol. 49, No. 10, pp. 88-93.

Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225.  

Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225.  

Brancheau, J. C., & Wetherbe, J. C. (1990). The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing. Information Systems Research, 1(2), 115-143.  

Brancheau, J. C., & Wetherbe, J. C. (1990). [http://inn.colorado.edu/Details/Paper/6658 The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing.] Information Systems Research, 1(2), 115-143.  

Carter Jr., F. J., Jambulingam, T., Gupta, V. K., & Melone, N. (2001). Technological innovations: A framework for communicating diffusion effects. Information & Management, 38(5), 277-287.  

Carter Jr., F. J., Jambulingam, T., Gupta, V. K., & Melone, N. (2001). Technological innovations: A framework for communicating diffusion effects. Information & Management, 38(5), 277-287.  

Grover, V. (1993). An empirically derived model for the adoption of customer-based interorganizational systems. Decision Sciences, 24(3), 603-640.  

Grover, V. (1993). An empirically derived model for the adoption of customer-based interorganizational systems. Decision Sciences, 24(3), 603-640.  

Grover, V., Fiedler, K., & Teng, J. (1997). Empirical evidence on swanson's tri-core model of information systems innovation. Information Systems Research, 8(3), 273-287.  

Grover, V., Fiedler, K., & Teng, J. (1997). [http://inn.colorado.edu/Details/Paper/6941 Empirical evidence on swanson's tri-core model of information systems innovation.] Information Systems Research, 8(3), 273-287.  

Grover, V., & Goslar, M. D. (1993). The initiation, adoption, and implementation of telecommunications technologies in U.S. organizations. Journal of Management Information Systems, 10(1), 141-163.  

Grover, V., & Goslar, M. D. (1993). [http://inn.colorado.edu/Details/Paper/10075 The initiation, adoption, and implementation of telecommunications technologies in U.S. organizations.] Journal of Management Information Systems, 10(1), 141-163.  

Hardgrave, B. C., Davis, F. D., & Riemenschneider, C. K. (2003). Investigating determinants of software developers' intentions to follow methodologies. Journal of Management Information Systems, 20(1), 123-152.  

Hardgrave, B. C., Davis, F. D., & Riemenschneider, C. K. (2003). [http://inn.colorado.edu/Details/Paper/7598 Investigating determinants of software developers' intentions to follow methodologies.] Journal of Management Information Systems, 20(1), 123-152.  

Hsu, C. L., Lu, H. P. and Hsu, H. H. (2007). Adoption of the mobile internet: an empirical study of multimedia message service (MMS), OMEGA: International Journal of Management Science, 35, 715-726.

Hsu, C. L., Lu, H. P. and Hsu, H. H. (2007). Adoption of the mobile internet: an empirical study of multimedia message service (MMS), OMEGA: International Journal of Management Science, 35, 715-726.

Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465-485.  

Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465-485.  

Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183-213.  

Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). [http://inn.colorado.edu/Details/Paper/83 Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs.] MIS Quarterly, 23(2), 183-213.  

Kautz, K., & Pries-Heje, J. (Eds.). (1996). Diffusion and adoption of information technology. London: Chapman and Hall.  

Kautz, K., & Pries-Heje, J. (Eds.). (1996). Diffusion and adoption of information technology. London: Chapman and Hall.  

Moore, G. C. (1987). "End user computing and ofice automation: A diffusion of innovations perspective. INFOR, 25(3), 214-235.  

Moore, G. C. (1987). "End user computing and ofice automation: A diffusion of innovations perspective. INFOR, 25(3), 214-235.  

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.  

Moore, G. C., & Benbasat, I. (1991). [http://inn.colorado.edu/Details/Paper/6664 Development of an instrument to measure the perceptions of adopting an information technology innovation.] Information Systems Research, 2(3), 192-222.  

Mustonen-Ollila, E., & Lyytinen, K. (2003). Why Organizations Adopt Information System Process Innovations: A Longitudinal Study using Diffusion of Innovation Theory, Information Systems Journal, 13(3), 275-297.  

Mustonen-Ollila, E., & Lyytinen, K. (2003). Why Organizations Adopt Information System Process Innovations: A Longitudinal Study using Diffusion of Innovation Theory, Information Systems Journal, 13(3), 275-297.  

Park, S., & Yoon, S. (2005). Separating early-adopters from the majority: The case of broadband internet access in korea. Technological Forecasting and Social Change, 72(3), 301-325.  

Park, S., & Yoon, S. (2005). Separating early-adopters from the majority: The case of broadband internet access in korea. Technological Forecasting and Social Change, 72(3), 301-325.  

Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362-379.  

Parthasarathy, M., & Bhattacherjee, A. (1998). [http://inn.colorado.edu/Details/Paper/6178 Understanding post-adoption behavior in the context of online services.] Information Systems Research, 9(4), 362-379.  

Peansupap, V., & Walker, D. (2005). Exploratory factors influencing information and communication technology diffusion and adoption within australian construction organizations: A micro analysis. Construction Innovation, 5(3), 135-157.  

Peansupap, V., & Walker, D. (2005). Exploratory factors influencing information and communication technology diffusion and adoption within australian construction organizations: A micro analysis. Construction Innovation, 5(3), 135-157.  

Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208-222.  

Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). [http://inn.colorado.edu/Details/Paper/6788 Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system.] Information Systems Research, 12(2), 208-222.  

Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). Implementation of electronic data interchange: An innovation diffusion perspective. Journal of Management Information Systems, 11(2), 157-186.  

Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). [http://inn.colorado.edu/Details/Paper/10123 Implementation of electronic data interchange: An innovation diffusion perspective.] Journal of Management Information Systems, 11(2), 157-186.  

Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117-135.  

Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117-135.  

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.  

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.  

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.  

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view.] MIS Quarterly, 27(3), 425-478.  

Wu, J., & Wang, S. (2005). What drives mobile commerce? an empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.  

Wu, J., & Wang, S. (2005). What drives mobile commerce? an empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.  

Zmud, R. W. (1983). The effectiveness of external information channels in facilitating innovation within software development groups. MIS Quarterly, 7(2), 43-58.  

Zmud, R. W. (1983). The effectiveness of external information channels in facilitating innovation within software development groups. MIS Quarterly, 7(2), 43-58.  

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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<br>