Diffusion of innovations theory

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Diffusion of innovations


Acronym

DOI

Alternate name(s)

Innovation Diffusion Theory (IDT)

Main dependent construct(s)/factor(s)

Implementation Success or Technology Adoption

Main independent construct(s)/factor(s)

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

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:

  • innovators - venturesome, educated, multiple info sources
  • early adopters - social leaders, popular, educated
  • early majority - deliberate, many informal social contacts
  • 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.

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

Rogers, Everett M. Diffusion of Innovations. 4thed. New York: Free Press,1995

Diffusion of Innovation Theory in IS

Moore and Benbasat (1991), working in an IS context, expanded upon the five factors impacting the adoption of innovations presented by Rogers, generating eight factors (voluntariness, relative advantage, compatibility, image, ease of use, result demonstrability, visibility, and trialability) that impact the adoption of IT. Scales used to operationalize these factors were also validated in the study.

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

Doi1.JPG

IS diffusion variance model:

Doi2.JPG

Originating author(s)

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

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.

Rogers, Everett M. (1962). Diffusion of Innovations. The Free Press. New York.

Rogers, Everett M & Shoemaker, Floyd F (1971). Communication of Innovations: A Cross-Cultural Approach (2nd ed.). New York: The Free Press.

Rogers, Everett M. Diffusion of Innovations. 4thed. New York: Free Press, 1995.

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

Originating area

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

Level of analysis

Group, Firm, Industry, Society

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. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582.

Armstrong, J. S., & Yokum, J. T. (2001). Potential diffusion of expert systems in forecasting. Technological Forecasting and Social Change, 67(1), 93-103.

Baskerville, R L & Pries-Heje, J (2001). A multiple-theory analysis of a diffusion of information technology case. Information Systems Journal, 11(3), 181-212.

Baskerville, R., & Pries-Heje, J. (2003). Diversity in modeling diffusion of information technology. Journal of Technology Transfer, 28(3-4), 251-264.

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., Zhang, C., and Chaudhury, A. (Forthcoming), “Social Media Assimilation in Firms: Investigating the Roles of Absorptive Capacity and Institutional Pressures,” Information Systems Frontiers, Springer.

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.

Blake, B. F., Neuendorf, K. A., & Valdiserri, C. M. (2005). Tailoring new websites to appeal to those most likely to shop online. Technovation, 25(10), 1205-1214.

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.

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.

Chen, L., Gillenson, M. L., & Sherrell, D. L. (2004). Consumer acceptance of virtual stores: A theoretical model and critical success factors for virtual stores. Database for Advances in Information Systems, 35(2), 8-31.

Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705-719.

Cheng, J. M. S., Kao, L. L. Y., & Lin, J. Y. (2004). An investigation of the diffusion of online games in taiwan: An application of roger's diffusion of innovation theory. Journal of American Academy of Business, Cambridge, 5(1/2), 439-445.

Cheung, C. M. K., Chan, G. W. W., & Limayem, M. (2005). A critical review of online consumer behavior: Empirical research. Journal of Electronic Commerce in Organizations, 3(4), 1-19.

Chircu, A. M., & Kauffman, R. J. (2000). Limits to value in electronic commerce-related IT investments. Journal of Management Information Systems, 17(2), 59-80.

Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123-139.

Crum, M. R., Premkumar, G., & Ramamurthy, K. (1996). An assessment of motor carrier adoption, use, and satisfaction with EDI. Transportation Journal, 35(4), 44-57.

Dos Santos, B. L., & Peffers, K. (1998). Competitor and vendor influence on the adoption of innovative applications in electronic commerce. Information & Management, 34(3), 175-184.

Eastin, M. S. (2002). Diffusion of e-commerce: An analysis of the adoption of four e-commerce activities. Telematics and Informatics, 19(3), 251-267.

Eder, L. B., & Igbaria, M. (2001). Determinants of intranet diffusion and infusion. Omega, 29(3), 233-242.

Fichman, R. G. (2004). Going beyond the dominant paradigm for information technology innovation research: Emerging concepts and methods. Journal of the Association for Information Systems, 5(8), 314-355.

Fichman, R. G. (2001). The role of aggregation in the measurement of it-related organizational innovation. MIS Quarterly, 25(4), 427-455.

Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3), 255.

Fichman, R. G., & Kemerer, C. F. (1997). The assimilation of software process innovations: An organizational learning perspective. Management Science, 43(10), 1345-1363.

Forman, C. (2005). The corporate digital divide: Determinants of internet adoption. Management Science, 51(4), 641.

Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4-5), 603-625.

Goslar, M. D. (1987). Marketing and the adoption of microcomputers: An application of diffusion theory. Journal of the Academy of Marketing Science, 15(2), 42-48.

Grantham, A., & Tsekouras, G. (2005). Diffusing wireless applications in a mobile world. Technology in Society, 27(1), 85-104.

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., & 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.

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.

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.

Hu, Q., Saunders, C., & Gebelt, M. (1997). Research report: Diffusion of information systems outsourcing: A reevaluation of influence sources. Information Systems Research, 8(3), 288-301.

Hung, S., Ku, C., & Chang, C. (2003). Critical factors of WAP services adoption: An empirical study. Electronic Commerce Research and Applications, 2(1), 42-60.

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.

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

Kocas, C. (2002). Evolution of prices in electronic markets under diffusion of price-comparison shopping. Journal of Management Information Systems, 19(3), 99-119.

Lai, V. S. (1997). Critical factors of ISDN implementation: An exploratory study. Information & Management, 33(2), 87-97.

Lee, M. K. O. (1998). Internet-based financial EDI: Towards a theory of its organizational adoption. Computer Networks and ISDN Systems, 30(16-18), 1579-1588.

Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in the implementation of new technology. Management Science, 34(10), 1252-1265.

Li, S. S. (2003). Electronic newspaper and its adopters: Examining the factors influencing the adoption of electronic newspapers in taiwan. Telematics and Informatics, 20(1), 35-49.

Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63-74.

Martins, C. B. M. J., Steil, A. V., & Todesco, J. L. (2004). Factors influencing the adoption of the internet as a teaching tool at foreign language schools. Computers and Education, 42(4), 353-374.

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.

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.

Nilakanta, S., & Scamell, R. W. (1990). The effect of information sources and communication channels on the diffusion of innovation in a data base development environment. Management Science, 36(1), 24-40.

O'Callaghan, R., Kaufmann, P. J., & Konsynski, B. R. (1992). Adoption correlates and share effects of electronic data interchange systems in marketing channels. Journal of Marketing, 56(2), 45-56.

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.

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.

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.

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.

Raho, L. E., Belohlav, J. A., & Fiedler, K. D. (1987). Assimilating new technology into the organization: An assessment of McFarlan and McKenney's model. MIS Quarterly, 11(1), 46-57.

Rajagopal, P. (2002). An innovation—diffusion view of implementation of enterprise resource planning (ERP) systems and development of a research model. Information and Management, 40(2), 87-114.

Ramamurthy, K., & Premkumar, G. (1995). Determinants and outcomes of electronic data interchange diffusion. IEEE Transactions on Engineering Management, 42(4), 332-351.

Ramamurthy, K., Premkumar, G., & Crum, M. R. (1999). Organizational and interorganizational determinants of EDI diffusion and organizational performance: A causal model. Journal of Organizational Computing & Electronic Commerce, 9(4), 253-285.

Ravichandran, T. (2000). Swiftness and intensity of administrative innovation adoption: An empirical study of TQM in information systems. Decision Sciences, 31(3), 691-724.

Reich, B. H., & Benbasat, I. (1990). An empirical investigation of factors influencing the success of customer-oriented strategic systems. Information Systems Research, 1(3), 325-347.

Rodger, J. A., Pendharkar, P. C., & Bhatt, G. D. (1996). Diffusion theory and the adoption of software innovation: Common errors and future issues. Journal of High Technology Management Research, 7(1), 1-13.

Roman, R. (2003). Diffusion of innovations as a theoretical framework for telecenters. Information Technologies & International Development, 1(2), 53-66.

Seyal, A. H., & Rahman, M. N. A. (2003). A preliminary investigation of e-commerce adoption in small & medium enterprises in brunei. Journal of Global Information Technology Management, 6(2), 6-26.

Shao, Y. P. (1999). Expert systems diffusion in british banking: Diffusion models and media factor. Information & Management, 35(1), 1-8.

Sharma, S., & Rai, A. (2003). An assessment of the relationship between ISD leadership characteristics and IS innovation adoption in organizations. Information and Management, 40(5), 391-401.

Straub, D. W. (1994). The effect of culture on IT diffusion: E-mail and FAX in japan and the U.S. Information Systems Research, 5(1), 23-47.

Swanson, E. B. (1994). Information systems innovation among organizations. Management Science, 40(9), 1069-1092.

Tam, K. Y. (1996). Dynamic price elasticity and the diffusion of mainframe computing. Journal of Management Information Systems, 13(2), 163-183.

Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington, Mass.: Lexington Books.

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.

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

Zhu, K., & Kraemer, K. L. (2005). Post-adoption variations in usage and value of E-business by organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1), 61-84.

Zmud, R. W. (1984). An examination of 'push-pull' theory applied to process innovation in knowledge work. Management Science, 30(6), 727-738.

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.

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

External links

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

http://www.anu.edu.au/people/Roger.Clarke/SOS/InnDiff.html, Roger Clarke presents a primer on DOI theory as a preparation to reading the relevant IS literature and a resource list including a number of references at http://www.anu.edu.au/people/Roger.Clarke/SOS/InnDiffISW.html

http://www.sigadit.org/, The AIS Special Interest Group on Adoption and Diffusion of Information Technology

http://www.isi.salford.ac.uk/tm/Diffusion.enl, Tom McMaster provides an EndNote library of DOI

http://www.context.org/ICLIB/IC28/AtKisson.htm, A role playing game called The Innovation Diffusion Game that is intended to demonstrate some basic principles of cultural change and DOI theory

http://disc-nt.cba.uh.edu/chin/digit98/panel2.pdf#search='innovation%20diffusion%20theory', A 1998 paper by Agarwal et. al. outlining extensions to DOI theory

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

Original Contributor(s)

Brent Furneaux

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