Innovation-Decision Model (also known as Diffusion of Innovations)

See more Change Theories »Summary written by Tessa Andrews, University of Georgia,

The innovation-decision model maps stages that individuals experience as they consider and adopt an innovation. It also outlines the features of innovations that make them more likely to be adopted.


The innovation-decision model aims to explain the process by which individuals adopt new innovations, and recognizes that a decision to use an innovation includes several steps (Rogers, 2003). Stages in this process include:

  • Knowledge - an individual becomes aware of an innovation and learns how it works
  • Persuasion - an individual forms a favorable or unfavorable attitude toward the innovation
  • Decision - an individual undertakes activities that lead to a choice to adopt or not adopt the innovation
  • Implementation - an individual uses the innovation
  • Confirmation - an individual seeks information and experiences that confirm the decision they already made, or reverse the previous decision in the face of conflicting messages

An individual may leave the innovation-decision process at any stage. Beyond these five stages of the process, this model stipulates prior conditions that may be important to the adoption of an innovation: previous practice, perceived needs or problems, innovativeness of the individual, and the norms of the relevant social system (Rogers 2003). It also distinguishes between awareness knowledge (knowing an innovation exists), how-to knowledge (knowing how to appropriately use an innovation), and principles knowledge (knowing why a strategy works). Lastly, the model outlines factors that influence whether an individual forms a favorable or unfavorable attitude about an innovation, including the relative advantage of the innovation, the compatibility of the innovation with current beliefs and practices, the complexity of the innovation, how easy it is to try the innovation (trialability), and whether the individual can watch someone else using the innovation before making a decision (observability).

Example of Use

Researchers have repeatedly used the innovation-decision model to investigate the adoption of evidence-based instructional practices among college STEM instructors. This model has been useful for in-depth studies of one or two instructors adopting new instructional strategies (e.g., Henderson, 2005; Marbach-Ad & Rietschel, 2017), studies of the factors influencing adoption of evidence-based strategies (e.g., Andrews & Lemons 2015; Lund & Stains, 2015), and large-scale studies of the current adoption status of hundreds of faculty (e.g., Henderson, Dancy, & Niewiadomska-Bugaj 2012). Some researchers have suggested modifications and limitations of the model for the context of STEM higher education, including Henderson (2005) and Andrews & Lemons (2015).

Assumptions & Limitations

One important limitation of the innovation-decision model is that it was developed primarily to explain the process of adopting innovations that are used "as is" rather than adapted. Henderson (2005) and others note that faculty rarely adopt evidence-based teaching strategies "off-the-shelf" and instead make many modifications to their context. Additionally this model does not fully capture the long-term and iterative process of changing one's teaching (Andrews & Lemons 2015), instead depicting change as a one-time decision. One assumption of the innovation-decision model is that the innovation should be adopted by individuals (Rogers 2003). Another potential short-coming is a laser focus on individuals as the key factor in change, with little recognition of the system in which they work. Lastly, this model does not recognize differences in social identity among individuals, such as race, gender, socioeconomic status, etc. that may influence the change process (Rogers 2003).

Original Publication of Theory

Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.

Other References

Andrews, T. C., & Lemons, P. P. (2015). It's personal: Biology instructors prioritize personal evidence over empirical evidence in teaching decisions. CBE—Life Sciences Education, 14(1), ar7.

Goodwin, E. C., Cao, J. N., Fletcher, M., Flaiban, J. L., & Shortlidge, E. E. (2018). Catching the Wave: Are Biology Graduate Students on Board with Evidence-Based Teaching?. CBE—Life Sciences Education, 17(3), ar43.

Henderson, C. (2005). The challenges of instructional change under the best of circumstances: a case study of one college physics instructor. Am J Phys 73, 778–786.

Henderson, C., Dancy, M. H. (2008). Physics faculty and educational researchers: divergent expectations as barriers to the diffusion of innovations. Am J Phys 76, 79–91.

Henderson, C., Dancy, M., Niewiadomska-Bugaj, M. (2012). Use of research-based instructional strategies in introductory physics: where do faculty leave the innovation-decision process? Phys Rev ST Phys Educ Res 8, 020104.

Lund, T. J., & Stains, M. (2015). The importance of context: An exploration of factors influencing the adoption of student-centered teaching among chemistry, biology, and physics faculty. International Journal of STEM Education, 2(1), 13.

Marbach-Ad, G., & Hunt Rietschel, C. (2016). A case study documenting the process by which biology instructors transition from teacher-centered to learner-centered teaching. CBE—Life Sciences Education, 15(4), ar62.

Note: This summary is written as a secondary resource to help researchers and practitioners learn about potentially relevant change theory. We encourage authors to read the original references rather than citing this summary in published work or grant proposals.