Innovation Complexity

The innovation is complicated, which may be reflected by its scope and/or the nature and number of connections and steps.

Complexity may be related to length (the number of sequential sub-processes or steps for using an innovation) and breadth (the number of choices presented at decision points) (Kochevar & Yano, 2006). Complexity is increased with higher numbers of potential target units (teams, clinics, departments) or types of people in the Inner Setting (Kochevar & Yano, 2006). The updated definition of complexity aligns with other published conceptualizations of complex innovations (Butler et al., 2017; Lewin et al., 2017; Moecker et al., 2021).


The original CFIR elaborated further on this construct, stating that appropriately diagnosing and assessing complexity is thought to benefit implementation by avoiding unintended consequences (Kochevar & Yano, 2006). Simple innovations are more likely to be effective (Greenhalgh, Robert, et al., 2004; Gustafson et al., 2003) because they increase user satisfaction and the speed required to be competent in using the innovations (Klein et al., 2001).

The type of innovation, whether a technical (e.g., a new computer module) or administrative (behavioral change) change, can contribute to perceptions of complexity. Technical innovations may include a purchased product, packaged service, or an automated production process (e.g., computerized order entry). Administrative innovations primarily affect social structures or processes within settings. Most innovations are a hybrid of both. Technical innovations tend to be more tangible and administrative innovations tend to be more complex and difficult to implement (Greenhalgh, Robert, et al., 2004). On the other hand, complex behavioral change innovations can generate heightened commitment when they are viewed as a welcomed fundamental change compared to settings that regard the innovation as a simple “plug-in” (Edmondson et al., 2001). Edmondson et al. describe a “technological frame” of thinking that influences implementation effectiveness. In their study of a new cardiac surgical approach involving behavior change and teaming, the sites with less successful implementation viewed the innovation as an (oversimplified) “plug-in technology” while those with better implementation effectiveness regarded the innovation “as fundamental change for the [operating] team,” (Edmondson et al., 2001) despite its complexity.

Inclusion Criteria

Code statements regarding the complexity of the innovation.

  • “There were so many pieces and parts to the process but we were able to get it going because we did it in phases.”
  • “It changed the whole way our team worked together – the workflow and roles during these surgeries is so different now.”

Exclusion Criteria

Exclude statements regarding the complexity of implementation and code to other appropriate CFIR codes, e.g., code difficulties related to space to Available Resources and code difficulties related to engaging participants in a new program to Engaging: Innovation Recipients.

Regarding quantitative measurement of this construct: In a systematic review of quantitative measures related to implementation, Lewis et al. identified nine measures (Lewis, Mettert, & Lyon, 2021). Using PAPERS measurement quality criteria with an aggregate scale ranging from -9 to +36 (Lewis, Mettert, Stanick, et al., 2021), the highest score was 4, indicating the need for continued development of high-quality measures.

Note: As we become aware of measures, we will post them here. Please contact us with updates.

Butler, M., Epstein, R. A., Totten, A., Whitlock, E. P., Ansari, M. T., Damschroder, L. J., Balk, E., Bass, E. B., Berkman, N. D., Hempel, S., Iyer, S., Schoelles, K., & Guise, J.-M. (2017). AHRQ series on complex intervention systematic reviews—paper 3: Adapting frameworks to develop protocols. Journal of Clinical Epidemiology, 90, 19–27.

Edmondson, A. C., Bohmer, R. M., & Pisana, G. P. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46(4), 685–716.

Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Q, 82(4), 581–629.

Gustafson, D. H., Sainfort, F., Eichler, M., Adams, L., Bisognano, M., & Steudel, H. (2003). Developing and testing a model to predict outcomes of organizational change. Health Serv Res, 38(2), 751–776

Klein, K. J., Conn, A. B., & Sorra, J. S. (2001). Implementing computerized technology: An organizational analysis. Journal of Applied Psychology, 86(5), 811–824.

Kochevar, L. K., & Yano, E. M. (2006). Understanding health care organization needs and context. Beyond performance gaps. J Gen Intern Med, 21 Suppl 2, S25-9.

Lewin, S., Hendry, M., Chandler, J., Oxman, A. D., Michie, S., Shepperd, S., Reeves, B. C., Tugwell, P., Hannes, K., Rehfuess, E. A., Welch, V., Mckenzie, J. E., Burford, B., Petkovic, J., Anderson, L. M., Harris, J., & Noyes, J. (2017). Assessing the complexity of interventions within systematic reviews: Development, content and use of a new tool (iCAT_SR). BMC Medical Research Methodology, 17(1), 76.

Lewis, C. C., Mettert, K. D., Stanick, C. F., Halko, H. M., Nolen, E. A., Powell, B. J., & Weiner, B. J. (2021). The psychometric and pragmatic evidence rating scale (PAPERS) for measure development and evaluation. Implementation Research and Practice, 2, 263348952110373.

Lewis, C. C., Mettert, K., & Lyon, A. R. (2021). Determining the influence of intervention characteristics on implementation success requires reliable and valid measures: Results from a systematic review. Implementation Research and Practice, 2, 263348952199419.

Moecker, R., Terstegen, T., Haefeli, W. E., & Seidling, H. M. (2021). The influence of intervention complexity on barriers and facilitators in the implementation of professional pharmacy services – A systematic review. Research in Social and Administrative Pharmacy, 17(10), 1651–1662.