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Criticism
[edit]Because there are more than four thousand articles across many disciplines published on Diffusion of Innovations, with a vast majority written after Rogers created a systematic theory, there have been few widely adopted changes to the theory[1]. Although each study applies the theory in slightly different ways, this lack of cohesion has left the theory stagnant and difficult to apply with consistency to new problems[2][3].
Diffusion is difficult to quantify because humans and human networks are complex. It is extremely difficult, if not impossible, to measure what exactly causes adoption of an innovation[4]. This is important, particularly in healthcare. Those encouraging adoption of health behaviors or new medical technologies need to be aware of the many forces acting on an individual and his or her decision to adopt a new behavior or technology. Diffusion theories can never account for all variables, and therefore might miss critical predictors of adoption[5]. This variety of variables has also led to inconsistent results in research, reducing heuristic value[6].
Various computer models have been developed in order to simulate diffusion. Veneris developed a systems dynamics computer model that considers various diffusion patterns modeled via differential equations.[7]
A number of criticisms imply limits to its usefulness for managers. First, technologies are not static. Continual innovation attracts new adopters all along the S-curve. The S-curve does not just 'happen'. Instead, the s-curve may be made up of a series of 'bell curves' at different sections of a population adopting different versions of a generic innovation.
Rogers placed the contributions and criticisms of diffusion research into four categories: pro-innovation bias, individual-blame bias, recall problem, and issues of equality. The pro-innovation bias, in particular, implies that all innovation is positive and that all innovations should be adopted.[8] Cultural traditions and beliefs can be consumer by another culture's through diffusion, which can impose significant costs on a group of people[9]. The one-way information flow, from sender to receiver, is another weakness of this theory. The message sender has a goal to persuade the receiver, and there is little to no reverse flow. The person implementing the change controls the direction and outcome of the campaign. In some cases, this is the best approach, but other cases require a more participatory approach.[10] In complex environments where the adopter is receiving information from many sources and is returning feedback to the sender, a one-way model is insufficient and multiple communication flows need to be examined[11].
- ^ Greenhalgh, T.; Robert, G.; Macfarlane, F.; Bate, P.; Kyriakidou, O.; Peacock, R. (2005). "Storylines of Research in Diffusion of Innovation: A Meta-narrative Approach to Systematic Review". Social Science & Medicine. 61: 417–430. doi:10.1016/j.socscimed.
- ^ Meyers, P; Sivakumar, K; Nakata, C (1999). "Implementation of Industrial Process Innovations: Factors, Effects, and Marketing Implications". The Journal of Product Innovation Management. 16 (3): 295–311. doi:10.1111/1540-5885.1630295.
- ^ Katz, E; Levin, M; Hamilton, H (1963). "Traditions of Research on the Diffusion of Innovation". American Sociological Review. 28 (2): 237–252.
- ^ Damanpour, F (1996). "Organizational Complexity and Innovaiton: Developing and Testing Multiple Contingency Models". Management Science. 42 (5): 693–716.
- ^ Plsek, P; Greenhalgh, T (2001). "The challenge of complexity in health care". Complexity Science. 323: 625–628.
- ^ Downs, GW; Mohr, LB (1976). "Conceptual Issues in the Study of Innovation". Administrative Science Quarterly. 21 (4): 700–714.
- ^ Veneris, Y. (1990). "Modelling the transition from the industrial to the informational revolution". Environment and Planning A. 22 (3): 399. doi:10.1068/a220399.
- ^ Cite error: The named reference
Rogers5was invoked but never defined (see the help page). - ^ Downs, GW; Mohr, LB (1976). "Conceptual Issues in the Study of Innovation". Administrative Science Quarterly. 21 (4): 700–714.
- ^ Giesler, Markus (2012). "How Doppelgänger Brand Images Influence the Market Creation Process: Longitudinal Insights from the Rise of Botox Cosmetic". Journal of Markeing. 76 (6): 55–68. doi:10.1509/jm.10.0406.
- ^ Robertson, M; Swan, Jacky; Newell, Sue (1996). "The Role of Networks in the Diffusion of Technological Innovation". Journal of Management Studies. 33 (3): 333–359.