A few noteworthy gems from Diffusion theory worth exploring before we move onto Christensen’ Disruption model. First is the Bass Forecasting Model created by Frank Bass in 1969, then a marketing professor at Purdue University. The Bass forecasting offers some plausible answers to the uncertainty associated with the introduction of a new product in the marketplace. The formula:
n(t) = p[m-N(t)] + q(N(t)/m)[m-N(t)]
where : m = the potential number of adopters n(t) = the number of adopters at time t N(t) = the cumulative number of adopters at time t p = coefficient of innovation q = coefficient of imitation The Bass Equation is interesting and worthy of a detailed discussion in of itself, however the key point is that Bass mathematically models diffusion and is consistent with the universal research findings that diffusion is essentially a social process occurring through interpersonal networks. Of specific interest are the two coefficients:
p = Coefficient of innovation – Mass media – influence of mass media is found through out the diffusion process but is concentrated in the relatively early time frames of diffusion. p typically has a value of 0.03
q = Coefficient of imitation – Interpersonal world-of-mouth channel – individuals adopting as a result of interpersonal message about a new product expand in numbers during the first half of the diffusion process and thereafter decline in numbers per time period creating the S-shaped diffusion curve. Value typically is around .38
Stated differently, the upward inflection in the S curve happens when work-of-mouth buzz begins to dominate the equation. Mass media is not sufficient to achieve market penetration. If we remember back to the dot com area, there is a long list of failed companies that thought pouring advertising dollars at the diffusion problem was sufficient for market acceptance. Grant you, dot coms had other issues, like the product wasn’t needed, but the advertising guys were skillful in convincing the diffusion neophytes that mass media was the solution to achieving market acceptance. If they only understood diffusion theory, maybe the outcomes would have been different – then again maybe not?
Bass modeling is a “look-like” forecast – i.e. uses a past innovation of similar attributes to start the model and define the coefficients. The devil has been and always will be in defining the coefficients to create a useful forecast model. Though the model has its constraints, I have seen examples of where companies have used it quite successfully in forecasting new product diffusion. Worth further exploring for those of you involved with forecasting.
Perceived Attributes of Innovation
The last nugget from the diffusion model that can help predict and understand if a new innovation will diffuse into a social network is what Rogers defines as the “Perceived Attributes of Innovation.” The perceived attributes of innovation are one important explanation of the rate of adoption of an innovation. According to Rogers “from 49 to 87 percent of the variance in the rate of adoption are explained by five attributes.” The attributes are:
Relative Advantage The degree to which an innovation is perceived as being better than the idea it supersedes. The degree of relative advantage is often expressed as economical profitability, social prestige or other benefits.
For example, the hybrid car delivers a perceived attribute as being economically beneficially with the added value of being socially responsible for protecting the environment. It’s also very hip in many circles.
Compatibility The degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters. Compatibility also has an infrastructure component where the new innovation is compatible with existing technologies (i.e. it interoperates and complements existing technologies).
For example, the cell phone while certainly technically radical, wasn’t that great of a stretch in terms of users understanding how to use the innovation (a telephone without wires) and because it could interoperate with existing phone networks, the value or utility of a limited number of phones was not constrained (i.e. Metcalf’s law of the value of the network wasn’t an issue).
Complexity The degree to which an innovation is perceived as relatively difficult to understand and use.
Again the cell phone provides a good example where the complexity of the innovation was relatively small for the target user to adopt. Whereas enterprise resource planning software is relatively difficult to understand and use.
Trialability The degree to which an innovation may be experienced with on a limited basis. Stated another way: the easier it is to try and quicker the benefit is understood and to dispel uncertainty about the new idea.
For example it is easy to go test drive a new hybrid car (supply not being an issue of course – but that’s another model we won’t dive into yet). And it was easy to use a friends cell phone to make a call.
Observability The degree to which the results of an innovation are visible to others.
The cell phone once again offers a very observable benefit. Whereas software, say location based services, are less observable (compatibility issues on behavior and infrastructure also come into play) and therefore will have a relatively slower rate of adoption.
Final words: perceptions count!
Recall that 49 to 87 percent of the variance in the rate of adoption are explained by five attributes As Rogers states “The receivers’ perception of the attributes of innovation, not the attributes as classified by experts or change agents, affect its rate of adoption.”
If we can understand the perceptions new users have of the innovation, we can better plan our product launch strategy over the lifecycle curve.
Onto more models related to product diffusion. In my next blog we will look at Christensen’s model of disruptive innovations.
Rock-on!