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“The lure of certainty is built into the brain at a very basic level. This is most poignantly demonstrated by split brain patients (…) The default state of the brain is indecisive disagreement; various mental parts are constantly insisting that the other parts are wrong. Certainty imposes consensus on this inner cacophony. It lets you pretend that your entire brain agrees with your behavior.”

Jonah Lehrer’s “How We Decide”

Let’s recap. In my last post I discussed the following:

• innovating is about delivering something new, “a first” that makes a difference,
• innovating involves change.

I also shared a quote from Pip Coburn’s book “The Change Function. Why Some Technologies Take Off and Others Crash and Burn,” which made me think of the need for gathering some information on known formulae for change. Note that the most popular equation on the subject is Gleicher’s Formula: C= D x V x F > R

• “C” stands for change.
• “D” relates to the level of dissatisfaction, the higher the more of a reason to embrace change.
• “V” is about having a leading vision in mind, an end in sight that is obtainable.
• “F” stands for the first milestone, the first step to get there.
• “R” is resistance to change, whether active, passive or just a subject of mere indecisiveness.

Being a product, D, V or F cannot be “0” for a favorable change to take place. Going back to Pip’s book. His change function = UC vs. TPPA

• “UC” means user crisis. This about problems serious enough to be worth addressing and solving, the opposite being a state of satisfaction or just indifference.
• “TPPA” is the total perceived pain of adoption. This is the sum of any unfavorable considerations and efforts that making a change would involve.

Accounting wise, appraising change is about estimating the trade off between the gains (or loss) and what needs to be given in exchange, which translates into the cost/benefit ratio:

• The benefits would need to outweigh the costs by an amount that is substantial enough to go through the trouble of changing,
• This also introduces the notion of opportunity cost, as the context and available options matter and, therefore, the relative appeal of the next best alternative.

Diffusion of innovation theory adds the following considerations:

• Communication channels, whether formal or informal, to convey the messages facilitating the discovery and early awareness, knowledge that can followed by persuasive communications and the individual’s own willingness to seek more information. 
• Rate (speed) of adoption.” This measured by the time that it takes: (1) to get the first user (and early adopters) to leverage the innovation, (2) adoption by specific user groups, market layers and segments and (3) to reach critical mass, market share targets, economies of scale, etc. just to name a few examples.
• Social systems formed by individuals that share an interest and/or experience the impact of the innovation. This leads to social network analysis, including the study of network effects.

In my next posts I will cover the Bass Diffusion Model and, at some point, I will circle back to the quote from “How We Decide” that I started this one with.

J. de Francisco blogging from Chicago on June 16, 2010

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