Platt Perspective on Business and Technology

Innovation, disruptive innovation and market volatility 3: modeling and understanding change and innovation per se 2

Posted in macroeconomics by Timothy Platt on July 2, 2014

This is my third posting to a series on the economics of innovation, and on how change and innovation can be defined and analyzed in economic and related risk management terms (see Part 1: considering businesses and outside investors and their dynamics and Part 2 where I began to develop and present a basic taxonomic model of innovation types.)

At the end of Part 2, I presented what amounted to an outline for what I plan on covering in this series as a whole, or at least of several of its key areas that I will be discussing. My intention is to frame at least most of that overall flow of discussion in terms of a basic four category model for distinguishing between types of innovation per se that I will focus on here in this series installment. And I begin presenting that from some core points that I raised in Part 2:

• I noted in Part 2, evaluating innovation for its economic value and potential is all about risk and benefits analysis.
• And it is one where the timeframes that these outcomes play out in are crucial.
• So as a basic organizing model, picture a two dimensional graphical layout with the horizontal axis representing time until at least break-even payoff on investment is reached, and where the vertical axis represents risk of not reaching even that minimal return on investment that would just cover costs for the effort made.

As a first real-world take on this conceptual model, both outside investors and in-house business owners and leaders seek more than simply breaking even when they make an investment in developing a new innovative offering. So in practice, assume that the time axis here, in fact represents reaching at least some minimal profit generating payoff that would be considered to reach at least a threshold-acceptable level of return on investment, given the overall risk taken in investing in and developing this innovation in the first place. So realistically, this is not just about reaching fiscal break-even where no one loses or gains out of this venture. This is about positive return on investment.

I note here in anticipation of discussion points to come that different observers/participants in this (e.g. business owners or alternatively venture capital investors) can have markedly different opinions as to what constitutes that “minimal acceptable return on investment” and that this means they can have very different ideas as to where a given innovation does or does not fit on this type of graph. I will discuss that and its implications in depth, but for now simply identify the horizontal axis as representing a timing axis and leave the details of “timing until what”, for later.

As graphically noted below, this model divides innovation development efforts into four quadrants which I identify as Roman numerals as:

I. High risk and short timeframe
II. High risk and long timeframe
III. Low risk and short timeframe, and
IV. Low risk and long timeframe


High Risk


I       |      II


— Short ———- Timeframe ———- Long —


III      |      IV


Low Risk


There are a number of ways to characterize the types of innovation contexts that would fit into these specific quadrants, but at least as a first cut here:

• Quadrant I is where you would find disruptive change and revolutionary innovation. The chances of this paying off are lower, as many and even most attempts at developing the truly innovative, game changing New do not succeed to the level of creating new markets or even just significant returns in more established ones. But when a quadrant I innovation does pay off, this is where blue ocean marketing and sales opportunity and massive returns on investment are possible.
• Quadrant IV is where you find slow and steady evolutionary change in already established product and service lines. Returns specific to any given incremental change are not likely to be very big but over an extended timeline, a succession of such innovative adjustments can lead to a steady ongoing return on investment and a secure level of ongoing profitability.
• Quadrant III is where you would find low risk, short shelf life fads that come and go, and the ongoing innovation that feeds fad-driven markets. If a particular fad really takes off, return on investment from it can be large, but most of them at best just break even and many individually even lose money. Fad market businesses seek a positive return on investment from an overall ensemble of potential fad successes that they invest in where a few successes can cover any collateral losses from the ones that do not make it and still leave an overall profit. I will discuss the issues of investing in portfolios of potential innovative winners and for quadrant I as well as for quadrant III innovations as a separate topic in this series and simply note it as an important approach here and for both developing businesses and supporting investors.
• Quadrant II represents a very special category where risk is high and any positive returns on investment would be long-term in arriving. Some of the best examples of innovative effort that would fit this pattern come not from the business sector per se, but from the public sector at least as an effort-organizing force. And addressing the long term challenges of global warming and climate change come immediately to mind here as (hopefully) working examples of quadrant II innovative efforts. Basic scientific research and even a measure of more applied research fit a quadrant II model approach and whether publically or private sector funded.

And with the four quadrant model at least in place as a quick outline and with some working categorical examples for each, I return to the top of this posting to consider how risk and timeframe evaluations can be perceived and set differently by different observers. I will delve into that set of issues in my next series installment, where I will discuss this model and its two organizing axes in at least semi-quantitative terms.

Meanwhile, you can find this and related postings at Macroeconomics and Business.

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