How Innovation Really Works [Book Review]

Happy New Year!

And with the beginning of 2018, it’s time for a new book review.

And like new year’s resolutions, we like to get straight to the point (and finish early). Not going to lie, reading this book felt like going through a “dumbed-down” version of a dissertation. It had the many qualities that you would find in such a document (and in this order):

  • What has happened in the past, and why it doesn’t work?
  • What did I do to help fix this problem?
  • What did I find? What were the results?
  • Look….. equations!
  • Conclusionss in each section AND in the conclusion chapter.

I was a little worried at first, because it kind of had a similar feel to a previous book I reviewed, “Life at the speed of light.” But this book stayed on target, and I was not disappointed in the book’s material. Additionally, the book was, ironically for this type of book, mentally engaging. Half of the chapters are based off of misconceptions that most readers would consider “strong” before reading this book. Concepts were challenged based off of a common sense mentality and discussed the reasoning behind why some methods worked and others just made things worse.

Spoiler alert:  The chapter titles did spoil half of the surprise on which practices were inefficient.

This book is about Research and Development (R&D), and how best to optimize it (from a corporate standpoint). To stay strong, most industrial corporations must stay ahead of (or at least keep up with) the wave of technological progression. If it doesn’t keep up, no one buys your stuff and the company economically dies.

So companies give smart people LOTS of money and hope for the best. That’s the cool part about R&D in general.

But how much money is enough……and how should it get invested? Do you invest most of it in research OR development? Where should R&D take place? Should it be internally conducted? Or should you just buy it from someone else and not even bother setting up a lab?

How Innovation Really Works, written by Anne Knott, dives into these questinos. And no, it wasn’t her PhD dissertation work re-written into a book format. [It was “Do Managers Matter?” (I haven’t found a free copy to read yet)]. And the main tool that she uses to determine R&D efficiency is RQ (research quotient), a unit-less measurement that was founded by the author of this book over 10+ years of research and evaluation.

RQ also trademarked……in case you were wondering.

To calculate a company’s RQ score, years of financial data are required. Harvesting the fruits of R&D labor typically occurs after years of effort, so ~5 years minimum of data are required for an accurate “estimation” of a company’s performance.  Consequently, a company’s stock prices (also required) are correlated with a company’s RQ score. This correlated time lag between these two values is taken into account into calculating a company’s RQ value. This information is then applied to a regression analysis to observe the impact of R&D funding has on company performance in the following equation:

Output ~ [R&D Budget]^(RQ) x [Capital/Labor/Advertising/Etc.]

Also for normalization purposes (and ease understanding), the values are adjusted to read similar to the IQ scale in determining a human’s “cognitive skills,” with an average score of a 100 for all companies.

RQ is not necessarily independent on HOW MUCH money the R&D budget is. Sometimes investing more into development can improve the sector’s potential. Alternatively, RQ can fall drastically if too much money is allocated due to inefficient monetary allocation. But the major player that impacts RQ are the corporation’s internal practices. Below are a list of some major factors:

  • The number of patents filed is NOT a strong measurement of R&D performance. Most companies only patent for legal protection and leverage, while keeping many “crucial” discoveries as internal secrets hidden from the public (and thus hard to measure externally).
  • A major shift in corporate practice has been the “external allocation” of technological advances. Unfortunately, this work is filled with proof demonstrating that this trend is detrimental to a company’s financial potential, but sometimes necessary if time is of the essence. Once intellectual property has been purchased (which is NEVER cheap), the company still has to spend internal R&D resources to build up it’s understanding and incorporation into current procedures, which is basically redoing the work that was previously done by others. As stated by the author, “outsourced R&D had an RQ of zero!”
  • Companies tend to be split between having a single R&D headquarters vs. multiple field-specific sites. While it looks good on paper, the latter method reduces the potential to network between business segments to combine multiple distinct ideas into novel products. Additionally, the concept of R&D “silos” has the additional disadvantage of multiple groups working on the same problem, possibly leading to competitive vs. collaborative efforts (or even worse, not even knowing that the problem has already been solved internally).
  • R&D efforts can be split between research AND development tasks (companies mostly do r&D, with a strong emphasis on development). It can also be divided between incremental and radical innovation. Radical work involves more “fancy” topics including fusion science, gene editing, and other “start-up” ideas. But incremental works are the major game changers for large companies, allowing manufacturing costs to be reduced and product specifications to be improved.

These ideas, and many others, can better be explained by the RQ methodology. It gives R&D and upper management a clearer windows to observe which practices improve a company’s competitive edge and how efficiently it does so. It also lays out the reasoning why this method is superior to previous methods of innovation ranking, including patent potential, total factor productivity (TFP), and Innovation Premium (IP).

The one slightly-obvious detrimental aspect to this book’s appeal is it’s portrayal on RQ; it’s too positive. Just like in the dissertation model, it’s almost a long advertisement for the author’s personal “invention” (for lack of a better word at the moment). Like it’s a tool that can be bought for consulting purposes.

Not surprisingly, this is what you get when you do a quick Google search on RQ ……..

RQ_Website.PNG

 

But the book still doesn’t state ANY methods of WHY it could possibly not work, or at least the possible limitations in this measurement. It lists some, but then right away “proves” that’s not the case with RQ.

Everything has some sort of weakness or limitation. Heroes can (and will) die. Corporate expansion plans can become disastrous if projected growth does not occur. Scientific theories are only applicable if certain caveats are ignored [From my experience, anything with the term nonlinear is always the exception rather than the rule]. And this RQ has got to have some limitation, with examples out there to prove them. While the concept is quite convincing, I can’t fully believe in the methodology until I have both sides of the story.

 

 

Side Rant: Leia should have died in space during that last movie. It would have made the movie more believable.

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