How Innovation Really Works: Using the Trillion-Dollar R&D Fix to Drive Growth

by Anne Marie Knott

Overview

Are you spending too much on R&D? Too little? Is your innovation program successful? And how do you measure that success?

Your company is spending millions on R&D every year, but despite your best efforts, that R&D isn’t driving growth. If you’re like 95% of firms, you aren’t investing the right amount, and the productivity of your R&D has fallen dramatically over the past several years. That’s because there hasn’t been a universal, uniform, and reliable measure of R&D—until now.

First introduced in Anne Marie Knott’s influential Harvard Business Review article, RQTM (Research Quotient) is a revolutionary new tool that measures a company’s R&D capability—its ability to convert investment in R&D into products and services people want to buy or to reduce the cost of producing these. RQ not only tells companies how “smart” they are, it provides a guide for how much they should invest in R&D to ensure that investment will increase revenues, profits, and market value.

Armed with insights from her experience as an R&D project manager, 20 years of academic research, and two National Science Foundation grants, Knott devised RQ and used the measure to test common innovation prescriptions across the full spectrum of U.S. companies engaged in R&D. The results are nothing short of game-changing.

In this essential guide, you will learn:

• how to use RQ to determine which R&D investments are most likely to drive growth—using the hard data you already have to better utilize the innovation tools you’re already using
• the 7 misconceptions about innovation trends—and how to avoid the ones that don’t work
• how investors can achieve 9x returns in the market and help companies in the process
• why corporate—and GDP—growth has stalled and how to restore it without R&D tax credits

This book promises to do for innovation and R&D what TQM did for manufacturing and what Sabremetrics did for baseball. It’ll show you How Innovation Really Works—with measurable results you can count on.

Product Details

ISBN-13:	9781259860942
Publisher:	McGraw Hill LLC
Publication date:	03/24/2017
Sold by:	Barnes & Noble
Format:	eBook
Pages:	288
File size:	19 MB
Note:	This product may take a few minutes to download.

About the Author

Anne Marie Knott is Professor of Strategy at Washington University, where her principle area of research is innovation. Her work has been published in Harvard Business Review, Management Science, Organization Science, and Strategic Management Journal, among others. Prior to receiving her PhD from UCLA, Professor Knott was a project engineer and program manager at Hughes Aircraft Company, developing missile guidance systems.

Read an Excerpt

How Innovation Really Works

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By Anne Marie Knott

McGraw-Hill Education

Copyright © 2017 Anne Marie Knott
All rights reserved.
ISBN: 978-1-259-86094-2

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CHAPTER 1

THE PROBLEM: Flying Blind

There is no question that innovation is important. Everywhere you turn, people are lauding its benefits. It's hard to open any popular business magazine and not find an article on innovation. This preoccupation with innovation comes from the belief that it is the key to growth. As Strategy& reported in its 2015 Global Innovation Study, "The results of our survey of 1,757 executives couldn't be clearer: innovation today is a key driver of organic growth for all companies — regardless of sector or geography." Indeed, the company reports its Innovation 1000 companies (top 1000 R&D spenders) invested $680 billion in R&D last year — up 5 percent from the prior year.

The benefits of innovation don't stop with companies. Historically R&D has been viewed as the engine of economic growth as well. This assumption was the foundation for President George W. Bush's America COMPETES (Creating Opportunities to Meaningfully Promote Excellence in Technology, Education, and Science) Act of 2007, whose goal was to invest in research and development to improve the competitiveness of the United States. Demonstrating that support for innovation is nonpartisan, President Barack Obama signed into law the America COMPETES Reauthorization Act of 2010 three years later.

Yet despite the importance of innovation to companies as well as to the broader economy, despite the growth in real R&D by both the government and companies, and despite all the experts dedicated to helping companies innovate, companies have become worse at it! The money companies spend on research and development is producing fewer and fewer results. In fact, the returns to companies' R&D spending have declined 65 percent over the past three decades. Not coincidentally, this decline coincides closely with the decline in U.S. GDP growth over the past 30 years (see Figure 1-1).

Given the tremendous importance of innovation, all the attention paid to it, and all the experts dedicated to advising companies on it, how is it possible that R&D has suffered such a severe decline in productivity?

I believe it is because everyone is flying blind with respect to innovation, because there has been no good way to measure its quality or productivity. Indeed, Industrial Research Institute (IRI) members report that the lack of good R&D measures is one of the top problems they face. They view measurement as important (a) to justify R&D investment to CEOs, the board, and investors, (b) to improve the efficiency of R&D, and (c) to estimate the value of R&D investment for future growth.

EXISTING MEASURES

This measurement problem isn't about a dearth of measures. In fact, it's the opposite problem — overabundance of measures. According to one study by the European Industrial Research Management Association, there are over 250 R&D metrics! Certainly one reason for the plethora of measures is that capturing project-level performance is quite different from capturing company-level performance. However, the more compelling reason appears to be that the measures are unsatisfactory: the data is difficult to collect, there aren't uniform standards across business units, the measures can be gamed to make sure a given group looks good, the measures focus on inputs and outputs (rather than the conversion of inputs to outputs), and perhaps most important, the measures aren't meaningful to shareholders.

Until now, academics have been unable to help companies solve the measurement problem. That's because their primary measure (patent counts) wasn't much better. Patents have a number of shortcomings that are acknowledged by academics and practitioners alike. In particular, patents are not universal, uniform, or reliable.

The first problem, universality, is that not all companies patent their inventions. In fact, less than 40 percent of companies who conduct R&D have any patents. Relatedly, even among the companies that do patent, few of them patent all their innovations. This is because patents are costly both financially (the cost to file and defend) as well as competitively (they require disclosure of the fundamental knowledge underpinning the innovation). Accordingly, companies file patents only under certain circumstances, such as to prevent copying when their innovations are easy to invent around. However, they also patent for strategic reasons, such as to block other companies' patents, to prevent lawsuits, to use in negotiations with companies who hold patents to necessary technology, or to enhance their reputation. Without universality (all companies patenting all innovations), it is difficult to use patents to compare companies on their innovativeness, or even to track any given company's innovativeness over time.

Patents also suffer the uniformity problem that they aren't all created equal. Compare for example the $2 billion in royalties for Kary Mullis's patent for the process to clone DNA5 to the value of the 97 percent of patents that are never commercialized. On average, less than 10 percent of patents account for 80 to 85 percent of the economic value of all patents. Without uniformity, the number of patents is not a meaningful measure of the value of innovations. While there are efforts to control for the uniformity problem through counting patent citations, these efforts are only partially effective, and they are only meaningful after adequate time has elapsed since the patent has been granted.

The final problem, reliability, is that patents don't predict the big outcomes that companies care about, such as revenues, profits, and market value. This is not surprising given the prior two problems. However, the most insightful answer to the question of why patents aren't reliable came from Dan Stern, a former vice president and chief scientist at Olin Corporation, who I run into periodically at the local happy hour. When I mentioned I was examining patents as a measure of innovativeness, Dan became very animated and said, "Patents don't measure anything! I know exactly how to increase patents — I merely tell my engineers they're going to get paid per patent."

Without universal, uniform, and reliable measures of innovation, it is almost impossible to identify best practices or establish top-level R&D strategy. In short, companies confront the classic problem "you can't manage what you can't measure," a quote that has been attributed to a number of people, dating back as far as 1883 to Lord Kelvin, and more recently to Peter Drucker, Fred Smith (FedEx founder), and Andy Grove (former Intel CEO). To make the "you can't manage what you can't measure" problem more concrete, let's review one example of how the problem manifests itself in the context of R&D.

In the summer of 2010, Mark Hurd was ousted as the CEO of Hewlett-Packard because there had been "violations of H.P.'s standards of business conduct." Joe Nocera, in his August 13 column in the New York Times that year, suggested the business conduct rationale was a ruse. Nocera cited Charles House, a former H.P. engineering manager, as saying, "the sexual harassment charge (against Mark Hurd) was a total red herring." Nocera goes on to report, "as many H.P. old-timers saw it ... Mr. Hurd was systematically destroying what had always made H.P. great. The way H.P. made its numbers, Mr. House said, was not just by cutting any old costs, but by 'chopping R&D,' which had always been sacred at H.P. The research and development budget used to be 9 percent of revenue, Mr. House told me; now it was closer to 2 percent."

Is Mr. House right? Did Mark Hurd destroy what made HP great? The problem with existing measures is that we have no way of knowing. We don't know (1) whether R&D capability has deteriorated at all, or if so by how much, and (2) whether the correct R&D investment is 9 percent or 2 percent. Without such basic knowledge it's almost impossible to manage R&D.

THE RQ SOLUTION

I confronted this measurement problem firsthand during a prior career managing missile guidance projects at Hughes Aircraft Company. Toward the end of my time there, I could see that changes in government acquisition policies were changing companies' incentives to conduct R&D. I could further see that Hughes's responses to these policies, as well as the company's response to being acquired by General Motors, were dramatically changing the way we organized R&D. I was concerned these changes would permanently degrade Hughes's R&D capability. Moreover, I suspected this was true not only for Hughes, but for all companies in the defense industry, and possibly other industries as well. The challenge at the time was that I couldn't convey the need for alarm. Without a good measure of R&D capability, there was no way to demonstrate there was a problem.

I became an academic in part to solve the R&D measurement problem. While the original insight for the solution occurred to me as a first-year PhD student, the hard work to implement and validate it has taken 20 years. The result of that work is a measure called RQTM (short for research quotient) — a name intentionally similar to individual IQ, to reflect the fact that both measure problem-solving capability. In fact, I originally called the measure IQ (innovation quotient). For individuals, IQ is measured as the speed and accuracy of solving problems of increasing difficulty. Within any given time constraint, individuals with a higher IQ solve more problems correctly than those with a lower IQ. For companies, RQ is efficiency solving new problems. For any given level of R&D spending, high RQ companies will generate more innovations, or for any given innovation, high RQ companies will invest less developing it. Accordingly, RQ is mapped onto the IQ scale (mean = 100, standard deviation = 15) to reinforce that intuition.

I argue that RQ is the most intuitive measure you could construct for R&D effectiveness. It captures a company's ability to generate value from its R&D investment in a very precise way. In particular, RQ is the percentage increase in revenue a company obtains from a 1 percent increase in R&D, while keeping everything else the same (the mathematical details are in Chapter 10).

Because RQ relates R&D to revenues, a company can have high RQ either by generating a large number of innovations and being reasonably effective exploiting them, or by generating a smaller number of innovations and being extremely effective exploiting them. One thing to note with this definition is that a company with a large number of patents or new products may not have a high RQ if it operates in small markets.

Accordingly, RQ doesn't fit everyone's definition of innovation. Some people prefer to think of innovation as the number of new things a company introduces. While this is important for some purposes, this isn't what RQ measures. Instead, RQ measures how much economic benefit the company derives from its R&D. The benefit can come from new products or services. However, it can also come from process innovation. In the case of product/service innovation, the economic benefit is reflected in higher revenue; in the case of process innovation, the economic benefit is reflected in lower costs.

What makes RQ so powerful as a measure of R&D is that it's derived from the "production function" in classic economics that relates a company's inputs to its output. This means that once a company knows its RQ, it can use economic relationships to forecast not only additional revenues from its R&D, but also profits, market value, and growth as well. Moreover, it can determine the optimal level of R&D investment. For all companies, there is a point at which the additional gross profit from R&D falls below the investment required to generate those gross profits. RQ allows companies to identify that optimal point precisely.

In addition to having a solid economic foundation, RQ solves the three problems with the patent measures. Because it is estimated entirely from standard financial data, RQ is universal. It can be computed for any company engaged in R&D. Second, because it is essentially a sophisticated ratio of output dollars to input dollars, RQ is unitless. Thus its interpretation is uniform across companies within an industry as well as across industries. Perhaps most important, RQ is a reliable measure of R&D productivity. The theoretical predictions relating RQ to company R&D investment, market value, and growth held up when tested with 47 years of financial data for all publicly traded companies in the United States.

Finally, RQ solves the main problems discussed earlier for the 250-plus measures currently in use: (1) the data to derive RQ is easy to collect (in fact, for public companies, it is data they are required to collect and report in their 10K), (2) there are uniform standards across business units that are imposed by FASB, (3) RQ can't be gamed to make sure a group looks good — the only way to look good is to have higher output or lower input costs, (4) RQ defines the relationship between inputs and outputs (rather than focusing on one or the other), and perhaps most important, (5) RQ is meaningful to shareholders, because now they, too, can predict how R&D spending will affect stock price. Moreover, as we will learn in Chapter 8, RQ can be used by investors to outperform the market.

Can't We Just Use Intuition?

In principle, measures aren't necessary if managers have good intuition about what bets to place and how to execute them. Certainly, none of the most famous entrepreneurial innovators needed measures of their R&D productivity: Henry Ford, Thomas Edison, or more recently Steve Jobs, Bill Gates, and Jeff Bezos. Each of them had a clear vision of what new product or service was needed, how valuable it would be, and what was necessary to execute that vision.

But what about the rest of us? How good are non-unicorns at gauging what drives innovation? Let's see. Test your own intuition by answering 12 questions about factors that are often associated with innovativeness (Figure 1-2).

Now check how you did. Give yourself one point for each answer that matches the answers in Table 1.1, column C. Column C provides the "truth" — what the data tell us about factors enhancing innovation. I will provide details on how I arrived at the truth in the remaining chapters.

Be generous — mark either 4 or 5 correct if the correct answer is 5, and mark either 1 or 2 correct if the correct answer is 1. If you scored a 9 or 10 you have exceptional intuition and may not need this book other than to provide deeper understanding of why your intuition is correct. If you scored 7 to 8, your intuition is better than the average manager's, but you can still benefit from understanding where your intuition is misleading you. If you scored 6 or below, you fall into the majority of managers and financial professionals who need a reliable measure to help make R&D decisions and gauge their effectiveness.

Now let's dig deeper. Rather than looking at overall score, look at your answers to specific questions. If you're like most managers inside operating companies, you provided the answers in column A. If you're like most professionals in the investment community, you provided the answers in column B. So the first thing to notice from comparing the two columns is that managers and investment professionals have different views on what makes R&D productive. For example, managers believe competition leads to greater innovation, while finance professionals believe it has no effect. Managers believe outside CEOs drive greater innovation, while investment professionals believe internal CEOs drive greater innovation. Perhaps most notably, investment professionals strongly believe decentralized decisions lead to greater innovation, while managers are split on whether centralized or decentralized R&D is more effective. These differences between the two sets of intuition could lead to problems if companies want to go left, but investors pressure them to go right.

The second thing to notice is that managers have only slightly better intuition than investment professionals: managers better gauge the truth for 6 of the 10 factors, while investment professionals better gauge the truth for 4 of the 10 factors. That should come as a surprise. Managers operating inside companies are much closer to "the innovation phenomenon" than investors, so they have more opportunity to develop and refine their intuition. The fact that managers don't have much better intuition suggests that being closer to the phenomenon doesn't help much. There are very few domains in which expertise and experience are unhelpful. The most notable popular example comes from Michael Lewis's book Moneyball, where experienced baseball scouts were poorer at choosing players than the sabermetrics employed by Billy Beane. Not surprisingly, the solution to poor R&D intuition is the same as the solution in Moneyball — replacing intuition with data and meaningful measures.

(Continues...)

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Excerpted from How Innovation Really Works by Anne Marie Knott. Copyright © 2017 Anne Marie Knott. Excerpted by permission of McGraw-Hill Education.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
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Table of Contents

Contents

Preface, vii,
Acknowledgments, xi,
1 THE PROBLEM: Flying Blind, 1,
2 MISCONCEPTION 1: Small Companies Are More Innovative, 23,
3 MISCONCEPTION 2: Uncontested Markets Are Good for Innovation, 45,
4 MISCONCEPTION 3: Spending More on R&D Increases Innovation, 63,
5 MISCONCEPTION 4: Companies Need More Radical Innovation, 89,
6 MISCONCEPTION 5: Open Innovation Turbocharges R&D, 109,
7 MISCONCEPTION 6: R&D Needs to Be More Relevant, 127,
8 MISCONCEPTION 7: Wall Street Rewards Innovation, 141,
9 THE PROMISE OF RQ: Restoring Growth, 161,
10 BEHIND RQ: What It Really Is and How to Find Yours, 183,
Appendix, 205,
Notes, 237,
Index, 245,