My initial insight for what would become RQ™ (short for research quotient) came from an earlier career in defense electronics at Hughes Aircraft Company. Until it was acquired by General Motors (GM) in 1985, Hughes had been a marvelous company of talented scientists and engineers working on exciting projects. To give you a sense of how special the company was, I once asked my customer how his agency chose when to award contracts to Hughes versus one of our competitors. He said, “I go to Hughes when I don’t know what I want. Once you help me discover that, I go to the other guys because they can build it more cheaply.”
At Hughes, we didn’t view our job as designing missiles; we viewed our job as pushing the knowledge frontier. So I became alarmed when the government changed its acquisition policies in ways that reduced companies’ incentives to conduct R&D. I became more alarmed after the GM acquisition, when management began asking us to include return on investment (ROI) estimates on proposals for R&D projects. Prior to that time, proposals merely identified the challenges prompting the need for the project, the doors the project would open, and the resources the project would require, and when it would require them. These proposals were all submitted to senior management (most of whom were themselves scientists and engineers), as the rest of us waited for the proverbial white smoke to appear. There was no need to quantify the returns because the company ethos was “do the right thing and profits will follow.” I felt moving toward ROI-based project selection was a death knell for the types of projects that had made Hughes so exciting—and successful. There was no market for communications satellites when Harold Rosen and Donald Williams originally proposed the project to Pat Hyland in 1959.1 Hughes created that market.
I was concerned that the combination of government policy changes and company strategy changes were going to permanently degrade Hughes’s ability to push the knowledge frontier, but I had no quantifiable means to demonstrate that. Because there was no good measure of R&D capability, I couldn’t show that it was deteriorating.
I became an academic in part to solve that problem. Over the course of a 20-year career doing research and consulting on innovation, I devised RQ as a measure of a company’s R&D capability—its ability to convert investment in innovation and R&D into products and services people want to buy or into lower cost for producing those products and services.
Once I had the RQ measure, I knew it was the holy grail I’d been seeking while at Hughes. My first step was to characterize the R&D productivity of all publicly traded U.S. companies going back as far as data would allow (1972). I learned that my concerns while at Hughes were valid—not only for Hughes but for the entire U.S. economy. The next step was to identify what caused the decline and, accordingly, how to reverse it. There was no easy way to do that because companies keep their R&D practices pretty close to their vests.
Fortunately, I was awarded two National Science Foundation (NSF) grants that allowed me to conduct ethnographic interviews with companies to understand their R&D programs and practices and to quantify the impact of these practices across the full spectrum of U.S. companies engaged in R&D. Through these two studies, I have been able to identify which popular innovation recommendations actually do work. The results are startling. Many of them don’t work. Not only do they fail to improve innovation, in many cases they actually make companies worse at innovation!
In this book, I share these results. My goal is to diffuse the RQ measure so that it can do for R&D what Total Quality Management (TQM) did for manufacturing, what hospital report cards are doing for morbidity, and what sabermetrics is doing for baseball. RQ not only tells companies how “smart” they are, it provides a guide for how much they should invest in R&D and innovation and how much that investment will increase revenues, profits, and market value. If companies use these findings to increase their RQs, it will not only result in their own desired growth, it should also restore economic growth to the rates we enjoyed in the mid-twentieth century.