In the News
March 21, 2017: Harvard Business Review "Is R&D Getting Harder, or Are Companies Just Getting Worse At It?" by Dr. Anne Marie Knott, examines why returns to companies’ R&D spending have declined 65% over the past three decades.
May 5, 2017: Dr. Anne Marie Knott presents "Rehauling the Broken Innovation Engine" at the UCLA Anderson Alumni Conference.
May 8, 2017: Dr. Anne Marie Knott was again a featured speaker at the annual meeting of the Industrial Research Institute, the preeminent R&D innovation membership organization
June 13, 2017: Dr. Knott discusses RQ, its applications and implications, with the American Management Association Edgewise podcast.
June 27, 2017: Dr. Knott presents at the National Academies of Science Conference "Beyond Patents: Assessing the Value and Impact of Patents" in Washington D.C.
amkANALYTICS provides tools to drive value from R&D using RQ™ (short for Research Quotient). RQ is derived from the production function from classic economics which defines the relationship between firm inputs and their output. The version seen in textbooks typically considers the two main tangible inputs: capital and labor, and is written as follows:
Y = KαLβ
Where Y is output, K is capital and L is labor. The exponents, α and β, called the output elasticity of capital and labor, respectively, tell us in a very precise way how productive each input is in generating output. A 1% increase in capital increases a firm’s output α%; a 1% increase in labor increases a firm’s output β%.
RQ is obtained by expanding the production function to include the two important intangible assets: R&D and advertising.
Y = KαLβRγAδ
The raw measure for RQ, γ, is the output elasticity of R&D-the percentage increase in revenue the firm gets by increasing its R&D 1%. Thus RQ is simply doing with R&D what everyone has been doing for years with capital and labor.
To support intuition, we rescale γ to match the human IQ scale. An RQ of 100 is the average across all US public firms engaged in R&D in the base year. The majority of firms (67%) have RQ's which fall between 85 and 115.
Because RQ is based on fundamental economics, it allows us to predict the expected revenues and profits from an increase in R&D. Better still, RQ matches the R&D productivity construct from endogenous growth theory, so it also links R&D spending to firm growth. Most important of all, these predictions have been validated with over 45 years of data as part of two National Science Foundation (NSF) studies. No other measure of R&D matches these predictions.
amkANALYTICS, LLC has a mission to grow the world economy through widespread adoption of RQ metrics and R&D best practices. Professor Knott’s analysis suggests that economic stagnation over the past 40 years stems in part from a 65% decline in firms’ RQ scores. Our hope is that through the R&D manager tools, firms can restore their prior RQs, and advanced economies can begin to enjoy the growth rates from the mid 20th century. We believe that R&D is the sleeping giant in our global economy.
About the Founder
Dr. Anne Marie Knott, PhD
Founder and Chief Scientist
Founder and Chief Scientist, Dr. Anne Marie Knott, is Professor of Strategy at the Olin School of Business, Washington University, where her principle area of expertise is innovation, both through entrepreneurship and large scale R&D. She has published books and numerous articles on innovation and entrepreneurship, and has received two National Science Foundation grants for her RQ work.
Professor Knott pioneered the RQ measure as part of a twenty-year academic career (the first half as an Assistant Professor of Entrepreneurship at Wharton, the second half as Professor of Strategy at Washington University in St. Louis) doing research and consulting on innovation. But RQ is not merely an academic exercise for her. She experienced declining innovativeness first hand during a prior career managing missile guidance projects at Hughes Aircraft Company. “I could see within Hughes that government policies and the company’s responses were forcing decisions that would permanently degrade R&D capability. Moreover, I suspected this was true not only for Hughes, but for all firms in the Defense sector, and possibly other sectors as well. The challenge at the time was I couldn’t convey the need for alarm, because there was no good measure of R&D capability”.
She became an academic (earning her PhD at UCLA, while continuing to work at Hughes) in part to solve that problem. Ultimately, she discovered RQ as a byproduct of answering a more fundamental question in the management of innovation. However, “once I had the measure, I knew it was the holy grail I’d been seeking”. The first thing she did with RQ was characterize the R&D productivity of all publicly traded US firms going back as far as data would allow (1972).
She learned her concerns while at Hughes were valid—not only for Hughes, but for the entire US economy (R&D productivity had declined 65%). Of course the next step was trying to identify how to reverse that trend. Fortunately, she was awarded two National Science Foundation (NSF) grants that allowed her to link RQ to R&D practices across the entire spectrum of US firms.
For more information on Dr. Knott please consult her website at Washington University