Organizational RQ is a measure of innovation effectiveness that assigns firms an "RQ score" analogous to an IQ score. It is derived from firms' production functions to quantify their ability to generate and exploit innovation. RQ has several advantages over traditional measures like R&D spending and patents - it is universal, uniform, and reliable. Firms can use their RQ to optimize R&D budgets and identify effective practices. Higher RQ is correlated with greater revenue growth, market value, and optimal R&D spending. The presenter argues RQ can help solve problems around setting R&D budgets and evaluating effectiveness, and help firms improve their innovation capabilities over time.

1. Organizational RQ
Anne Marie Knott
Professor of Strategy, Washington University
Director, Berkeley Research Group
_______________________________________________
April 17, 2012
SIKM Leader’s Community Call

2. My life before academia
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3. The innovation engine is broken
 Historically R&D viewed as engine
Impact of R&D on GDP growth
of economic growth 14.00%
 Obama in State of the Union 12.00%
address holds it up as solution to 10.00%
8.00%
growth problem 6.00% GDP growth
 Valid through the space race 4.00%
2.00%
R&D intensity
 No longer seems to be the case 0.00%
1950 1960 1970 1980 1990 2000 2010
Axis Title
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4. The Problem: Firms and policymakers fly blind
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 No good measures of R&D effectiveness
– R&D spending is an input measure (51% of firms using)
– Patents neither universal or uniform
• Less than 50% of firms doing R&D patent; universality
• 10% of patents comprise 85% of economic value; uniformity
– Neither Patents nor R&D spending is reliable
• Anomaly in their relationship to market value
• Booz study each year says no correlation with firm growth
 Implications
– Front-end: Firms use rules of thumb to choose R&D investment levels
– Back end: Can’t tell whether given practice helps or hurts capability
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5. Front-end: unsure how much to spend
10000 Majority of firms are spending sub-optimally
8000
6000
Actual R&D
4000
2000
0
0 20000 40000 60000 80000
Optimal R&D (R*)
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6. Back end: Unsure what makes them effective
Increasing IQ
Cross Plot of Old IQ versus New IQ
200
Changes appear random
150
Number of Mean Statistically
100
Firms Change Significant
Increasing IQ 178 13.0 70
Decreasing IQ 184 -12.5 65
50
0
0 50 100 150 200
Firm IQ 1987-1996
Decreasing IQ
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7. A real world illustration of both problems
Charles House, a former longtime H.P. engineer …now at Stanford
University, openly rejoiced when he heard that Mr. Hurd was leaving. “I think the
sexual harassment charge was a total red herring,” Mr. Hurd was systematically
destroying what had always made H.P. great. … The research and development
budget used to be 9 percent of revenue, … now it was closer to 2 percent.
 Is Mr House right: Did Hurd destroy what made HP great?
 With existing measures, we have no way of knowing!
– Whether R&D capability has deteriorated, or by how much
– Whether the correct investment is 9% or 2%
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8. Solution: Organizational IQ…well now RQ
 Derived from firm’s
production function
Y = A aB b
Output = Capitala *Laborb
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9. Organizational RQ extends the PF in two ways
 Expand to include R&D:
Output = Capitala *Laborb * R&Dg * Spilloversd * Advertisinge
 Make all the exponents firm-specific:
– Raw RQ is the exponent, g, on R&D
• “firm-specific output elasticity of R&D”
• % increase in output from 1% increase in R&D
– Reported RQ rescales g to the human IQ scale (m=100, s=15)
 Interpretation:
– Analog to individual IQ:
• Those with higher IQ solve more problems per unit of input:
• time (individuals), money (firms)
– Need to generate AND exploit innovation
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10. Some properties of RQ
Variation (within industry) >
Normally distributed
Variation (across industries)
Distribution of Firm IQ 10
300
IQs by Industry
150
130
Firm-Level IQ
110 IQ Mean
200
90 IQ Max
IQ Min
70
50
30
100
34
35
44
72
76
77
51
61
63
74
79
90
25
42
45
61
70
72
73
28
28
28
35
35
35
36
36
36
36
36
36
38
38
38
38
73
73
73
Industry SIC
0
0 50 100 150 200
Q
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11. Its most important properties
 Universal: RQ can be estimated for all firms doing R&D
 Uniform: An RQ of 100 (or any other number) means
the same thing across all firms regardless of currency
 Reliable: It “predicts” firm behavior and market value
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12. The IQ Guessing Game: The Business Week 5
Distribution of IQ guesses for BW5
1.0
Percentage of guesses
0.9
0.8 Apple
0.7
Google
0.6
0.5 Microsoft
0.4
IBM
0.3
0.2 Toyota
0.1
0.0
<85 85-95 95-105 105-115 >115
IQ guess
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13. Comparing RQ to other innovation indices
Business Week Rank vs RQ Forbes 100 vs RQ
130 180
160
120
Organizational IQ (2004-2010)
140
110 120
IQ 2004-2010
100
100
80
90 60
40
80
20
70 0
0 10 20 30 40 50 0 20 40 60 80
Business Week Innovation Rank 2010 Innovation Premium
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14. Using RQ immediately: Set R&D budget
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 IQ defines optimal R&D investment Optimal R&D and Market Value
both increase with RQ
d Õ d é b 1 b 2 RQ b 4 b 5 b 0
= ë K L R S A e - (dK + L + R + A)ù
û
dR dR 80 3.5
70 3.0
1
æ 1 ö RQ-1
R* = ç ÷
60
2.5
Optimal R&D spending
b1 b 2 b 4 b 5 b 0
è (RQ)K L S A e ø 50
R&DIQ
2.0
40
 Theory tells us profits should increase by 30
1.5
é R RQ - R RQ ù - R
opti
ë 2 1 û
1.0 mal
20 R
 Both predictions hold in the real world! 10 0.5
 Firms with Higher RQ tend to spend
0 0.0
0.02.03.04.05.06.07.08.09 0.11.12.13.14.15.16.17.18.19 0.21.22.23.24.25
0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0.2 0 0 0 0
more on R&D IQ
 Market value increases if you
RQ
increase R
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15. The REAL promise of RQ: firms can change theirs
 Firms can change their RQ!
– Though currently random Growth versus RQ
 There is no theory to tell us how 4.00
3.50
much this increases growth, but…
ln(revneue growth 2000-2007)
3.00
– Empirics tell us 10% increase in RDg 2.50
2.00
increased growth rate 3.2% 1.50
 The holy grail: RQ will do for R&D 1.00
0.50
– What TQM did for manufacturing cost 0.00
– What hospital report cards do for -1.00 -0.50 -0.50 0.00 0.50 1.00 1.50 2.00
-1.00
morbidity and mortality
-1.50
 Firms are real ln(RDg)
implementors, ….but
analysts are fuel!
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16. RQ Summary
 The most intuitive measure you can construct for R&D effectiveness
 It’s universal, uniform and reliable
 Application
– Solves the up-front problem of setting R&D budgets:
• Immediate benefit: $1 trillion increased market cap just for top 20 firms!
• Medium term benefit: Better R&D allocation should increase RQprofits and
market value
– Solves back-end problem of knowing effectiveness:
• Medium term benefit: Keeping what’s effective (dropping what isn’t) should
increase RQprofits and market value
 To learn more:
– Harvard Business Review article “The Trillion Dollar R&D Fix” hits online
and newsstands today: http://hbr.org/magazine
• Includes online tool to lookup firms’ RQ
– www.amkANALYTICS.com offers firm reports and subscriptions to RQ data
– For help implementing RQ:www.brg-expert.com/professionals-anne_marie_knott.html
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