1. Know-How and Asset Complementarity and Dynamic Capability Accumulation: The Case of R&D
Constance E. Helfat
Strategic Management Journal

2. Dynamic Capability
“The subset of the competence/capabilities which allow the firm to create new products and processes and respond to changing market circumstances” (Teece and Pisano, 1994).

3. Complementary knowledge (economies of scope)
Oil Industry
Complementary knowledge (economies of scope)
Firms’ response √
$ $$$ OR
Coal Conversion R&D
AND
Physical assets

4. Data
Source
US Department of Energy (DOE) – 26 largest energy firms
Financial Reporting System (FRS) – Firm-level breakdown of R&D expenditures (e.g. oil and gas recovery, refining) and balance sheet information
Time

5. Historical Timeline Increased power of OPEC Oil Price $ $$$ $$$ $$$$$
Two sorts of R&D
Conventional  incremental improvement to current products and processes
Less developed  major technology improvement
Increased power of OPEC
Oil Price $
$$$
$$$
$$$$$ $
1970
1973
1974
1975
1976
1978
1979
1980
1981
1987
1989
Investment of R&D $
Investment of R&D $$$
Unique opportunity for testing

6. 6 times more than Oil Shale and Tar Sand
Energy-related R&D
L : less developed technology
C: conventional technology
6 times more than Oil Shale and Tar Sand

7. What is coal gasification/liquefaction?
The technology is to produce a natural gas substitute of pipeline quality(via gasification) or to produce substitutes for traditional refined oil products (via liquefaction) (Helfat, 1988)
1st generation: Lurgi process  Btu gas  costly, low productivity, limited usage
2nd generation: low cost, widely usable
Core technology: refining
Technological commonalities between coal conversion and oil refining:
high-temperature, high-pressure treatment of hydrocarbon fluids, catalytic processing of hydrocarbons, and fluidized bed design and hydrogenation (Teece, 1980; Federal Energy Administration, 1974)

8. Hypothesis 1a
Economies of scope  related business activities  accumulated knowledge for speculative technology
Economies of scope could have lowered the costs of R&D needed to commercialized the technology
Unrelated diversification is unlikely to enhance technological complementarities (i.e. economies of scope) (Mahoney and Pandian 1992)
H1a: Firms that had larger stocks of knowledge from past refining R&D were likely to have undertaken larger amounts of coal gasification/liquefaction R&D

9. Hypothesis 1b
A firm’s accumulated refinery assets provide a rough measure of the extent of a firm’s experience in utilizing established refinery technologies (expertise of refinery operations)
H1b: Firms that had larger accumulated refinery assets were likely to have undertaken larger amounts of coal gasification/liquefaction R&D
(may not hold because of output substitutability)

10. Hypothesis 1c
R&D on coal conversion also benefit from knowledge acquired from prior R&D in technologically related but more speculative technologies (such as oil shale and tar sands)
H1c: Firms that had larger stocks of knowledge from past R&D on other synthetic fuels were likely to have undertaken larger amounts of coal gasification/ liquefaction R&D.

11. Hypothesis 2 A key asset for R&D in coal conversion is coal
Firms want to exploit their prior holdings of coal reserves
Hypothesis 2: Firms that had larger accumulated coal assets were likely to have undertaken larger amounts of coal gasification/liquefaction R&D

12. coal conversion R&D spending
Empirical Method
All within-firm variables include a firm sales revenue divisor (“intensity”)  control of firm size; but H1a and H1c do not exclude firm size effect, because larger firm has larger coal conversion R&D expenditure
coal conversion R&D spending
= f[complementary knowhow + complementary coal assets + other sorts of resources and knowledge +control (oil price) + control (idiosyncratic firm effects)]
!!!!!!!!!!!!!

13. Variable names
Know-how
Physical assets
Know-how
Know-how

14. Empirical Method
Tobit regression (OLS will yield inconsistent estimates of the regression coefficients (Maddala, 1983))
Assumption of the regression: at the start of each year, managers made decisions to expand their firms’ stocks of knowledge pertaining to coal conversion technology, based on the information and resources available at the time.

15. Empirical Results Spillover effect Fixed effect regression
H1a
H1a
H1c H2
Spillover effect
Cumulative learning in R&D

16. Conclusion
Positive coefficients on the refining R&D capital stock variable suggest that firms may have sought to benefit from complementary knowledge accumulated from past refining R&D, while increasing coal conversion R&D in response to environmental change
The insignificant coefficients on oil and gas recovery R&D capital stock (with few complementary know-how) lend further support to the interpretation that the significance of the refinery R&D variable reflects complementary know-how
The fixed effect regressions suggest that the increase in coal conversion R&D during the period was positively related to possession of complementary physical assets
An interaction term between refining R&D capital stock and coal assets lacks significance in the regressions  a separate rather than a joint relationship of each variable is related to coal conversion R&D

17. Conclusion
The other synfuels R&D capital stock variable lacks significance, except in a regression which omits the refining R&D capital variable (the omission of common knowledge would cause other synfuels R&D to be significant)
H1b is not supported by the empirical results
Inappropriate proxy for accumulated knowledge of refinery operations
Output substitutability
Variables for past coal conversion R&D (lagged dependent variable) and financial resources (profitability) have positive and significant coefficients
Spillover effect is negative and significant
Spillovers from other firms’ R&D partially substituted for own-firm R&D
High levels of R&D capital possessed by others deterred rivalrous R&D spending
The significance of firm dummy variables  idiosyncratic firm-level factors & coal conversion R&D

18. Discussion
The author points out a limitation about the proxy for the expertise of refinery operations in the paper. Is there a better proxy?
The insignificance of the interaction variable (knowledge * physical assets)is somewhat counter-intuitive, how to explain it?