Planning your firm's R&D investment

Research Technology Management

Volumn 49, Issue 2, 2006, Pages 25-36

  Hartmann, George C ^a   Myers, Mark B ^b   Rosenbloom, Richard S ^c  

^a XEROX CORPORATION   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c HARVARD BUSINESS SCHOOL   (United States)

Author keywords

R D Gain; R D level; Revenue growth quantification

Indexed keywords

DATA ACQUISITION; DECISION MAKING; INVESTMENTS;

R&D GAIN; R&D LEVEL; REVENUE GROWTH QUANTIFICATION;

RESEARCH AND DEVELOPMENT MANAGEMENT;

EID: 33646429214     PISSN: 08956308     EISSN: None     Source Type: Journal    
DOI: 10.1080/08956308.2006.11657366     Document Type: Article

References (32)

1. See, for example, G. K. Morbey, R&D: Its Relationship to Company Performance, J. Product Innovation Management, 5, pp. 191-200, 1988.
2. G. K. Morbey and R. N. Rethner, How R&D Affects Sales Growth, Productivity and Profitability, Research-Technology Management, 33 (3), pp. 11-14, 1990, who report a significant correlation between R&D intensity and subsequent revenue growth, using a database of 727 companies, as well as a positive correlation between profit margins and R&D expenditure per employee.
3. R. Henderson, I. Cockburn, Scale, Scope and Spillovers: the Determinants of Research Productivity in Drug Discovery, RAND Journal of Economics, 27, pp. 32-59, 1996, report on a study of patent data for pharmaceutical firms, which found that size enables an advantage due to shared knowledge and infrastructure. They noted that competitive advantage correlates with the absolute amount of R&D, rather than R&D intensity.
4. B. Lev, Intangibles Management, Measurement, and Reporting, Washington D.C., Brookings Institution Press, 2001, cites evidence (pp. 53-58) that investors believe that R&D investment enhances the firm's value, and that investors discern the contribution of R&D across industries, firm sizes, and stages of maturity.
5. P. Boer, Linking R&D to Growth and Shareholder Value, Research-Technology Management, 37 (3), pp. 16-22, 1994, examines how increases in earnings growth can be realized from R&D investments.
6. B. Hall, The Stock Market's Valuation of R&D Investment During the 1980's, AEA Papers and Proceedings, 83, pp. 259-264, 1993, introduces the idea that a firm's value depends on various forms of capital, including the intangible value of sustained R&D investment. In later work,
7. B. Hall, Innovation and Market Value, in Ray Barrell, Geoffrey Mason and Mary O'Mahoney (eds.), Productivity, Innovation and Economic Performance, Cambridge, Cambridge University Press, 2000, analyzes the relationship between market value and innovation, and shows that financial markets value R&D.
8. J. Armstrong. It's Possible To Spend Too Much on RD&E. Research-Technology Management, 40 (5), pp. 10-11, Sept.-Oct. 1997.
9. K. Hansen, M. Weiss and S. Kwak. Allocating the R&D Resources: A Quantitative Aid to Management Insight. Research-Technology Management, 42, (4) pp. 44-50, July-Aug., 1999, describe a hierarchy of three decision levels to determine the R&D budget. Their study focuses on the second level.
10. R. G. Cooper, S. J. Edgett and E. J. Kleinschmidt, Portfolio Management for New Products, pp. 16-35, Portfolio Study, McMaster University, Hamilton, Ontario, Canada, 1997, have described portfolio management for new product development.
11. G. Mitchell and W. Hamilton, Managing R&D as a Strategic Option, Research-Technology Management, 27 (3), pp. 15-22, May-June 1988,
12. and also R. Gunther McGrath and I. C. MacMillan, Assessing Technology Projects Using Real Options Reasoning, Research-Technology Management, 43 (4), pp. 35-49, July-Aug. 2000, have addressed the management of uncertainty in each phase of R&D, and discussed how to use real options reasoning to guide investment decisions with high uncertainty.
13. G. C. Hartmann, and M. B. Myers, Technical Risk, Product Specifications, and Market Risk, in Taking Technical Risks, Lewis M. Branscomb and Philip E. Auerswald (eds.), Cambridge: MIT Press. pp. 30-43, 2001, have examined the interplay of product development risk, market risk and investment amount.
14. R. S. Rosenbloom and W. J. Spencer, (eds.), Engines of Innovation, Boston: Harvard Business School Press, 1995, have argued that this rationale is no longer valid.
15. Interviews were held by Mark B. Meyers in 2000 with Paul Horn, Sr. Vice President for Research and staff at IBM Watson Research Center, Yorktown Heights, New York and Robert L. Martin, CTO, at the Bell Laboratories, Murray Hill, New Jersey.
16. If carried out systematically, the product portfolio approach can provide estimates of the return to be expected from the R&D investment. But product portfolio planners usually do not develop a future revenue scenario for each future product, and do not aggregate them across the firm and over time. Consequently there is no systematic information on how future revenues will change, including the impacts of declining revenues from legacy products.
17. See, for example, the review by J. R. Hauser and F. Zettelmeyer, Metrics to Evaluate RD&E, Research-Technology Management, 40 (4), pp. 32-38, 1997, of various metrics in use.
18. M. Patterson. Linking Product Innovation to Business Growth, J. Product Innovation Management, 15, pp. 390-402, 1998.
19. G. C. Hartmann. Linking R&D Spending to Revenue Growth, Research-Technology Management, 46 (1), pp. 39-46, 2003.
20. G. C. Hartmann. Performance Trends for Marking Technologies in Copiers and Printers. Journal of Imaging Science and Technology, 44 (3), pp. 204-209, May/June (2000).
21. The sources for historical revenue and R&D information are Standard and Poor's Compustat and company annual reports. R&D excludes market research, customer or government sponsored research and development, and routine ongoing engineering expenses to maintain or improve existing product lines. Minor acquisitions were treated as business as usual, except for HP, where revenue and R&D associated with Agilent's spin-off in June 2000 is excluded from 1996 to 2000. Xerox revenue and R&D expenses for 1997-2000 were taken from the 2001 annual report.
22. The time required for product development depends on product complexity, often measured using metrics such as the number of drawings, parts and lines of computer code. Products from Xerox and IBM have similar levels of complexity; hence, for purposes of illustration we assumed the product development times for both companies were similar. The duration of the revenue stream from a product line depends on how often it is replaced in the market by a new generation of products, which in turn depends on factors including initial product price and rate of technological progress. Our assumption is that in the main, products from these two companies had a similar spectrum of price, supplies and service. Consequently, for the purpose of our example, we have assumed a similar duration of the revenue waves.
23. p = 2.8 years.
24. p changes by one year.
25. The firms were the largest (by annual revenues) on the 2002 list of the Fortune 500, except for computers, where Dell was omitted because it is not a significant performer of R&D. Revenue and expenditure amounts were restated as constant dollars, for reasons previously discussed.
26. The revenue and R&D time series from 1980 to 2000 was acquired from public sources and expressed in 1990 constant dollars. Acquisitions or divestitures were treated as business as usual, except for Pfizer where the financial impact was large. Unconsolidated revenue and R&D prior to the merger in 2000 with Warner Lambert is available in Pfizer annual reports up to and including 1999. The product development cycle was modeled by waves with short, medium and long durations (Table 2). Long product development times were assumed for semiconductor and pharmaceutical/health care firms, reasoning that long times are required for development of major product platforms and major manufacturing capabilities. Following the same logic, medium product development times were assumed for office product firms, and medium and short development times were assumed for firms offering computer products. The product revenue stream was modeled by waves with short, intermediate and long duration (Table 2). For pharmaceutical/health care firms, a long revenue wave was assumed, recognizing that products from these firms often generate revenue for a decade or more. Intermediate-duration revenue waves for revenue streams lasting about 5 years were assumed for IBM and Xerox, which develop complex equipment. The shortest revenue wave was assumed for firms that supply desktop products frequently replaced by customers, and for semiconductor firms, which frequently upgrade the performance of their processors.
27. The correlation is robust across lags of 3-5 years. A similar strong relation was found when the running average time was reduced to 3 years.
28. ROA is proportional to revenue growth rate according to the sustainable growth rate equation, with coefficient (equity/assets)(profits after tax)/(Δ retained profits). From financial data during the mid'90s, the average value for the ten firms of the coefficient is 1.1, within 10% of the value from the correlation, suggesting that as a group, the firms were near their sustainable growth rates.
29. The key uncertainties inherent in this analysis are our assumptions about the shapes of the investment and revenue waves for all but two of the firms. What is needed is a Gain estimate independent of these shapes for the major product platforms of each firm. One approach would be to compute the Gain directly from data on product price, cost, and quantity sold.
30. N. A. Nichols. Scientific Management at Merck: An Interview with CFO Judy Lewent. Harvard Business Review, pp. 89-99, Jan.-Feb. 1994.
31. Because the method involves a simultaneous fit of the parameters to the data, in principle the values of all parameters for all years are affected each time a new year is added to the series. However, if the data for the newest year are close to the projections used in the previous computation, the effect on values for earlier years is small.
32. k+1)/(2a + b), with b/a equal to 1, 2 or 3, depending on the curvature. The resulting Gain time series was smoothed in the same way.