r&d intensity and the new product development portfolio

664 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 60, NO. 4, NOVEMBER 2013

R&D Intensity and the New ProductDevelopment Portfolio

Raul O. Chao and Stylianos Kavadias

Abstract—A key metric for assessing innovative activity at thefirm level is R&D intensity. R&D intensity is the ratio of a firm’sR&D investment to its revenue (the percentage of revenue thatis reinvested in R&D). Empirical and anecdotal evidence suggeststhat R&D intensity within an industry tends to be remarkably con-sistent. Despite this consistency in R&D spending, however, firmsdiffer with respect to their new product development (NPD) port-folio strategy and overall performance. This paper seeks to explainhow R&D intensity can be so consistent at the aggregate level,while NPD portfolio strategies and firm performance are so var-ied at the firm level. We develop a model that considers firm levelfactors, such as the NPD portfolio composition and risk levels, aswell as industry level factors, such as competition intensity and en-vironmental stability. We show how a simple evolutionary processlinks aggregate R&D intensity and firm level portfolio choices. Ourresults highlight that R&D intensity alone does not explain firmperformance. Rather, it is the proper alignment between R&D in-tensity (how much the firm invests) and an NPD portfolio strategy(how the firm invests the money) that drives profitability.

Index Terms—Evolutionary systems, innovation, new productdevelopment (NPD), portfolio management, research and develop-ment, R&D intensity.

I. INTRODUCTION

AKEY metric for the assessment of innovative activity atthe firm level is R&D intensity, defined as the ratio of a

firm’s R&D investment to its revenue (the percentage of revenuethat is reinvested in R&D). The consistency in R&D spendingwithin an industry is a well-documented phenomenon [7], [8].As an example, consider the R&D intensity for Pfizer, Inc.,(see Fig. 1). Over a ten year span between 1995–2005, Pfizer’squarterly revenue grew from approximately $2 Billion to al-most $15 Billion. Over the same period of time, Pfizer’s R&Dintensity was approximately 13–15%. Despite an eight-fold in-crease in quarterly revenue, the percentage of revenue investedin R&D remained relatively constant. Consistency in R&D in-tensity is not unique to Pfizer. Fig. 2 provides the distributionof R&D intensities for firms in the automotive and pharma-ceutical industries. The data show that consistency in R&Dintensity is a common trait across these industries. In the auto-motive industry, R&D intensity typically falls between 3–5%.

Manuscript received November 8, 2011; revised October 9, 2012 and Febru-ary 7, 2013; accepted April 3, 2013. Date of publication May 2, 2013; date ofcurrent version October 16, 2013. Review of this manuscript was arranged byDepartment Editor S. (Sri) Talluri.

R. O. Chao is with the University of Virginia, Darden School of Business,Charlottesville, VA 22903 USA (e-mail: [email protected]).

S. Kavadias is with the University of Cambridge, Judge Business School,Cambridge CB2 1AG, U.K. (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEM.2013.2257792

Fig. 1. Pfizer, Inc., (PFE) R&D intensity and revenue over time (1995–2005).

Fig. 2. Distribution of R&D intensities in the automotive and pharmaceuticalindustries.

In the pharmaceutical industry, R&D intensity is approximately14–16%.

On the managerial front, informal interactions with senior ex-ecutives suggest that while R&D intensity is more-or-less con-sistent [4], [12], [19], firms tend to adopt significantly differentresource allocation and new product development (NPD) portfo-lio strategies [9], [26], [32]. It is common to hear a manager saythat R&D intensity is simply the “cost of doing business” in aparticular industry. This argument does little to explain how thedifferences between firms emerge in terms of their NPD port-folio strategies and subsequently their financial performance.On the academic front a number of researchers, primarily in

0018-9391 © 2013 IEEE

CHAO AND KAVADIAS: R&D INTENSITY AND THE NEW PRODUCT DEVELOPMENT PORTFOLIO 665

the domain of economics, take note of the consistency in R&Dintensity. Almost without exception their research focuses onthe industry as a unit of analysis in an effort to show that R&Dintensity exhibits less variance within an industry than betweenindustries. The extant research in economics remains silent asto the implications at the firm level (NPD portfolio strategy andfinancial performance). Interestingly, both practice and theoryacknowledge that firms are differentiated with respect to theirNPD portfolio strategy. This implies that firms operate accord-ing to different potential reward, timing, and risk considerationsfor R&D investments.

In light of these observations, we explore how R&D inten-sity is so consistent at the aggregate level while NPD portfoliostrategies are so different at the firm level. We also hope toshow how the combination of these two factors determines firmperformance. We begin our analysis at the individual firm leveltaking into account the firm’s R&D investment decision andits effect on sales growth. We then extend our analysis to theindustry level where multiple firms conduct R&D in the faceof competition. A key component of our analysis lies in un-derstanding how the R&D investment and its allocation acrossa portfolio of innovation initiatives jointly evolve over time todrive firm performance.

We show that R&D intensity for the single firm depends onthe firm’s NPD portfolio strategy (reward, risk, and timing) andcontextual variables such as cost of sales and competition inten-sity. Lower R&D return, risk, and cost of sales drive higher R&Dintensity. Conversely, lower competition intensity drives lowerR&D intensity. At the industry level, we show that the exis-tence of a simple evolutionary process drives the consistency inR&D intensity—firms within an industry tend to converge to anequilibrium in terms of R&D spending. This result echoes pastfindings in the Economics literature [18]. However, R&D inten-sity alone does not determine firm performance. It is the properalignment between R&D intensity and NPD portfolio strategythat determines firm performance. In particular, the proper align-ment is dictated by two industry level parameters: competitionintensity (the degree of lost sales due to competition) and envi-ronmental stability (the rate at which firms become extinct andthe subsequent effect of new firms that enter the environment).

Our work makes a number of contributions to theory andpractice. On the theoretical side, we link industry level eco-nomic concepts to operational NPD portfolio strategy. Moreimportantly, our results add to the discussion on R&D intensityby addressing how the R&D investment is implemented througha portfolio of projects, and how these two concepts together im-pact firm performance. From the practical side, our study opensthe door for managers to understand the combined impact ofR&D investment and the NPD portfolio strategy in the longrun. Specifically, given their NPD portfolio strategy and indus-try characteristics, managers may be able to use firm level datato estimate whether they are over or under invested in R&D.A novel aspect of this study from the perspective of methodol-ogy is that we develop an analytic model and nest it within anevolutionary (agent-based) analysis. While these methods areoften considered separately, we show how they can be used to-gether to glean insights about complex systems and managerialdecisions.

II. RELATED LITERATURE

A number of economic researchers have studied problemsthat are closely related to the issue of R&D intensity [8], [16],[18], [23]. Similar to our work, the majority of these effortsare based on analytic models of R&D spending. However, withrare exception, these efforts decouple revenue from the R&Dinvestment—they consider the case of exogenous growth. Thisdecoupling transforms the problem from one of R&D investmentand innovation to one of consumption and wealth accumulation.The focus on consumption and wealth accumulation is justifiedbecause the goal of the this stream of research is to provideguidelines for long-term economic growth.

The interest in long-term economic growth and the need tobetter understand what drives this growth has given rise to en-dogenous growth models, which have an important relationshipwith our study. Whereas neoclassical economic growth modelsassume that the long-run rate of growth is exogenously deter-mined (i.e., it is determined by an exogenous rate of techno-logical progress or an exogenous rate of labor force growth),endogenous growth theory presumes that firm activity impactsthe rate of technological progress. One important factor thatdrives this is investment in R&D and innovation. Beginningwith the work of Solow [29] and advanced through the workof Romer [25], Aghion and Howitt [1], and Aghion and Ti-role [2], the endogenous growth literature attempts to explainlong-term economic growth rates. Still, the high-level macroe-conomic view does not describe the operational details of R&Dinvestment and the varied resource allocation and NPD portfoliodecisions made by managers.

There is research in finance that is tangentially related to theR&D intensity question primarily from the viewpoint of invest-ment strategy [5], [22]. The focus of the finance literature iscapital structure (debt to equity ratio and dividend payments).In that light, R&D is conceptually related to finance in the sensethat an investment decision is made taking into account potentialreturns [13], [14], [28]. However, R&D investment is definedby a number of properties that go beyond standard financial in-vestments. The most important of these properties are nonlinearreturns and the fact that R&D investment endogenously alterspotential value and probability of success [20].

Relative to the existing research in economics and finance,our study provides a greater level of operational detail with re-spect to how the R&D investment is allocated across a portfolioof innovation initiatives. Thus, our analysis is similar in terms ofthe level of detail to previous work in R&D portfolio manage-ment [6], [20]. This detail allows us to make the necessary linkbetween economic factors, firm strategy, and more operationaldecisions such as how to structure the NPD portfolio.

III. MODEL OF R&D INVESTMENT FOR THE SINGLE FIRM

Our model consists of a nested analysis in which the singlefirm exists within an industry that is subjected to evolutionaryforces. Evolutionary perspectives have recently been advocatedin managerial settings characterized by technological innovationand change [10], [11], [15], [21], [30], [31]. We first developan analytic model of R&D investment for a single firm (see


Section III), and we use those results in an evolutionary analysisat the industry level (see Section IV).

The nested structure is a novel element of our model, anddeserves a bit more discussion. The fact that we nest an analyticmodel within an industry level simulation reflects an implicithierarchy of decisions within firms. We posit that resource de-cisions are the more flexible ones, since resources can be addedor subtracted from the ongoing portfolio of projects. These de-cisions tend to have, usually, more direct effects as well onsales. At the same time, portfolio level decisions, i.e., alloca-tion across different high-level projects, are not as flexible. Inother words, they are not manipulated as often as the resourcebudget decisions. The reason for such rarer changes stems fromthe reality of R&D portfolios where ideas need to be found andviable business cases need to be established before the start ofprojects, the scope of several projects that tends to be longer termand their results require multiple years to unfold. Therefore, weclaim that changes in the portfolio compositions happen not al-ways based on some optimization rule but they tend to react toperformance. We capture such evolution of portfolio decisionsthrough our evolutionary model.

A. R&D Investment and Sales Growth

We begin by considering the infinite horizon problem of in-vesting in R&D to drive sales growth. The infinite horizon struc-ture is appropriate since senior managers are expected to makeR&D investment decisions for the foreseeable future withoutany artificial end-of-horizon modeling effects. Let r(t) be thefirm’s R&D investment at time t ∈ (t0 ,∞). This R&D invest-ment may drive different monetary outcomes, depending on thequalitative properties of the projects funded by it. We identifythree distinct properties that define these effects of R&D invest-ment: first, the potential impact on sales (more R&D dollarsallow the capture of larger market shares); second, the timing atwhich this impact will take place (short-term versus long-term);and third, the possibility that the R&D investment will capturethe potential sales.

The R&D investment has a positive impact on sales throughan R&D return function f(r), which is increasing and con-cave on (0,∞) with f ′(0) = ∞ and f ′(∞) = 0. To capturethe time lag in effectiveness of R&D investments, we defineω(t − τ) ∈ (0, 1) as the portion of the R&D investment madeat time τ that has an impact on sales at time t ≥ τ . With thisdefinition in hand, we can write the total portion of the R&D in-vestment made at τ that has an impact on sales at any time in thefuture as

∫ ∞τ ω(t − τ)dt = μ ∈ (0, 1).1 Finally, the 0 < μ < 1

scalar represents the ability to capture fully (or not) the im-pact of R&D investments over time: not all R&D dollars endup being effective because of the inherent uncertainty of R&D

1Our modeling of the lag function ω(t − τ ) is slightly unorthodox with re-spect to the prior literature [33], [34]. The novelty of our approach is that itallows us to capture a lagged investment effect without full return of our invest-ment without violating the mathematical conditions for the long run equilibriumderivation. An alternative formulation that would come closer to the traditionalliterature formulations is as follows:

∫ ∞τ

ω(t − τ )dt = 1, but then the laggedeffect is scaled by a factor μ. We would like to thank a reviewer for pointing outthis divergence of our model from the established literature.

projects. Thus, despite the significant resources allocated to cer-tain projects, these projects are not successful and they get com-pleted or abandoned without ever having an impact on sales.In this sense, we treat the scalar μ as a surrogate metric forthe risk-driven R&D productivity losses from the firm’s R&Dinvestment. Hence, R&D investments that are inherently moreuncertain would exhibit higher R&D productivity losses, be-cause such higher risk encompasses greater potential to disruptthe R&D productivity both in the short and long term, and resultin a lower μ effect; in contrast, R&D investments that supportrelatively low risk ideas should exhibit less losses and carry ahigher μ.

The triplet π = {f(·), ω(·), μ} offers a broad characterizationof the firm’s NPD portfolio strategy. Each of the elements of πrepresents different properties of the R&D overall investmentthat jointly characterize the allocation strategy and define theNPD portfolio. We represent the portfolio strategies in such ageneral way (as opposed to a more detailed project level anal-ysis), because it allows us to capture key properties of R&Dinvestments without the cumbersome complexity of a projectlevel analysis. We recognize that these different properties ex-hibit specific characteristics contingent on the amount of in-vestment risk assumed in the portfolio. Thus, if a firm has apreponderance of incremental NPD programs in its portfolio,then one would expect the following implications: the potentialsales impact (f(·)) will be relatively small given that incremen-tal programs address well understood needs and exploit existingmarkets as opposed to creating new markets and therefore largechanges on the potential market shares; the timely effect of in-cremental programs tends to be short, and therefore ω(·) willbe such that the time lapse from R&D investment to payoff issmall; finally incremental programs have relatively low risk andtherefore rarely leads to failures. In that sense, the risk-drivenproductivity losses from the R&D investments will be relativelysmall, i.e., equivalently μ will be relatively large. Conversely,a firm that has a substantial number of radical NPD programsin the portfolio will have f(·) that is relatively large, ω(·) suchthat the time lapse from R&D investment to payoff is relativelylong, and μ that is relatively small.

These π characteristics that are contingent on the portfoliorisk point to a well-documented tradeoff in the portfolio litera-ture [6] (Cooper et al. 1996) regarding the portfolio risks andrewards. Thus, the latter description for π is aligned with a lowrisk, low reward strategy, whereas the former captures a highrisk, high reward NPD portfolio strategy. We limit our study tofirms that exist along a continuum from low-risk, low-reward tohigh-risk, high-reward. This convention ensures that our modelremains unbiased in terms of firm strategies. Alternatively, ifwe allow firms with strategies such as low-risk, high-reward toenter into our analysis, they would clearly dominate all otherstrategies. Fig. 3 provides a schematic representation of poten-tial NPD portfolio strategies.

Given the firm’s R&D investment and portfolio strategy,we can define the impact of R&D on sales. Let dS/dt =∫ t

−∞ f [r(τ)]ω(t − τ)dτ − δS(t) be the change in sales at t. Thechange in sales is the difference between growth due to the returnfrom previous R&D investments and decline due to lower sales


Fig. 3. Representation of NPD portfolio strategies (adapted from [9]).

because of customers shifting to other products, less customersremaining to purchase the new product, cannibalization fromcompetitive products and/or technologies. While many factorsmay ultimately lead to sales loss, we focus our attention on theeffects of competition intensity. Therefore, we say that δ ∈ [0, 1]is the per-period percentage loss in sales that the firm suffers.We attribute that to the firm’s product, technology, and marketpositioning relative to other firm’s in the industry within whichthe firm operates. Note that growth in sales at time t depends onR&D investments made prior to t. The time at which the R&Dinvestments actually deliver results (if they deliver results at all)depends on the form of ω(·) and the value of μ. Having definedthe firm’s R&D investment and its impact on sales, we can writethe firm’s profit maximizing problem as

V (S0) = maxr(t)

∫ ∞

t0

[(1 − c(t)

)S(t) − r(t)

]e−ρtdt (1)

where c(t) is the cost of sales (as a percentage of sales) and ρ ∈(0, 1) is the discount factor. The maximization in (1) is subjectto dS/dt =

∫ t

−∞ f [r(τ)]ω(t − τ)dτ − δS(t), S(t0) = S0 > 0,and

∫ ∞τ ω(t − τ)dt = μ ∈ (0, 1). We allow process improve-

ment and learning to take place so that dc/dt < 0, d2c/dt2 > 0,and limt→∞ c(t) = c. Thus, the cost of sales is decreasing overtime, but cannot be reduced beyond a limiting value c. The valuec is a useful descriptive parameter of an industry characteristic,as it indicates the cost of sales at a steady state, once the initialcosts from entering the industry have been put under control.

B. Equilibrium R&D Intensity

In this section, we discuss the analytic solution to the problempresented previously. To ease exposition, all technical detailsare presented in the Appendix and functional notation is sup-pressed when the intended meaning is obvious. In describing theanalytic solution, we first characterize the firm’s optimal R&Dinvestment and then proceed to analyze the equilibrium valuesfor R&D investment, sales, and R&D intensity. The problemstated in (1) results in an intuitive solution for the firm’s opti-mal R&D investment over time. We state this result formally inProposition 1.

Proposition 1—Optimal R&D Investment: The firm’s optimalR&D investment, r∗(t), is defined implicitly by ∂f/∂r∗

∫ ∞t λ

(τ)ω(τ − t)dτ = 1.The firm’s optimal R&D investment equates the expected

marginal benefit from R&D (i.e., the expected impact that R&Dinvestment has on sales including all future benefits) to themarginal cost of R&D at time t. This insight is aligned with pre-vious work in economics [7], [8], and in NPD [20]. Based on thisresult, we seek long-run equilibrium values for the firm’s R&Dinvestment and sales rate. The long run equilibrium offers a real-istic representation of managerial decision making for a numberof reasons. There are many parameters that affect the successor failure of projects that go into the portfolio. Such complexityrenders finite horizon optimization decisions uninformative andlimited. Instead senior management can treat the portfolio deci-sion as an allocation decision over an infinite horizon. This alsorepresents the reality that product lifecycles and competitiveactions cannot always be known. In addition to the long run sta-tionary equilibrium offering a surrogate metric for the optimalR&D investment over an infinite horizon, we should note thatit has been previously used in various managerial contexts [27].Even if the initial sales are different than the stationary long-runequilibrium solution, there exists always an optimal (Pontrya-gin) path that takes the initial sales to the optimal level. Anequilibrium is achieved when two conditions are satisfied. First,the marginal benefit from all future sales (discounted to t) isbalanced against the marginal benefit at t. Second, the expectedincrease in sales at t (due to all previous R&D investments) isbalanced against the decline in sales (due to δ). Together theseconditions define a stationary equilibrium [17], [27]. Proposi-tion 2 characterizes the equilibrium conditions.

Proposition 2—Equilibrium Conditions: r = g−1 [(ρ + δ)/(μ − cμ)] where g−1(·) is a decreasing and convex function de-fined by g(r) = ∂f/∂r. Equilibrium Sales Rate: S = f(r)μ/δ.Equilibrium R&D Intensity: There exists an equilibrium R&Dintensity given by: β = r/S.

The equilibrium R&D investment balances the marginal ex-pected benefit from R&D expenditure (in terms of R&D returnand higher sales) with the cost of the investment. Because c(t)is decreasing over time, it can be shown that the R&D invest-ment increases over time until reaching the equilibrium value.This R&D investment drives sales growth over time. The equi-librium sales rate is defined at the point at which the expectedsales growth due to R&D expenditure offsets the sales decline.Because the R&D expenditure increases toward the equilibrium,it follows that the sales rate increases until reaching the equi-librium value S. Given the equilibrium R&D expenditure andsales rate, we define the equilibrium R&D intensity as the ratioof R&D expenditure to sales.

The existence of an equilibrium R&D intensity is guaranteedif the firm optimally invests in R&D at the point at which thebenefits from the R&D expenditure balance against the cost ofthe investment. Moreover, for any initial sales state, there isalways an optimal path to the equilibrium [27]. An importantelement of our analysis lies in understanding the factors thatdrive lower or higher R&D intensity. We state this formally inProposition 3.


Proposition 3—Comparative Statics Analysis for R&D In-tensity: β is higher if: (i) f(·) is lower, (ii) μ is lower, (iii) δ ishigher, (iv) c is lower.

The comparative statics results in Proposition 3 hint at thefactors that drive R&D intensity lower or higher for an individualfirm.2 Equilibrium R&D intensity is higher, ceteris paribus, ifthe firm is subject to conditions that result in lower sales (i.e.,lower f(·), lower μ, and higher δ). In such cases, the firm mustinvest more in R&D to maintain a comparable level of sales andthe result is a higher equilibrium R&D intensity. It is interestingto note that f(·) and μ are portfolio metrics that are internal tothe firm while δ is an external effect. Lower cost of sales (c)drives higher R&D intensity because the firm can invest moredollars in R&D without sacrificing profit. Considered together,the effects cited above hint at the link between R&D intensityand the NPD portfolio strategy. Proposition 4, next, offers aformal statement of this link.

Proposition 4—Heterogeneity of Portfolio Strategies: Twodifferent firms may reach the same equilibrium R&D in-tensity, β, through substantially different portfolio strategiesπ = {f(·), ω(·), μ}.

The statement in Proposition 4 is a straightforward outcomeof the comparative statics offered previously. As shown there,the equilibrium R&D intensity is higher when the return, f(·),and the risk μ of the portfolio strategy are lower. However, aswe point out in our discussion of Fig. 3, typical NPD portfoliostrategies encountered in practice tend to exist on a reward-riskcontinuum. In that sense, a lower f(·) is often associated witha higher μ and vice versa. Combining the two results, one no-tices that similar equilibrium R&D intensities may emerge fromvery different portfolio strategies. This realization should notgo unnoticed as it states an important managerial insight fromour model analysis: it formally establishes that the multitudeof portfolio allocation strategies observed in practice exists de-spite the robustness of the overall amount of funds allocated toR&D. The results discussed previously apply to a single firm.We come back to this observation later in the next section oncewe analyze the evolution of R&D and portfolio strategies acrossfirms within an industry.

IV. EVOLUTIONARY MODEL OF R&D INVESTMENT

We now extend our analytic model of the single firm into anevolutionary simulation which allows us to analyze the R&D in-vestments within an industry [24]. That is to say, multiple firmsinteract and firm strategies evolve over time based on variation,selection, and retention mechanisms. An evolutionary perspec-tive is appropriate given the fact that firms must often makeR&D investment decisions without knowing the strategies ordecisions made by competitors. At the same time, the sheer

2In our analysis we have not assumed any structural relationship between theπ elements, despite our consideration about their cross dependence being “real-istic” (i.e., all three elements follow a loosely defined higher risk, higher rewardrelationship). As such our comparative statics explore local perturbations. Theanalysis of a more structured relationship between the portfolio risk and rewardelements is beyond the scope of our study here. Future research should addressin more depth this fundamental risk-reward tradeoff.

amount of parameters that determine the success or failure ofportfolio projects makes any optimization challenging. Insteadan evolutionary approach captures better the bounded rational-ity of managerial decision making [3]. Indeed, corporate R&Dinvestments and NPD portfolio decisions are highly guardedsecrets and are only common knowledge ex post.

A. Competition and Environmental Stability

A significant portion of the failures associated with R&Dinvestments stems from two distinct factors. First, and mostimmediate, is lost sales due to a firm’s strategic position thatallows for insufficient differentiation, or a competition intensityeffect. Second, there is a real threat that some firms may ceaseto exist before their R&D investments pay dividends. We callthis an environmental stability (turbulence) effect, and we intro-duce it more formally in the next subsection. In order to capturethese effects, we consider firms that find themselves compet-ing with one another. In such cases, the effects of competitionand stability highlight the importance of relative performance(profitability) between firms. In fact, poor performing firms donot exist in perpetuity. This final insight merits study becauseprevious work in economics that analyzes equilibrium R&D in-tensity does not account for the implementation of the R&Dinvestment (through the NPD portfolio) and the performanceimpact of these decisions.

B. Evolution: Variation, Selection, and Retention

Simulation of the evolutionary model takes place through adiscrete time approximation to the analytic (single firm) model.To implement the simulation, we define variation, selection, andretention mechanisms for the population of firms. Aligned withour analytic model, we assume that firms are initially differenti-ated with respect to R&D intensity (βi), portfolio strategy (πi),competition intensity (δi), and cost of sales (ci). The discountfactor ρ is assumed to be the same for all firms. The follow-ing steps take place in each period t = 0, 1, 2, . . . for each firmi = 1, 2, . . . , N .

1) Variation occurs as firm i determines its R&D investment,ri = βiSi where βi is firm i’s R&D intensity. Firm i’sR&D investment has an impact on future sales: Si(t +1) = (1 − δi)Si(t) +

∑tτ =t0

fi [ri(τ)]ωi(t − τ)μi . Notethat the firm’s portfolio strategy (fi(·), ωi(·), and μi) de-termines the impact of R&D investments on future sales.

2) Selection occurs in each period based on firm profit. Profitfor firm i is Πi(t) = (1 − ci)Si(t) − ri(t) and the lowest(1 − s) · 100% of firms (in terms of profit) are eliminatedfrom the population. The parameter s ∈ (0, 1) capturesthe stability of the environment. Stability determines theentry and exit rates within an industry; see Klepper [18]for a detailed treatment. Such entry and exit is subject tomany external factors beyond the firm performance (e.g.,overall economy performance, policy changes, etc.). Itis represented through the proportion of low performingfirms that cease to exist and is related to the notion of en-vironmental change or turbulence. The use of the phrase


Fig. 4. Illustrative example of the distribution of firm R&D intensities at t = 0 and t = 100 (steady state) for a sample simulation run.

“ceases to exist” does not necessarily imply physical ter-mination. It could be the case the the firm ceases to existin its current organizational structure or form. Low sta-bility implies that the technological market space changesquickly and many firms become extinct in each period (theequivalent of a fluid phase in the industry lifecycle [35].Conversely, high stability implies that few firm becomeextinct in each period (the equivalent of maturity in thetechnology/market space). The same number of new firmsenter the population with random portfolio strategies andtechnology/market position.3

3) Retention occurs as new firms that enter the populationcopy R&D intensity from the highest performing firms (interms of profit). We posit it is realistic to assume that firmscan benchmark R&D intensity. However, it does not makesense to use NPD portfolio strategy as a retention mecha-nism because the composition of the NPD portfolio tendsto be a highly guarded corporate secret—not viable forbenchmarking. Therefore, new firms enter the populationwith random portfolio strategies (πi) and competition in-tensity (δi). R&D intesnity (βi) is copied randomly fromone of the top performing firms (top 10% in terms ofprofit).

The above process of variation, selection, and retention takesplace until the system reaches steady state, which we define asthe period after which parameter values do not change by morethan 0.50% for 100 consecutive periods. The steady state isachieved at or around t = 100 for the majority of experiments.

C. Experimental Design for the Evolutionary Model

In this section, we detail the functional forms, parametervalues, and experimental design for the evolutionary model.The functional form of f(r) ensures that R&D productivity issubject to diminishing returns. The functional form of the timelag of R&D effectiveness is a Gamma function with parameters(n1 , n2). Recall that μ ∈ (0, 1) limits the total effectiveness of

3The assumption that firms enter the population at random positions in notas limiting as it seems. If firms were to behave strategically, they would posi-tion themselves so as to maximize the distance between one another. This isessentially what we achieve through random initial positioning.

R&D investment

f(r) = A[1 − e−ar

](2)

ω(t) =nn2

1 t(n2 −1)e−n1 t

(n2 − 1)!. (3)

Throughout the simulation there are a number of parametersthat remain static. In each experiment, we limit the number offirms to N = 500. We conducted 100 replications to test forinitialization bias and found that our results are robust. We alsofix the shape parameter for ω(t) at n1 = 2. Finally, we assumethat the number of firms that enter the population at each pointin time is equal to the number that exit. The convention thatpopulation size remains constant is aligned with other work inevolutionary systems and population dynamics.

D. Equilibrium R&D Intensity

Our first challenge in analyzing the results of the evolutionarymodel is to confirm that R&D intensity indeed reaches an equi-librium. We then concern ourselves with the question of howthe equilibrium R&D intensity aligns with the NPD portfoliostrategy to determine performance. Fig. 4 depicts the distri-bution of R&D intensities at t = 0 and t = 100 (steady state)for a sample numerical experiment of the evolutionary process.Fig. 4 illustrates that an equilibrium R&D intensity exists whena population of firms is subject to competition and evolution-ary mechanisms. Extensive experimentation confirms that theconvergence result depicted in Fig. 4 is robust. In particular, thevariance in R&D intensity at t = 100 (steady state) was substan-tially lower than the variance at t = 0 for all experiments. Notethat the initial (t = 0) distribution of R&D intensities rangesfrom approximately 0.10 to 0.40. This reflects the fact that firmsinitially differ with respect to R&D intensity. At t = 100 (steadystate) the distribution of R&D intensities is characterized bysubstantially lower variance. This result indicates a convergenceto an equilibrium R&D intensity.

We now turn our attention to the value of the equilibriumR&D intensity. Table I depicts the experimental design used toverify the comparative statics results of Proposition 3. For eachexperiment, we report the parameter value that changes relative


TABLE IPARAMETER VALUES USED TO TEST THE COMPARATIVE STATICS RESULTS OF PROPOSITION 3

TABLE IIEQUILIBRIUM PROFIT, SALES, R&D INVESTMENT, AND R&D INTENSITY AT t = 100 (STEADY STATE)

to the base case (all other parameters are the same as the basecase experiment).

Table II shows the mean of the distributions at t = 100 (steadystate) for profit, sales, R&D investment, and R&D intensity forthis set of numerical experiments. Each experiment (E1-E5)highlights a changed parameter relative to the base case experi-ment. We should note here an important feature of our analysis:all firms in the population start off with asymmetric values aboutall relevant variables and parameters.4 In other words, we donot impose any symmetry among the firms to ensure that we donot bias the resulting distributions. We also report the standarderrors for each output measure to highlight the fact that conver-gence is robust. The results in Table II mirror the comparativestatics analysis of Proposition 3. As with the analytic result, wenote that the equilibrium R&D intensity is lower if R&D returnis higher, uncertainty is lower, competition intensity is lower,and cost of sales is higher. Based on the results in Table II, wecan explain the factors that drive a change in equilibrium R&Dintensity for a population of firms. In experiment E1, both theequilibrium sales and equilibrium R&D investment are higher.The equilibrium R&D intensity is lower because the sales effect

4We would like to thank one of the reviewers for pushing us to clarify thispoint.

dominates the R&D investment effect (the same reasoning is truefor experiment E2 and E3). Conversely, experiment E4 resultsin lower equilibrium R&D investment and sales. For this ex-periment, R&D intensity is lower because the R&D investmenteffect dominates the sales effect. Interestingly, experiments E1,E2, and E3 consist of factors that have a direct impact on salesgrowth or decline while experiment E4 directly impacts firmprofitability.

E. R&D Intensity and NPD Portfolio Strategy of the TopPerforming Firms

The analysis aforementioned shows that R&D intensity in-deed reaches an equilibrium value, but it says nothing of firmperformance or the portfolio strategy that must be implementedto deliver this performance. We now go deeper into the dis-tribution of R&D intensities to understand how a firm’s R&Dinvestment and portfolio strategy impact the performance. Weperformed an additional 30 × 30 = 900 experiments to un-derstand how stability and competition intensity impact theequilibrium R&D intensity and the NPD portfolio strategy ofthe top performing firms. We define the top performing firms asthe highest 10% in terms of profit once the system reaches thesteady state.


Fig. 5. Equlibrium R&D intensity and NPD portfolio innovativeness of the top performing firms (top 10% in terms of profit) as a function of competition intensityand stability.

TABLE IIICOMPLETE EXPERIMENTAL DESIGN FOR THE EVOLUTIONARY

ANALYSIS OF PORTFOLIO STRATEGIES

Table III depicts the complete experimental design forthe evolutionary analysis of the portfolio strategies. Our fullexperimental design in this section varies δ and s as shown inTable III (30 × 30 = 900 experiments). We control for initial

sales by setting S0 = 30 for all firms. This ensures that successis determined by strategy rather than a favorable initial condi-tion. We tested the robustness of the model with respect to S0and found that our insights remain qualitatively the same.

Fig. 5 is a contour graph that presents iso-R&D-intensity andiso-portfolio-strategy regions. The left panel of Fig. 5 depictsthe equilibrium R&D intensity of the top performing firms asa function of competition intensity and stability.5 For a givenlevel of stability, the R&D intensity of the top performing firmsincreases in competition intensity. The evolutionary model con-firms the directional results of the analytic model, and highlightsthe magnitude of the effects. For low levels of competition in-tensity (0.00 < δ < 0.10), the R&D intensity does not changeacross the different values of stability. However, for higher lev-els of competition intensity, R&D intensity increases in sta-bility. Note that there is a second-order effect of stability andcompetition intensity on equilibrium R&D intensity for the topperforming firms, and it appears that competition intensity isthe dominant driver of equilibrium R&D intensity for the topperforming firms.

While competition intensity is the key driver of equilibriumR&D intensity for the top performing firms, the same cannot besaid for the NPD portfolio strategy. The right panel of Fig. 5depicts the NPD portfolio strategy of the top performing firms

5Our results are robust if we define the top performing firms as the highest15%, 20%, and 25% in terms of profit. Of course, the variance of firm strategiesincreases using these scenarios.


as a function of competition intensity and environmental sta-bility. We operationalize NPD portfolio strategy in terms ofthe level of risk/reward in the portfolio, a proxy for the port-folio’s “innovativeness.” Given the underlying relationship be-tween the different dimensions of portfolio strategies (see dis-cussion in secton 3.1), we use one of the parameters to illus-trate innovativeness, namely the return-function parameter A inf(r) = A

[1 − e−ar

].

Fig. 5 shows that for a given level of competition inten-sity, the portfolio strategy of top performing firms seeks higherrisk/reward as stability increases. Said differently, lack of sta-bility leads to an incremental focus in the NPD portfolio. Thisobservation echoes earlier observations made in Chao and Kava-dias [6]. From a managerial perspective, in an environment char-acterized by low stability, radical innovation initiatives that arehigh risk/reward do not have time to deliver results and the out-come is that top performers tend to be those firms that pursueincremental innovation. Note that the effect of competition in-tensity on the NPD portfolio strategy for the top performingfirms is smaller compared to the effect due to stability. For lowlevels of stability, the top performing firms reach an equilibriumcharacterized by an incremental strategy, while for higher levelsof stability, the equilibrium portfolio strategy is balanced.

These results shed further light on the need for a combinedperspective on R&D intensity and R&D portfolio strategy un-der the different conditions (stability, and average competitionintensity). Whereas R&D intensity always converges to an in-dustry average (as observed in reality), it follows a robust mech-anism: successful firms tend to increase R&D intensity as thecompetition intensity increases. Said differently, cut-throat com-petition tends to favor R&D spending. At the same time, port-folio strategies tend to be more disperse (i.e., not a clear con-vergence to a robust portfolio choice). Moreover, the successfulfirms tend to seek riskier portfolios only when there exists in-dustry stability (i.e., a relatively fixed number of competitorsfighting each other with few exits/entries over time). So, over-all, we do find a relatively rich set of potential choices when itcomes to how much to spend on R&D, and how to spend theR&D budget.

V. DISCUSSION AND CONCLUSION

The goal of this study is to explore and understand an often-observed phenomenon in industries: R&D intensity is character-ized by consistency at the moment that different firms appear tofollow different resource allocation strategies and to define dis-similar portfolios of new products and projects. To accomplishthis goal, we developed an analytic model of R&D investmentfor a single firm. We then extended the analytic model to acompetitive setting in which a population of firms evolves overtime until reaching an equilibrium state. Our results show thatR&D intensity for the single firm depends on a combinationof portfolio metrics (i.e., risk, reward, and timing) and contex-tual variables such as cost of sales, competition intensity, andstability. Our analysis also shows, though, that the same R&Dintensity can be derived from diverse portfolio metrics, indicat-ing that it is possible to obtain a common R&D intensity but

Fig. 6. Aligning R&D intensity and the NPD portfolio strategy.

differing portfolio strategies. Thus, lower R&D return and costof sales drive higher R&D intensity. Conversely, lower uncer-tainty and competition intensity drive lower R&D intensity. Atthe industry level, we show that a simple evolutionary processdrives the consistency in R&D intensity. The evolutionary modelsuggests that multiple firms within an industry tend to convergeto an equilibrium R&D intensity. It also depicts that portfoliostrategies for the top performing firms tend to be diverse asargued in the analytical model.

Our preceding analyses imply that R&D intensity (how muchthe firm invests) and the NPD portfolio strategy (how the firm in-vests this money) must be correctly aligned during the differentstages of an industry lifecycle. Early in the industry lifecycle,when competition intensity and stability are both low, profitabil-ity is a result of a lower R&D intensity and a more incrementalportfolio. At this stage, incremental implies exploitation of afirm’s current position rather than radical, long-term initiatives.Later in the industry lifecycle, when competition intensity andstability are both high, the best performing firms have higherR&D intensity and a more balanced portfolio. Depending onhow these two industry factors evolve, managers must maintaincareful alignment between R&D intensity and the NPD portfoliostrategy.

Our results mimic the ample evidence that industry evolutionproceeds from low stability and low competition intensity tohigh stability and high competition intensity. Early in an indus-try lifecycle, many firms tend to take “creative bets”—differentapproaches that are distant from one another in terms of tech-nologies or target markets, and do not cannibalize other offeringsin the industry. As time passes, new entrants emulate the posi-tioning of successful firms and competition intensity increases.Low performing firms cannot survive in part due to the risk oftheir creative bets, management capabilities, and cash position,among others.

Fig. 6 highlights these potential evolutionary pathways foran industry, and the accompanying R&D intensity and the NPD


portfolio strategy that together deliver superior performance ateach stage. If competition intensity ramps up before the industrybecomes stable, then firms should increase R&D intensity whilemaintaining an incremental focus in the NPD portfolio. This sce-nario calls for investing more dollars in relatively safe projects(exploitation). Conversely, if the industry becomes stable andthen competition intensity increases, firms should maintain arelatively low R&D intensity and shift resources to more radicalNPD initiatives. This scenario calls for investing less dollars inmore risky projects (exploration).

Our results contribute to both the theory and practice of R&Dinvestment. From the theoretical side, we “open the black box”to discuss how the NPD portfolio strategy affects and is affectedby the consistency of R&D investment within an industry. Mostimportantly, we note that the only way that firms can ensuresuccess at different stages of the industry lifecycle is throughcareful alignment between R&D intensity (how much money toinvest) and NPD portfolio strategy (how to invest that money).We find that how the industry evolves is as important as whenit evolves. This is an important theoretical contribution as itadds a performance dimension to a long standing question inthe economics of R&D investment.

From the practical side, this study opens the door for man-agers to understand the long-run impact of R&D investment andthe NPD portfolio strategy. Specifically, firm level data such ascost of sales, overall NPD portfolio composition, and R&D in-tensity can be used to estimate whether a firm is over or underinvested in R&D. Furthermore, managers can benchmark in-dustry competitors to ensure that their R&D intensity and theNPD portfolio strategy are aligned. We view our work as animportant step that can help academics and practitioners de-velop a better understanding of an often-observed phenomenonin R&D investment. Indeed, understanding where the R&D in-vestment comes from, what drives this investment, and how itdelivers performance is the first step toward effective resourceallocation and NPD portfolio management.

APPENDIX

In, we provide proofs for Propositions 1, 2, and 3. We restateequations from the manuscript when necessary to ease exposi-tion. As a convention, we denote the partial derivative of anyvariable x with respect to its argument as x′ when the argumentis unambiguous. We denote the time derivative for any variablex as x.

A.1 Proofs

The problem stated in (1) is solved using the optimal controltheory [17], [27]. We state the current value Hamiltonian (H)as follows:

H = [1 − c(t)]S(t) − r(t) + f [r(t)]∫ ∞

t

λ(τ)ω(τ − t)dτ

− λ(t)δS(t). (4)

The necessary conditions for optimality are:

∂H

∂r= 0 (5)

λ − ρλ = −∂H

∂S. (6)

Equation (5) is the necessary first-order conditions for the opti-mal R&D investment r∗. Equation (6) is the necessary conditionfor the costate variable λ(t) (marginal value function for sales).We can now state the proof for Proposition 1.

Proposition 1—Optimal R&D Investment: The optimal R&Dinvestment r∗(t) is defined implicitly by ∂f/∂r∗

∫ ∞t λ(τ)ω(τ −

t)dτ = 1.Proof of Proposition 1: The proof follows directly from the

necessary condition in (5). Differentiating H with respect to rand setting this term equal to zero gives: ∂f/∂r∗

∫ ∞t λ(τ)ω(τ −

t)dτ = 1. The left-hand side of this expression is the marginalexpected benefit from a dollar invested in R&D. The right-handside of the expression is the marginal cost of investing the dollarin R&D. Note that the marginal expected benefit takes intoaccount all future marginal benefits from the R&D investment(captured in the term

∫ ∞t λ(τ)ω(τ − t)dτ ). QED.

Based on the result in Proposition 1, we seek long-run equi-librium values for the firm’s R&D investment and sales rate.The necessary conditions for a long-run equilibrium are:

λ = 0 (7)

S = 0. (8)

Proposition 2—Equilibrium R&D investment: r = g−1 [(ρ +δ)/(μ − cμ)], where g−1(·) is a decreasing and convex func-tion defined by g(r) = ∂f/∂r. Equilibrium Sales Rate: S =f(r)μ/δ, where f(·) is an increasing and concave function.Equilibrium R&D Intensity: There exists an equilibrium R&Dintensity given by: β = r/S.

Proof of Proposition 2: To prove the first part of Propo-sition 2, we make use of (6) and (7). These conditions de-fine a long-run equilibrium for the costate variable (marginalvalue function). We have λ = 0 ⇒ ∂H/∂S = ρλ ⇒ λ = (1 −c)/(ρ + δ), where λ is the equilibrium value of the costate vari-able. Note that we have assumed that sufficient time has passedso that c = 0 and the cost of sales has reached its limiting value(a valid assumption given that (7) defines a long-run equilib-rium). We now make use of Proposition 1 and the expressionfor the optimal R&D investment. We can remove λ(τ) from theintegral because it is no longer a function of time. Also, recallthat we define ω(·) such that

∫ ∞t ω(τ − t)dτ = μ. Finally, let

∂f/∂r = g(r) and note that g(r) is decreasing and convex in rbecause f(r) is increasing and concave in r. We can now writethe complete expression for the equilibrium R&D investment asr = g−1 [(ρ + δ)/(1 − c)μ].

To prove the second part of Proposition 2, we make use of(8) along with the state equation that defines the change insales. From (8), we have S = 0 ⇒

∫ t

−∞ f [r(τ)]ω(t − τ)dτ =δS, where S is the equilibrium sales rate. To find the equilibrium


sales rate, we make use of the equilibrium R&D investmentestablished above and we note that

∫ t

−∞ ω(t − τ)dτ → μ ast → ∞. This leaves us with S = f

[g−1(1/λμ)

]μ/δ.

To prove that there exists an equilibrium R&D intensity givenby β = r/S, we consider the locus of points in (S, λ) space forwhich the equilibrium conditions in (7) and (8) are satisfied. Thecurve defined by the points for which S = 0 is an increasing andconcave function of λ. Using similar logic, the curve defined bythe points for which λ = 0 is not a function of S. The pointat which these two curves intersect in (S, λ) space defines thelong-run equilibrium R&D intensity. QED.

Proposition 3 is a comparative statics analysis for β. Thecomparative statics allows us to understand the factors that drivelower or higher equilibrium R&D intensity.

Proposition 3—Comparative Statics Analysis for R&D In-tensity: β is higher if: (i) δ is higher, (ii) μ is lower, (iii) f(·) islower, (iv) c is lower, (v) ρ is lower.

Proof of Proposition 3: Using the results established in Propo-sition 2, we can define the equilibrium R&D intensity as animplicit function of the other parameters in the problem:

F (·) = βf(g−1(y)

)μ − g−1(y)δ = 0 (9)

where y = ρ+δ(1−c)μ . For any parameter x, the comparative statics

are given by:

dβ

dx= −∂F/∂x

∂F/∂β. (10)

Note that ∂F/∂β = f(g−1(·))μ > 0 so we need to onlyconsider the sign of ∂F/∂x in our analysis. We proveeach part of Proposition 3 in succession. (i) ∂F/∂f(·) =βμ > 0 ⇒ dβ/df(·) < 0. (ii) ∂F/∂μ = βf(g−1(·)) > 0 ⇒dβ/dμ < 0. (iii) ∂F/∂δ = −g−1(·) < 0 ⇒ dβ/dδ > 0. (iv)∂F/∂c = βμ(∂f/∂g−1) − δ < 0 ⇒ dβ/dc < 0. QED.

ACKNOWLEDGMENT

The authors thank S. Talluri and two anonymous reviewers fortheir helpful and constructive comments throughout the reviewprocess. They also thank seminar participants at the Universityof Utah Product Development Mini-Conference, the Universityof Minnesota Carlson School of Management, and the Depart-ment of Computational Social Science in the Krasnow Institutefor Advanced Study at George Mason University. This researchwas generously supported by grants from the Batten Instituteat the University of Virginia, the Darden School Foundation,and 3M Corporation. As far as we know, there are no errors oromissions.

REFERENCES

[1] P. Aghion and P. Howitt, “A model of growth through creative destruction,”Econometrica, vol. 60, no. 2, pp. 323–351, 1992.

[2] P. Aghion and J. Tirole, “The management of innovation,” Quart. J. Econ.,vol. 109, no. 4, pp. 1185–1209, 1994.

[3] W. B. Arthur, “Inductive reasoning and bounded rationality,” Amer. Econ.Rev., vol. 84, no. 2, pp. 406–411, 1994.

[4] J. Banovitz, Personal communication with John Banovitz, Strategic Busi-ness Development Manager, 3M Corporation, 2009.

[5] M. Bradley, G. Jarrell, and H. Kim, “On the existence of an optimal capitalstructure: Theory and evidence,” J. Finance, vol. 39, no. 3, pp. 857–878,1984.

[6] R. Chao and S. Kavadias, “A theoretical framework for managing the NPDportfolio: When and how to use strategic buckets,” Manag. Sci., vol. 54,no. 5, pp. 907–927, 2008.

[7] W. Cohen and S. Klepper, “The anatomy of industry R&D intensity dis-tributions,” Amer. Econ. Rev., vol. 82, no. 4, pp. 773–799, 1992.

[8] W. Cohen and S. Klepper, “A reprise of size and R&D,” Econ. J., vol. 106,no. 437, pp. 925–951, 1996.

[9] R. Cooper, S. Edgett, and E. Kleinshmidt, Portfolio Management for NewProducts. New York, NY, USA: Perseus Books, 1998.

[10] L. Fleming and O. Sorenson, “Technology as a complex adaptive system:Evidence from patent data,” Res. Pol., vol. 30, no. 7, 2001.

[11] L. Fleming and O. Sorenson, “Science as a map in technological search,”Strateg. Manag. J., vol. 25, no. 8/9, pp. 909–929, 2004.

[12] P. Freyre, Personal communication with Pedro Freyre, Senior Manager,Bain and Company, 2006.

[13] B. Hall, “Investment and R&D at the firm level: Does the source offinancing matter?” NBER Working Paper Series, No. W4096, 1992.

[14] B. Hall, “The financing of research and development,” Oxford Rev. Econ.Policy, vol. 18, pp. 35–51, 2002.

[15] M. Hannan and J. Freeman, “The population ecology of organizations,”Amer. J. Sociol., vol. 82, no. 5, pp. 929–964, 1977.

[16] M. Kamien and N. Schwartz, “Self financing of an R&D project,” Amer.Econ. Rev., vol. 68, no. 3, pp. 252–261, 1978.

[17] M. Kamien and N. Schwartz, Dynamic Optimization: The Calculus ofVariations and Optimal Control in Economics and Management. Ams-terdam, the Netherlands: Elsevier, 1991.

[18] S. Klepper, “Entry, exit, growth, and innovation over the product lifecycle,” Amer. Econ. Rev., vol. 86, no. 3, p. 562, 1996.

[19] J. Kloeber, Personal communication with Jack Kloeber, Directorof Portfolio Management, Johnson and Johnson Pharmaceuticals,2007.

[20] C. Loch and S. Kavadias, “Dynamic portfolio selection of NPD programsusing marginal returns,” Manag. Sci., vol. 48, no. 10, pp. 1227–1241,2002.

[21] C. Loch and S. Kavadias, New Product Development and Management.London, U.K.: Elsevier, 2007.

[22] S. Myers, “The capital structure puzzle,” NBER Working Paper Series,No. 1393, 1984.

[23] R. Nelson, “Modelling the connections in the cross section between tech-nical progress and R&D intensity,” RAND J. Econ., vol. 19, no. 3, pp. 478–485, 1988.

[24] R. Nelson and S. Winter, An Evolutionary Theory of Economic Change.Cambridge, MA, USA: Belknap Press, 1982.

[25] P. Romer, “Endogenous technological change,” J. Polit. Econ., vol. 98,no. 5, pp. 71–102, 1990.

[26] P. Roussel, K. Saad, and T. Erickson, Third Generation R&D. Cam-bridge, MA, USA: Harvard Univ. Press, 1991.

[27] S. Sethi and G. Thompson, Optimal Control Theory: Applications to Man-agement Science and Economics. Norwell, MA, USA: Kluwer, 2000.

[28] L. Shyam-Sunder and S. Myers, “Testing static tradeoffs against peckingorder models of capital structure,” J. Financial Econ., vol. 51, pp. 219–244, 1999.

[29] R. Solow, “A contribution to the theory of economic growth,” Quart. J.Econ., vol. 70, pp. 65–94, 1956.

[30] T. Stuart and J. Podolny, “Local search and the evolution of technologicalcapabilities,” Strateg. Manag. J., Summer 1996.

[31] M. Tushman and P. Anderson, “Technological discontinuities and orga-nizational environments,” Administ. Sci. Quart., vol. 31, pp. 439–465,1986.

[32] S. Wheelwright and K. Clark, Revolutionizing Product Development:Quantum Leaps in Speed, Efficiency, and Quality. New York: The FreePress, 1992.

[33] R. Hartl and S. Sethi, “Optimal control of a class of systems with continu-ous lags: Dynamic programming approach and economic interpretations,”J. Opt. Theory Appl., vol. 43, no. 1, pp. 73–88, 1984.

[34] J. Carrillo and C. Gaimon, “Improving manufacturing performancethrough process change and knowledge creation,” vol. 46, no. 2, pp. 266–288, 2000.

[35] W. Abernathy and J. Utterback, “Patterns of innovation in technology,”MIT Technol. Rev., vol. 80, pp. 40–47, 1978.


Raul O. Chao received the B.S. degree in chemi-cal engineering from Johns Hopkins University andthe Ph.D. degree in operations management fromthe Georgia Institute of Technology. He is an As-sistant Professor of Business Administration at theDarden School of Business, where he teaches op-erations management, innovation, and new productdevelopment courses in Darden’s MBA and Exec-utive Education programs. Raul’s research interestsinclude understanding how innovation, new productdevelopment, and R&D processes operate in large

organizations. Much of his research is based on fieldwork and case studies con-ducted at companies such as 3M, Microsoft, Whirlpool, and Lockheed Martin,among others. Prior to joining the Darden faculty, Raul worked as a ManagementConsultant serving U.S. and Latin American organizations in a wide range ofindustries including pharmaceutical, biomedical, healthcare, financial services,hospitality, and media among others.

Stylianos Kavadias received the M.S. degree in elec-trical and computer engineering from the NationalTechnical University Athens, Greece, and the Ph.D.degree in operations management from INSEAD,France.

He is a Professor of Operations Management at theJudge School of Business, University of Cambridge.His research interests include the effectiveness of newproduct development (NPD) decisions with a partic-ular focus on the decisions that concern: (i) strategyimplementation through the appropriate resource al-

location rules and the definition of the “right” portfolio of new projects andproducts; (ii) the R&D ideation, search and experimentation process both at afirm level and the project team level; and (iii) the effects of the organisational de-sign and the associated incentive schemes on the product development outcome.Professor Kavadias serves as an Associate Editor for Management Science’s En-trepreneurship and Innovation department, and as the Department Editor for theR&D, New Product Development and Project Management department of Pro-duction and Operations Management. Prior to joining the faculty at Cambridge,Professor Kavadias was the Steven A. Denning Professor of Technology &Management, as well as an Associate Professor of Operations Management, atthe College of Management at Georgia Tech. He has also been a Fellow at theBatten Institute of Innovation and Entrepreneurship at the Darden School ofBusiness.

r&d intensity and the new product development portfolio

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