The effect of the marketing–R&D interface on new product performance: The critical role of...

eting 25 (2008) 56–68www.elsevier.com/locate/ijresmar

Intern. J. of Research in Mark

The effect of the marketing–R&D interface on new product performance:The critical role of resources and scope☆

Mark A.A.M. Leenders a,⁎, Berend Wierenga b

a University of Amsterdam, Amsterdam Business School, Roeterstraat 11, 1018 WB Amsterdam, The Netherlandsb Erasmus University Rotterdam, Rotterdam School of Management, The Netherlands

First received in September 21, 2005 and was under review for 7 months, Area editor Eitan Muller

Abstract

Although the integration of marketing with R&D is widely recognized as a critical factor for a new product's success, this study shows that notall companies benefit equally from more integration. Using data from the worldwide pharmaceutical industry, the authors show that integration isparticularly effective in conjunction with high levels of new product development resources. In addition, the effect of the interaction betweenintegration and new product development resources is stronger for companies with a narrow strategic scope. So, although broadly focusedcompanies often have an advantage with respect to innovation because they can more easily leverage resources to different markets, our resultsindicate that narrowly focused companies can compensate for this by developing and integrating their marketing–R&D interface.© 2008 Elsevier B.V. All rights reserved.

Keywords: R&D–marketing integration; NPD resources; Strategic scope; Performance

1. Introduction

The survival and growth of companies is increasinglydependent on their ability to develop and market successfulnew products. As a result, companies need to accumulate, protect,and effectively use critical new product resources and capabilitiesthat maximize their return in terms of new product outcomes(Day, 1994; McEvily, Eisenhardt, & Prescott, 2004; Nerkar &Roberts, 2004; Sorescu, Chandy, & Prabhu, 2003). At a basiclevel, a company can simply ensure that technological resourcesand marketing resources are present at a sufficient level in theorganization. However, merely possessing such resources doesnot guarantee their optimal use. To create value in the market-place, these resources have to be combined into new capabilitiesto be exploited (Sirmon, Hitt, & Ireland, 2007).

☆ This paper has benefited from many useful suggestions made by JehoshuaEliashberg and George Day (The Wharton School, University of Pennsylvania).We would also like to thank the editor and the three anonymous reviewers fortheir constructive comments and suggestions.⁎ Corresponding author. Tel.: +31 20 5256078.E-mail address: [email protected] (M.A.A.M. Leenders).

0167-8116/$ - see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.ijresmar.2007.09.006

Our study focused on a factor that is expected to play a keyrole in the combination and exploitation of new product de-velopment (NPD) resources, namely the integration of market-ing with R&D. Various terms are used in earlier research todescribe the notion of people working together to accomplishnew product tasks, for example “integration,” “collaboration,”“cooperation,” and “harmony”. The common denominator is thejoint behavior toward some goal of common interest (Pinto,Pinto, & Prescott, 1993). However, the integration of marketingwith R&D does not come about without effort, and organiza-tional measures and investments are often required to ac-complish more integration; examples of such measures includeinterdisciplinary project teams, training programs, the hiringof cross-functional specialists, job rotation, relocation and physi-cal facilities design, and collaborative ICT systems (Griffin &Hauser, 1996).

Practitioners, consultants, and scholars believe that integrationacross functional and disciplinary specialties has a positive effecton new product performance (e.g., Griffin & Hauser, 1996;Hoopes & Postrel, 1999; Ittner & Larcker, 1997). This effect hasreceived considerable attention in the marketing literature. Forexample, Gupta, Raj, and Wilemon (1985, 1986) showed thatthere is an “integration gap” between R&D and marketing (i.e., a

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57M.A.A.M. Leenders, B. Wierenga / Intern. J. of Research in Marketing 25 (2008) 56–68

difference between the need for integration and the integrationachieved), and that the larger the gap, the lower the innovationsuccess. Studies such as Pinto et al. (1993) found that higherlevels of integration lead to better task outcomes and to moredesirable psychosocial outcomes. After reviewing fifteen studieson the marketing–R&D interface, Griffin and Hauser (1996)concluded that higher levels of integration (also called “colla-boration,” “cooperation,” or “harmony”) between R&D andmarketing produce better new product performance. However,Henard and Szymanski (2001) presented evidence of a weakereffect in their meta-study. Based on fifteen studies that included ameasure of integration, they found that the mean correlationbetween cross-functional integration and new product perfor-mance was positive (r=.23) but not significant. Henard andSzymanski argue that their findings bring into question theblanket need for integration across functional areas, and that moreintegration may not always be a productive approach for directlyimproving new product performance.

In the research so far, there seems to be a lack of attention tothe conditions that make integration more or less valuable forfirms. The studies mentioned above mostly studied integrationin relative isolation. Many did not include the underlying NPDresources, let alone the interaction between integration and NPDresources. The aim of our study was to fill this gap in theliterature. Our main premise was that the effect of integration isembedded in the broader organizational context of NPDresources, and that the way in which the resources are used(in terms of scope in the market) affects the interaction betweenintegration and NPD resources. We tested this key hypothesiswith international survey data from the pharmaceutical industryand validated our results with factual data on the number of“blockbusters” (products with more than $1 billion in sales)produced by the firms in our sample. We found that the effect ofintegration does depend on the underlying NPD resources,which implies that most integration effect models that focusedon simple effects without taking into account the underlying

Fig. 1. The study's und

NPD resources are misspecified. Second, we found evidencethat the interaction effect between integration and NPDresources is stronger for companies with a narrow marketfocus in NPD than for companies with a broad focus. We endthis paper by discussing our results and providing managerialinsights derived from these findings.

2. The framework

Fig. 1 shows how the interplay of NPD resources, theintegration of R&D with marketing, and the strategic scope isexpected to affect new product performance.

2.1. New product performance

In our study, new product performance was defined as theextent to which a company has output and market success withits new products (see, for example, Griffin & Page, 1993).“New” was defined in the most general sense as new-to-the-company, including the full range of me-too products and new-to-the-world innovations.

2.2. NPD resources

NPD resources are the tangible and intangible assets thatenable a firm to produce and market new products that havevalue for some market segment (e.g., Day, 1994). It isdemonstrated in the literature that both technological andmarketing resources have a positive effect on new productperformance (see, for example, Dutta, Narasimhan, & Rajiv,1999; Nerkar & Roberts, 2004). Sorescu et al. (2003) found thatfor firms with more marketing and technology support, thefinancial value of innovations is higher. In our study, we definedNPD resources broadly, encompassing both technology as wellas marketing resources. Similarly broad resource constructs areused in other studies (e.g., Gatignon & Xuereb, 1997).

erlying framework.

58 M.A.A.M. Leenders, B. Wierenga / Intern. J. of Research in Marketing 25 (2008) 56–68

2.3. NPD resources and integration

The research on technological and marketing resources oftenfinds positive interactions between the two (Moorman &Slootgraaf, 1999). Teece (1980) provides several examples toillustrate the interdependence of R&D and marketing in thedevelopment and marketing of new products ranging frommedicines to new cars. This interdependency suggests that theintegration of marketing with R&D has a positive effect on theeffectiveness of NPD resources. We define integration as thedegree to which there is collaboration, communication, andgood relationships between marketing and R&D (Pinto et al.,1993). This definition takes into account that there can beconflict between different functional areas as long as the conflictis constructive (Souder, 1988).

Lawrence and Lorsch (1967) elaborate on the need for bothintegration and differentiation (specialized resources) inorganizations. Specialization alone is not sufficient for highperformance. The specialized groups need to be integrated inorder to accomplish the organization's overall objectives (seealso Madhavan & Grover, 1998). In general, it is clear that acompany needs technology and marketing resources and theability to integrate these two, and that technology and marketingresources interact. For example, integration is known to lead tomore creativity among employees. Creative employees combineand exploit ideas in both marketing and R&D, which leads topotentially more ideas, as well as to more feasible and moreeffective ideas (Postrel, 2002). In addition, integration can leadto a more effective and more efficient use of resources, as it canreduce coordination problems between marketing and R&Dresources in the NPD pipeline (Griffin & Hauser, 1996).

2.4. Strategic scope

Although resources are only one source of variance incompetitive heterogeneity, the resource deployment strategyand especially the scope of the deployment have to be includedin any discussion (cf. Cool & Schendel, 1987; Hoopes, Madsen,& Walker, 2003; Penrose, 1959; Sorescu et al., 2003). In ourstudy, scope is defined as the breadth of the company's targetmarket in terms of customers and needs. It refers to whichopportunities are pursued and for which markets. As we explainbelow, scope may be especially important with respect to theneed for joint marketing and R&D involvement in NPD1. Anarrow scope involves achieving leadership in specific markets(or market segments) with selected products, while a broadscope involves targeting a broad spectrum of markets with agreat variety of products. The narrower the scope, the easier it isto develop market knowledge and to support new products inthat particular area. However, knowledge of other markets is

1 In previous scope research, like in the studies on integration, the findings onthe effect of scope on performance have been mixed. Negative, positive, andinverted U-shaped relationships have been found (e.g., Hoskisson & Hitt,1988). In some studies, little or no evidence of a significant effect was reported(Cool & Schendel, 1987).

likely to be less and the ability to leverage certain technologiesto other markets is likely to be lower.

Companies that have a narrow strategic scope – such as theDutch pharmaceutical company Organon BioSciences (whichspecializes in oral contraceptives and has few other products) –operate in selective markets. However, in these markets, theycan operate at a “deep” level by offering a broad spectrum ofproducts and by catering to a larger number of different marketsegments or sub-segments2. Treacy and Wiersema (1997) callthe underlying value discipline “customer intimacy,” which isfocused on tailoring products for specific customer needs. Forexample, Organon developed some new contraceptives basedon innovative technologies that can be implanted in the arm andstay active for many years. Thus, although new products tend tobe close to the customer base, they can be very innovative.

On the other hand, firms that have a broad strategic scopehave more opportunities to leverage certain technologies acrossdifferent markets. As a result, they may rely more heavily on theopportunities created only by their R&D, at least until thelaunch approaches. They may also experience fewer up-frontmarket constraints, which may be positive for their technolo-gical breakthroughs and for their radical innovation output ifthere are actual markets with great customer needs for theproduct (Postrel, 2002). Since there is less need to focus on keyproducts, there is less need to closely coordinate whichtechnologies are to be developed for which markets. Giventhe lack of a footprint and high costs related to acquiring in-depth market knowledge in those segments, there is probablyless market knowledge available to combine with R&Dknowledge and to exploit in the first place.

Thus, companies can basically follow a narrow or a broadstrategy in their NPD policy. Recent studies show indications ofa positive effect of a broad strategy (as compared to a narrowstrategy) on overall NPD results. For example, Wuyts, Dutta,and Stremersch (2004), who studied the consequences ofinterfirm R&D agreements, found that agreements covering amore diverse set of technologies (a proxy for a broad scope)produce more innovations. According to these authors, greatertechnological diversity reduces a firm's tendency to be locked inprior knowledge, and technological diversity enhances radicalinnovation. According to Wuyts et al. (2004, p. 89), the poten-tial drawbacks – for example, that technological diversity mayimpede a clear focus and complicate the development ofspecialist competences – are outweighed by the positive effects.Sorescu et al. (2003) found that a broader product scope (i.e., amore diverse portfolio of products) has a significant positiverelationship with the financial value of an innovation.

Can narrow-scope companies be successful, too? The answeris “yes”. See, for example, the Organon example just mentionedor Allergan, which is focusing on skin and eye care. Somecompanies in the pharmaceutical industry actively pursue anarrow-scope strategy, for example by buying generic (off-patent) drugs to complement their portfolio. This trend startedmany years ago when pharmaceutical companies tried to

2 It can be asked whether Organon can keep up its performance now that ithas become part of Schering Plough (a company with a broader scope).


integrate healthcare providers. This is likely to be driven bymarketing considerations (such as an effort to strengthencustomer relationships) and by a need to offer a completerange of products in a certain product-market area, includingolder and cheaper products along with innovative and newmedicines. Many years ago, Teece (1980) showed that thesuccess of new drugs in new markets is often dependent onspecialized sales support and on the availability of a full rangeof products in specific categories. This, again, emphasizes theneed for concentrating within a limited scope.

We hypothesized that a critical success factor for narrow-scope companies is that they can benefit from a strongintegration of R&D and marketing. Narrow-focus companiesdevelop innovations that are often driven by deep customerknowledge and involvement. To be successful with such astrategy, both R&D and marketing need processes andstructures to coordinate the many activities and decisionsrelated to the selection of key products, key market segments,and key technologies. Here, knowledge creation by means ofjoint marketing and R&D sessions, the use of market researchand feasibility studies, the (joint) selection of product features,the packaging, the launch, and the product lifecycle manage-ment all play an important role. For these companies, suchknowledge is likely to be obtainable in a cost-effective waybecause of the strong footprint in their markets (Gatignon &Xuereb, 1997). If managed adequately, this can be an importantstrategic advantage for focused companies over companies withbroad-scope strategies. In the studies cited above, where it wasshown that broad-scope strategies are, in general, moresuccessful than narrow-scope strategies, no variable measuringthe integration of R&D with marketing was included. There-fore, it was not possible to examine whether the potentiallynegative effect of a narrow scope could be compensated for by ahigher level of integration of R&D with marketing. Weaddressed this issue in the present study, guided by thehypothesis formulated below.

Key hypothesis. There is an interaction effect between integra-tion and NPD resources with respect to new product performance(i.e., higher levels of integration increase the effect of NPDresources). This interaction is stronger for companies with anarrow scope than for companies with a broad scope.

2.5. Control variables

Damanpour (1991) and others have shown that formalizationand centralization affect new product performance. As keyaspects of organizational structure, formalization and centraliza-tion seem to affect innovation negatively because a weakemphasis on work rules is known to facilitate innovation, whilea concentration of decision-making authority hampers thedevelopment of innovative solutions (e.g., Hage & Aiken,1967; Thompson,1967). As a result, we included centralizationand formalization as control variables in our analysis. In addition,we took company size into account. For example, all other thingsbeing equal, large companies may generate more new productsthan may small companies (Henderson & Cockburn, 1996).

3. Methods

3.1. The pharmaceutical industry context

Companies in the pharmaceutical industry were selected asthe research context for several reasons. New products play avery important role in this industry. It is a large and competitiveindustry in which many companies are active. It is also a verytransparent and well-documented industry, one that is oftenused to study such phenomena as NPD resources, strategicgroups, and NPD (e.g., Cool & Schendel, 1987; Henderson &Cockburn, 1994; Nerkar & Roberts, 2004; Sorescu et al., 2003;Wuyts et al., 2004). We recognize the potentially idiosyncraticnature of the industry by paying attention to the validation ofearlier research findings, by discussing our measures in detail,and (in the “Conclusion and limitations” section) by elaboratingon generalizations to other industries.

3.2. International survey

Data were collected by means of an international mailsurvey. A random sample of 1000 pharmaceutical companieswas drawn from a worldwide ESOMAR (European Society forOpinion and Marketing Research) database and from theEPhMRA (European Pharmaceutical Marketing ResearchAssociation) database, both of which contain a broad range ofinformation on organizations, the names of employees, jobtitles, and addresses.

We selected the most senior executive from each companybased on job title; if two or more executives were at the samelevel, we chose randomly. This person could be expected tohold a position that makes him or her the most knowledgeableabout the issues being researched (e.g., Campbell, 1951;Seidler, 1974).

3.3. Measurement

We used existing scales as much as possible, adapted someexisting scales from the literature, and developed new scales ifadequate measures were not available.

For the integration of R&D with marketing, we adapted anexisting scale developed by Pinto et al. (1993) in which the mostimportant elements of integration are present. This scaleincludes items on communication, collaboration, and coopera-tive relationships. This scale, along with all of the other scalesdiscussed in this section, is presented in Appendix A. As anexample of the nature of the adaptations, we altered an originalitem such as “A friendly attitude exists among project teammembers” to “A friendly attitude exists among R&D andmarketing”. In earlier research, this scale proved to be one-dimensional (Pinto et al., 1993), which was validated before usehere.

The NPD resources of a company were measured in terms ofa set of specialized NPD resources in R&D and marketing.Resources refer to uniquely valuable assets for a company,usually accumulated over a large number of years. There wereno scales available in the literature that we could use, so we


developed a scale using relevant insights from the literature(Dierickx & Cool, 1989; Dutta et al., 1999; Gatignon & Xuereb,1997; Teece et al., 1997). As mentioned, this is a joint scale forR&D and marketing resources. As is suitable for valuable,scarce, and difficult to imitate resources, our scale containsmany elements that cannot simply be bought on the market,such as goodwill, networks and relationships with outsideparties, broad information access, databases, and marketknowledge. The scale for NPD resources can be interpreted asa formative (formed) scale; in other words, the elements coverdifferent aspects of the phenomenon and have to be summed toget the overall picture of the state of the company's NPDresources (Rossiter, 2002).

The strategic scope measure (wherein higher values indicatea broader scope) is based on product range and is partly foundedon Cool and Schendel (1987). The items (a narrow versus abroad product range, etc.) were previously used to clusterpharmaceutical companies into strategic groups and to measurethe scope of a pharmaceutical company's strategy. We includedthe item about the number of therapeutic areas as an additionalindicator.

To measure new product performance, items were collectedfrom the new product performance literature (e.g., Griffin &Page, 1993). According to Loch, Stein and Terweish (1996),there is a difference between process performance (e.g.,decision-making) and outcome performance (e.g., the numberof products and their performance in the market). The focus ofour measure is on outcomes, measured in terms of theprogression in the product development pipeline, the numberof new products, and the market performance of new products.The progression of medicines in the pipeline proved to be animportant market success indicator for industry professionals.

In the questionnaire, respondents were asked to assess theperformance of their company, compared to companies of similarsize, over a period of five years. We asked this information for theprevious five years, and also asked about the expected newproduct performance scores for the coming five years. This formatof seeking performance information (including perceptualinformation) relative to similar companies in the industry tendsto produce findings that are consistent with factual measures andalso directly controls for size (Dess&Robinson, 1984). So a scoreof 5 on the item “The performance of the products that have beenlaunched in the last five years” means that the company is in thetop 20% of all firms in the industry of similar size.

The centralization and formalization measures were takenfrom Hage and Aiken (1967) and Zaltman, Duncan andHolbeck (1984).

All of the measures were reviewed by four academics andeight industry executives working in the regions where mostpharmaceutical companies are located (USA, Europe, andJapan). In the pretest, the substantive validity of the items wastested by means of the procedure developed by Anderson andGerbing (1991). Senior managers in this industry speak Englishvery well and use a similar (i.e., industry) vocabulary. Thismade us decide to develop a single, English-languagequestionnaire, which is often done in these types of situations(e.g., Hofstede, 1976).

3.4. Questionnaire

The introduction letter, reminder, and draft questionnaire wedeveloped were pretested by two academics and two pharma-ceutical managers. This resulted in minor refinements before the20-page questionnaire was finalized (in the form of a smallbooklet that took about 15 min to complete). We used theguidelines provided byDillman (1978) to give the questionnaire agood look and feel, and to ensure that respondents could progressquickly through it. All questionnaires carried a stamped number inorder to be able to add factual company data afterwards. Forexample, factual data on the number of blockbusters (productsthat generate more than $1 billion in sales) were obtained fromtrade journals and annual reports and were added to the dataset.

The questionnaire contained a brief introduction, and respon-dents whose company had a multidivisional form were explicitlyasked to consider only the pharmaceutical part of their company(the respondents were always from the pharmaceutical part).

3.5. Mailing procedure and response

The mailing consisted of a first wave, a reminder, and asecond wave for those who had not responded after three weeks.A total of 211 questionnaires were returned unopened (wrongaddress, the respondent moved, etc.). Therefore, the effectivesample size was 789. A total of 148 usable questionnaires werereturned (19%). This response rate is satisfactory compared toother international surveys, where response rates are “often insingle digits” (Jobber et al., 1985). Of all responses, 29.7%came from U.S. companies, 14.9% from the UK, 12.8% fromJapan, 10.1% from Germany, and 8.1% from Switzerland. Theremainder came from companies in other European countries.All companies have both marketing and R&D. The companiestend to be large, generating revenues of more than US$3 billionon average (US$1.1 billion was the median). This is in line withour objective to have a sample of pharmaceutical companiesinstead of R&D-driven biotech companies.

A comparison of the respondent profiles and the companycharacteristics of early and late responses revealed no significantdifferences at the α=.05 level. A pooling test on all constructs inthe model and on the characteristics of the companies such asrevenues and the number of employees (the means andvariances) revealed no significant differences between respon-dents with a marketing background (61%) and respondents withan R&D background (39%). In addition, sample means seemedto resemble the population means very well. The companies inthe sample invest an average of 16% of sales in R&D, and 33%of sales are generated by products introduced in the previous fiveyears, which is representative of the industry at large accordingto Scrip Magazine's annual reviews over the last ten years.

4. Results

4.1. Reliability and validity

To examine the reliability and validity of our measures, wefirst screened the item-to-item correlations and the item-to-total

Table 1The determinants of new product performance

Variables Model 1 (OLS)beta

Model 2 (GLS)beta

Model 3 (GLS)beta

Integration (INT) .07 (.14) .06 (.14) .10 (.14)NPD resources (RES) .51 ⁎⁎⁎ (.01) .48 ⁎⁎⁎ (.01) .45 ⁎⁎⁎ (.01)Strategic scope (SCO) .08 (.09) .09 (.09) .10 (.09)Centralization − .08 (.12) − .12 ⁎ (.13) − .12 ⁎ (.13)Formalization − .14⁎ (.12) − .13⁎ (.12) − .14 ⁎⁎ (.12)

2-wayINT ⁎RES .13 ⁎⁎ (.02) .19 ⁎⁎⁎ (.02)INT ⁎SCO − .06 (.18)RES ⁎SCO .15 ⁎⁎ (.01)

3-wayINT ⁎RES ⁎SCO − .17 ⁎⁎ (.03)N 121 121 121R2 .36 .37 .41Adjusted R2 .34 .34 .36F-value 13.26 ⁎⁎⁎ 11.07 ⁎⁎⁎ 8.48 ⁎⁎⁎

Standard errors in parentheses.⁎ Significant at pb .10.

⁎⁎ Significant at pb .05.⁎⁎⁎ Significant at pb .01.


correlations; they were all satisfactory. With respect to thevariable NPD resources, it turned out that the companies in oursample are active in both R&D and marketing. Although this isnot strictly necessary for the NPD resource variable (since this isa formative scale), the NPD resources items are stronglycorrelated and positive, ranging form r=.20 to r=.80. Thissupports our bringing together of R&D and marketing resourcesin one NPD resources scale.

Confirmatory factor analysis (CFA) was used to assess theoverall measurement model. The CFA containing all of themeasures and all of the items resulted in an adequate fit(RMSEA=.062). The standardized loadings were all significant.However, the number of data points per parameter may haveresulted in estimates that are not trustworthy. Therefore, a partiallydisaggregated model was composed by averaging sets of items toobtain only three composite indicators for the integrationmeasureand three composite indicators for the resources measure(cf. Bagozzi & Edwards, 1998). This resulted in a good fit aswell (χ2=722, df=545, p=.0001, RMSEA=.051, CFI= .90,TLI=.90). The discriminant validity was assessed by means ofthe inter-factor correlations and their confidence intervals. Settingthe inter-factor correlations to 1 resulted in very poor-fittingmodels in all instances. In addition, the confidence intervals forthe ϕ's did not contain the value of 1.

To establish the internal consistency of the measures, wecomputed Cronbach's alpha coefficients to calculate the reliabilityof the scales. All scales exceed the .70 level: the integration of R&Dwith marketing (15 items, α=.91), the NPD resources (10 items,α=.86), the strategic scope (3 items, α=.77), formalization(4 tems, α=.76), centralization (6 items, α=.78), future new prod-uct performance (4 items, α=.90), and new product performance(4 items, α=.83). Finally, since the number of items affects the αfavorably, sensitivity analysis was conducted on smaller sets ofthree items per construct. These alpha coefficients still exceeded theα=.70 level. As a result, composites were calculated by averagingthe scores of all of the items for all constructs.

4.2. Correlations

The correlations between the dependent and the independentvariables are presented in Appendix D. Interestingly, the bivariatecorrelation between integration and newproduct performance is .20(pb .05), and that between integration and the expectations aboutfuture new product performance .24 (pb .01). These values areclose to the average value of .23 found by Henard and Szymanski(2001) in their meta-study. The NPD resource variable clearly hasthe strongest correlations with the new product performancemeasures: r=.54, pb .01 (for new product performance), r=.40,pb .01 (for expected future new product performance), andr=.42, pb .01 (for blockbusters). In line with the studies mentionedearlier, a broader scope seems, in general, to be associated withbetter new product performance (r=.12, pb .10).

4.3. Analysis

To test our hypothesis, we estimated four regressionmodels, including moderated regressions with interactions

(Jaccard et al., 1990). Because of the multicollinearitybetween interactions and its component variables, we mean-centered the variables nested in the interactions (Jaccard et al.,1990). In addition, we applied generalized least squares (GLS)to come up with efficient estimates that take into account theinherent heteroscedasticity from the model specification(Hildreth & Houck, 1968; Gatignon & Vosgerau, 2006). SeeAppendices B and C for the specification of the inherentheteroscedasticity.

Table 1 shows the results of the regressions. Note that the firsttwo models are preparatory stages for the comprehensive Model3. We tested the hypothesis with the comprehensive model, butthe numerical results of the nested models are interesting inthemselves. The results for Model 1 provide strong evidence forthe importance of NPD resources (beta= .51, pb .01). Model 1explains 36% of the variance in new product performance(adjusted R2=0.34). Model 2 includes the interaction betweenintegration and NPD resources, which obtains a significantcoefficient (beta= .13, pb .05). The other coefficients do notsubstantially change. The increase in R2 is small but significant(pb .05). The implication is that, in itself, the integration of R&Dwith marketing seems to have a limited direct effect on newproduct performance; however, integration does have a signifi-cant effect in conjunction with more NPD resources. This is inagreement with the first part of our hypothesis, but we have to takeinto account that these models are basically misspecified becausescope is disregarded. The control variables, centralization andformalization, attained generally negative coefficients. This is inline with meta-studies on the effects of formalization andcentralization (e.g., Damanpour, 1991) and reflects the “threat-rigidity” cycle.

Fig. 2. The relationship between integration and new product performance.

Fig. 3. The relationship between integration and new product performance forcompanies with a narrow or a broad strategic scope.


4.4. NPD resources, integration, and strategic scope

Model 3 is our comprehensive and adequately specifiedmodel. It is clear that there is a significant main effect of NPDresources (beta= .45, pb .01), a significant interaction of R&D–marketing integration with NPD resources (beta= .19, pb .05), asignificant interaction of NPD resources with scope (beta= .15,pb .05), and a significant 3-way interaction among NPDresources, integration, and scope (beta=− .16, pb .05). Thelast-mentioned is a first step toward confirming our keyhypothesis, but we still have to look at the nature of thisinteraction. For now, it shows that the 3-way interaction explainsadditional variance in new product performance that is notexplained by its components.

To visualize the nature of the interactions, we first discuss therelationship between integration and resources on new productperformance, and then include scope in the discussion. Fig. 2presents the relationship between integration and performancefor different levels of the moderator variable (i.e., NPDresources) in a graphical representation. The sample is par-titioned by means of a median split to illustrate the nature of theinteraction only.

The plots show a clear difference in slopes. For companiesthat score high on NPD resources, more integration leads tobetter new product performance. Here, the effect of integrationis considerable. For a company that scores high on NPDresources, it can mean the difference between performing justabove the industry average (a new product score around 3) orbeing among the best-performing 20% with respect to newproducts (a new product score N4). For companies that scorelow on NPD resources, the effect of integration on performanceis hardly visible, and even tends to be negative rather thanpositive. So, for companies with few NPD resources, puttingmore effort into the integration of R&D with marketing mayyield limited returns.

To interpret the 3-way interaction in Model 3, we divided thesample into companies with a narrow strategic scope andcompanies with a broad strategic scope (again, by means of amedian split). For each sub-sample, the relationship betweenintegration and new product performance is presented for agroup of companies with a relatively high level of NPDresources and a group with a relatively low level of NPDresources. To find support for our hypothesis, the slopes (i.e., thedirection coefficients, or Dc) of the regression lines betweenintegration and new product performance need to be steeper(more positive) for the narrow-scope sample than for the broad-scope sample.

Fig. 3 shows that the effect of integration is the strongestfor the group with a narrow strategic scope. The differencebetween the effect of the low NPD resources group and thatof the high NPD resources group is considerable, since the

Table 2The determinants of the expected future new product performance

Model 4 (GLS)Variables beta

Integration (INT) .21 ⁎⁎⁎ (.17)NPD resources (RES) .32 ⁎⁎⁎ (.15)Strategic scope (SCO) .03 (.12)Centralization .04 (.15)Formalization − .09 (.15)

2-wayINT ⁎RES .13 ⁎ (.05)INT ⁎SCO − .01 (.22)RES ⁎SCO .15 ⁎⁎ (.02)

3-wayINT ⁎RES ⁎SCO − .16 ⁎⁎ (.06)N 121R2 .24Adjusted R2 .17F-value 3.71 ⁎⁎⁎

Standard errors in parentheses.⁎ Significant at pb .10.⁎⁎ Significant at pb .05.

⁎⁎⁎ Significant at pb .01.

Table 3The determinants of the number of blockbusters in the firm (a factual measurefor 65 companies in the sample)

Model 5 (GLS)Variables beta

Integration (INT) − .09 (.32)NPD resources (RES) .46 ⁎⁎⁎ (.02)Strategic scope (SCO) .17 ⁎⁎ (.19)Centralization − .04(.24)Formalization .21 ⁎⁎ (.25)

2-wayINT ⁎RES .16 ⁎⁎ (.05)INT ⁎SCO .03 (.40)RES ⁎SCO .20 ⁎⁎ (.02)

3-wayINT ⁎RES ⁎SCO − .08 (.06)N 65R2 .40Adjusted R2 .34F-value 7.29 ⁎⁎⁎

Standard errors in parentheses.⁎ Significant at pb .10.

⁎⁎ Significant at pb .05.⁎⁎⁎ Significant at pb .01.


increase in direction coefficient is very large (ΔDc= .59(246%)). This indicates that for a company in this group,increases in integration have relatively high returns, espe-cially if integration is accompanied by higher levels of NPDresources. The lower part of Fig. 3 shows that the interactioneffect of integration is weaker for companies that have abroad strategic scope. There is a slightly negative slope forthe high NPD resources sample (Dc=−.04), while the lowNPD resources group has a somewhat stronger negative slope(Dc=− .08).

4.5. Validation

Working with survey data has methodological limita-tions. One element we addressed is the issue of causality.Although an inverse causal relationship – namely that newproduct performance would generate more NPD resourcesand more integration – is theoretically less plausible, it isworthwhile to carry out an additional analysis. In the phar-maceutical industry, development cycles tend to be longand therefore investments in NPD resources and in R&D–marketing integration may also be expected to also have apositive effect on future new product performance. Withrespect to future new product performance, reverse causalityis even less plausible. Therefore, we also carried out theanalysis with future new product performance as the depen-dent variable.

The results of Model 4 (Table 2) are encouraging becausethey find similar results as were found when current newproduct performance was used. The signs and significances ofthe coefficients generally show a picture similar to that inTable 1. The main effect of integration is somewhat strongerand is significant (beta= .21, pb .01), although this effect has

to be interpreted with care as there are significant interactionswith integration in the model. The key 3-way interactionobtains a similar coefficient as before (beta=− .16, pb .05). Weconclude that our findings hold not only for current newproduct performance, but also for future new product per-formance, and this strengthens the belief in our theoreticalmodel. Structural characteristics, such as centralization(beta= .04, p=.73) and to a lesser extent formalization (beta=− .09, p=.35), seem less important for future new productperformance than do the other variables, such as integration,resources and scope.

Another potential problem with using survey data is thepossibility of mono-method bias. In the analysis so far, wehave used perceptual data for the performance measure.Although in other studies the approach developed by Dess andRobinson (1984) resulted in strong correlations betweenperceptual and factual performance measures, we tried tovalidate our results with objective data. We had factualinformation about the number of blockbusters that each of 65companies had in the year under study. This is a measure thatis conceptually very close to our new product performancemeasure, as it is an output measure in terms of the number ofblockbusters and also has a market performance side, as itonly counts products that have very large sales (i.e., sales ofover $1 billion). Since one of the items in our performancemeasure is the “number of breakthroughs,” one would expecta significant correlation. This is indeed the case; thecorrelation between blockbusters (as measured by the factualinformation) and our subjective performance measure isr=0.47 (pb0.01). Next, we ran the complete model as inTable 1 and used the factual blockbuster measure in ouranalysis. The results are presented in Table 3.


Table 3 shows that the coefficients are largely similar tothe ones in Table 1. The 3-way interaction has the same sign,but is not significant. This is probably related to the smallersample size and the lack of statistical power. However, thetwo 2-way interactions that were significant in Model 2 aresignificant again, indicating the interdependence betweenintegration, NPD resources, and scope, as before. Overall, thepattern in the data seems to be very similar. Formalizationnow has a positive and significant coefficient (beta= .21, pb.05). However, since the blockbuster measure is not con-trolled for size (unlike our perceptual measures), this variableis probably picking up the effect of size that plays a rolein NPD, as larger firms are known to be more formalized.Overall, we find good support for our models with differenttypes of data.

5. Conclusion and limitations

As the importance of new products for companies increases,so does the attention that is paid to the marketing–R&Dinterface in academic research and in the popular press. Much,but not all, of the academic research and anecdotal evidencesuggests that more integration of R&D with marketing (i.e.,communication, collaboration, a cooperative relationship, andcommon “thought worlds”) leads to better new productperformance. Many consulting companies are involved inimplementing cross-functional structures, new IT solutions, andhuman resource management approaches that build theintegration of marketing with R&D.

Our research shows that the positive effect of integrationon new product performance is conditional upon certainfactors, and that these factors have to be taken into accountwhen studying and managing the marketing–R&D interface.With high levels of NPD resources, there is a strong positiveeffect of integration on new product performance. If acompany has limited NPD resources, however, investmentsin integration may not produce the desired beneficial effects.Furthermore, companies that do not have precise targetmarkets and that spread their efforts over many marketsegments and product groups (i.e., companies with a broadfocus), will experience fewer benefits from increased integra-tion in conjunction with NPD resources than will companiesthat compete in fewer segments with a selected number ofproducts and that pursue a strong footprint in certain product-market areas. We found that in companies with a narrowstrategic scope, more integration results in important increasesin new product performance, especially if the company hashigh levels of NPD resources.

We note that, with the comprehensive model, up to 41% ofthe variance in new product performance is explained; this isgood compared to other studies (see, for example, Henard &Szymanski, 2001). A large proportion of the remaining variancemight be explained by such factors as specific entrepreneurialindividuals, serendipity in the laboratory, and pure luck. Thereseem to be natural limits to what can be explained in this type ofresearch. Also, environmental factors (e.g., technological oreconomic turbulence) could play some role, even among

companies in the same industry. Finally, we did not payattention to the costs of integration, which would give aninvestment perspective to the question of whether or not topursue more integration.

Although our empirical findings relate to a specificindustry, our decision to focus on this industry was notarbitrary. Apart from the considerations mentioned before, itis important to note that pharmaceuticals are a core part ofthe life sciences that together represent about 40% of allR&D expenses. In 2006, the pharmaceutical industry spentabout $80 billion on R&D (Scrip R&D data book andyearbook 2006). If the integration of R&D with marketingis so important even in an industry with development cyclesthat are as long as they are in the pharmaceutical industry,mindful integration is definitely important in industries withshorter development cycles and shorter times to market. Apositive consequence of concentrating on one industry is theopportunity to study in depth the effect of companycharacteristics on new product performance without toomuch interference from external “noise”. The fact that wefocused only on companies that have both marketing andR&D in their business model further reduces potentialbiases.

Our model is general, is not based on any industry-specificknowledge, and can easily be tested in a broad range of in-dustries. Applications to other industries would enrich ourinsights with respect to the role of the R&D–marketingintegration.

6. Managerial implications

Our results have several implications for managers in market-ing, strategy, and product development, and for consultants whoare working on improving the new product performances ofcompanies.

The main managerial implication is that managementshould not automatically think of increasing the integrationof R&D with marketing in the case of poor new productperformance. Instead, there are specific conditions with re-spect to NPD resources and strategic scope that determinewhat priority management should give to increasingintegration. The conditions are presented schematically inFig. 4.

Consider a company with low NPD resources and a broadstrategic scope that aims to improve its new product per-formance (see the lower-left cell of Fig. 4). Our study showsthat in such a situation, investments in integration are ex-pected to yield modest returns. For these companies, it wouldbe more profitable to invest the efforts and resources usedfor achieving more integration into something else, for ex-ample, hiring better scientists or marketers, or into develop-ing R&D relationships with partners in order to becomemore dominant in terms of NPD resources. In contrast, acompany with a narrow strategic scope and large NPDresources (see the upper-right cell) can obtain substantiverewards by increasing integration. In addition, integration islikely to be somewhat beneficial for companies with low

Fig. 4. Summary of the findings of this study.


NPD resources and a narrow strategic scope (see the upper-left cell), and somewhat beneficial for companies with highNPD resources but a broad strategic scope (see the lower-right cell).

We can illustrate this with the case of the Allergancompany. At first, Allergan management did not seem verysatisfied with the efforts to increase integration3 (ScripMagazine, March 1996). Our research enables us to explainthis dissatisfaction. Initially, Allergan was a company with abroad strategic scope and limited NPD resources (AllerganAnnual Report, 2001), which placed it in the lower-left cell ofFig. 4. For such a company, increasing the integration of R&Dwith marketing becomes worthwhile only after it has acquiredmore NPD resources (which moves the company to the lower-right cell). In 2002, Allergan sold off some parts of its non-core business and used the money to invest in specific eye-care and skin-care areas, resulting in more focus in their NPDstrategy and additional NPD resources. Our model predictsthat Allergan is now in a position (with more NPD resourcesand a narrower strategic scope) to harvest its investments inthe marketing–R&D interface. Given that the company isachieving considerable sales growth with new products suchas Botox® and Acular®, and that the stock price hasperformed relatively well compared to the industry averageover the last years, this may indeed be the case (AllerganAnnual Report, 2004).

7. Future research

So far, research has been dominated by studies that identifycritical success factors for new product performance (Henard &Szymanski, 2001; Montoya-Weiss & Calantone, 1994). How-ever, effects on new product performance do not come from

3 “Consulting battalions are wandering across the corridor” (citation from theR&D director, Scrip Magazine March 1996).

single factors. Depending on the specific situation of thecompany, factors can be more or less critical, because it is acombination of factors that determines success (see also Olson,Walker, Ruekert, & Bonner, 2001).

More work is needed on the role of the marketing–R&Dinterface in influencing new product performance. Theapproach taken in this paper can be extended to differentindustries. An interesting question, for example, is whetherthe same relationships will be found in industries that have amuch lower R&D expenditure level or in less technology-intensive industries, such as the fast-moving consumer goodsindustry. Also, Henard and Szymanski (2001) propose thatsome factors, such as market orientation, may be lessimportant in low-technology markets than in high-technologymarkets. Second, as a follow-up to our survey researchdesign, it might be worthwhile to gather additional insightsinto the role of the marketing–R&D interface by monitoringthe results from actual efforts to achieve more integration ofR&D with marketing in companies. Sometimes companiesmake sudden changes in the way R&D and marketing areorganized or physically located, which offers the possibilityof “natural experiments” (e.g., Van den Bulte & Moenaert,1998).

Appendix A. Measures used in this study

A.1. Integration of R&D with marketing (1 = strongly disagree,5 = strongly agree)

A friendly attitude exists between R&D and marketingOpen communication of relevant information occursbetween R&D and marketingR&D and marketing intentionally provide each other withmisleading informationR&D and marketing search for solutions that are agreeable toeach otherR&D and marketing are more like teammates thancompetitorsIf disagreements arise between them, R&D and marketingare usually able to resolve the disagreementsR&D and marketing openly share their ideas with eachotherR&D and marketing help each other to more effectivelyperform their tasksR&D and marketing often fail to communicate informationto each other (Reverse)R&D and marketing are always blaming each other forfailures (R)It is difficult for R&D and marketing to contact each other(R)Conflicts between R&D and marketing are of a constructivekindR&D and marketing perceive their problems as mutualproblemsR&D and marketing recognize each other's talents andexpertiseR&D and marketing share resources to complete tasks


A.2. NPD resources (1 = among the 20% of companies ofcomparable size with the fewest resources, 5 = among the 20%of companies of comparable size with the most resources)

The sophistication of R&D equipmentGoodwill at medical institutesDatabase and library facilitiesContacts with universitiesWorldwide market informationTop scientistsContacts with top medical specialistsCooperative R&D relationshipsRelationships with governmental bodiesKnowledge of competitors

A.3. Strategic scope (The first two items are 5-point semanticdifferential scales and the third item has discrete choices [1–3],[4–6], [7–9], [10–12], [13b])

Narrow product range–broad product rangeFew market segments–many market segmentsThe number of therapeutic areas that the company isinvolved in with respect to marketing (e.g., cancer, antibiotics,cardiovascular)

A.4. Formalization (1 = strongly disagree, 5 = strongly agree)

In my company, formal procedures are followed beforemaking a decisionIn my company, many paper forms are usedIn my company, decision-making responsibilities within ajob are described in detailIn my company, employees have detailed task descriptions

A.5. Centralization (1 = strongly disagree, 5 = strongly agree)

My company has a flat organizational structure (R)In my company, departments have a large degree of autonomy(R)In my company, many decisions are taken low in thehierarchical structure of the organization (R)The organization of my company is very centralizedMaking decisions in my company is strongly bound tohierarchical linesIn my company, most decisions have to be approved byhigher management

A.6. New product performance (1 = among the 20% ofcompanies of comparable size with the worst performance, 5 =among the 20% of companies of comparable size with the bestperformance)

The performance of the products that have been launched inthe last five yearsThe number of new products in the last five yearsThe number of breakthroughs in the last five yearsThe quality of the R&D pipeline in the last five years

Appendix B. GLS for Model 2

For Model 2 (see Table 1), the response equation is:

yi ¼ b0þ b1i x1i þ Ai ð1Þ

with the following moderating equation:

b1i ¼ c0þ c1i x2i þ ei ð2Þwhere:

y =new product performancex1=NPD resourcesx2= integration.

When we substitute Eq. (2) in Eq. (1), we obtain Eq. (3)

y ¼ b0þ c0x1i þ c1ix1ix2i þ ex1i þ Ai: ð3Þ

The error term in Eq. (3) now shows heteroscedasticity withV [εx1i+μi]=σε

2 (x1i)2 +σμ

2. This implies that a generalized leastsquares estimator that takes into account the mechanism thatcreates the error term is more efficient.

Appendix C. GLS for Model 3

For Model 2 and Model 3 (see Table 1) and for the models inTables 2 and 3, we have the following response Eq. (4) andmoderating Eqs. (5) and (6):

yi ¼ b0þ b1i x1i þ b2i x2i þ Ai ð4Þwith the following moderating equations:

b1i ¼ c0þ c1i x2i þ ei ð5Þ

c1i ¼ d0þ d1x3i þ di ð6Þwhere:

y =new product performancex1=NPD resourcesx2= integrationx3=scope.

Substituting Eq. (6) in Eq. (5) leads to:

b1i ¼ c0þ d0x2i þ d1x3i x2i þ di x2i þ ei: ð7Þ

Substituting Eq. (7) in Eq. (4) leads to:

y ¼ b0þ c0x1i þ d0x2i x1i þ d1x3i x2i x1i þ di x2i x1iþ ex1i þ b2x2þ Ai: ð8Þ

Again, Eq. (4) shows heteroscedasticity, but in a somewhatdifferent form. Var (δi x2i x1i+εi x1i+μi)=σδ

2 (x2i x1i)2 +σε

2

(x1i)2 +σμ

2 . As a result, a generalized least squares estimatorthat takes this into account is, again, more efficient.


Appendix D. Descriptive statistics and the correlations between the variables

No.
Variable Mean Standarddeviation
(1)
(2) (3) (4) (5) (6) (7)
(1)
Integration 3.50 .57 (2) NPD 3.48 .65 .23⁎⁎⁎
Resources
(34.8) (6.5) (3) Strategic scope 3.25 .86 .16⁎ .11⁎
(4)
Centralization 3.13 .66 − .33⁎⁎⁎ − .22⁎⁎⁎ − .04 (5) Formalization 3.25 .71 − .02 − .10 .22⁎⁎ .42⁎⁎⁎
(6)
New product performance 3.14 1.00 .20⁎⁎ .54⁎⁎⁎ .12⁎ − .27⁎⁎⁎ − .16⁎ (7) Future new product performance 3.20 1.10 .24⁎⁎⁎ .40⁎⁎⁎ .08 − .16⁎ − .05 .53⁎⁎⁎
(8)
Blockbusters 1.3 1.6 .10 .42⁎⁎⁎ .27⁎⁎⁎ − .02 .12 .43⁎⁎⁎ .27⁎⁎⁎
The composite score of the 10-item formative NPD resource scale is given in parentheses.⁎pb .10 (two-tailed).⁎⁎pb .05.⁎⁎⁎pb .01.

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