complementarity, capabilities, and the …willm/bio/cv/papers/smj2009... · ate opportunities for...

Strategic Management JournalStrat. Mgmt. J., 30: 1065–1091 (2009)

Published online EarlyView in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.769

Received 31 October 2007; Final revision received 19 February 2009

COMPLEMENTARITY, CAPABILITIES, AND THEBOUNDARIES OF THE FIRM: THE IMPACT OFWITHIN-FIRM AND INTERFIRM EXPERTISE ONCONCURRENT SOURCING OF COMPLEMENTARYCOMPONENTS

ANNE PARMIGIANI1* and WILL MITCHELL2

1 Lundquist College of Business, University of Oregon, Eugene, Oregon, U.S.A.2 Fuqua School of Business, Duke University, Durham, North Carolina, U.S.A.

Theories of the firm raise conflicting arguments about how complementarities between two ormore components affect firms’ knowledge and production boundaries. Traditional arguments inthe boundaries of the firm literature suggest that firms will tend to produce sets of complementarycomponents internally, while more recent modularity studies argue that firms can outsource togain flexibility. We resolve these views by examining concurrent sourcing, which arises when firmsboth make and buy the same components. We argue that concurrent sourcing of complementarycomponents becomes more common in two cases: when firms have relevant knowledge about thecomponents in conjunction with suppliers (interfirm expertise) and, perhaps more surprisingly,within the firm (within-firm shared expertise). The results suggest that firms often need to makein order to know, but can partially outsource if they possess sufficient expertise. Copyright 2009 John Wiley & Sons, Ltd.

INTRODUCTION

Theories of the firm traditionally posit that firmsdetermine their boundaries by choosing whether tomake or buy individual components (e.g., Coase,1937; Williamson, 1975). In practice, however,firms may make joint sourcing decisions for mul-tiple components rather than treat them indepen-dently due to complementarities that stem frominterrelated business activities, such as shared

Keywords: complementarity; sourcing; firm capabilities;vertical integration; knowledge∗ Correspondence to: Anne Parmigiani, Lundquist College ofBusiness, University of Oregon, Eugene, OR 97403, U.S.A.E-mail: [email protected]

production equipment and technological interde-pendence (Teece, 1982; Helfat and Raubitschek,2000; Panzar and Willig, 1981; Santos and Eisen-hardt, 2005). Strategy research dating back toscholars such as Penrose (1959), Chandler (1962),and Cyert and March (1963) has viewed the abilityto manage interdependencies that involve phys-ical goods and/or business activities as a coreelement of business strategy, and an importantsource of a firm’s competitive advantage (Porter,1996). Arguments that neglect such interdepen-dencies by treating sourcing decisions for com-plementary components atomistically are likely tomisinterpret firms’ boundary choices and, in turn,misconstrue key aspects of business strategy byfocusing on individual rather than joint decisions.

Copyright 2009 John Wiley & Sons, Ltd.

1066 A. Parmigiani and W. Mitchell

A growing body of research has begun to con-sider how complementarity among physical goodsand/or business activities influences firm bound-aries. In perhaps the most formal treatment, Mil-grom and Roberts (1990, 1995) define complemen-tarity in terms of the joint returns from carryingout different activities. In their terms, activitiesinvolving physical goods and/or business processesare complementary if doing more of one activityincreases the returns from doing more of anotheractivity. Related research—which spans neoclas-sical economics, transaction cost economics, andthe capabilities literature—has considered comple-mentarity in terms of economies of scope that cre-ate opportunities for cost savings in production andgovernance activities (Panzar and Willig, 1981;Teece, 1982, 1986; Helfat and Eisenhardt, 2004),as well as technological opportunities to sharedesign and production activities (Arrow, 1975;Armour and Teece, 1978; Helfat and Raubitschek,2000; Kogut and Zander, 1992). The conventionalconclusion concerning firm boundaries is that firmshave strong incentives to integrate sets of com-plementary components (in which the concept ofcomponents includes both physical goods and ser-vice tasks) in order to reduce production costs,protect specialized assets, and draw on tacit inter-related skills.

The traditional conclusion that firms will preferto integrate complementary components now faceschallenges, however, because conventional argu-ments underestimate the benefits of outsourcingcomplementary components. Milgrom and Roberts(1990: 526) conclude that outsourcing complemen-tary components creates flexibility in the face ofsupply and demand uncertainties. These uncertain-ties are common in interrelated systems of businessactivities, quite often dominating integration incen-tives. They cite the example of lean productionsystems, such as the Toyota Production System(TPS), in which suppliers undertake many com-plex interrelated tasks for end-product manufac-turers. Brusoni and Prencipe (2001) and Prencipe,Davies, and Hobday (2003), meanwhile, highlightthe growth of modularity in product markets, argu-ing that firms can act as system integrators by coor-dinating outsourced production activities of mul-tiple suppliers. Boeing, for instance, gains muchof its competitive advantage in aircraft productionthrough its integration of components produced bythousands of suppliers.

Nonetheless, pure outsourcing of complemen-tary components can be risky for firms due toloss of control, leakage of proprietary information,and lack of opportunities to gain knowledge vialearning by doing (Pisano, 1994). Indeed, empiri-cal evidence finds that many firms—such as Toy-ota—commonly produce a substantial degree oftheir complementary components internally. Boe-ing, meanwhile, has encountered delays in its cur-rent 787 project, in part because it has extendedoutsourcing of assembly tasks well beyond its tra-ditional practice (Clark, 2007). Thus, there is atension between traditional integration argumentsand recent insights about the benefits of outsourc-ing complementary components.

An emerging literature discusses concurrentsourcing as an alternative to the traditionaldichotomy of make versus buy boundary deci-sions, thereby identifying a potential solution tothe tension that arises between the incentives tointegrate and the benefits of outsourcing comple-mentary components. Concurrent sourcing, whichis sometimes referred to as tapered integration ordual distribution (Adelman, 1949; Harrigan, 1986),occurs when firms both make and buy some oftheir requirements for a component. Conceptually,Bradach and Eccles (1989), Hennart (1993), andGulati and Puranam (2006) describe concurrentsourcing as a plural governance mode. Empiri-cally, scholars have studied concurrent sourcing ina variety of industries and settings, including truck-ing, computing, restaurants, general manufactur-ing, and research activities (Bradach, 1997; Cassi-man and Veugelers, 2006; He and Nickerson, 2006;Heide, 2003; Rothaermel, Hitt, and Jobe, 2006).Concurrent sourcing is common in sales forces,where firms use both internal agents and outsiderepresentatives (Dutta et al., 1995), and also occurswith the simultaneous use of employees and con-tract labor (Davis-Blake and Uzzi, 1993; Lepakand Snell, 1999). Parmigiani (2007) shows thatconcurrent sourcing of individual components iscommon when a firm and its suppliers jointly pos-sess substantial relevant expertise.

No study, however, has addressed conditionsunder which firms will concurrently source setsof complementary components. This study arguesthat both greater interfirm and greater within-firmshared expertise relating to a set of complementarycomponents increase firms’ incentives to concur-rently source rather than integrate the compo-nents. In this view, firms’ knowledge boundaries

Copyright 2009 John Wiley & Sons, Ltd. Strat. Mgmt. J., 30: 1065–1091 (2009)DOI: 10.1002/smj

Complementarity, Capabilities, and the Boundaries of the Firm 1067

influence their production boundaries (Brusoni,Prencipe, and Pavitt, 2001; Takeishi, 2002), some-times in nonintuitive ways. Figure 1 depicts thefocus of the study. We test the predictions using asample of joint sourcing decisions for die design,die construction, and end-part machining servicesof 110 North American metal stamping and pow-der metal firms in 2002. Die design and die con-struction are complements due to economies ofscope, while die design and end-part machiningare largely independent components.

The study makes three contributions in extend-ing our understanding of the interplay betweencomplementarity, capabilities, and firm bound-aries. First, we demonstrate that accounting forfirm capabilities, which highlights the distinctionbetween knowledge and task boundaries, helpsresolve an apparent contradiction in firms’ sourc-ing choices for complementary components. Assuch, we extend capability-based theory to sug-gest that interfirm and within-firm expertise can bemechanisms for knowledge spillover across com-ponents. In doing so, we provide both theoreti-cal logic and empirical exploration regarding howfirms incorporate the attributes of related compo-nents into their sourcing decisions, including bothsimilar and systemic interdependencies (Richard-son, 1972). Through concurrent sourcing, firmscan obtain a deeper understanding of the inter-dependencies among complementary components

as well as benefit from the broader knowledge ofsuppliers while simultaneously balancing shirkingand slacking (Hennart, 1993). Moreover, concur-rent sourcing opens the firm to learning arisingfrom the spillovers from related components anddifferent production methods of the firm and itssuppliers.

Second, we demonstrate that a firm’s knowledgeboundaries relate more tightly to its productionboundaries than the recent modularity view sug-gests. While firms may ‘know more than theymake’ (Brusoni et al., 2001), they often need tomake in order to know. Nonetheless, firms do notneed to make all of their components to knowenough to outsource. Instead, they can solve thisparadox by concurrently making some portion oftheir needs and buying the rest.

Third, we fill a gap between the levels of anal-yses of transaction cost and capability-based the-ories. While the transaction cost approach empha-sizes decisions concerning assets underlying indi-vidual transactions and components (Williamson,1975), the capabilities view tends to emphasize abroad approach consisting of a myriad of relatedactivities (Penrose, 1959; Barney, 1991). Our workhelps bridge this gap by analyzing how, on anintermediate level, firms source sets of comple-mentary goods

Note: As indicated above, goods can be produced inside the firm, outsourced, or concurrentlysourced (on the boundary between the firm and supplier). This study addresses the intersectingcircles on the boundary, that is, concurrent sourcing of complementary goods.

Firm Supplier

Figure 1. Sourcing choices for independent and complementary components, depicted by single vs. intersecting circles



BACKGROUND

We first define complementarity, discuss the liter-ature on sourcing choices, and outline traditionalarguments concerning boundary choices for com-plementary components.

Complementarity

Complementarity arises when doing more of oneactivity increases the returns from doing more ofanother activity (Milgrom and Roberts, 1995: 181).Formally, complementarity can either involvesmooth functions of incrementally divisiblechoices, or arise from indivisible choice vari-ables. Complementarity involving smooth func-tions occurs when the cross-partial derivativesinvolving two or more activities within a pay-offfunction are positive: the marginal returns to oneactivity increase as a firm does more of the otheractivities (Milgrom and Roberts, 1990). How-ever, complementarity does not require functionalsmoothness—indeed, in practice, complementar-ity commonly involves non-convexity such thatchanges in one activity require non-incrementalchanges in other activities (Penrose, 1959). Thus,as Milgrom and Roberts (1995) point out, com-plementarity most generally involves benefits thatarise from making joint decisions about multiplegoods and activities. This definition of comple-mentarity encompasses the concept of synergy aswell as the idea of system effects that arise whenthe whole is greater than the sum of its parts.

Complementarity stands in contrast to indepen-dence and substitution. Independence among a setof components arises when changes in one activ-ity do not change the value of other activities; forexample, vehicle audio systems are independent ofpower train components. Substitution arises whendoing more of one activity reduces the value ofanother activity (Rothaermel and Hess, 2006); forexample, selling economy vehicles from the samelocation as luxury vehicles may reduce the valueof the luxury vehicles.

Complementarity may be widespread or morefocal. Widespread complementarity involves exten-sive systems of business activities, such as keyelements of the Toyota Production System orSouthwest Airlines’s business model, which Porter(1996) refers to as activity systems. Milgrom andRoberts (1990) use the example of the shift frommass production to lean production manufacturing

as a comparison of two complementary systems ofbusiness activities. More focally, complementaritymay involve as few as two interrelated physicalgoods and/or business activities, such as the pro-duction of automobile engine blocks and pistons,the design of semiconductor wafers and productionequipment, and the management of savings andinvestment accounts within financial institutions.

Whether widespread or focal, complementarityinvolving two or more components originates fromeconomies of scope in the value chain of devel-opment, production, sales, and other commercialactivities. We use economies of scope as a generalconcept that includes improving a firm’s ability tocreate new goods and services, as well as reduc-ing the cost of goods and services. Economiesof scope that reduce the cost of current activitiesarise when firms gain efficiencies by jointly coor-dinating multiple activities (Helfat and Eisenhardt,2004; Panzar and Willig, 1981; Teece, 1982). Suchactivities may include colocating fruit orchardsand bee apiaries, or producing fertilizer from by-products of mineral smelting processes. Cost sav-ings can result from more efficient utilization of asharable resource, such as capital, labor, or man-agerial expertise. Such resources can include com-plementary assets (Teece, 1986), such as brandreputation and distribution knowledge, which firmscan leverage over multiple products. For exam-ple, offering financing along with the productionof durable goods allows a firm to gain from estab-lished client relationships. These sorts of efficien-cies are often the basis for seemingly unrelateddiversification (Campbell, Goold, and Alexander,1995; Rumelt, 1982) because firms can share par-enting skills, administrative expertise, allocationability, and managerial talent across divisions thatoperate in different end-product markets.

Scope economies that improve a firm’s ability tocreate new goods and services arise when a firm’sinnovative activities require knowledge of mul-tiple aspects of design, production, distribution,and/or other elements of the value chain across twoor more components. Such technologically basedscope economies occur when changes in the designof one component require changes in another com-ponent (Dosi, 1988). For example, advances inmetallurgy for automobile engine blocks in turnrequire changes in piston design. Scope economiesresulting from technological complementarities canreflect pooled knowledge bases, sequential opera-tions, and/or reciprocal production processes



(Thompson, 1967). Similarly, technologicallybased scope economies can result from the needfor integrative knowledge about complex businessactivities (Armour and Teece, 1978; Brusoni andPrencipe, 2001; Helfat and Raubitschek, 2000),such as the ability to produce new generations ofsemiconductors that combine knowledge of multi-faceted technical advances, production skills, andchanges in customer demands.

Although it is possible to identify conceptualdifferences in the antecedents of scope economies,these differences can be difficult to untangle empir-ically because they often occur jointly. Distin-guishing between scope economies that producecurrent savings versus scope economies that facili-tate innovation can be difficult when assessing firmbehavior and decisions. For example, Lear Corpo-ration produces automotive seats and dashboards.Lear has a considerable history in the automotiveindustry, and has a strong reputation and extensivedistribution resources that it can leverage acrossthe two products. This results in scope economiesthat reduce the costs of current activities. In addi-tion, Lear has expertise in ergonomics, electronics,and materials that it can use in development activ-ities and innovation for both goods, yielding tech-nologically based scope economies. Fortunately,complementarity has a common implication forboundary choices, whether the complementarityarises from current efficiencies or from contribu-tions to innovation. In either case, firms benefit bycoordinating activities needed to produce comple-mentary components within a business system.

The empirical boundaries of complementaritywithin a business system can also be difficultto identify. There are few opportunities to for-mally estimate the change in value that arisesacross a system when one element of the sys-tem changes, whether calculations involve differ-entials of a smooth function or order statisticsfor step functions. Instead, identifying the bound-aries of complementarity typically requires judg-ment by managers and/or scholars. Some exam-ples of complementarity that the literature hasused are quite sweeping, such as overall produc-tion systems that clearly consist of multiple com-plementary subsystems rather than single, fullyintegrated, complementary systems (e.g., Milgromand Roberts, 1995; Porter, 1996; Siggelkow 2001;Porter and Siggelkow 2008). Even subsystems orsubassemblies, such as vehicle braking systems

or dashboards, typically consist of multiple, inter-related, complementary components (Novak andStern, 2009; Sako, 2003). The empirical manage-rial and research challenge arises in identifyinglogical combinations of two or more componentsthat share economies of scope, while being at leastpartially separable from other sets of activities. Wewill return to this issue in the analysis.

Boundary choices: make, buy, and concurrentsourcing

Traditionally, the boundaries of the firm literaturefocused on the distinction between make and buydecisions. The core conclusion about incentives inthis literature, which includes both transaction costeconomics and capabilities arguments, is that firmstend to purchase goods and services through exter-nal sourcing when the exchanges depend on gen-eral investments (Coase, 1937; Williamson, 1975),or when external suppliers have substantial capa-bility advantages (Argyres, 1996; Barney, 1986).By contrast, these studies suggest that firms tendto integrate activities when they require specializedinvestments and/or when the firm’s capabilitiesprovide substantial production, sales, or develop-ment advantages.

Transaction cost and capabilities arguments arisefrom different premises and different units of anal-ysis, but reach similar conclusions about inter-nal and external boundary choices for componentsinvolving idiosyncratic assets (Conner, 1991; San-tos and Eisenhardt, 2005). Transaction cost eco-nomics stresses the use of vertical integration toprotect the firm from transaction hazards such asholdup based on specialized investments, slackingdue to an inability to accurately measure inputperformance prior to use, and knowledge appro-priation by suppliers.1 Transaction cost logic takes

1 Transaction cost arguments do not address concurrent sourcing.The idea of concurrent sourcing may appear to arise whenWilliamson (1985) suggests that ‘. . . where firms are observedboth to make and to buy an identical good or service, the internaltechnology will be characterized by greater asset specificitythan will the external technology, ceteris paribus’ (Williamson,1985: 96), but technologies with different asset specificity arenot identical. Williamson also discusses cases of intermediatelevels of asset specificity, which he expects to result in ‘. . .mixed governance, in which some firms will be observed tobuy, others to make, and all express “dissatisfaction” with theircurrent procurement solution’ (Williamson, 1985: 94), but thisprediction addresses differences across firms rather than firm-level concurrent sourcing (i.e., the argument does not suggestthat firms may be able to split their requirements to limit thisdissatisfaction).



production costs as given, in which learning influ-ences production costs, and focuses on the gov-ernance costs and benefits of different boundarychoices, emphasizing the protection elements ofgovernance. By contrast, capabilities argumentssuggest that firm boundaries influence produc-tion costs because boundary placement influenceslearning opportunities. This literature emphasizesgains from engaging in activities internally basedon common knowledge, resulting in reduced costsand greater development ability.

Beyond the discrete make and buy distinction,the buyer-supplier literature sometimes raises con-current sourcing as a third sourcing option, high-lighting benefits stemming from short-term effi-ciencies and longer-term capability development.The earliest arguments suggested that concurrentmake and buy strategies help firms hedge againstdemand uncertainty (Adelman, 1949; Harrigan,1986), as well as gain an understanding of produc-tion process to better monitor suppliers (Cannon,1968; Harris and Wiens, 1980; Porter, 1980). Morerecent studies show that opportunities for cost shar-ing and learning among buyers and suppliers createincentives for concurrent sourcing (Bradach, 1997;Cassiman and Veugelers, 2006; He and Nicker-son, 2006; Heide, 2003). In turn, franchising stud-ies (Bradach, 1997; Combs, Michael, and Castro-giovanni, 2004; Lafontaine and Shaw, 1999) andwork by Parmigiani (2007) suggest that concur-rent sourcing is often a stable choice rather than atransitory state.2

Although most discussion of boundaries hasfocused on individual components, the boundariesliteratures do offer several arguments concerningsourcing options for complementary components.As we discussed earlier, most arguments empha-size incentives to internalize the components. Ini-tial arguments focused on technological comple-mentarities (Arrow, 1975; Armour and Teece,1978), with the premise that firms can share infor-mation across technologically interdependent com-ponents (see also Kogut and Zander, 1992). Panzarand Willig (1981) then suggested that firms willinternalize assets underlying transactions for whicheconomies of scope offer efficiencies, although

2 Concurrent sourcing will be most common when scale econo-mies are limited. With strong increasing returns to scale, firmsare more likely either to fully integrate or outsource. Manyphysical goods and business processes exhaust scale economieswithin relevant production ranges (Scherer and Ross, 1990),however, so that concurrent sourcing is often viable.

Teece (1982) argued that the internalization incen-tives occur only when the economies of scope arisefrom co-specialized assets. By contrast, Milgromand Roberts’s (1990) formal model suggested thatfirms gain flexibility by outsourcing complemen-tary components in the face of technology andmarket uncertainties.

Nonetheless, as we will discuss below, firms alsohave incentives to concurrently source sets of com-plementary components. To date, the literature hasnot explored this argument. We will use the term‘concurrently source complementary components’to refer to cases in which a firm both makes andbuys two or more complementary components.

PREDICTIONS

We will now explore how firm and supplierexpertise shape firms’ incentives to concurrentlysource sets of complementary components. We willrestrict our comparison to concurrent sourcing ver-sus vertical integration of the set of complemen-tary components, because vertical integration isthe traditional expectation based upon cost sharingand technological interdependencies (the empiri-cal analysis reports other comparisons). We focusthe discussion on complementary rather than inde-pendent components because complementary com-ponents will be affected by expertise that spanscomponents; the empirical analysis will test thisassumption.

We define expertise as the firm’s understandingof the skills associated with a particular compo-nent, including design, production, and marketingknowledge, as well as other skills related to a goodor service (Grant, 1996; Wernerfelt, 1984). Firmswith expertise related to a particular componenttypically are able to produce the component moreefficiently and effectively than firms without suchexpertise because they possess appropriate person-nel, equipment, and knowledge (Penrose, 1959;Conner, 1991; Grant, 1996; Rubin, 1973). Compo-nents that fit closely into the firm’s constellationof knowledge tend to be produced internally, whilethose that do not match well with the firm’s exper-tise tend to be outsourced (Argyres, 1996; Connerand Prahalad, 1996, Grant, 1996).

We are considering the joint sourcing decisionfor a set of complementary components, and there-fore focus on expertise relevant to the set of com-ponents. We refer to within-firm shared expertise



as knowledge within a firm that spans two or morecomponents, such as possessing a deep understand-ing of the scientific technology related to a familyof goods (Danneels, 2007; Dosi, 1988; Helfat andRaubitschek, 2000; Penrose, 1959). In parallel, weuse the term interfirm expertise to refer to thedegree to which a firm and its potential supplierseach have skills relevant to a particular compo-nent and/or set of components (Dyer and Singh,1998; Lavie, 2006). In the following predictions,we discuss how within-firm shared expertise andinterfirm expertise create incentives that affect thejoint sourcing decision of complementary compo-nents.

In considering interfirm expertise, we expect thatconcurrent sourcing will be particularly commonwhen firms and potential suppliers have substantialinterfirm expertise relevant to a set of complemen-tary components. In such cases, firms have incen-tives to both leverage their own skills and gainaccess to suppliers’ skills. Interfirm expertise typ-ically involves both specialized skills (skills heldby only one party) and redundant skills (equiva-lent skills possessed by two or more parties). Evenin the face of redundancy, firms can gain flexi-bility in achieving economies of scope for a setof complementary components by making someof their requirements and outsourcing the balancefrom strong suppliers. For example, most fast-foodfranchisors have company-owned stores as well asfranchised stores; they also conduct national adver-tising while relying on their franchisees for localadvertising (Blair and Lafontaine, 2005). Thus,franchisors concurrently source the complemen-tary activities of advertising and food production,leveraging the scope economy provided by theirbrand strength and reputation. They prefer knowl-edgeable franchisees who best understand theirlocal market and can adapt product and advertisingofferings accordingly, while also maintaining inter-nal competencies that assist in systemwide learning(Bradach, 1997).

Substantial levels of interfirm expertise typi-cally involve some degree of skill specializationin which the buyer and supplier each understanddifferent aspects of the components. As a result,two or more strong firms bring differentiated valueto a product, which creates an incentive to bothmake and buy. When interfirm expertise for a set ofcomplementary components is sufficiently strong,a firm may elect to use concurrent sourcing, rather

than vertically integrate, in order to gain flexibil-ity and access to the specialized skills of a partner.For example, Cassiman and Veugelers (2006) findthat firms often concurrently source research anddevelopment (R&D) activities when they rely onuniversities for basic research.

Parmigiani (2007) found that greater combinedfirm and supplier expertise for a single com-ponent led to concurrent sourcing of that com-ponent. This component-level interfirm expertisewas grounded in superior skills, progression upthe learning curve, and the associated reducedproduction costs for the focal component. Thecurrent study expands the view of the poten-tial influence of interfirm expertise by consid-ering how such expertise may affect the sourc-ing decision for a set of complementary compo-nents rather than a single component. The ear-lier study (Parmigiani, 2007) did not address thispossibility (moreover, support for the predictionin the earlier study concerning individual compo-nents does not guarantee support for the predic-tion concerning complementary components in thisstudy). Here we focus on the spillover effect ofinterfirm expertise of one component as it affectsthe sourcing choice for the set of complementarycomponents.

The presence of skill specialization and thepotential for spillover across knowledgeable firmscreate a learning incentive for concurrent sourcingof complementary components. A firm will oftengain knowledge from buying components froma highly skilled supplier. Such learning oppor-tunities will be most productive when the firmalso has strong relevant expertise, because itsinternal skills create an absorptive capacity thatenables knowledge transfer (Cohen and Levinthal,1990). For example, with the increased impor-tance of electronics in automotive systems andthe interdependence between these systems, Toy-ota chose to source related electrical compo-nents both internally and from Denso in orderto better understand the technology and learnindirectly about competitors (Lincoln, Ahmadjian,and Mason, 1998). The logic leads to the firsthypothesis.

Hypothesis 1: The greater the interfirm expertiseof a firm and its potential suppliers for comple-mentary components, the more likely the firmwill concurrently source the set of componentsrather than vertically integrate.



We next consider how within-firm shared exper-tise, which is the focal firm’s skill relevant to aset of complementary components, will influencesourcing choices for complementary components.The discussion produces a prediction that may atleast initially appear counterintuitive.

The seemingly intuitive expectation concern-ing within-firm shared expertise might be thatgreater within-firm shared expertise will lead firmsto vertically integrate complementary componentsin order to capture and benefit from knowledgespillovers. Firms tend to accumulate tangible andintangible resources that can be used throughout afamily of related products (Helfat and Raubitschek,2000; Nelson and Winter, 1982). Thus, one mightexpect that a firm will make sets of comple-mentary components when it has an opportunityto leverage a particularly strong set of internalskills, rather than outsourcing part of its require-ments. These skills can include organizationalcapabilities that enable the firm to exploit scopeeconomies and/or technical skills that assist thefirm in managing technological interdependen-cies.

An intriguing counterpoint to the seeminglyobvious expectation emerges from the modularityliterature, which suggests complementary compo-nents can be successfully outsourced, as long asclearly described interfaces exist between com-ponents (Baldwin and Clark, 2000; Sanchez andMahoney, 1996). One example is the possibilityof separating product design from production suchthat a firm may internally produce designs andoutsource subsequent manufacture of the products.While these two activities are clearly complemen-tary, the forces motivating the firm to integratethe activities may differ from those that motivateinternalization of either activity (Dutta et al., 1995;Ulrich and Ellison, 2005). A considerable body ofwork has also explored system integrators, typ-ically large firms that outsource much of theiractivities while retaining expertise in the under-lying technologies (Brusoni and Prencipe, 2001;Prencipe et al., 2003). The benefits of outsourc-ing include flexibility in the face of uncertainty(Milgrom and Roberts, 1990), as well as opportu-nities to learn from external partners (von Hippel,1988).

An implication of the modularity research isthat a firm’s knowledge boundaries may be con-siderably broader than its production boundaries

(Brusoni et al., 2001). Firms must preserve inter-nal knowledge, however, in order to best under-stand the interrelationships between componentsbased upon technologies that may be changingat different rates. Quoting Pavitt (2003), ‘Firmsspecializing in product development and systemsintegration will need to maintain competencies inrelated manufacture, components, and subsystems’(Pavitt, 2003: 86).

The location of a firm’s boundaries affects thefirm’s ability to monitor and learn from suppli-ers, however, suggesting limits to the benefitsof outsourcing. From a capabilities standpoint,some degree of tacit knowledge and understandingwill be gained and applied only through internalproduction. Hoetker’s (2006) study of the com-puter industry shows that product modularity doesnot map cleanly onto organizational modularity;instead, firms prefer internal suppliers for both sys-temic and modular goods. Takeishi (2002) arguesthat, particularly for new technologies, architec-tural knowledge is insufficient to manage interre-lated components and unravel engineering prob-lems. Firms require component-level knowledgethat they ostensibly can only obtain through pro-duction. As Dosi et al. (2003) suggest, ‘Clearly,the balance between what these types of firms“know” and what they directly “make” will con-tinue to depend upon product- and technology-specific patterns of knowledge accumulation andtheir interfaces’ (Dosi et al., 2003: 109). From agovernance perspective, some hazards and risksare only contained through internalization, chosenonly as a last resort (Williamson, 1985). Mayerand Salomon (2006) show that technical exper-tise reduces holdup risks by improving a firm’sability to craft contracts that govern external trans-actions involving specialized assets, and also findthat technical expertise does not enable the firm toavoid evaluation problems or knowledge leakagein outsourced exchanges.

Evaluation and knowledge issues will tend tobecome even more important when goods are com-plementary, further limiting the incentives for pureoutsourcing. In turn, this opens up the possibilityfor concurrent sourcing, because firms need notcompletely vertically integrate to gain productionknowledge, but rather can produce a small portionof their requirements. For example, a firm mayoperate a pilot plant in order to better understandand monitor its suppliers’ processes (Oster, 1994).



Concurrent sourcing helps resolve the problemof attempting to achieve the seemingly conflict-ing goals of deep component knowledge, flexibil-ity, and protection. Firms need not produce theirfull requirements of complementary componentsin order to obtain deep component knowledge andmaintain sufficient absorptive capacity to sourceeffectively (Adelman, 1949; Cohen and Levinthal,1990; Oster, 1994; Parmigiani, 2007). This sug-gests a greater overlap in production and knowl-edge boundaries than the modularity or systemsintegration views might suggest.

The benefits of overlap and the need to produceinternally arise from the tacit nature of productionand the interrelatedness of the goods. When com-ponents are complementary, the sourcing decisionsfor the two components become intermingled, suchthat concurrent sourcing of one commonly leads toconcurrent sourcing for the other. This combineddecision making involves both technological andorganizational complexity. In turn, this complexityrequires that the firm possess a significant degreeof within-firm shared expertise across the compo-nents to utilize the concurrent sourcing strategy.The payoff of this difficult-to-manage mode is aricher understanding of the set of components andthe underlying body of technical knowledge. Thislogic leads to the second hypothesis.

Hypothesis 2: The greater the within-firm sharedexpertise for complementary components, themore likely the firm will concurrently source thecomponents rather than vertically integrate.

In sum, we expect both greater interfirm exper-tise and greater within-firm shared expertise toincrease the use of concurrent sourcing over verti-cal integration. The analysis will also assess howfirm and supplier expertise for individual compo-nents affects sourcing choices.

CONTEXT, DATA, AND METHODS

We studied sourcing decisions of North Americanmetal stamping and powder metal firms for pro-duction tooling and services. These two sectors ofthe metal forming industry consist of many inde-pendent small firms, most of which are privatelyheld. This setting allowed us to focus on straight-forward buyer-supplier relationships, since these

firms rarely use sophisticated alliances or joint ven-tures for sourcing. Both sectors share relativelyhomogeneous production processes and a commonend-product market of the automotive industry,which stresses the importance of production effi-ciency and technological progress from upstreamsuppliers (Womack, Jones, and Roos, 1990).

We first conducted exploratory interviews withmanagers of 11 metal forming firms. The inter-views helped us understand their production pro-cesses and identified three components that allfirms sourced: die design (progressive stampingdies in the metal stamping industry and pow-der compaction dies in the powder metal indus-try), die construction, and end-part machining ser-vices. These three components were common toall firms and are strategically significant.3 Thesefirms use dies to create their products and mustsource both the design and construction of thedies. Die design is a complex process in whichdesigners must select the proper type of steel, con-sider press features, and optimize the layout ofthe die to maximize its productivity (Poli et al.,1993). Dies involve numerous interrelated compo-nents, including top and bottom shoes, punches,bearings, springs, and guide pins. Die construc-tion typically requires tool-grade steel and employscomputer numerical controlled (CNC) mills andelectric-discharge machining (EDM) centers. Sub-stantial technical expertise is required for diedesign and construction, as Walsh (1994) discussesin his machining handbook: ‘. . . die design anddie making are complex arts as well as technolo-gies that require considerable skill, knowledge, andpractical experience’ (Walsh, 1994: 792). End-partmachining is a downstream operation that thesefirms undertake in order to meet customer specifi-cations by adding features that cannot be stampedor molded into the part, such as slots or holes.

Although we do not have a direct measureof complementarity, we identified a complemen-tary dyad and an independent dyad among thethree components. Die design and die construc-tion are complementary components because theyenjoy scope economies arising from both cost sav-ings and technological interdependence (primarilysequential interdependence, in Thompson’s [1967]

3 We also collected data about die maintenance and end-partcoating, but did not include them in this analysis becauseof infrequent outsourcing for die maintenance and infrequentinternal production for end-part coating.



terms, with die design preceding die construc-tion). The efficiencies arise due to firm reputa-tion, common personnel, and administrative skillsneeded to manage the overall process. Technologi-cal interdependence of die design and constructionarises because both draw upon a common bodyof knowledge, including mechanical engineering,metal working, and press operations (Walsh, 1994).Many managers we interviewed were keen to dis-cuss the intricacies and interdependencies of sourc-ing tooling designs and dies. As one stated, ‘Justlooking at the (end) part doesn’t tell you how tomake it—just that it can be done . . . [it’s the]‘magic of the tool,’ who designs it, and how itruns.’ The managers also acknowledged the impor-tance of expertise in the sourcing decision, assert-ing they desired suppliers that are up to date ontechnology, bring ‘intellectual ability to the table,’and can ‘grow with us and form creative solutions.’At the same time, the executives were concernedabout supplier opportunism, particularly for dieconstruction. They noted that ‘when tool shops arebusy, prices go up two times and delivery increasesfrom 2–3 weeks to 10–12 weeks,’ and were waryof the ‘kitchen remodeler trick’ in which supplierswould agree to a job but later negotiate for higherprices and/or later deliveries due to unforeseen cir-cumstances.

The relationship between production and design,such as in tooling dies, is a classic example of com-plementarity, in which it is possible that firms canhave strong expertise in one activity and not theother. Some firms may excel at such design capa-bilities as engineering, creating specifications, andenvironmental scanning. Other firms may be supe-rior manufacturers, with a keener focus on processefficiency and meeting specifications. Dependingupon how the activities are divided, it is some-times possible to create a ‘black box’ specificationfor suppliers, such that they follow predeterminedrules of coordination between the different subsys-tem components, so that firms can make separatesourcing decisions for the design and productionstages (Brusoni et al., 2001; Petersen, Handfield,and Ragatz, 2005). However, a firm’s technicalknowledge, its past experience, and its reputationcreate strong incentives to make joint sourcingdecisions for complementary components.

In contrast with die design and construction, diedesign and end-part machining are largely indepen-dent of each other. The activities involve different

kinds of expertise, have little or no work flow inter-dependencies, and create few economies of scope.Die design requires skills in CAD software to pro-duce the specifications for an individual design, forexample, while end-part machining requires skillsin managing high quantity production, often withclose tolerances using metal processing equipmentsuch as mills or lathes.

We also assessed die construction and end-partmachining as a possible pair of components, butcould not cleanly categorize them as complemen-tary or independent. The two activities use somesimilar equipment, but the materials and productvolumes differ and the workflow interdependenceis minimal. Hence, we focused on the two moreclearly categorical pairings of design-construction(complementary) and design-machining (indepen-dent).

Survey administration and response

Based upon our understanding of the firms and thethree inputs, we designed a survey booklet withsections for data on each input, plus a section foroverall firm information. We chose scales basedon reviews of the literature, refined by discussionsduring the preliminary interviews. Most items usedseven-point scales (Fowler, 1995). The 24-pagesurvey contained over 300 items covering sev-eral aspects of the sourcing decision. We pretestedthe survey by soliciting feedback from academiccolleagues, conducting interviews with managers,obtaining reviews from industry association exec-utives, and performing a pilot test with managersthat replicated final survey conditions. We solicitedthe help of industry associations for metal stamp-ing (the Precision Metalforming Association) andpowder metal (the Metal Powder Industry Federa-tion). From these groups, we were able to obtainmembership lists that we used as the basis forour sample. We called each of the 509 memberfirms in order to identify the correct contact per-son to whom we should send the survey. In thefall of 2002, we sent the survey to the 453 firmsthat provided us with contact information. Afterthe initial call and survey mailing, we conductedup to four follow-up contacts by fax, phone, andmail. Through these efforts, we obtained a 43percent usable response rate, substantially higherthan the 20 percent rate that is common for firm-level surveys (Paxson, Dillman, and Tarnai, 1995).



Our dataset included 809 distinct sourcing deci-sions from 193 firms. Because we are investigat-ing the sourcing choice for pairs of components,we ruled out firms that did not source all threecomponents of interest. Our final data include 110firms that sourced both the complementary pair ofcomponents (die design and construction) and theindependent pair of components (die design andend-part machining).

We used a key informant approach for the sur-vey. While in some cases it is preferable to havemultiple survey respondents, we believed it wasbetter to request information from one knowledge-able respondent in this study, due to the smallsize of these firms (mean of 75 employees, with95% having fewer than 500 employees) and thetechnical and specialized nature of the survey.The key informant approach is appropriate whenone can identify respondents who, by virtue oftheir positions in an organization’s hierarchy, areable to provide opinions and perceptions that arevalid reflections of other key decision makers inthe firm (Li and Atuahene-Gima, 2002; Phillips,1981). For many questions, the most qualified rateris a senior manager of the firm, who is likely tohave the broadest interactions as well as the bestoverview of the firm’s strategy and performance(Glick et al., 1990; Parkhe, 1993).

In our study, the most common respondent wasthe president or general manager. Senior managersof metal forming firms typically have consider-able experience in buying production components,particularly because metal forming is a matureindustry, based upon established technology, withwell established buying and supplying firms. Oneof the authors worked as a purchasing managerin this industry for several years and can attestto the fact that a single senior executive typicallyhas oversight of sourcing decisions and managingsuppliers. Our first-hand knowledge of this con-text and the fact that we asked about their sourcingexperience over the past year also gave us confi-dence in using a single, well-qualified respondent.

We recognize that consistency artifacts and acommon method variance bias can result from col-lecting dependent and independent variables fromthe same respondent. While this is a drawback ofthis type of survey design, we sought to avoid con-sistency artifacts by placing more subjective items,such as supplier expertise, before objective ones,such as firm size (Salancik and Pfeffer, 1977). We

investigated common method variance by conduct-ing the Harman one-factor test, which involvesentering all the independent and dependent vari-able items into a factor analysis (Podsakoff andOrgan, 1986). A principal component factor analy-sis of all measurement items yielded seven factorswith an eigenvalue exceeding one. These factorsaccounted for 57 percent of the variance. The fac-tor with the greatest eigenvalue accounted for 15percent of the variance. Because no single fac-tor emerged as a dominant factor accounting formost of the variance, common method variance isunlikely to be a serious problem in the data.

Variables

Dependent variable

The sourcing choice variable reflects the sourcingfirm’s procurement choice for the pair of compo-nents over the prior year. Including a time periodis essential for questions that seek to understandbehavior (Fowler, 1995). We measured the depen-dent variable based on a question that asked ifthe firm sourced each item internally, externally,or concurrently (see Appendix A). For concurrentsourcing, we obtained the percentage of internalproduction. The survey provided six choices ratherthan relying on a respondent’s ability to recall aprecise percentage. This procedure improves accu-racy and reduces response distortion by cueingthe respondent’s memory, pushing him or her toconsider the different options and choose the bestone (Fowler, 1995). Consistent with prior work(Harrigan, 1986; Jacobides and Hitt, 2005; Parmi-giani, 2007; Sa Vinhas, 2002), we used a 10 per-cent cutoff, such that components that firms pro-duced at least 90 percent internally were consid-ered ‘made,’ those that firms outsourced 90 percentor more were ‘bought,’ and the rest concurrentlysourced.

We investigated whether firms’ use of concur-rent sourcing tended to be stable or representeda transitory state. The data in this study appearto reflect stable boundary choices, based on sur-vey responses to items measuring longevity (‘Howlong has the good been sourced this way?’) andlikelihood of change (‘Do you plan to changesourcing modes within the next two years?’); themeans for concurrently sourced items did notdiffer significantly from those made or bought.Nearly 60 percent of the goods had always been



sourced in the mode indicated, and less than12 percent were likely to change modes, indicatingstability.

Because there are three options (make, buy,concurrent) for each component, the joint sourc-ing decision for a pair of components includesnine options. For clarity, we focus on four of thejoint options, when firms: made both components(make/make), concurrently sourced both compo-nents (concurrent/concurrent), bought both com-ponents (buy/buy), and used some combination ofchoices (mixed mode). Hypotheses 1 and 2 com-pare the concurrent/concurrent to the make/makeoption.

We chose to pool the six combined options intothe mixed mode classification for both concep-tual and econometric reasons. Conceptually, ourkey explanatory variables involve expertise andour main comparison of interest is between con-currently sourcing both goods versus making bothgoods. We have no reason to predict that interfirmor within-firm shared expertise will influence thechoice of concurrent/concurrent versus make/buyany differently than that of concurrent/concurrentversus buy/make, nor does the mixed choice speakclearly to firm boundary issues addressed in thisstudy. Given that our main interest involved com-plementary pairs of goods, which we believedwould be more likely sourced by the same mode,no explanatory power is lost by grouping the mixedmodes together. Moreover, reducing the choiceoptions from nine to four reduced the number ofcomparisons from 36 to six, a much more man-ageable number.

Combining the categories and pooling the dataalso helps resolve an econometric issue, becausethe data were not well balanced for each pair ofgoods by sourcing mode. Tables 3a and 3b demon-strate this point. For example, firms made dies andconcurrently sourced designs in only three of the110 cases. A drastically uneven number of casesin each category creates a sparse cell count prob-lem and, if left uncorrected, can bias parameterestimates in multinomial logit models (Agresti,1996). Hence, we pooled the mixed mode observa-tions (i.e., make/buy, make/concurrent, buy/make,buy/concurrent, concurrent/make, concurrent/buy)into one category. Our dependent variable for thecomplementary pair thus results in four categories:make/make (25 cases), buy/buy (18 cases), concur-rent/concurrent (25 cases), and mixed mode (42cases).

Explanatory variables

We created expertise variables with questionnaireitems that used a seven-point Likert-type scale.Since the variables used multiple items, we usedexploratory factor analysis (EFA) and then con-firmatory factor analysis (CFA) to investigate therelationships between the items and the variables(Anderson and Gerbing, 1988). The EFA resultsindicated that each of the variables were uni-dimensional and distinct. We created CFA sub-models for each individual variable and thenaggregated a final, full model. All of the CFAmodels used full information maximum likeli-hood, with evaluation based on six indices (Huand Bentler, 1998). The final model sufficientlyfit the data (chi-square = 1521.642, 421 degreesof freedom, p < 0.001, chi-square/dof = 3.614,TLI = 0.972, CFI = 0.976, RMSEA = 0.057). Allparameter estimates were significant (p < 0.02),supporting convergent validity, and none of thecovariances were significantly close to one, sup-porting divergent validity (Bollen, 1989; Bagozzi,1994). Reliability estimates for the firm expertisevariables were all over 0.60, suggesting adequateconsistency among the items (Nunnally, 1967).The loadings from this final CFA model pro-vided item weighting factors to construct aggregatescores for each variable (Pedazhur and Schmelkin,1991); the subsequent analysis used these scores.Appendix A lists items for the explanatory andcontrol variables.

Firm component expertise

We constructed measures of firm expertise for allthree components, to assess the extent to whicha firm had substantial skills and capabilities forproducing the good and an understanding of theunderlying technology. Four items measured firmcomponent expertise and five measured suppliercomponent expertise. Some of these items weredrawn from prior work (Noordewier, John, andNevin, 1990; Walker and Weber, 1984) and oth-ers were original. This approach assumes that firmshave knowledge of supplier expertise, which is rea-sonable in a clearly defined, mature industry. Theseitems are perceptual and, while they could poten-tially be influenced by the strength of the supplierrelationship, prior work has indicated that theseeffects are independent (Parmigiani and Mitchell,2005).



Within-firm shared expertise

This variable represents a firm’s internal expertiseacross pairs of components. The measure was con-structed by multiplying the mean-differenced firmcomponent expertise scores for the two compo-nents. This interaction assumes the expertise of onecomponent can enhance the expertise of anotherdue to shared skills and other resources, makingthe aggregate expertise greater than the sum of itsparts.

Interfirm expertise

This variable reflects the aggregate expertise of afirm and its potential suppliers related to a compo-nent. The measure was constructed by multiplyingthe mean-differenced firm and supplier expertisevariables for each component. Using this type ofmeasure captures interfirm expertise based on dis-similar factors (e.g., a firm’s skilled labor versusa supplier’s unique equipment) and related factors(e.g., a firm’s experienced engineers and the sup-plier’s use of advanced design software).

Control variables

Several items addressed alternate explanations.In addition to supplier component expertise andwithin-supplier shared expertise for the pair ofcomponents, item-level control variables includedasset specificity, performance uncertainty, volumerequirements, and component homogeneity. Firmcontrol variables included scope economies forthe pair of components and firm size (the num-ber of employees). Table 1 provides details on theoperationalization of the explanatory and controlvariables, as well as descriptive statistics. Table 2provides descriptive statistics and correlations forthe expertise variables.

Methods

To test the hypotheses, we created multinomiallogit models for the pair of complementary com-ponents using the four categories (make/make:MM; buy/buy: BB; concurrent/concurrent: CC;and mixed mode: Mix). Our equation for thesemodels is as follows:

SMODEij = FEXi + FEXj + SUPEXi

+ SUPEXj + (FEXi)∗(FEXj)+

(SUPEXi)∗(SUPEXj) + (FEXi)

∗(SUPEXi)

+ (FEXj)∗(SUPEXj) + FSCOPEij + ASi+

ASj + PUi + PUj + VOLi + VOLj

+ ALLSAMEi + ALLSAMEj + EMPEES

In this model, SMODEij indicates the sourcingmode choice for the pair of components, whileFEXk and SUPEXk reflect firm and supplier com-ponent expertise. Within-firm shared expertise isrepresented by the mean-differenced interaction ofthe firm expertise variables for both components,while interfirm expertise is represented by themean-differenced interaction of the firm and sup-plier expertise variables for each component. Con-trol variables include asset specificity, performanceuncertainty, volume requirements, and homogene-ity for each component, as well as firm size andfirm scope economies for the pair of components.

Multinomial logit is appropriate because wehave independent choice categories (Maddala,1983). Altogether, the models provide six compar-isons involving the four modes (CC v. MM, CC v.BB, CC v. Mix, MM v. BB, MM v. Mix, BB v.Mix). Consistent with our conceptual intent, ourdiscussion compares concurrently sourcing bothcomponents with making both components (CC v.MM); Appendices B and C report all six compar-isons.

RESULTS

Tables 3a and 3b provide descriptive informationconcerning the firms’ boundary choices for com-plementary (die design and die construction) andindependent (die design and end-part machining)components. Higher figures along the diagonals ofthe tables indicate the use of common sourcingmodes for the pairs of components. Table 3a showsparticularly high figures on the diagonal (62% ofthe cases), indicating that firms often source com-plementary components in the same manner. Bycontrast, Table 3b shows that common sourcingmodes for independent components are less preva-lent (45% of the cases fall along the diagonal). Thedifference is statistically significant (p < 0.005).Thus, firms are more likely to use common sourc-ing modes for complementary components than forindependent components.

In turn, Tables 3a and 3b show that firms con-currently sourced complementary components



Table 1. Operationalization and descriptive statistics for explanatory and control variables (110 cases)

Variable name Operationalization Mean Standarddeviation

1 Firm expertise—die design 4 item, Likert scale 5.59 1.212 Firm expertise - die construction 4 item, Likert scale 4.99 1.533 Firm expertise - machining 4 item, Likert scale 5.41 1.144 Supplier expertise—die design 5 item, Likert scale 3.48 1.065 Supplier expertise—die construction 5 item, Likert scale 3.78 0.966 Supplier expertise—machining 5 item, Likert scale 3.43 0.947 Interfirm expertise—die design #1 ∗ #4 −0.59 1.498 Interfirm expertise - die construction #2 ∗ #5 −0.44 1.609 Interfirm expertise - machining #3 ∗ #6 −0.45 1.1710 Within-firm shared expertise - die design and

die construction#1 ∗ #2 0.48 2.13

11 Within-firm shared expertise - die design andmachining

#1∗ #3 0.13 1.26

12 Within-supplier shared expertise - die designand die construction

#4∗ #5 0.60 1.53

13 Within-supplier shared expertise - die designand machining

#4∗ #6 0.25 0.94

14 Firm scope economies die design and dieconstruction

Summed scores from two 2-itemLikert scales

8.46 2.52

15 Firm scope economies - die design andmachining

Summed scores from two 2-itemLikert scales

8.85 2.42

16 Asset specificity—die design 3 item, Likert scale 4.06 1.2817 Asset specificity—die construction 3 item, Likert scale 3.98 1.2618 Asset specificity—machining 3 item, Likert scale 3.81 1.1819 Performance uncertainty—die design 5 item, Likert scale 2.96 0.9320 Performance uncertainty—die construction 5 item, Likert scale 2.89 0.9721 Performance uncertainty—machining 5 item, Likert scale 2.85 0.8722 Volume requirements—die design Number of designs, 5 point scale

from 1 to 200+2.70 1.52

23 Volume requirements—die construction Number of dies, 5 point scale from 1to 200+

2.79 1.53

24 Volume requirements—machining Number of part numbers, 5 pointscale from 1 to 100+

2.70 1.53

25 Item homogeneity, die design 1 item, Likert scale 3.62 1.8727 Item homogeneity, die construction 1 item, Likert scale 3.65 1.7528 Item homogeneity, machining 1 item, Likert scale 3.48 1.8529 Firm size Number of employees (5 point scale) 2.56 1.11

Notes (1) To construct aggregate scores for variables using items with Likert scales, we used loadings from a CFA model to determineitem weights; scores can range from 1 to 7.(2) Die design = Design of progressive stamping (conventional molding) dies; die construction = construction of progressive stamping(conventional molding) dies; machining = machining of end parts.(3) To create the interfirm expertise and within-firm shared expertise variables, the firm (supplier) expertise variables were mean-differenced and then multiplied together.

(25/110 cases = 23%) more often than indepen-dent components (16/110 = 15%). The differenceis moderately significant (p = 0.06), and likelyarises because greater economies of scope basedon cost savings and technological interdependencecreate stronger incentives for common sourcing.

Table 4 tests the predictions. Models 1a to 1dexamine concurrent sourcing of complementarycomponents (die design and die construction).

Model 1a reports the control variables. Models 1band 1c add interfirm expertise then within-firm andwithin-supplier shared expertise separately. Model1d then includes the full set of interfirm, within-firm, and within-supplier shared expertise vari-ables. We will focus the discussion on Model 1d,because the results are consistent with the simplerpreceding models. Model 1d fits the data quitewell, with a McFadden’s pseudo r-squared value



Tabl

e2.

Des

crip

tive

stat

istic

san

dco

rrel

atio

nsfo

rex

pert

ise

vari

able

s(1

10ca

ses)

Mea

ns.

d.1

23

45

67

89

1011

1213

1Fi

rmex

pert

ise—

die

desi

gn5.

591.

211

2Fi

rmex

pert

ise

-di

eco

nstr

uctio

n4.

991.

530.

26∗

13

Firm

expe

rtis

e—m

achi

ning

5.41

1.14

0.09

0.16

14

Supp

lier

expe

rtis

e—di

ede

sign

3.48

1.06

−0.4

6∗−0

.04

0.00

15

Supp

lier

expe

rtis

e—di

eco

nstr

uctio

n3.

780.

96−0

.34∗

−0.3

0∗0.

090.

59∗

16

Supp

lier

expe

rtis

e—m

achi

ning

3.43

0.94

−0.0

7−0

.13

−0.4

3∗0.

26∗

0.31

∗1

7In

terfi

rmex

pert

ise—

die

desi

gn−0

.59

1.49

0.40

∗0.

19∗

−0.0

5−0

.38∗

−0.3

2∗−0

.07

18

Inte

rfirm

expe

rtis

e-

die

cons

truc

tion

−0.4

41.

600.

050.

28∗

0.03

0.17

0.02

−0.0

20.

141

9In

terfi

rmex

pert

ise—

mac

hini

ng−0

.45

1.17

−0.0

5−0

.13

0.24

∗−0

.01

0.06

−0.1

3−0

.06

0.00

110

With

in-fi

rmsh

ared

expe

rtis

e-

die

desi

gnan

ddi

eco

nstr

uctio

n0.

482.

13−0

.33∗

0.06

−0.1

10.

19∗

0.05

−0.0

2−0

.34∗

−0.3

0∗0.

091

11W

ithin

-firm

shar

edex

pert

ise

-di

ede

sign

and

mac

hini

ng0.

131.

26−0

.09

−0.1

40.

14−0

.07

−0.0

2−0

.06

−0.1

1−0

.02

0.21

∗0.

42∗

1

12W

ithin

-sup

plie

rsh

ared

expe

rtis

e-

die

desi

gnan

ddi

eco

nstr

uctio

n0.

601.

53−0

.25∗

0.13

0.19

∗0.

33∗

0.20

∗−0

.07

−0.4

5∗0.

29∗

0.04

0.09

−0.0

51

13W

ithin

-sup

plie

rsh

ared

expe

rtis

e-

die

desi

gnan

dm

achi

ning

0.25

0.94

−0.0

90.

13−0

.01

−0.0

3−0

.11

−0.0

6−0

.07

0.02

−0.2

3∗0.

030.

110.

35∗

1

∗p

<0.

05



Table 3a. Sourcing choices for die design and die construction (complementary components)

Make dies Buy dies Concurrently source dies

Make designs 25 17 9 51Buy designs 1 18 3 22Concurrently source designs 3 9 25 37

29 44 37 110

Table 3b. Sourcing choices for die design and end part machining (independent components)

Make machining Buy machining Concurrently source machining

Make designs 28 9 14 51Buy designs 6 5 11 22Concurrently source designs 18 3 16 37

52 17 41 110

Comparisons from Tables 3a and 3b(1) Firms were more likely to commonly source complementary components than independent components (68/110 = 62% in Table 3av. 49/110 = 45% in Table 3b, p < 0.005).(2) Firms were moderately more likely to concurrently source complementary components than independent components (25/110 inTable 3a=23% v. 16/110 in Table 3b=15%; p = 0.06).

of 0.48. This model also satisfies the Wald test thatindicates none of the categories can be combined(Long and Freese, 2001).

The results in Model 1d of Table 4 partly sup-port Hypothesis 1. Greater interfirm expertise fordie design contributes to concurrent sourcing (p <

.0.05). That is, when both a firm and its potentialsuppliers have substantial expertise relevant to diedesign in the set of complementary components,the firm is more likely to concurrently source ratherthan make both components in the complementaryset. By contrast, although interfirm expertise fordie construction also has a positive coefficient, theeffect is not statistically significant. The differencein the two types of components may suggest thatthe more complex technical knowledge that under-lies die design has a greater effect on sourcingchoices than the more procedural knowledge thatunderlies die construction.

In turn, the results in Model 1d strongly sup-port Hypothesis 2. As expected, greater within-firmshared expertise leads to more use of concurrentsourcing of the pair of complementary compo-nents, rather than producing the components inter-nally (p < 0.05). That is, the firm’s own skill baseappears to provide absorptive capacity that allowsit to manage the outsourcing relationships that arepart of a concurrent sourcing strategy.

For completeness, Appendix B provides the fullset of results for Model 1d, showing comparisonsbetween concurrent sourcing, making both goods,buying both goods, and mixed sourcing. Thesecomparisons provide further support for concurrentsourcing over mixed sourcing modes in the case ofwithin-firm shared expertise, and also suggest thatfirms will be less likely to make both goods if theyhave extensive interfirm die design expertise.

Model 2 of Table 4 reports the results for thepair of independent components, as a comparisonwith the complementary components in Model1d (simpler nested models for the independentcomponents produced equivalent results). Model 2shows that neither interfirm expertise nor within-firm shared expertise influence concurrent sourcingof the independent components. The null effectsare consistent with the logic of Hypotheses 1 and2, given that the independent components have fewshared scope economies.

Several control variables from Model 1d inTable 4 provide useful results concerning incen-tives to undertake concurrent sourcing of comple-mentary components. First, firm component exper-tise for die design in the complementary pair pro-motes vertical integration of the system of comple-mentary components. However, firm componentexpertise for die construction has no significant



Table 4. Multinomial logit models of joint sourcing decisions for complementary components (die design and dieconstruction) and independent components (die design and end part machining): concurrently source both components(CC) versus make both components (MM)(positive coefficient indicates that concurrent sourcing is more likely thanmaking both components)

1. Complementary components: CC v. MM 2. Independentcomponents:

1a 1b 1c 1d CC v. MM

Interfirm expertise—die design (H1: +) 0.87∗∗ 0.92∗∗ 0.62(0.39) (0.49) (0.40)

Interfirm expertise—die construction (H1: +) 0.08 0.54 −0.43(0.43) (0.66) (0.57)

Within-firm shared expertise (H2: + for 1.17∗∗ 1.26∗∗ −0.07complementary) (0.67) (0.64) (0.48)

Within-supplier shared expertise −0.17 0.12 0.05(0.37) (0.41) (0.47)

Firm expertise- die design −0.59 −0.78 −1.28∗∗ −1.54∗∗ −1.02∗∗

(0.55) (0.59) (0.70) (0.73) (0.57)Firm expertise - die construction 0.97∗∗ 1.23∗∗ 0.56 0.96 −0.01

(0.48) (0.54) (0.66) (0.70) (0.52)Supplier expertise- die design −0.20 −0.51 −0.06 −0.31 −0.32

(0.53) (0.59) (0.56) (0.64) (0.52)Supplier expertise- die construction 0.01 0.20 −0.04 −0.46 −0.36

(0.53) (0.77) (0.56) (1.02) (0.58)Firm scope economies −0.36∗∗ −0.40∗∗ −0.40∗∗ −0.43∗∗ #

(0.19) (0.20) (0.20) (0.21)Asset specificity- die design −0.17 −0.16 −0.09 −0.06 −0.37

(0.33) (0.34) (0.33) (0.36) (0.34)Asset specificity- die construction −0.49 −0.45 −0.49 −0.50 −0.21

(0.32) (0.33) (0.34) (0.35) (0.38)Performance uncertainty- die design −0.08 −0.11 −0.10 −0.12 0.37

(0.37) (0.39) (0.38) (0.39) (0.40)Performance uncertainty - die construction −0.82∗∗ −0.90∗∗ −1.02∗∗∗ −1.07∗∗∗ −0.09

(0.40) (0.43) (0.43) (0.45) (0.46)Volume requirements- die design −0.08 0.10 −0.10 0.11 0.22

(0.25) (0.28) (0.26) (0.29) (0.29)Volume requirements - die construction −0.20 −0.12 −0.18 −0.08 −1.01∗∗∗

(0.28) (0.30) (0.28) (0.30) (0.38)Item homogeneity - die design −0.09 −0.10 −0.08 −0.08 0.83∗∗

(0.21) (0.21) (0.21) (0.23) (0.39)Item homogeneity - die construction −0.37 −0.46∗∗ −0.30 −0.40 #

(0.24) (0.26) (0.25) (0.27)Firm size 0.91∗∗∗ 0.96∗∗∗ 0.81∗∗ 0.87∗∗ #

(0.37) (0.40) (0.40) (0.44)Log likelihood (d.f.) −83.8(42) −79.85(48) −80.4(48) −75.79(54) −81.5(45)Pseudo r-squared 0.43 0.46 0.45 0.48 0.32Cases 110 110 110 110 110

∗∗∗ p < 0.01; ∗∗ p < 0.05 (one-tailed tests); reported results omit estimates for constants.# Model 2 omits the firm scope economies and item homogeneity control variables in order to obtain convergence.Note: The table reports the focal comparison (CC v. MM); Appendices B and C report the full set of sourcing comparisons (CC v.Buy/Buy (BB), CC v. Mixed mode (Mix), MM v. BB, MM v. Mix, BB v. Mix).

impact once within-firm shared expertise across thepair of components is added to the analysis. Thus,when a firm is strong in die design expertise butnot in die construction expertise, it prefers to makeboth components, rather than attempt to manage

the more complicated process of concurrent sourc-ing.4 This conclusion reinforces the logic that

4 This conclusion emerges if one sets the value of the component-level die construction expertise variable to zero in Model 1d,



underlies Hypothesis 1, and again may arise fordie design rather than die construction because ofgreater technological complexity of design.

Second, firms tend to vertically integrate whenthey enjoy substantial internal economies of scope(the negative effect of ‘firm scope economies’ onconcurrent sourcing), which will allow them tocapture the cost savings. Third, performance uncer-tainty increases the incentive for vertical integra-tion (with a statistically significant impact for thedie construction variable), most likely because theuncertainty creates measurement problems. Fourth,larger firms are more likely to concurrently sourcecomplementary components, because they havemanagerial resources needed to manage this morecomplex sourcing mode. Finally, neither suppliercomponent expertise nor within-supplier sharedexpertise affects the decision to use concurrentsourcing rather than internal production. These nullresults may suggest that firms are more concernedabout their internal skills than suppliers’ skillswhen considering concurrent sourcing. Moreover,they may not want to depend upon strong andpotentially opportunistic suppliers.

We undertook several sensitivity analyses. Weadded the square of the asset specificity termsto check whether concurrent sourcing arose fromintermediate levels of specificity; the squared termswere not significant and our predicted results con-tinued to be significant. Our predicted resultsheld when we included measures of technologi-cal uncertainty for die design and die construction.We combined the two component-level measuresof interfirm expertise into a single interfirm exper-tise measure (essentially a four-way interaction)and added this variable to Model 1d in Table 4.The combined measure had a significantly posi-tive impact on CC v. MM, reflecting the impactof interfirm die design expertise, but did not sig-nificantly improve the explanatory power of themodel. Finally, we pooled the complementary andindependent sets within a single model. Our pre-dicted results continued to hold but the model’sexplanatory power was not as great as the twoseparate models, most likely because the sourcingdecisions for the two groups manifested differentfunctional forms.

which means that the within-firm shared expertise variable alsowill be zero, leaving only the negative effect of the non-zerocomponent-level die design expertise variable.

Overall, the results show that expertise rele-vant to complementary components, both withinfirms and between firms, strongly influences firms’boundary decisions. Strong interfirm expertise ofa company and its potential suppliers enables con-current sourcing of complementary components, atleast with respect to die design expertise. Perhapsmost strikingly, strong within-firm shared expertiseprovides the absorptive capacity that firms requireto manage concurrent sourcing relationships, ratherthan having to rely solely on internal production.

DISCUSSION AND CONCLUSION

This study investigates how firms source com-plementary components, adding to economic andcapability theories of the firm that typically con-sider only sourcing decisions of individual items.Our finding that firms often concurrently sourcecomplementary sets of components resolves a ten-sion between the traditional view that firms willvertically integrate to coordinate technical inter-faces and gain scope economies (Arrow, 1975;Panzar and Willig, 1981), with the emerging viewthat firms will outsource these components toobtain flexibility (Milgrom and Roberts, 1990;Sanchez and Mahoney, 1996; Brusoni et al., 2001).Concurrent sourcing helps solve the coordinationversus flexibility quandary because it permits bothactivities, but requires knowledgeable suppliersand a substantial degree of internal expertise aboutthe components.

Implications

The results provide insight into the distinctionbetween a firm’s production and knowledge bound-aries, as well as the need for overlap between theseboundaries. Because firms learn through internalproduction, they cannot fully outsource key activ-ities and hope to preserve their skills. However,they need not produce the entirety of their require-ments. The study offers a better understanding ofthe activity systems of firms such as Toyota Motorsand Southwest Airlines, which utilize concurrentsourcing for electronic components and aircraftmaintenance.

Knowledge is a key driver toward concurrentsourcing of complementary components. In par-ticular, interfirm expertise leads firms toward thissourcing mode because they can benefit from



the combination of their own and their suppliers’understanding of the components. Whether suppli-ers have highly specialized or somewhat redundantskills, firms benefit from the broad and diversetypes of knowledge they acquire through sup-ply relationships, because this learning augmentsknowledge that the firm obtains through internalproduction and provides the firm with a greaterdegree of absorptive capacity. Supply relationshipsalso open the firm to serendipitous learning arisingfrom the spillovers from related components anddifferent production methods of the firm and itssuppliers. Thus, firms that enjoy a high degree ofinterfirm expertise related to a set of complemen-tary components often will concurrently sourcerather than produce these goods only internally.

Perhaps the most intriguing result is that firmswith substantial expertise that spans the com-plementary components often concurrently sourcerather than integrate. This contrasts with traditionalboundaries arguments, which argue that skilledfirms should internalize production of related com-ponents to gain scope economies and better coor-dinate technical change. Instead, our results alignbetter with the more recent modularity and systemsintegration literatures, which posit that firms canoutsource effectively if they retain understandingof the technology (Brusoni et al., 2001). In par-allel, we suggest that firms can outsource someof their requirements if they have an underlyingexpertise in the base technology.

Hence, there may be a virtuous cycle in whichknowledge of how to produce enhances the firm’sability to partially outsource, thereby augmentingtheir knowledge base as they learn from their sup-pliers, which, in turn, enables them to producemore effectively. In this way, concurrent sourc-ing of complementary components can be a sta-ble, reinforcing sourcing strategy. This approachbridges the gap between the traditional and mod-ularity arguments by suggesting that firms oftendo not need to unilaterally make or buy a set ofcomponents, but can sometimes do both.

Our study also bridges the gap between the lev-els of analysis of the transaction cost and capa-bilities perspectives. Consider four levels of anal-ysis: the transaction level (e.g., buying one die),the good level (annual die purchases), the activ-ity level (designing and constructing dies whichtogether constitute die creation), and the systemlevel (die creation, pressing, heat treating, andinspection activities that together comprise a work

cell). The theoretical standpoint of transaction costlogic emphasizes the lowest level, although empir-ical studies of transaction costs tend to be at thelevel of the good due to data limitations (e.g., theinability to obtain sourcing decisions about oneparticular item at one given time, such as individ-ual shipment or purchase order line data). Look-ing specifically at concurrent sourcing, Parmigiani(2007) and He and Nickerson (2006) conduct theirwork at the level of the good. Studies that take acapabilities approach have tended to be at the moregeneral system level, such as Bradach’s (1997)study on franchisors, Harrigan’s (1984) work onstrategic business units, and Rothaermel et al.’s(2006) study of computer firms. Our work connectsthe transaction/good levels of analysis with the sys-tem level by exploring complementary goods thatmake up a related set of activities.

Two managerial implications emerge from thisstudy. First, when considering sourcing optionsfor complementary components, firms neither needto solely vertically integrate nor completely out-source. Instead, they can consider doing both inorder to take advantage of the increased learn-ing and flexibility from concurrent sourcing. Whiletypically not relevant for sets of independentcomponents, concurrent sourcing may be espe-cially valuable for complementary components,because it enables the firm to better understandthe scope economies and technological interrela-tionships between the components, and withstanddifferent levels of change and uncertainty. Second,managers, however, should keep in mind that firmsneed to have a thorough understanding of the basetechnologies for the complementary componentsin order to undertake concurrent sourcing. If theylack this breadth of experience (perhaps knowing alot about one component but little about the other),they may be better off producing both internally,until the firm gains more experience.

Is concurrent sourcing relevant in practice?

During the course of the study, one question thatarose was whether concurrent sourcing is suffi-ciently common in practice to merit extensivestudy. We have cited multiple academic studiesof concurrent sourcing dating back to the 1940s,which have included both mature and develop-ing technologies in manufacturing and serviceindustries. Nonetheless, it is possible that suchacademic attention simply focuses on uncommon



events. Therefore, we took advantage of a class-room opportunity to determine whether experi-enced managers recognized the concept of concur-rent sourcing and whether it was relevant for theirfirms.

We asked a class of senior executive MBAstudents—that one of the authors was teach-ing—whether their firms engaged in concurrentsourcing. We received 25 examples of concurrentsourcing (from about half of the students partic-ipating in the discussion), including cases of ITand software development, R&D, production (con-sumer goods, metal production, chemicals, elec-tronic components, and computers), engineeringshops, financial services, insurance, and hospitallab testing. The most common reason that the stu-dents cited for use of concurrent sourcing wasdealing with demand uncertainty (14/25 cases),followed by gaining access to suppliers’ skills (10cases). Other reasons included taking advantage ofsupplier cost advantages, responding to customerrequirements, and transitioning to fully internal orexternal sourcing. We believe that the responsesdemonstrate that practicing managers readily rec-ognize the concept of concurrent sourcing and thatconcurrent sourcing is common in their experi-ence.

Given its prevalence in practice, why has aca-demic research on firm boundaries research paidrelatively little attention to concurrent sourcing?We believe that a core reason for the lack of studyis that theoretical concepts often create empir-ical blinders. Such blinders have been particu-larly common in the dominant recent paradigmfor firm boundaries studies, transaction cost eco-nomics, with its emphasis on the dichotomouschoice between markets and hierarchies, leavenedby use of alliances as an intermediate governancemode. Hence, studies sometimes neglect to ask iffirms use modes other than make or buy, code datainto just two categories, or exclude data that do notfit the categories.

Thus, theory is a powerful two-edged sword.One edge pares away detail in order to allow schol-ars to focus on key phenomena. At the same time,however, the other edge of the sword risks chop-ping up important phenomena, thereby distortingthe realities of what firms actually do. Hence, oneof our jobs as scholars is to create new types ofswords that allow us to do battle with understudiedconcepts and facts.

Limitations and further research

This study has limitations in context and methodthat suggest further research. These findings mightbe most common for small manufacturing firmsand fairly low levels of technological change; itwould be informative to investigate joint sourc-ing decisions among larger firms in more volatileenvironments. We did not include buyer/supplieralliances or other more complicated forms oforganizing. It would be interesting to understandhow sourcing of complementary components fitsinto these relationships. This study used a cross-sectional survey method and, as such, does notconsider the dynamics of joint sourcing deci-sions; further study could investigate how sourc-ing modes influence the development of firmexpertise.

Other extensions would be valuable. Additionalwork could explore possible differential effects ofcost-based and technologically based elements ofscope economies. In addition, due to our emphasison firm production boundary decisions, we focuson complementarities in production, rather thancomplementarities that might arise in the use ofcomponents.5

Both types of complementarities involve reduc-ing costs through the grouping of related products,but reduce costs for different parties. Combinationsof products that are complementary for users maynot result in production economies for manufactur-ers, and may actually raise production costs. Fur-ther research could explore implications for firmboundaries that might arise from these differentcomplementarities. For example, complementari-ties to users that do not offer scope economies toproducers might lead toward more alliances, whileproduction complementarities might lead to moremergers.

Understanding more specifically how firms con-currently source, such as whether they use sin-gle or multiple suppliers and what proportionof their requirements they choose to outsource,will also advance work in this area. It would beinformative to consider more complicated systems

5 Some product features may have both user and producercomplementarities, while others may favor one or the other.For example, the initial push to add electronic systems intoautomotive dashboards had greater benefits for users than forproducers. Once these are incorporated, however, adding anotherelectronic feature (e.g., an MP3 connector) is relatively easy andhas benefits to both users and producers. We are grateful to areviewer for suggesting the implications of this distinction.



of components, with a greater number of com-ponents and more complex interactions betweenthem. Scholars could also investigate performanceimplications of the sourcing strategy chosen forthese components, perhaps contrasting individualcomponent performance versus that of the systemusing multistage modeling techniques. Longitudi-nal studies would also be useful in determiningwhether firms continue to source subsequent gener-ations or particular families of products in the samemanner over time, and how the associated inter-firm relationships evolve. It also would be usefulto better understand the supplier’s perspective inparticipating with the firm in sourcing these typesof components. We look forward to studies thatexplore how firms source the myriad of productsthey require, how they manage the associated rela-tionships, and how the knowledge and productionboundaries of the firms evolve.

ACKNOWLEDGEMENTS

We appreciate financial support from the Institutefor Supply Management, the American Produc-tion and Inventory Control Society, the Universityof Michigan Business School, and the LundquistCollege of Business. Thanks to the seminar par-ticipants at the West Coast Research Symposiumand attendees at our Academy of Management ses-sion. We also appreciate the efforts of the editorand reviewers in helping us develop this work. Anyremaining errors are our own.

REFERENCES

Adelman MA. 1949. The large firm and its suppliers.Review of Economics and Statistics 31: 113–118.

Agresti A. 1996. An Introduction to Categorical DataAnalysis . Wiley: New York.

Anderson E. 1985. The salesperson as outside agentor employee: a transaction cost analysis. MarketingScience 4: 235–254.

Anderson E, Schmittlein DC. 1984. Integration of thesales force: an empirical examination. RAND Journalof Economics 15: 385–395.

Anderson JC, Gerbing DW. 1988. Structural equationmodeling in practice: a review and recommended two-step approach. Psychological Bulletin 103: 411–423.

Anderson SW, Glenn D, Sedatole KL. 2000. Sourcingof complex products: evidence on transaction costs,high powered incentives, and ex-post opportunism.Accounting, Organizations, and Society 25: 723–749.

Argyres N. 1996. Evidence on the role of firmcapabilities in vertical integration decisions. StrategicManagement Journal 17(2): 129–150.

Armour HO, Teece DJ. 1978. Vertical integration andtechnological innovation. Review of Economics andStatistics 470–474.

Arrow KJ. 1975. Vertical integration and communication.Bell Journal of Economics 6: 173–183.

Bagozzi RP. 1994. Principles of Marketing Research .Blackwell Publishers: Oxford, UK.

Baldwin CY, Clark KB. 2000. Design Rules: The Powerof Modularity . MIT Press: Cambridge, MA.

Barney JB. 1986. Strategic factor markets: expectations,luck, and business strategy. Management Science 32:1231–1241.

Barney JB. 1991. Firm resources and sustained com-petitive advantage. Journal of Management 17(1):99–120.

Blair RD, Lafontaine F. 2005. The Economics ofFranchising . Cambridge University Press: New York.

Bollen KA. 1989. Structural Equations with LatentVariables . Wiley: New York.

Bottum P. 1992. Technology management through theselection of alternative sourcing strategies. PhD diss,University of Minnesota: Minneapolis, MN.

Bradach JL. 1997. Using the plural form in themanagement of restaurant chains. AdministrativeScience Quarterly 42: 276–303.

Bradach JL, Eccles RG. 1989. Price, authority, and trust:from ideal types to plural forms. Annual Review ofSociology 15: 97–118.

Brusoni S, Prencipe A. 2001. Unpacking the black box ofmodularity: technologies, products, and organizations.Industrial and Corporate Change 10: 179–205.

Brusoni S, Prencipe A, Pavitt K. 2001. Knowledgespecialization, organizational coupling, and theboundaries of the firm: why do firms know morethan they make? Administrative Science Quarterly 46:597–621.

Campbell A, Goold M, Alexander M. 1995. Corporatestrategy: the quest for parenting advantage. HarvardBusiness Review 73: 120–132.

Cannon JT. 1968. Business Strategy and Policy . Har-court, Brace, & World: New York.

Cassiman B, Veugelers R. 2006. In search of comple-mentarity in innovation strategy: internal R&D andexternal knowledge acquisition. Management Science52: 68–82.

Chandler A. 1962. Strategy and Structure: Chapters inthe History of the Industrial Enterprise. MIT Press:Cambridge, MA.

Clark N. 2007. Boeing delays deliveries of 787. New YorkTimes 10 October. Available at: http://www.nytimes.com/2007/10/10/business/10cnd-boeing.html.

Coase RH. 1937. The nature of the firm. In Organiza-tional Economics: Toward a New Paradigm for Under-standing and Studying Organizations , Barney JB,Ouchi WG (eds). Jossey-Bass: San Francisco, CA.

Cohen WM, Levinthal DA. 1990. Absorptive capacity:a new perspective on learning and innovation.Administrative Science Quarterly 35: 128–152.



Combs JG, Michael SC, Castrogiovanni GJ. 2004. Fran-chising: a review and avenues to greater theoreticaldiversity. Journal of Management 30: 907–931.

Conner KR. 1991. A historical comparison of resource-based theory and five schools of thought withinindustrial organizational economics: do we have anew theory of the firm? Journal of Management 17:121–154.

Conner KR, Prahalad CK. 1996. A resource-basedtheory of the firm: knowledge versus opportunism.Organization Science 7: 477–501.

Cyert RM, March JG. 1963. A Behavioral Theory of theFirm . Prentice Hall: Englewood Cliffs, NJ.

Danneels E. 2007. The process of technological com-petence leveraging. Strategic Management Journal28(5): 511–533.

Davis-Blake A Uzzi B. 1993. Determinants of employ-ment externalization: a study of temporary workersand independent contractors. Administrative ScienceQuarterly 38: 195–223.

Dosi G. 1988. Sources, procedures, and microeconomiceffects of innovation. Journal of Economic Literature20: 1120–1171.

Dosi G, Hobday M, Marengo L, Prencipe A. 2003.The economics of systems integration: towards anevolutionary interpretation. In The Business of SystemsIntegration , Prencipe A, Davies A, Hobday M (eds).Oxford University Press: New York; 78–91.

Dutta S, Bergen M, Heide JB, John G. 1995. Under-standing dual distribution: the case of reps and houseaccounts. Journal of Law, Economics, & Organization11: 189–204.

Dyer JH, Singh H. 1998. The relational view: cooperativestrategy and sources of interorganizational competitiveadvantage. Academy of Management Review 23:660–679.

Fowler FJ. 1995. Improving Survey Questions: Designand Evaluation . SAGE Publications: Thousand Oaks,CA.

Glick WH, Huber GP, Miller CC, Doty DH, Sut-cliffe KM. 1990. Studying changes in organizationaldesign and effectiveness: retrospective event histo-ries and periodic assessments. Organization Science1: 293–313.

Grant RM. 1996. Toward a knowledge-based theoryof the firm. Strategic Management Journal , WinterSpecial Issue 17: 109–122.

Gulati R, Puranam P. 2006. Complementarity and con-straints: why firms both make and buy the samething. Working paper. Northwestern University Gradu-ate School of Management, Evanston, IL. Available at:C:\Users\bby327\AppData\Local\Microsoft\Windows\Temporary Internet Files\Low\Content.IE5\6DUV5JV5\Ranjay Gulati, Phanish Puranam, [1].pdf.

Harrigan KR. 1984. Formulating vertical integrationstrategies. Academy of Management Review 9(4):638–652.

Harrigan KR. 1986. Matching vertical integration strate-gies to competitive conditions. Strategic ManagementJournal 7(6): 535–555.

Harris RG, Wiens EG. 1980. Government enterprise: aninstrument for the internal regulation of industry.Canadian Journal of Economics 13: 125–132.

He D, Nickerson JA. 2006. Why do firms make and buy?Efficiency, appropriability, and competition in thetrucking industry. Strategic Organization 4: 43–69.

Heide JB. 2003. Plural governance in industrial purchas-ing. Journal of Marketing 67: 18–29.

Helfat CE, Eisenhardt KM. 2004. Inter-temporal econo-mies of scope, organizational modularity, and thedynamics of diversification. Strategic ManagementJournal 25(13): 1217–1232.

Helfat CE, Raubitschek RS. 2000. Product sequencing:co-evolution of knowledge, capabilities, and products.Strategic Management Journal , October–NovemberSpecial Issue 21: 961–979.

Hennart J-F. 1993. Explaining the swollen middle:why most transactions are a mix of ‘market’ and‘hierarchy.’ Organization Science 4(4): 529–547.

Hoetker G. 2006. Do modular products lead to modularorganizations? Strategic Management Journal 27(6):501–518.

Hu LT, Bentler PM. 1998. Fit indices in covariancestructure modeling: sensitivity to underparameterizedmodel misspecification. Psychological Methods 3:424–453.

Jacobides MG, Hitt LM. 2005. Losing sight of theforest for the trees? Productive capabilities and gainsfrom trade as drivers of vertical scope. StrategicManagement Journal 26(13): 1209–1227.

Kogut B, Zander U. 1992. Knowledge of the firm,combinative capabilities, and the replication oftechnology. Organization Science 3: 383–397.

Lafontaine F, Shaw KL. 1999. The dynamics of franchisecontracting: evidence from panel data. Journal ofPolitical Economy 107: 1041–1080.

Lavie D. 2006. The competitive advantage of intercon-nected firms: an extension of the resource-based view.Academy of Management Review 31: 638–658.

Lepak DP, Snell SA. 1999. The human resourcearchitecture: toward a theory of human capitalallocation and development. Academy of ManagementJournal 24: 31–48.

Li H, Atuahene-Gima K. 2002. The adoption ofagency business activity, product innovation, andperformance in Chinese technology ventures. StrategicManagement Journal 23(6): 469–490.

Lincoln JR, Ahmadjian CL, Mason E. 1998. Organiza-tional learning and purchase-supply relations in Japan.California Management Review 40: 241–264.

Long JS, Freese J. 2001. Regression Models for Cate-gorical Dependent Variables Using Stata . Stata Press:College Station, TX.

Maddala GS. 1983. Limited-dependent and QualitativeVariables in Econometrics . Cambridge UniversityPress: New York.

Mayer KJ, Salomon RM. 2006. Capabilities, contractualhazards, and governance: integrating resource-basedand transaction cost perspectives. Academy ofManagement Journal 49: 942–959.



Milgrom P, Roberts J. 1990. The economics of modernmanufacturing: technology, strategy, and organization.American Economic Review 80: 511–528.

Milgrom P, Roberts J. 1995. Complementarities andfit: strategy, structure, and organizational change inmanufacturing. Journal of Accounting and Economics19: 179–208.

Nelson RR, Winter SG. 1982. An Evolutionary Theoryof Economic Change. Harvard University Press:Cambridge, MA.

Noordewier TG, John G, Nevin JR. 1990. Performanceoutcomes of purchasing arrangements in industrialbuyer-vendor relationships. Journal of Marketing 54:80–93.

Novak S, Stern S. 2009. Complementarity among verticalintegration decisions: evidence from automobileproduct development. Management Science 55(2):311–332.

Nunnally JC. 1967. Psychometric Theory . McGraw-Hill:New York.

Oster SM. 1994. Modern Competitive Analysis . OxfordUniversity Press: New York.

Panzar JC, Willig RD. 1981. Economies of scope.American Economic Review 71: 268–272.

Parkhe A. 1993. Strategic alliance structuring: a gametheoretic and transaction cost examination of interfirmcooperation. Academy of Management Journal 36:794–829.

Parmigiani A. 2007. Why do firms both make and buy?An investigation of concurrent sourcing. StrategicManagement Journal 28(3): 285–311.

Parmigiani A, Mitchell W. 2005. How buyers shapesupplier performance: can governance skills substitutefor technical expertise in managing outsourcingrelationships? Academy of Management Best PaperProceedings.

Pavitt K. 2003. Specialization and systems integration:where manufacture and services still meet. InThe Business of Systems Integration , Prencipe A,Davies A, Hobday M (eds). Oxford University Press:New York; 78–91.

Paxson MC, Dillman DA, Tarnai J. 1995. Improvingresponse to business mail surveys. In Business SurveyMethods , Cox BG, Binder BA, Chinnappa BN, Chris-tianson A, Colledge MJ, Kott PS (eds). Wiley: NewYork: 303–316.

Pedhazur EJ, Schmelkin LP. 1991. Measurement, Design,and Analysis . Lawrence Erlbaum Associates: Hills-dale, NJ.

Penrose ET. 1959. The Theory of the Growth of the Firm .Wiley: New York.

Petersen KJ, Handfield RB, Ragatz GL. 2005. Supplierintegration into new product development: coordinat-ing product, process and supply chain design. Journalof Operations Management 23: 371–388.

Phillips LW. 1981. Assessing measurement error inkey informant reports: a methodological note onorganizational analysis in marketing. Journal ofMarketing Research 18: 395–415.

Pisano G. 1994. Knowledge, integration, and the locusof learning: an empirical analysis of process

development. Strategic Management Journal , WinterSpecial Issue 15: 85–100.

Podsakoff PM, Organ DW. 1986. Self-reports in organi-zational research: problems and prospects. Journal ofManagement 12: 531–544.

Poli C, Dastidar P, Mahajan P, Graves RJ. 1993. Designfor stamping: analysis of part attributes that impactdie construction costs for metal stampings. Journal ofMechanical Design 115: 735–743.

Poppo L, Zenger T. 1998. Testing alternative theories ofthe firm: transaction cost, knowledge-based, and mea-surement explanations for make-or-buy decisions ininformation services. Strategic Management Journal19(9): 853–877.

Porter ME. 1980. Competitive Strategy . Free Press: NewYork.

Porter ME. 1996. What is strategy? Harvard BusinessReview 74: 61–78.

Porter ME, Siggelkow N. 2008. Contextuality withinactivity systems and the sustainability of competitiveadvantage. Academy of Management Perspectives 22:34–56.

Prencipe A, Davies A, Hobday M. 2003. The Businessof Systems Integration . Oxford University Press: NewYork.

Richardson GB. 1972. The organisation of industry.Economic Journal 82(327): 883–896.

Rothaermel FT, Hess A. 2006. Innovating within a newtechnological paradigm: the role of individual, firm,and network-level effects in building capabilities.Academy of Management Best Paper Proceedings,C1–C6.

Rothaermel FT, Hitt MA, Jobe LA. 2006. Balancingvertical integration and strategic outsourcing: effectson product portfolio, product success, and firmperformance. Strategic Management Journal 27(11):1033–1056.

Rubin PH. 1973. The expansion of firms. Journal ofPolitical Economy 71: 936–949.

Rumelt RP. 1982. Diversification strategy and profitabil-ity. Strategic Management Journal 3(4): 359–369.

Sa Vinhas A. 2002. Dual distribution channels inbusiness-to-business marketing: a transaction interde-pendencies view. PhD diss., INSEAD: Fountainbleu,France.

Sako M. 2003. Modularity and outsourcing: the nature ofco-evolution of product architecture and organizationarchitecture in the global automotive industry. InThe Business of Systems Integration , Prencipe A,Davies A, Hobday M (eds). Oxford University Press:New York; 229–253.

Salancik GR, Pfeffer J. 1977. An examination of need-satisfaction models of job attitudes. AdministrativeScience Quarterly 22: 427–456.

Sanchez R, Mahoney JT. 1996. Modularity, flexibility,and knowledge management in product and organi-zation design. Strategic Management Journal , WinterSpecial Issue 17: 63–76.

Santos FM, Eisenhardt KM. 2005. Organizational bound-aries and theories of organization. Organization Sci-ence 16: 491–508.



Scherer FM, Ross D. 1990. Industrial Market Structureand Economic Performance. Houghton Mifflin:Boston, MA.

Siggelkow N. 2001. Change in the presence of fit: therise, the fall, and the renaissance of Liz Claiborne.Academy of Management Journal 44: 838–857.

Stump RL, Heide JB. 1996. Controlling supplier oppor-tunism in industrial relationships. Journal of Market-ing Research 33: 431–441.

Takeishi A. 2002. Knowledge partitioning in the interfirmdivision of labor: the case of automotive productdevelopment. Organization Science 13: 321–338.

Teece DJ. 1982. Towards an economic theory of themultiproduct firm. Journal of Economic Behavior &Organization 39: 39–63.

Teece DJ. 1986. Profiting from technological innovations:implications for integration, collaboration, licensing,and public policy. Research Policy 15: 283–303.

Thompson JD. 1967. Organizations in Action . McGrawHill: New York.

Ulrich K, Ellison D. 2005. Beyond make-buy: inter-nalization and integration of design and produc-tion. Production and Operations Management 14:315–330.

von Hippel E. 1988. The Sources of Innovation . OxfordUniversity Press: New York.

Walker G, Weber D. 1984. A transaction cost approachto make-or-buy decisions. Administrative ScienceQuarterly 29: 373–391.

Walsh RA. 1994. McGraw-Hill Machining and Metal-working Handbook . McGraw-Hill: New York.

Wernerfelt B. 1984. A resource-based view of the firm.Strategic Management Journal 5(2): 171–180.

Williamson OE. 1975. Markets and Hierarchies: Analysisand Antitrust Implications . Free Press: New York.

Williamson OE. 1985. The Economic Institutions ofCapitalism . Free Press: New York.

Womack JP, Jones DT, Roos D. 1990. The Machine thatChanged the World . Harper Perennial: New York.



APPENDIX A: SURVEY ITEMS

Dependent variable

For the past fiscal year, which best describes howyou source progressive stamping dies?

� All done internally (either within your plant orfrom a division with which your firm shares acommon corporate parent)

� All purchased from external suppliers� Both done internally and purchased from exter-

nal suppliers(i.) If you marked this response, what % of your

requirements did you produce internally (pleasemark one)?

� 0–10% � 26–49% � 75–90%� 11–25% � 50–74% � Over 90%

� Don’t use this input.

Explanatory variables

Each item included a response scale of 1 to 7,indicating totally true to totally untrue. Items wereedited to reflect each different good (e.g., ‘dies’was replaced with ‘end part machining’). Thisresulted in three similar four-page sections, onesection for each good. (Items adapted from priorwork are designated by citations; other items areoriginal.)

Firm expertise

1. Our manufacturing staff can/could easily pro-duce dies.

2. Making dies requires a deep expertise that ourfirm understands (Walker and Weber, 1984).

3. We have internally produced dies for years.4. The skills used to make dies are closely related

to those that we use to make other similarproducts.

Supplier expertise

1. The leading die suppliers have proprietaryknowledge that gives them an advantage overother firms (Walker and Weber, 1984).

2. We rely on our suppliers to help us keep upwith die technology (Stump and Heide, 1996).

3. There is very little difference between the pro-cess we would use to make dies and that usedby a supplier (reversed).

4. As compared to suppliers, our internal produc-tion of dies is/would be higher in price (Ander-son, 1985; reversed).

5. As compared to our suppliers, our internal pro-duction of dies is/would be lower in quality(Anderson, 1985; reversed).

Firm scope economies

1. By making our own dies, we do/could reduceour overall production costs of other products.

2. We do/could better utilize our labor and equip-ment by making dies in addition to our otherproducts.

Asset specificity

1. The skills needed to create dies are generic andwidely available (reversed).

2. Numerous capable die suppliers exist in themarket (Walker and Weber, 1984; reversed).

3. Switching die suppliers would be quick andeasy to do (Poppo and Zenger, 1998; reversed).

Performance uncertainty

1. We can easily describe dies to our suppliersthrough printed/electronic descriptions and/ordrawings (reversed).

2. Through a simple inspection, we can predicthow well the die will function in our down-stream production processes (Bottum, 1992;reversed)

3. We use several forms of inspection and severaldifferent metrics to evaluate die quality (Ander-son, Glenn, and Sedatole, 2000).

4. When there is a problem with a die, we usuallycan determine its cause (reversed).

5. It is difficult to equitably measure one supplier’sdie versus another supplier’s (Anderson andSchmittlein, 1984).

Volume requirements

1. Approximately how many progressive stampingdies did you require in the most recent fiscalyear?

� 1–25 � 51–100 � Over 200� 26–50 � 101–200 � I don’t know.



Item homogeneity

1. The dies we require are basically all the same.

FirmsSize

1. How many employees work for your firm?

� 1–49 � 100–249 � 500or more� 50–99 � 250–499 � I don’t know.

Appendix B: Multinomial logit model of joint sourcing decisions for complementary components (a positive coefficientindicates that the first choice is more likely than the second combination)

1. CC v.MM

2. CC v.BB

3. CC v.Mix

4. MM v.BB

5. MM v.Mix

6. BB v.Mix

Interfirm expertise—die design 0.92∗∗ −1.01 −0.02 −1.92∗∗ −0.94∗∗ 0.99(0.49) (0.91) (0.48) (0.94) (0.50) (0.89)

Interfirm expertise—die construction 0.54 0.47 0.55 −0.07 0.01 0.08(0.66) (0.84) (0.68) (0.66) (0.43) (0.53)

Within-firm shared expertise 1.26∗∗ 0.38 0.94∗∗ −0.88 −0.32 0.56(0.64) (0.70) (0.55) (0.72) (0.53) (0.55)

Within-supplier shared expertise 0.12 −0.41 −0.33 −0.53 −0.45 0.08(0.41) (0.59) (0.37) (0.60) (0.39) (0.50)

Firm expertise - die design −1.54∗∗ 1.93∗∗ −0.98∗∗ 3.47∗∗∗ 0.56 −2.91∗∗∗

(0.73) (1.01) (0.59) (1.09) (0.56) (1.01)Firm expertise - die construction 0.96 3.13∗∗∗ 2.42∗∗∗ 2.17∗∗∗ 1.46∗∗∗ −0.72

(0.70) (0.97) (0.66) (0.87) (0.56) (0.69)Supplier expertise - die design −0.31 −1.33 −1.20∗∗ −1.02 −0.89 0.13

(0.64) (1.25) (0.65) (1.22) (0.61) (1.12)Supplier expertise - die construction −0.46 −0.09 0.28 0.37 0.74 0.37

(1.02) (1.13) (0.94) (0.91) (0.63) (0.75)Firm scope economies −0.43∗∗ −0.76∗∗ −0.13 −0.32 0.30 0.63∗∗

(0.21) (0.35) (0.20) (0.35) (0.19) (0.32)Asset specificity - die design −0.06 0.27 −0.08 0.33 −0.02 −0.35

(0.36) (0.55) (0.36) (0.55) (0.33) (0.49)Asset specificity - die construction −0.50 −0.66 −0.16 −0.17 0.33 0.50

(0.35) (0.52) (0.36) (0.53) (0.36) (0.48)Performance uncertainty - die design −0.12 0.44 −0.15 0.55 −0.04 −0.59

(0.39) (0.74) (0.40) (0.73) (0.36) (0.69)Performance uncertainty - die construction −1.07∗∗∗ −0.25 −0.62 0.81 0.45 −0.36

(0.45) (0.62) (0.46) (0.63) (0.43) (0.56)Volume requirements- die design 0.11 −0.21 0.43 −0.32 0.32 0.64∗∗

(0.29) (0.41) (0.30) (0.41) (0.28) (0.36)Volume requirements - die construction −0.08 −0.59 −0.61∗∗ −0.52 −0.53∗∗ −0.01

(0.30) (0.46) (0.31) (0.48) (0.29) (0.45)Item homogeneity - die design −0.08 0.15 −0.22 0.23 −0.14 −0.37

(0.23) (0.37) (0.22) (0.37) (0.22) (0.33)Item homogeneity - die construction −0.40 −0.38 0.19 0.01 0.59∗∗∗ 0.58∗∗

(0.27) (0.34) (0.26) (0.35) (0.24) (0.31)Firm size 0.87∗∗ 1.26∗∗ 0.25 0.39 −0.62 −1.01∗∗

(0.44) (0.62) (0.39) (0.64) (0.41) (0.57)

∗∗∗ p < 0.01; ∗∗ p < 0.05 (one-tailed tests); results omit estimates for constantsNotes1. CC=Concurrently source both components; MM=Make both components; BB=Buy both components; Mix= different sourcingmode for each component.2. Column 1 is Model 1d from Table 4.Discussion: Concurrent sourcing was more likely for firms with considerable within-firm shared expertise and for larger firms. Makingboth components was unlikely if firms had high interfirm die design expertise or low levels of component expertise. Buying bothcomponents was unlikely if firms had component expertise.



Appendix C: Multinomial logit model of joint sourcing decisions for independent components (a positive coefficientindicates that the first choice is more likely than the second combination)

1. CC v.MM

2. CC v.BB

3. CC v.Mix

4. MM v.BB

5. MM v.Mix

6. BB v.Mix

Interfirm expertise—die design 0.62 −4.10 −0.12 −4.72 −0.73∗∗∗ 3.98(0.40) (3.53) (0.35) (3.53) (0.30) (3.51)

Interfirm expertise - end part machining −0.43 −3.23 0.14 −2.81 0.57 3.37(0.57) (3.25) (0.48) (3.24) (0.45) (3.23)

Within-firm shared expertise −0.07 1.67 0.06 1.74 0.13 −1.61(0.48) (2.15) (0.41) (2.13) (0.33) (2.11)

Within-supplier shared expertise 0.05 2.19 0.23 2.13 0.17 −1.96(0.47) (2.55) (0.42) (2.53) (0.35) (2.52)

Firm expertise - die design −1.02∗∗ 6.89 0.40 7.90 1.42∗∗∗ −6.48(0.57) (6.61) (0.44) (6.61) (0.46) (6.60)

Firm expertise - end part machining −0.01 6.62 0.48 6.63 0.49 −6.15(0.52) (5.84) (0.44) (5.84) (0.38) (5.84)

Supplier expertise- die design −0.32 −9.08 −0.43 −8.76 −0.12 8.65(0.52) (7.72) (0.48) (7.71) (0.37) (7.70)

Supplier expertise—end part machining −0.36 −5.99 −1.04∗∗ −5.63 −0.68 4.95(0.58) (5.69) (0.52) (5.69) (0.46) (5.66)

Asset specificity - die design −0.37 −1.27 −0.21 −0.90 0.16 1.06(0.34) (2.01) (0.31) (2.00) (0.24) (1.99)

Asset specificity - end part machining −0.21 −0.15 −0.28 0.07 −0.07 −0.14(0.38) (1.06) (0.35) (1.04) (0.29) (1.00)

Performance uncertainty - die design 0.37 −2.24 0.34 −2.61 −0.03 2.57(0.40) (2.98) (0.37) (2.97) (0.31) (2.96)

Performance uncertainty - end part machining −0.09 −0.03 0.04 0.06 0.12 0.06(0.46) (1.29) (0.44) (1.26) (0.35) (1.22)

Volume requirements - die design 0.22 −0.12 0.20 −0.34 −0.02 0.32(0.29) (0.77) (0.26) (0.76) (0.22) (0.74)

Volume requirements - end part machining −1.01∗∗∗ −0.99 −1.11∗∗∗ 0.01 −0.11 −0.12(0.38) (0.75) (0.38) (0.68) (0.20) (0.66)

Firm size 0.83∗∗ −0.41 0.61∗∗ −1.24 −0.22 1.02(0.39) (1.42) (0.36) (1.41) (0.29) (1.39)

∗∗∗ p < 0.01; ∗∗ p < 0.05 (one-tailed tests); results omit estimates for constantsNotes1. CC=Concurrently source both components; MM=Make both components; BB=Buy both components; Mix= different sourcingmode for each component.2. Column 1 is Model 2 from Table 4.Discussion: Overall, and consistent with the logic of our arguments, expertise had little impact on the joint sourcing choice ofindependent components.


complementarity, capabilities, and the …willm/bio/cv/papers/smj2009... · ate opportunities for...

Documents