Beyond the “Mirroring” Hypothesis: Product Modularity and Interorganizational Relations in the...

OrganizationScienceArticles in Advance, pp. 1–18issn 1047-7039 �eissn 1526-5455 doi 10.1287/orsc.1110.0655

© 2011 INFORMS

Beyond the “Mirroring” Hypothesis: Product Modularityand Interorganizational Relations in the

Air Conditioning Industry

Anna CabigiosuDepartment of Economics, University of Padova, 35123 Padova, Italy, [email protected]

Arnaldo CamuffoDepartment of Management and Technology and Center for Research on Organization and Management, Bocconi University,

20135 Milan, Italy, [email protected]

This study explores whether, to what extent, and under which conditions modular products are associated with modularorganizations (the “mirroring” hypothesis). We analyze the product and organizational architectures of three firms in

the air conditioning industry through an original data set of 100 components and supply relationships. Applying a varietyof regression methods, we show that, under the condition of product architecture stability at the component level, supplierrelations for loosely coupled components are characterized by less information sharing, which implies that the degree ofcoupling of product components varies directly with the degree of coupling of organizations (the “mirroring” hypothesis).Also, the performance of supply relationships depends on the amount of buyer–supplier information sharing but not on thedegree of component modularity, which supports the relational view and confirms that product modularity does not haveunambiguous effects on organizational performance. Moreover, the degree of component modularity negatively moderatesthe impact of buyer–supplier information sharing on supplier-relationship performance, which confirms that componentmodularity works as an ex ante, embedded substitute for high-powered interorganizational integration mechanisms. Finally,contingent on firms’ strategies, organizational structures, and capabilities, we argue that at the firm level, higher productmodularity may be associated either with less information sharing with suppliers, which implies that the mirroring effectmight hold also at the firm level, or with more information sharing with suppliers, which implies that there may be increasingreturns to modularity in design efforts because of interorganizational integration (the “complementarity” hypothesis).

Key words : loose coupling; modularity; product architecture; organizational architecture; supplier relations;complementarities

History : Published online in Articles in Advance.

IntroductionResearch on the relationship between the degrees of cou-pling between product and organizational architectureshas flourished during the last two decades (Langlois andRobertson 1992; Sanchez and Mahoney 1996; Baldwinand Clark 1997, 2000; Schilling 2000; Hoetker 2006;Fixson and Park 2008; Tiwana 2008; Colfer and Baldwin2010). This literature builds on Simon’s (1962) intuitionthat complex systems such as products, technologies,and organizations are adaptive if modular (i.e., hierar-chical and nearly decomposable), and this literature hastentatively explored how the architecture of a system(e.g., a product) may affect the architecture of other sys-tems (e.g., an organization) as well as their behavior andperformance (Ethiraj and Levinthal 2004). Within thisbody of research, some studies recently investigated therelationship between the degree of product modularity,the nature of vertical interorganizational relationships,and organizational performance (Jacobs et al. 2007, Lauet al. 2007, Ro et al. 2008, Gomes and Joglekar 2008).

These studies are of interest because although organiza-tional economics and supply chain management researchwidely converge on the idea that the performance of sup-ply relationships depends heavily on buyer–supplier inte-gration (Vickery et al. 2003), integrative supply contracts(Mayer and Argyres 2004), collaborative arrangements(Helper et al. 2000), and relational contracting (Dyerand Singh 1998), the level of understanding about thedirection and intensity of the relationship between mod-ularity in product design and modularity in organizationremains unsatisfactory (Hoetker 2006, Baldwin 2008).

Sanchez and Mahoney (1996, p. 64, emphasis in orig-inal) first formulated the “mirroring” hypothesis, i.e.,the idea that “the [loosely coupled] standardized compo-nent interfaces in a modular product architecture providea form of embedded coordination that greatly reducesthe need for overt exercise of managerial authority toachieve coordination of development processes, therebymaking possible the concurrent and autonomous devel-opment of components by loosely coupled organizationstructures.” Since then, however, diverse organizational

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Published online ahead of print May 17, 2011

Cabigiosu and Camuffo: Product Modularity and Interorganizational Relations in the AC Industry2 Organization Science, Articles in Advance, pp. 1–18, © 2011 INFORMS

patterns have been observed across industries so thatconventional views are now considered too simplistic,and many studies indicate the need for a more nuancedtheory of mirroring (Colfer and Baldwin 2010). Over-all, two different theoretical positions have emergedon the topic as a consequence of this contradictingevidence (Colfer and Baldwin 2010). The first main-tains that modularity in product design reduces theneed for “hand-in-glove” supply relationships, becauseknowledge encapsulation within modules lowers inter-firm interdependence and, hence, coordination and con-trol needs (Sanchez and Mahoney 1996, Langlois 2003,Sosa et al. 2004, Baldwin 2008). The second maintainsthat modularity in product design increases the need for“thick” supply relationships (Hsuan 1999). Products canbe modularized only if buyers “know more than theymake” (Brusoni et al. 2001, p. 597) and engage suppli-ers in collaborative relationships that component mod-ularity may eventually only facilitate (Hoetker 2006).In addition, interorganizational interdependencies tend toremain ubiquitous, continually emerging ex post, despitemodular design efforts to limit them (Staudenmayeret al. 2005).

These conflicting theoretical predictions largelydepend on the fact that most of the studies underlyingthem tend to focus on whether the “mirroring” hypoth-esis holds, not on under which conditions it may holdand should be tested. These conditions would include(a) the nature of product and industry architectures aswell as their coevolution (Garud 1995; Argyres andBigelow 2007, 2010); (b) the distinction between indus-tries characterized by architectural or systemic innova-tion and fluid vertical contracting structures vis-à-visindustries characterized by “frozen” product architec-tures, autonomous innovation, and stable vertical rela-tionships (Henderson and Clark 1990, Garud 1995,Baldwin 2008); (c) the presence of industry standards(Schilling 2000, Campagnolo and Camuffo 2009); and(d) firms’ strategies, organizations, and capabilities interms of product and supply chain design (Hoetker et al.2007, Fixson and Park 2008).

One way to contextualize the investigation around the“mirroring” hypothesis is to test it under the conditionof product architecture stability. If product architecturesare fluid, the boundaries of the components/moduleschange, making it hard or even impossible to analyze therelationship between moving/ambiguous/shifting objects(components and supplier organizations). Product archi-tecture instability may generate a variety of potentialrelationships between product and organizational archi-tectures, and it makes the link between product andprocess designs and its transactional boundaries moreambiguous. Thus, as product and process designs changeso will firms transactional boundaries, but not in a pre-dictable way (Baldwin 2008). If the product architectureis stable, it is possible to isolate the relationship between

the degree of coupling of product components and thedegree of coupling of organizations, which otherwisewould be subject to the effects of product technologychanges and, hence, difficult to analyze.

This study tests the “mirroring” hypothesis under thecondition of product architecture stability, correlating, atthe component and firm levels, the degree of informa-tion sharing between buyer and supplier (used as a proxyfor the degree of coupling between organizations) with aformal measure of component modularity based on engi-neering design (used as an inverse proxy for the degreeof coupling between product components). Also, thisstudy investigates how the degree of coupling of prod-uct components and organizations separately and jointlyaffects supplier relations performance. Finally, it inves-tigates how firms’ strategies, organizational structures,and capabilities contribute to shaping the relationshipbetween product and organizational architectures.

The rest of this study is organized as follows. Thenext section sets the theoretical framework, reviews therelevant literature, and illustrates the research constructs.The subsequent sections present the research hypotheses;describe the industry context, the data, the sample, theresearch method, and the measures; and present and dis-cuss the tests, statistical methods, and findings. The con-clusion section highlights the study limitations, drawssome research implications, and points out some futureresearch directions.

Theory and Research ConstructsWe frame our investigation around the “mirroring”hypothesis within modularity theory as located at thecrossroads of organizational economics, system theoryapplied to product and organization design, and thetheory of relational contracts applied to supply chainmanagement.

The Degree of Coupling of Product Architectures:Component ModularityProducts are complex systems in that they comprisea large number of components with many interactionsbetween them. The scheme by which a product’s func-tions are allocated to its components is called its “archi-tecture” (Ulrich 1995). Modularity is a concept thathelps us to characterize different product architectures.It refers to the way in which a product design is decom-posed into different parts or modules. Research at thecrossroads of management and engineering proposes avariety of definitions of product modularity, highlight-ing what features may characterize a product’s com-ponent as “a module” (Ulrich 1995, Gershenson et al.2004, Mikkola 2006, Salvador 2007, Campagnolo andCamuffo 2009). Despite this variety, there is agreementon the concepts that lie at its heart: the notion of inter-dependence within modules and independence between

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Cabigiosu and Camuffo: Product Modularity and Interorganizational Relations in the AC IndustryOrganization Science, Articles in Advance, pp. 1–18, © 2011 INFORMS 3

modules. The latter concept is referred to as “loose cou-pling” (Orton and Weick 1990). Modular designs areloosely coupled in that changes made to one modulehave little impact on the others. Sosa et al. (2004) arguethat “modules” are characterized by independence acrossand interdependence within their defined boundaries.This independence is achievable through the adoption ofstandard interfaces that decouple the development andthe inner working principles of a product’s components.Moreover, if a component fully and exclusively imple-ments few functions, it should be easier to isolate itsdevelopment from the rest of the system and to evaluateand ensure certain performance levels (Salvador 2007).

The Degree of Coupling of OrganizationalArchitectures: Buyer–Supplier Information SharingThe degree of coupling of organizational architec-tures depends on the extent to which the organizationsinvolved in the design and production of the componentsthat make a product communicate and exchange busi-ness and technological information, coordinating theirdecisions, actions, and efforts (Paulraj et al. 2008).Communication and information sharing are integrationmechanisms that coordinate interorganizational relation-ships (Ring and Van De Ven 1992). Whenever the com-plexity of vertical interorganizational relationships isnonnegligible, communication and information sharingcan improve the ordering, logistics, inventory manage-ment, and new product development processes, facilitat-ing the understanding of supply chain dynamics (Grant1996) and fostering cross-firm learning and problemsolving (Helper et al. 2000). This study analyzes thedegree of coupling between organizations, focusing onthe intensity of buyer–supplier information sharing andconcentrating on three key interconnected business pro-cesses: new product development, contract/price negoti-ation, and logistics.

The Performance of Interorganizational(Supply) RelationshipsWe build on Howard and Squire (2007), Lau et al.(2007), and Jacobs et al. (2007) and use measures ofinterorganizational (supply) relationship performance tounderstand how performance varies contingent on thedegree of coupling of product and organizational archi-tectures. Two dimensions are fundamental in defininga performance criterion for supply relationships. First,approaches and measures are contingent on the adoptedperspective: the supply chain perspective (a systemic,global view), the buyer’s perspective, or the supplier’sperspective (Chen et al. 2004). Second, measures canfocus on a single performance dimension or encompassmultiple performance dimensions (Paulraj et al. 2008).Our study measures the performance of buyer–supplierrelationships from the buyer’s perspective, which isappropriate because, in the industry we investigate

(air conditioning), product and organizational architec-tures are largely defined by the buyer. In addition,we adopt the purchasers’ perception of supplier per-formance to measure the degree of buyer’s satisfactionregarding its relationships with suppliers, which is con-sistent with classical research on the topic (Bensaouand Venkatraman 1995, Bensaou and Anderson 1999).Finally, we prefer operational performance measures tofinancial performance measures because they providea more direct indication of the effects of the buyer–supplier relationship (Chen et al. 2004).

HypothesesThe “Mirroring” HypothesisIn the case of full product modularity, all the compo-nents exclusively perform one or a few functions, andthe interfaces among them are completely open standard.As a consequence, all the suppliers that design and pro-duce a given component do not need to discuss withthe buyer how this should be designed to fit the prod-uct because a change in the design of one componentdoes not require compensating changes in the designs ofthe other components (Schilling 2000). Because compo-nents’ design and development can be isolated and con-ducted separately by suppliers within a “frozen” productarchitecture, codevelopment practices might be unneces-sary and the advantages of relational quasi rents (Dyerand Singh 1998) negligible. Buyers and suppliers neednot to engage in thick relationships through which theycontinuously improve products and processes, controlopportunism, and share risk. They have less incentive to“learn by monitoring” (Helper et al. 2000, p. 443). Ashighlighted previously, because modularity is a propertyof the engineering of a product and information sharingis a behavior of organizational actors, whether and howthey are related is a matter of debate. Thus, our firsthypothesis is as follows.

Hypothesis 1 (H1) (Component Mirroring). Whena buyer designs more modular components (ex ante),there is less information sharing between the buyer andcomponent suppliers (ex post).

The “Performance” HypothesesIt is widely held that information sharing between sup-ply chain partners reduces product and performance-related errors, thereby enhancing quality, time, andcustomer responsiveness (Novak and Stern 2008).Through information sharing, buyers and suppliers nur-ture the formation of relational rents (Baker et al. 2002),which facilitates problem solving, improves the qual-ity of component design, shortens customer responsetime, reduces “hold-up” effects, and saves cost throughproduct design and operational efficiency (Argyres 1999,Vickery et al. 2003). Our second hypothesis is asfollows.

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Hypothesis 2A (H2A). The performance of supplyrelationships (as perceived by the buyer) is positivelycorrelated with the amount of buyer–supplier informa-tion sharing.

Although the direct effect of product modularity onthe performance of interorganizational relationships isa matter of debate (Chesbrough and Kusunoki 1999,Fleming and Sorenson 2001, Ethiraj et al. 2008), thedegree of coupling between product components doesaffect, although indirectly, interorganizational relationsperformance. Suppliers that design and produce modu-lar components face less uncertainty in transaction eval-uations and make less transaction-specific investments(Williamson 1985), because modular components can fit,with minor adjustments, a variety of products, to thesatisfaction of several buyers. Buyers of modular com-ponents are also likely to suffer less from hold-up prob-lems because potential suppliers’ opportunism is curbedby the presence of competitors, which increases sub-stitutability and reduces switching costs (Klein et al.1978). More generally, the information cost for supply-ing loosely coupled components is lower either becauseless information exchange is required or because moreinformation is already available (Galbraith 1973). There-fore, under the condition of product architecture stability,component modularity works as an ex ante, embeddedsubstitute for interorganizational coordination (Sanchezand Mahoney 1996) and as a functional equivalentof high-powered interorganizational integration mecha-nisms. The next hypothesis is as follows.

Hypothesis 2B (H2B). Component modularity neg-atively moderates the positive relationship betweenbuyer–supplier information sharing and the perfor-mance of the supply relationships.

The “Complementarity” HypothesisThe fact that, under the condition of product architec-ture stability, interfirm transactions “mirror” the modu-lar structure of product components within firms does notexclude the possibility that firms may achieve higher lev-els of modularity across all of components if they sharea lot of information with their suppliers, i.e., that mod-ularity and information sharing may go hand in handat the firm level. To date, the literature has concep-tualized two alternative approaches about how productand organizational architectures can be combined. Thefirst approach (“complementarity”) maintains that a moremodular overall product design requires an ex ante jointinvestment in modular interfaces and, hence, more infor-mation sharing between buyers and suppliers across theboard. Loosely coupled product architectures are typi-cally the result of interorganizational codevelopment andrequire interaction times and comprehensive efforts byboth assemblers and suppliers (Hsuan 1999). Firms needto make to know (Parmigiani and Mitchell 2009), or

at least they need to keep access to component-specificknowledge via intense supply relationships (Zirpoli andCamuffo 2009). A product can be modularized only ifbuyers and suppliers make joint investments in compo-nent modularity (Brusoni and Prencipe 2001, Brusoniet al. 2001) and continue to engage in hand-in-gloverelationships that component modularity may eventuallyonly facilitate. Besides, product architectures (and, withinthem, product components) are never “perfectly modu-lar” because of uncertainty, unexpected problems, andunanticipated technological, economic, and social vari-ation. Thus in practice, interorganizational interdepen-dencies remain ubiquitous and continually emerge eventhroughout the product development process, despiteefforts to limit them (Staudenmayer et al. 2005). There-fore, component modularity and information sharing maybe complements1 in the sense that more of one makesmore of the other more valuable: modularization nurturesrelational quasi rents, and buyer–supplier informationsharing facilitates modularization. The second approach(“trade-off”) maintains that a more modular overall prod-uct design allows, via knowledge encapsulation, to econ-omize on information sharing, lowering transactionaluncertainty and/or asset specificity (Langlois 2003) orreducing the information required to perform a task (Gal-braith 1973). This may happen either because inter-faces are exogenously defined (i.e., industry standards) orbecause they are established top-down by the buyer (i.e.,proprietary standards). Thus, Hypothesis 3 is as follows.

Hypothesis 3 (H3) (Complementarity). A moremodular product design is associated with more infor-mation sharing between the buyer and component sup-pliers during the design process.

Data, Method, and MeasuresThe Air Conditioning IndustryThe worldwide air conditioning market is worth approx-imately E35 billion, and the European market approxi-mately E10 billion. Italy is the main European market,with a share of approximately 29%, and is also thelargest European producer, with output amounting to70% of total European production (Furlan et al. 2006).The air conditioning industry provides an ideal settingto investigate whether, to what extent, and under whichconditions the degree of coupling between product andorganizational architectures mirror. First, high-precisionconditioners and chillers are complex products madeof tens of components whose design principles arelocated at the crossroads of several technologies. Sec-ond, in the air conditioning industry, original equip-ment manufacturers (OEMs) are assemblers who buy50%–70% of the product value from external and inde-pendent suppliers, which, depending on the compo-nent, may have diverse technological capabilities. Third,

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within the air conditioning industry, product architec-tures are stable (remaining almost unchanged during thelast decade). The air conditioning industry is mature, andthe technological improvements take place incrementallyat the component level. The study focuses on a spe-cific product, high-precision air conditioners that providehigh-precision cooling, ventilation, humidity, and tem-perature control in household, business, and communityenvironments through a process called the refrigerationcycle. This cycle was originally discovered in the 19thcentury, and the first modern electrical air conditionerwas invented by Willis Haviland Carrier in 1902 (Kren1997). Current product technology is still grounded onthe refrigeration cycle and relies on a set of key compo-nents (e.g., compressor, ventilator, condensing battery)that implement the typical functions of the refrigerationcycle. Within the cycle, functions need to be performedin a given order, which, product-design-wise, constrainsthe sequencing of product components interfaces. Inthe last two decades, important technological improve-ments have been introduced (reduction of air condition-ers’ size, advancements in remote control technologyand energy savings, improvement of indoor air quality).However, because the technology, the main components,and their relationships inside the refrigeration cycle haveremained basically unchanged, the product architecturecan be considered stable. Data on patent applicationsprovide additional support for this assertion: during thelast 15 years (1990–2004), Italian heating, ventilation,and air conditioning producers (including both OEMsand suppliers) applied for only 60 patents, of whichonly two dozen were actually related to product innova-tions. Similar data can be found at the European level(Prometeia/Anima 2006). The stability of the techno-logical architecture of high-precision conditioners wasalso confirmed by several independent interviews withrefrigeration technology scholars at the Physics Depart-ment of the University of Padova, Italy, who have longbeen involved in international research and technologydevelopment, as well as industrial collaborations, withthe industry. Finally, we analyzed the evolution of theproduct lineups of several competing producers, observ-ing that the average product life cycles are similar andrelatively long (at least five years with no significantchanges) and that the core technologies underlying theproducts are the same over time.

Sample and Research MethodsWe analyze the product architectures and supply rela-tionships of three air conditioning OEMs, similar inregard to size, product range, scope of activities, andfinal markets. To select them we referred to previ-ous research on the Italian air conditioning industry(Camuffo et al. 2007). A preliminary analysis of theexisting air conditioning OEMs led to a short list of

five producers from which we selected three OEMs pro-ducing high-precision air conditioning units, chillers,and modular floors. These three OEMs are among themost important Italian players (their combined outputaccounts for more than 60% of domestic production).We focused on high-precision air conditioners becausethey account for the largest share of the OEMs’ revenuesand represent their most complex and valuable products.For each OEM, we chose, within their product lineup,one specific model of high-precision air conditioner. Weselected the three models among the top selling ones,making sure that they were competing products, homo-geneous technology-wise, and targeted to the same mar-ket segments. We also double-checked that the threemodels were at similar stages in their product life cyclesand that no significant change in their product architec-ture had intervened at least during the last five years.The chief engineers of the three OEMs assisted us inmaking these choices. Data gathering required extensivefieldwork at the OEM locations. We piloted the method-ology with one of them (April–October 2006) and then,after checking and refining our methods, proceeded withthe other two (March–October 2007).

The three OEMs provided, for the selected models,the bills of materials and other technical informationabout the products. Based on the bills of materials, weconducted a Pareto analysis of the product componentspurchased from suppliers, ranking them according tothe proportion of their purchasing cost to the product’stotal manufacturing cost. For each of the three selectedmodels, we took into consideration a number of compo-nents whose aggregate value amounted roughly to 80%of the full manufacturing cost of the analyzed products.We did not include components with negligible valueor simple parts (e.g., screws and bolts), and we con-sidered only components at the first level of the prod-uct hierarchy. We ended up with 39 components forthe first company (OEM1), 31 for the second company(OEM2), and 30 for the third company (OEM3). Ouranalysis of the degree of coupling between components,i.e., of the degree of component modularity, was con-ducted directly with the three OEMs’ chief engineers,whom we interviewed extensively. When necessary orrequired, further assistance from senior engineers ortechnology specialists was also provided. After analyz-ing the product architectures, we moved to the analysisof the interorganizational (supply) relationships. In thisphase of the research, we asked for the support of thepurchasing departments of the three OEMs. We chose toinvolve different organizational departments (R&D andPurchasing) to keep the analysis of product architecturesseparate from that of interorganizational relations andto avoid common method bias (Podsakoff et al. 2003).We did not give the purchasing department informa-tion on the product architecture analysis we had con-ducted with the engineering department. Similarly, the

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engineering department was not involved in the anal-ysis of the supply relationships. With the help of thepurchasing departments of the three OEMs, we identi-fied, for all the sampled components, the relevant sup-pliers and chose among them the main one, i.e., thesupplier with the highest share of the OEM’s purchas-ing turnover for the analyzed component. After matchingcomponents with suppliers, we proceeded with the anal-ysis of the supply relationships. For each relationship,we gathered information on the degree of buyer–supplierinformation sharing and its performance. To do this, weinterviewed the OEMs’ purchasing managers and buyersand asked them to fill a structured questionnaire derivedfrom previous work on supply relationship management.In fact, the questionnaire’s structure, questions, items,and scales had been tested previously in similar stud-ies (Bensaou and Anderson 1999, Furlan et al. 2006).Each interviewee filled in one questionnaire for eachsupplier relation–component couple. The questionnairewas multipurpose and designed as a complement tothe function–component and the design structure matrix(DSM)-based analysis2 of product modularity. Overall,it contains 52 items specifically geared toward captur-ing the organizational implications of product modular-ity. The questions/items for the purpose of this studyare articulated in three sections: (a) the performanceof the supply relationships (9 items), (b) the degree ofinformation sharing between the supplier and the buyer(10 items), and (c) the general information about thesupplier, the component, the market, and the relation-ship (8 items). The questionnaire items touch upon threekey business processes: component development, logis-tics, and price/contract negotiation. To avoid commonmethod bias (Posdakoff et al. 2003), we had the buy-ers answering the questions regarding the degree ofinformation sharing as well as the general informationabout the supplier, the component, the market, and thesupply relationship, whereas the purchasing managersanswered the questions regarding the performance ofthe supply relationships. In all of the three analyzedfirms, purchasing managers supervise the buyers, and thebuyers have to report regularly to the purchasing man-agers. The purchasing managers gather information onthe available suppliers (both current and perspective),visit them, and then select them on the basis of vendorrating systems that, although similar, differ across firms.The buyers manage the day-to-day relationships with thesuppliers, once the purchasing manager has evaluatedand selected them, and report directly to the purchasingmanager. Their patterns of communication differ amongfirms. OEM1 has an M-form organizational structure,and each division (business unit) has a functional struc-ture. OEM1’s buyers work in the Czech Republic, andthe purchasing managers are located in Italy. The buyersare located in the Czech Republic because of lower costand because several of OEM1’s suppliers are located

in Eastern Europe. The purchasing managers work inItaly because they are involved in corporate-level activ-ities. Purchasing managers and buyers meet regularlytwice a month. OEM2 has a process-based organiza-tion. The buyers are responsible for everyday activities,whereas the purchasing managers set and drive the over-all sourcing strategy. Buyers and purchasing managersoperate at the same company location. The buyers, asprocess owners, all work in a large open space (OEM2has a unique huge room with a table approximately100 meters long, and all the process owners/buyers worksitting around this table). Buyers and purchasing man-agers usually meet monthly or twice a month. OEM3 hasa functional organization. Purchasing managers and buy-ers all work inside the same building, and they usuallymeet, as at OEM2, once or twice a month. Purchasingmanagers select the suppliers, monitor the components’market, define the sourcing strategy, and control buyers’activities.

The questionnaire’s constructs, items, and definitions,as well as the reliability analysis (Cronbach’s �5, aresummarized in Tables 1, 2, and 3. Scales range from1 to 5. We use Cronbach’s � > 0060 as a threshold toindicate that the items in the section are internally con-sistent, and hence, the constructs are reliable (Allen andYen 2002). We also computed the corrected item-to-total correlation, which was negligible across the board,and thus no item was eliminated. Overall, we conducted100 interviews with the OEMs’ buyers responsible formanaging the supplier relations for the analyzed com-ponents. The interviews, besides offering a better under-standing of the supply chain and supply network of thethree OEMs, allowed a smoother filling of the question-naires, with the authors assisting the interviewees andensuring consistency on how the questionnaires werefilled in. On average, each interview required 90 min-utes to be completed. In the end, our aggregate data setis made of a sample of 100 observations, one for eachcomponent/supplier.

MeasuresComponent Modularity. We use the degree of compo-

nent modularity as an inverse proxy for the degree ofcoupling of product architectures. We adopt the follow-ing measure of component modularity (CM), which is amodified version of Fine et al. (2005)3 measure:

(CM5=1

4F + I51

where F is the number of functions implemented by thecomponent, and I is the number of closed interfaces ofthe component. CM refers to a specific product compo-nent, and it is close to 1 when the component imple-ments one or few functions, and most of its interfacesare open standard. In this case, the degree of compo-nent modularity is high, and the analyzed component is

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Table 1 Buyer–Supplier Information Sharing

Questionnaire items, scales,Variable and reliability analysis Description

Buyer–supplierinformation sharing:

4IS5=

∑ni Vi

N

N = 10(�= 0091)1–5 scale: 5 = “I completelyagree”; 1 = “I completelydisagree”

(1) You frequently exchange, with the analyzed supplier, detailed informationconcerning new product development and new component development.(2) You frequently exchange, with the analyzed supplier, detailedinformation concerning inventory levels. (3) You frequently exchange, withthe analyzed supplier, detailed information about productionplans/demand forecasts. (4) You frequently exchange, with the analyzedsupplier, detailed information about the attributes and performanceparameters of the product and of the analyzed component. (5) Youfrequently exchange, with the analyzed supplier, detailed informationabout the cost breakdown structure of the analyzed component. (6) Youfrequently exchange, with the analyzed supplier, detailed informationabout delivery schedules and other supply services. (7) You frequentlyexchange, with the analyzed supplier, detailed information about itsproduction capacity and its level of saturation. (8) You frequentlyexchange, with the analyzed supplier, detailed information about changesin volume, mix, and sequencing of production. (9) You frequentlyexchange, with the analyzed supplier, detailed information about keyfinancials, such as turnover, cash flows, and profitability ratios. (10) Youfrequently exchange, with the analyzed supplier, detailed informationabout supplier’s R&D investment and innovation efforts (product,processes, etc.).

Notes. Variables, questionnaire items, scales, and reliability analysis are shown. N = 100.

loosely coupled with the others. CM is close to 0 whenthe component implements several functions and/or mostof the interfaces are closed. In this case, the degree ofcomponent modularity is low, and the analyzed com-ponent is tightly coupled with the others. We gatheredthe number of functions performed by each componentwithin the relevant product using a list (function break-down structure) for a generic high-precision air condi-tioning unit (this step benefited from help of two seniorresearchers in the Physics Department at the Univer-sity of Padova specializing in air conditioning systemsand technology), which was discussed with the OEMs’chief engineers, who commented and in some casesadjusted it. With regard to interfaces, we conducted aDSM analysis on the three analyzed products (Pimm-ler and Eppinger 1994, Sosa et al. 2004) to provide anobjective way to identify and count interfaces betweencomponents.

Buyer–Supplier Information Sharing. We use thedegree of buyer–supplier information sharing as aproxy for the degree of coupling between organizations.The degree of buyer–supplier information sharing (IS)is measured as the mean value of the interviewees’responses to the 10 items of the questionnaire dedicatedto this variable. The details about the measures, scaling,and reliability analysis are reported in Table 1.

The negative moderating effect of component mod-ularity on the positive relationship between buyer–supplier information sharing and the performance of thesupply relationships (Hypothesis 2B) is tested using, asa moderator, a purposely defined variable: CM × IS.

This variable is the interaction effect between compo-nent modularity and information sharing, both definedand measured as stated above.

Supply Relationship Performance. We measure theperformance (P5 of supply relationships as the degreeof the OEM’s satisfaction with its relationships withsuppliers regarding component development, logistics,and price and contract negotiation. More specifically, Pis the purchasers’ perception of supplier performanceand is measured as the mean value of the interviewees’responses to the nine items of the questionnaire dedi-cated to this variable. The details about the measures,scaling, and reliability analysis are reported in Table 2.

Controls. Buyer–supplier information sharing andsupply relationship performance might differ across buy-ers and across suppliers within the same industry forseveral reasons related to market structure and dynamics,component technology, buyers’ sourcing strategies, andsuppliers’ capabilities. Following Bensaou and Ander-son (1999), Bensaou and Venkatraman (1995), andCamuffo et al. (2007), our analysis takes into accountthis variation by including six control variables:4 geo-graphical proximity, supplier’s size, supplier’s capabil-ities, technological change, demand predictability, andstandards. These variables are measured as the value ofthe interviewees’ responses to each of the eight items(questions) of the questionnaire dedicated to informationabout the supplier, the component, the market, and therelationship. The details about the measures, scaling, andreliability analysis are reported in Table 3.

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Table 2 Supply Relationship Performance


Supply relationshipperformance:

4P 5=

∑ni Vi

N

N = 9(�= 0067)1–5 scale: 5 = “I completelyagree”; 1 = “I completelydisagree”

(1) The component designed and produced by the analyzed supplier is, asregards product technology innovation, more innovative than competitors’.(2) The analyzed supplier’s component development time and designcosts are better than competitors’. (3) To manufacture the analyzedcomponent, the analyzed supplier uses process technologies that aremore advanced than competitors’. (4) You are very satisfied with thequality of the component supplied by the analyzed supplier (defect-freeproduct). (5) You are very satisfied with the quality of the supply servicesof the analyzed supplier (on-time delivery, etc.). (6) You are very satisfiedwith the price of the analyzed component purchased from the analyzedsupplier because it is lower than competitors’. (7) The analyzed supplieris very flexible concerning the customization of the analyzed component.(8) The analyzed supplier is very flexible concerning batch sizes. (9) Youare very satisfied with the analyzed supplier’s order to delivery lead time.


Table 3 Control Variables


Supplier’s size N = 11–5 scale

Number of employees; 1 = 0–10 employees; 2 = 11–50 employees;3 = 51–250 employees; 4 = 251–1,000 employees; 5 =>1,000 employees

Geographical proximity N = 11–5 scale

Distance of the supplier’s plant from the buyer’s plant; 1 = 0–15 km,2 = supplier located in the same province, 3 = supplier located in thesame Italian region, 4 = supplier located in Italy, 5 = supplier locatedabroad

Supplier’s capabilities N = 2(�= 0068)1–5 scale: 5 = “I completelyagree”; 1 = “I completely dis-agree”

(1) The analyzed supplier’s product capabilities (product technologyknowledge, design processes and methods, etc.) are much better thancompetitors’. (2) The analyzed supplier’s process capabilities (processtechnology, production planning and control, industrial methods, workorganization, etc.) are much better than competitors’.

Technological change N = 2(�= 0082)1–5 scale: 5 = “I completelyagree”; 1 = “I completely dis-agree”

(1) The market for the analyzed component is characterized by rapid andfrequent product technology changes. (2) The production processes ofthe analyzed component are characterized by rapid and frequenttechnological changes.

Demand predictability N = 11–5 scale: 5 = “I completelyagree”; 1 = “I completely dis-agree”

(1) The market demand for the analyzed component is very predictable.

Standards N = 11–5 scale: 5 = “I completelyagree”; 1 = “I completely dis-agree”

(1) The majority of the producers of the analyzed component useinternational/national standards that regulate the production processesand/or the component’s quality.


The variable standards, defined as the availability, fora given component, of international/national standardsregulating the processes and/or the component’s quality,is used to determine selection in the Heckman procedureapplied in testing H1, and, because it should positivelyaffect the perceived performance of the supply relation-ship, is also subsequently used as a control variable inthe performance equations. We control for cross-OEMvariation by introducing two dummy variables (OEM1and OEM2).

Tests and FindingsTable 4 reports the descriptive statistics and correlationmatrix for all of the variables.

The table shows different correlation coefficientsbetween the dummy variables OEM1 and OEM2 andthe variables CM and IS. These data suggest that thethree OEMs might have diverse product design and sup-ply relationship management strategies. Therefore, webroke down the analysis by OEM to verify whether thereare significant cross-company differences that may affect

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Table 4 Descriptive Statistics and Correlations

Variables Mean SD 1 2 3 4 5 6 7 8 9 10 11

P 304 0055 1000CM 0037 0030 −0011 1000IS 3023 0087 0040∗ −0040∗ 1000Supplier’s size 3000 0099 −0002 −0002 0038 1000Geo. proximity 2061 1052 −0022∗ 0043 −0011 0031 1000Supplier’s capabilities 3060 0081 0057∗ −0021∗ 0042∗ −0006 −0006 1000Technological change 3001 0098 0031∗ −0006 0059∗ 0029∗ 0029∗ 0038∗ 1000Demand predictability 3037 0094 0000 −0012 0032∗ −0015 −0015 −0034∗ −0030∗ 1000Standards 3047 1010 0023∗ 0006 −0005 −0007 0002 0013 0006 −0029∗ 1000OEM1 0039 0049 0047∗ 0001 0064∗ 0004 0008 0002 0024∗ 0052∗ 0005 1000OEM2 0031 0046 0002 0021∗ −0062∗ −0019∗ 0004 0009 −0002 −004∗ −0068∗ 0016∗ 1000

Note. N = 100.∗p ≤ 001.

Table 5 Descriptive Statistics for the Three Firm-Level Subsamples: OEM1 (N = 39), OEM2 (N = 31), and OEM3 (N = 30)

OEM1 OEM2 OEM3

Variables Mean SD Min Max Mean SD Min Max Mean SD Min Max

P 3072 0053 2056 4044 3039 0041 2056 4011 2095 0037 2044 4067CM 0038 003 0007 1 0047 0035 0004 1 0027 0023 0008 1IS 3093 0048 208 409 2045 0085 102 3065 3016 004 201 306Supplier’s size 301 0091 2 5 301 102 1 5 208 0089 1 5Geo. proximity 2064 1068 1 5 2086 1038 1 5 2037 105 1 5Supplier’s capabilities 3085 0071 3 5 3064 0099 105 5 303 0047 3 4Technological change 3065 0082 105 5 2053 0087 1 4 2078 0073 1 4Demand predictability 3061 0078 2 4 2031 0071 2 4 4003 0018 4 5Standards 3054 1025 1 5 30174 1006 1 5 301 0084 2 4

our results. Table 5 shows, for each OEM, the mean,standard deviation, minimum, and maximum for eachanalyzed variable.

The data highlight cross-OEM differences in meansfor CM and IS. More specifically, OEM1 uses a lot ofbuyer–supplier information sharing but lies among theother OEMs in regard to component modularity. OEM2’saverage degree of component modularity is larger thanthe other OEMs’, but its level of buyer–supplier informa-tion sharing is the lowest. Finally, OEM3 lies in betweenOEM1 and OEM2 in terms of buyer–supplier informa-tion sharing but has the lowest level of component mod-ularity. Moreover, Table 5 shows nonoverlapping rangesof the IS variable across the three OEMs. Therefore, thethree OEMs seem to be pursuing different strategies asregards component modularity and buyer–supplier infor-mation sharing, and consequently, we need to assesswhether this heterogeneity affects our results concerningH1, i.e., if the coefficients of CM (1) are the same for thethree OEMs and/or, if different, (2) remain negative andsignificant. Table 4 also suggests that, because the vari-able supplier’s capabilities is strongly and significantlycorrelated with IS and P , it might be endogenous in H1as well as in H2A and H2B. However, because supplier’scapabilities is assessed by the buyers who also assessthe level of buyer–supplier information sharing (IS), our

major concern is the potential endogeneity of the variablesupplier’s capabilities in H1.

Test of H1 4the “Mirroring” Hypothesis5. To beginwith, we test the “mirroring” hypothesis using anordinary least squares (OLS) model in which IS (buyer–supplier information sharing) is the dependent variableand CM (component modularity) is the independent vari-able. The model also includes the five control variablesand the two dummies described in the previous sections.The second column of Table 6 reports the regressionresults.

The regression coefficient for CM is negative andsignificant (−00751 p ≤ 0001), which supports H1. Theregression equation we estimate includes a constant(2.80, p ≤ 0001). This value of the dependent variable IScorresponds to CM = 0, i.e., to nonmodular components.But CM’s range is 60117. When CM equals 1, i.e., in thecase of perfectly modular components, the value of ISdecreases but does not go to 0, implying that nonnegligi-ble levels of buyer–supplier information sharing persisteven in the case of perfect component modularity. Theresults also show that the two dummies have significantcoefficients with opposite signs.

The correlation matrix in Table 4, the OEM-specificdescriptive statistics in Table 5, and the results of thefixed effects OLS model used to test H1 (see Table 6)

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Table 6 OLS Results for Hypotheses H1

Variables IS

Constant 2080∗∗∗

400485CM −0075∗∗∗

400205Supplier’s size 0002

400065Geographical proximity −0001

400045Supplier’s capabilities 0025∗∗∗

400085Technological change 0019∗∗∗

400065Demand predictability −0018∗∗

400105OEM1 0048∗∗∗

400125OEM2 −0086∗∗∗

400235R 2 0073

Notes. Robust standard errors are in parentheses. N =

100.∗∗p ≤ 005; ∗∗∗p ≤ 0001.

suggest the presence of a self-selection bias and that theOEMs’ strategies are ordered. To avoid the risk of get-ting flawed results, we moved to an endogenous switch-ing regime model, which allows for nonrandom selectionof firm strategy. We applied an ordered probit modelas the first stage of a Heckman procedure,5 the resultsof which are reported in Table 7. We first performedthe Heckman procedure with the selection equation fordummy variable OEM1 and then with the selection equa-tion for dummy variable OEM2. The first column ofTable 7 shows the standard OLS results for H1 with-out the dummies. The other two columns include theresults of the two Heckman selection models (for vari-ables OEM1 and OEM2). As requested by the procedure,the two selection equations include one additional vari-able that is not included in the outcome equation. Weused the variable standards, described in the previoussection, to determine selection.

The regression coefficients for CM of the twoHeckman selection models (−0.68 and −1.20) remainnegative and significant, confirming the results of thebasic OLS model for H1. However, they diverge, sug-gesting that the three firms are heterogeneous. The factthat, in both selection models, rho—the cross-equationcorrelation of the error terms—is different from 0(respectively, 0.30 and −0.66) also seems to support het-erogeneity. Nevertheless, applying the Wald test, whichtests the hypothesis that rho = 0, we cannot reject thenull hypothesis (p-values of 0.97 and 0.33, respectively).Therefore, we cannot conclude that rho is statisticallydifferent from 0 either. Furthermore, as another check

of the presence of the selection bias, we estimated threeOLS models, one for each OEM, to test H1 at the firmlevel, and we then applied a Chow test to verify whetherthe regression coefficients differ across firms. The threeOLS models, one for each OEM, are reported in Table 8.

The CM regression coefficients of the three OEM-specific regression models remain negative and signif-icant (−0069) for OEM1, p ≤ 0053 −1006 for OEM2,p ≤ 005, and −0093 for OEM3, p ≤ 00015, suggestingthat despite differences in product architecture strategiesand supply relationship management strategies, a neg-ative relationship between IS and CM holds across theOEMs. The Chow test results (F 4121795 = 4025, p =

00000) confirm that the regression coefficients of thevariables across firms are not the same. We then testedwhether the three slopes of CM in the three separateOLS regressions are significantly different or if the dif-ferences are driven solely by the intercepts. The resultsof the tests (F -tests) indicate that the slopes are not sta-tistically different although the intercepts are. We cannotreject the null hypothesis that the slopes are equal (withp-values of 0.54 for OEM1 and OEM2, 0.70 for OEM1and OEM3, and 0.77 for OEM3 and OEM2), althoughwe can reject the null hypothesis that the intercepts areequal (with p-values of 0.00 for OEM1 and OEM2, 0.03for OEM1 and OEM3, and 0.02 for OEM2 and OEM3).On the basis of this additional data, we can infer that nofirm drives disproportionately the negative relationshipbetween component modularity and buyer–supplier inte-gration, but these three firms share systematically dif-ferent amounts of information with their suppliers, andtheir strategies are ordered: OEM1 shares systematicallymore information than OEM3, which shares more infor-mation than OEM2.

Above, we anticipated the issue of the endogeneityof supplier’s capabilities in H1. Supplier’s capabilitiesmight be endogenous in H1 because buyers might per-ceive that the suppliers they share information with aremore capable, or perhaps buyers seek to share informa-tion with more-capable suppliers. This is true, given thatbuyers from the three OEMs assessed both the degree ofIS and the degree of supplier’s capabilities through thequestionnaire (common method bias). Moreover, endo-geneity is driven by the cross-sectional nature of ourdata. To solve the endogeneity problem of the variablesupplier’s capabilities, we used two instrumental vari-ables that are theoretically correlated with supplier’scapabilities but not with IS. These variables are prod-uct/process innovation in vendor rating and productionflexibility in vendor rating. As stated previously, thesevariables are items on the questionnaire we asked thebuyers to complete for each component/supplier. Thetwo variables measure whether and to what extent prod-uct/process innovation and production flexibility are keyfactors in vendor rating, i.e., in a supplier’s selection

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Table 7 Ordered Probit Model for H1 (the “Mirroring” Hypothesis)

Basic model Heckman–OEM1 Heckman–OEM2

IS IS Selection equation IS Selection equation

Constant 1032∗∗∗ 3003 −4025∗∗∗ 2024∗∗ 4004∗∗

400515 480175 410485 400725 410895CM −0094∗∗∗ −0068∗∗ 0032 −1020∗∗∗ 1017∗

400275 400345 420285 400365 400665Supplier’s size −0014 0000 −0029 −0012 0039

400095 400445 400235 400115 400285Geo. proximity 0009∗ 0005 0026∗ −0019 −0049∗∗

400055 400375 400135 400135 400225Supplier’s capabilities 0025∗∗∗ 0021 0020 0036∗∗∗ 0048∗∗

400085 400425 400515 400115 400285Technological change 0047∗∗∗ 0028 0089∗∗∗ 0009 −0081∗∗∗

400085 410305 400235 400165 400225Demand predictability 0011 −0024 0015 0012 −1039∗∗∗

400085 400195 400385 400225 400275Standards −0001 0004

400175 410195R 2 0053Censored 61 69Uncensored 39 31Rho 0.30 −0.66Wald test 33.38 126.24Wald test of �= 0 0.97 0.33

Notes. The dependent variable is IS and the independent variable is CM. Robust standard errorsare in parentheses. N = 100.

∗p ≤ 001; ∗∗p ≤ 005; ∗∗∗p ≤ 0001.

Table 8 OEM-Specific OLS Models for H1

IS

OEM1 OEM2 OEM3

Constant 3036∗∗∗ 2055∗∗∗ 2068∗∗

400685 400755 410085CM −0069∗∗ −1006∗∗ −0093∗∗∗

400265 400395 400235Supplier’s size 0002 −0005 0019

400075 400115 400155Geo. proximity 0003 −0026∗∗ −0000

400035 (011) 400055Supplier’s capabilities 0020 0043∗∗∗ 0006

400145 400105 400175Technological change 0022∗∗∗ 0002 0006

400085 400145 400115Demand predictability −0025∗∗ −0010 0006

400135 400165 400185R2 0063 0071 0046

Notes. Robust standard errors are in parentheses. OEM1, N = 39;OEM2, N = 31; and OEM3, N = 30.

∗∗p ≤ 005; ∗∗∗p ≤ 0001.

for a specific component. The three OEMs have a ven-dor rating system, and we wanted to know whether andto what extent product/process innovation and produc-tion flexibility in vendor rating are important as suppli-ers’ selection criteria. Because these criteria are defined

ex ante at the firm level for each component and drivethe supplier’s selection process, product/process innova-tion in vendor rating and production flexibility in ven-dor rating may be used as instruments for supplier’scapabilities. Buyers do not necessarily perceive thatthe suppliers they share information with are those forwhich product/process innovation in vendor rating andproduction flexibility in vendor rating are high. Simi-larly, buyers do not necessarily seek to share informa-tion with suppliers for which product/process innova-tion in vendor rating and production flexibility in ven-dor rating are important. However, it is likely that formore-capable suppliers, product/process innovation andproduction flexibility are important factors in the suppli-ers’ evaluation and selection system. Consequently, thetwo instruments should be significantly correlated withthe variable supplier’s capabilities but are not endoge-nous in IS. Before using the identified instruments tomove to a two-stage least squares (TSLS) model, wetested the endogeneity of the variable supplier’s capa-bilities (we followed Wooldridge 2002). First, we per-formed an OLS regression of supplier’s capabilities onproduct/process innovation in vendor rating and produc-tion flexibility in vendor rating and retrieved the fittedvalues. The instruments are positively correlated withthe variable supplier’s capabilities, and the regressioncoefficients are positive and significant (0.31, p < 0001

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for product/process innovation in vendor rating; 0.30,p < 0001 for production flexibility in vendor rating).Morevoer, the two instrumental variables’ sample meansare statistically different from 0 (t-test performed), andthe instruments are not weak (F -statistic > 10) (Staigerand Stock 1997). Second, we performed an OLS regres-sion on the auxiliary regression model of IS, which alsoincluded the fitted values retrieved previously. Third, wetested the null hypothesis that the coefficient of the resid-uals is 0 (F -test), rejecting it (p = 0000). This test isasymptotically equivalent to the Hausman test but moreconvenient and more efficient with small samples. Onthe basis of these results, we concluded that supplier’scapabilities is endogenous in H1. Hence, we moved toa TSLS model. The TSLS model, included in Table 9,shows that our results for H1 are robust, i.e., that ourdata support the “mirroring” hypothesis after check-ing for endogeneity of the variable supplier’s capabil-ities. The CM regression coefficient (−0075, p < 0001)remains negative and significant.

Test of H2 4the “Performance” Hypotheses5. Wetested H2A and H2B with a difference-in-differencesspecification (see Table 10). In both models the depen-dent variable is P (supply relationship performance). Inthe OLS model that tests H2A, the independent vari-able is IS. This hypothesis is supported because theregression coefficient for IS is positive and significant(00151 p ≤ 005). In the OLS model that tests H2B, theindependent variables are CM, IS, and the interactionterm CM × IS. The product term CM × IS has a neg-ative and significant regression coefficient (−00241 p ≤

005), which supports H2B. In this case we used Lance’s(1998) residual centering procedure for computing the

Table 9 TSLS Results for H1

IS

Constant 2015∗∗∗

400415CM −0075∗∗∗

400205Supplier’s size −0002

400065Geographical proximity −0002

400045Supplier’s capabilities 0025∗∗∗

400085Technological change 0019∗∗∗

400065Demand predictability −0018∗∗

400105OEM1 0048∗∗∗

400125OEM2 −0086∗∗∗

400235R 2 0073

Note. N = 100.∗∗p ≤ 005; ∗∗∗p ≤ 0001.

Table 10 OLS Results for H2A and H2B

H2A H2Bperformance performance

Variables (P ) (P )

Constant 1047∗∗∗ 1016∗∗∗

400385 400415CM −0009 −0008

4000145 400135IS 0015∗∗ 0015∗∗

400085 400075CM× IS −0024∗∗

400115Supplier’s size 0004 0004

400055 400055Geo. proximity −0006∗ −0006∗

400035 400035Supplier’s capabilities 0020∗∗∗ 0021∗∗∗

400085 400075Technological change −0007 −0007

400085 400085Demand predictability 0008 0009

400065 400055Standards 0008∗ 0007

400045 400045OEM1 0062∗∗∗ 0060∗∗∗

400115 400115OEM2 0062∗∗∗ 0060∗∗∗

400185 400175R 2 0058 0060

Notes. Robust standard errors are in parentheses. N = 100.∗p ≤ 001; ∗∗p ≤ 005; ∗∗∗p ≤ 0001.

interaction term CM × IS to correct the problem ofpartial coefficient distortion faced in the simultaneousanalysis of main effects and interaction terms becauseof their correlation. Lance’s procedure is made of twosteps: in the first, the product term CM × IS is regressedon its components (component modularity and buyer–supplier information sharing); in the second step, cross-product residuals are constructed and then used in thefull equation regression.6

Interestingly, in all of the OLS models, the regres-sion coefficient for the control variable supplier’s capa-bilities is positive and significant, suggesting that sup-pliers’ capabilities favor interorganizational coordinationvia information sharing and positively affect supply rela-tionship performance. Although this effect is reasonable,it might be driven by the fact that supplier’s capabilitiesis endogenous. In the performance equations, the controlvariable supplier’s capabilities (assessed by the buyers)might be too close to the dependent variable perfor-mance (assessed by the purchasing managers). Similarly,we also need to address a potential endogeneity issue forthe variable IS (assessed by the buyers), because buy-ers who have relatively close relationships with certainsuppliers might speak well of them to purchasing man-agers (who assessed the performance), thus creating a

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potentially spurious correlation between the variables ISand performance. To test the potential endogeneity ofthe variable supplier’s capabilities in H2A and H2B, weused the same variables used to address the issue ofthe endogeneity of supplier’s capabilities in H1: prod-uct/process innovation in vendor rating and productionflexibility in vendor rating. First, we performed an OLSregression of supplier’s capabilities on product/processinnovation in vendor rating and production flexibility invendor rating and retrieved the fitted values for H2A andH2B. The two instruments are positively correlated withsupplier’s capabilities, their sample means are statisti-cally different from 0 (t-test performed), and they arenot weak (F -statistic > 10) in either model (Staiger andStock 1997). Second, we performed an OLS regressionon the auxiliary regression models for H2A and H2B,which included the fitted values previously retrieved.Then, we tested the null hypothesis that the coefficientsof the residuals are equal to 0 (F -test). Because thep-values of the F -test are, respectively, 0.81 and 0.71,we cannot reject the null hypothesis. Therefore, the vari-able supplier’s capabilities is not endogenous in H2Aand H2B. This test is asymptotically equivalent to theHausman test statistic but more accurate and efficientwith small samples.

To test the potential endogeneity of the variable ISin H2A and H2B, we used two instrumental variables:technological change and demand predictability. Thesetwo instrumental variables are significantly correlatedwith IS (see Table 4). The variable technological changemight be an instrument for IS because the higher thetechnological uncertainty, the higher the buyer–supplierinformation sharing needed to manage the correspondingsupply relationship should be. Similarly, demand pre-dictability might be an instrument for IS because thehigher the demand uncertainty, the higher the buyer–supplier information sharing needed to manage the cor-responding supply relationship should be. Moreover, thetwo instruments are not significantly related to the vari-able performance in H2A and H2B. These assumptionsare rooted in classic contingency theories of organiza-tions (Galbraith 1973) as well as in supply relation-ship management literature (Bensaou and Venkatraman1995). First, we performed an OLS regression of ISon technological change and demand predictability andretrieved fitted values for H2A and H2B. The two instru-mental variables’ sample means are statistically differentfrom 0 (t-test performed) even if they are weak instru-ments (F -statistic < 10) in both models (Staiger andStock 1997). Second, we performed an OLS regressionon the auxiliary regression models of H2A and H2B,which included the fitted values previously retrieved.Then, we tested the null hypothesis that the coefficientsof the residuals are equal to 0 (F -test). Because thep-values of the F -test are, respectively, 0.89 and 0.26,we cannot reject the null hypothesis. Therefore, also, thevariable IS is not endogenous in H2A and H2B.

Figure 1 Regression Functions and Average Levels ofComponent Modularity for OEM1, OEM2, and OEM3

1

2

3

4

5

0 1Component modularity

Info

rmat

ion

shar

ing

0.38

0.470.27

OEM1OEM2OEM3

Notes. OEM1 shares more information than OEM2 and OEM3 forall levels of component modularity. OEM2 has the highest aver-age level of component modularity, followed by OEM1 and OEM3.OEM1’s product design and sourcing strategy seems to be con-sistent with the “complementarity” hypothesis (combination of highlevels of product modularity and thick relationships with suppliers).OEM2’s product design and sourcing strategy seems consistentwith the “trade-off” hypothesis (combination of high levels of prod-uct modularity and “loose” relationships with suppliers).

Test of H3 4the “Complementarity” Hypothesis5. Totest H3 we used the data from the three separate regres-sions contained in Table 8 and the OEM-specific sub-sample means of CM shown in Table 5. H3 would besupported if the three lines representing the three regres-sions in Table 8 (H1 tested for each firm) were separateand nonintersecting, and with higher actual levels of CMon each higher line. The “complementarity” hypothe-sis would be rejected if we had one line characterizingall three firms. Other combinations of outcomes wouldprovide mixed results. Testing H1, we have alreadyestablished that we have three different lines with signif-icantly different intercepts but nonsignificantly differentslopes. Therefore, the three firms have ordered strate-gies: for each possible level of CM, OEM1 shares moreinformation with its suppliers than OEM3, which sharesmore information than OEM2. The “complementarity”hypothesis would be strongly supported if OEM1, whichshares more information across all components, achievedthe highest average level of modularity. The “trade-offtheory” would be upheld if OEM1, which shares moreinformation across all components, achieved the lowestaverage level of modularity in its product design. Thedescriptive statistics in Table 6 provide the data to com-plete the test: OEM1 has a mean modularity level that ishigher than that of OEM3 but lower than that of OEM2.Interestingly, OEM2, which does the least informationsharing, also obtains the highest level of modularity. Fig-ure 1 helps visualize the results of the test of the “com-plementarity” hypothesis.

Overall, our results show that the “complementarity”hypothesis might hold for OEM1, which achieves highlevels of modularity combined with high informationsharing. This is not true, however, for OEM2, whichobtains even higher levels of modularity with the lowestlevel of information sharing.

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DiscussionOur study supports the “mirroring” hypothesis (H1)(Baldwin and Clark 1997, Schilling 2000, Baldwin 2008,Colfer and Baldwin 2010) at the component level andunder the condition of product architecture stability.The results also confirm that the performance of sup-ply relationships depends on the amount of buyer–supplier information sharing (H2A) (Helper et al. 2000,Vickery et al. 2003)—which is consistent with the the-ory of relational contracts (Baker et al. 2002)—but isnot affected by the degree of component modularity,which implies that modularity may have different andcontradictory effects on performance as regards effi-ciency, quality, and innovation within firms and acrosssupply chains (Fleming and Sorenson 2001, Pil andCohen 2006, Lau et al. 2007, Ethiraj et al. 2008).Moreover, our findings confirm that high levels of com-ponent modularity negatively moderate the direct rela-tionship between buyer–supplier information sharing andthe performance of supply relationships (H2B), whichis consistent with the idea that component modular-ity works as an ex ante, embedded, partial substitutefor high-powered interorganizational integration mecha-nisms (Sanchez and Mahoney 1996). When, as in theair conditioning industry, product architectures are sta-ble, buyers and suppliers of loosely coupled componentscan act more independently than buyers and suppliersof tightly coupled components, because high levels ofcomponent modularity work as a functional equivalentof high-powered interorganizational coordination mech-anisms allowing to economize on information cost.

At the firm level, we find that product modularitymay be alternatively associated with either plenty ofinformation sharing with suppliers (the “complementar-ity” hypothesis) or with little information sharing withsuppliers (the “trade-off” hypothesis). OEM1’s strategyseems consistent with the “complementarity” hypothesis,whereas OEM2’s seems consistent with the “trade-off”hypothesis. Overall, our findings suggest that firms mayopt for alternative strategies differently combining prod-uct and organizational architectures. These options maybe conceptualized as lying within a continuum definedby two opposing strategies: “complementarity” (productmodularity and buyer–supplier information sharing gohand in hand because of synergistic effects and increas-ing returns to adopting them simultaneously) and “trade-off” (product modularity is a substitute of buyer–supplierinformation sharing and works as a functional equivalentof high-powered interorganizational coordination mech-anisms). High levels of product modularity might beachieved either by extensively sharing information withsuppliers (the case of OEM1) or by ex ante defining theinterfaces and then maintaining (ex post) a low level ofinformation sharing with suppliers (the case of OEM2).One strategy does not exclude the other.

Although we observe strategic heterogeneity, we cansay nothing about what drives such cross-firm diver-sity. Therefore, the findings above should be interpretedvery cautiously because we have no specific informa-tion about the strategic intent underlying the observedOEMs’ levels of component modularity and informationsharing. Further caution in interpreting these findingsderives from the nature of our information sharing mea-sure, which is broad and comprises different aspects ofthe supply relationship such as component design, logis-tics, and contract/price negotiation. The breadth of thismeasure, although aligned with the outstanding modu-larity literature (Jacobs et al. 2007, Lau et al. 2007),implies that the “complementarity” hypothesis might nothold even for OEM1 information sharing is referred onlyto the component development process.

In terms of theory, our findings offer a new perspec-tive on the debate about the “mirroring” hypothesis andthe outstanding modularity literature. The two litera-tures’ positions we stylized in the Theory and ResearchConstructs section can now be reinterpreted not as twocontradicting theories with conflicting supporting evi-dence but as the seeds of a contingent theory of the rela-tionship between product and organizational modularity.The first literature’s position, modular products associ-ated with modular organizations (Sanchez and Mahoney1996), in our study finds support at the component levelbut only under the condition of product architecture sta-bility. However, our findings are not conclusive and donot exclude that, under different conditions, the “mir-roring” hypothesis might not hold or be difficult to betested. Air conditioners and the air conditioning industryrepresent a somewhat special case. In industries charac-terized by more complex products, more dynamic tech-nologies, architectural innovation, and a fluid verticalcontracting structure, organizational architectures maynot mirror product architectures in terms of degree ofcoupling. Moreover, as Wolter and Veloso (2008) sug-gest, different types of technological shocks may havedifferent effects on the integration level of an industry.For example, when modular innovations are introduced,the overall level of vertical integration of an industrydecreases. Consequently, the possibility to meaningfullytest the “mirroring” hypothesis in different industriesis conditional on the nature of the path of technologi-cal change, even when product architectures are stable.The second literature’s position, which sees modular-ity in design as the outcome of buyer–supplier code-velopment and, hence, of high-power interorganizationalcoordination mechanisms, in this study finds partial sup-port at the firm level. This is consistent with whatwas found in other industries (Argyres 1999, Brusoniand Prencipe 2001, MacDuffie 2008) and suggests that(a) modularization may not eliminate interdependen-cies between assemblers and suppliers, and hence theneed for thick, collaborative supply relationships persists

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even in the presence of “perfectly” modular components;(b) modularization may derive from the joint efforts ofboth assemblers and suppliers, which tend to remainengaged in “hand-in-glove” relationships that compo-nent modularity may eventually only enhance; and (c)there may be increasing returns to modularity-in-designefforts because of buyer–supplier integration, i.e., theymay be complementary or supermodular (Topkis 1998),with modularization nurturing relational quasi rents andbuyer–supplier information sharing facilitating modu-larization. However, complementarity between productmodularity and organizational integrality remains onlyone of the alternative strategic options that firms mayadopt to differently combine product and organizationalarchitectures.

ConclusionThis study contributes to the theoretical debate and theempirical work around the “mirroring” hypothesis bycorrelating the degree of information sharing betweenbuyer and supplier (used as a proxy for the degree ofcoupling between organizations) with a formal measureof component modularity based on engineering design(used as an inverse proxy for the degree of couplingbetween product components). This original researchapproach applies multiple methodologies that draw upona variety of fields including product engineering, supplychain management, strategy, and organization theory. Byincluding a variety of variables and measures from dif-ferent fields, and by using first-hand, firm-level data, thisstudy improves the empirical foundation of research onthis topic and offers an original methodological roadmapthat could be replicated in other settings.

The support we find for the “mirroring” hypothe-sis under the condition of product architecture stabilityas well as the observation of strategic heterogeneityamong firms (diverse combinations of product and orga-nizational modularity) (a) suggest that the outstand-ing alternative theoretical positions are both empiricallygrounded; (b) advance the understanding of the rela-tionship between the degree of coupling of product andorganizational architectures, beyond the universalistic,deterministic (and at times tautological) view that prod-uct and organizational architectures simply mirror andthat this relationship is technology driven; and (c) are inline with the recent calls for a contingent theory of the“mirroring” hypothesis (Colfer and Baldwin 2010).

With regard to management practices, our findingshave straightforward implications: under the conditionof product architecture stability, component modular-ization allows to economize on high-powered interor-ganizational coordination mechanisms. However, thisdoes not imply that modularization is a perfect sub-stitute for interorganizational integration mechanismsbecause complex products typically comprise a mix

of components, some of which are tightly coupledto others and some of which are relatively indepen-dent; therefore, for some components it is inevitable toengage in thick collaborative relations with suppliers.Moreover, buyer–supplier information sharing remains,overall, necessary to globally optimize a supply chainbecause (a) it is the logical antecedent and the con-dition on which any modularization process is based;(b) it complements modularization ex post, allowingfor problem solving for unforeseen design and sup-ply chain management issues; and (c) it may facilitatemodularization-nurturing relational quasi rents in verti-cal interfirm relationships. Our findings also underlinethe impact of internal engineering and suppliers’ capa-bilities on interorganizational relationship performance.Finally, future research will have to overcome the limita-tions of this work. Multiple cross-industry studies will benecessary to compare situations with diverse degrees ofproduct architecture stability and different technologicaland industrial dynamics. Also, further studies will haveto investigate what really drives strategic heterogeneityand cross-firm differences with regard to product andorganizational modularity, as well as variation in cross-firm performance. It would be relevant to build newstudies that explicitly analyze the strategic intent behindthe observed different levels of buyer–supplier informa-tion sharing and product modularity. Finally, whereasour study remains prevalently descriptive, it would beinteresting to establish the causal relationship betweenproduct modularity and buyer–supplier information shar-ing, defining “what comes first” and speculating on theperformance implications of these strategies.

AcknowledgmentsThe authors thank Carliss Baldwin, Erich Battistin, StefanoBrusoni, Sendil Ethiraj, Sebastian Fixson, Andrea Furlan,Anna Grandori, Rob Grant, Richard Langlois, John PaulMacDuffie, Anne Parmigiani, Andrea Prencipe, FabrizioSalvador, Melissa Schilling, Giuseppe Soda, Francisco Veloso,Francesco Zirpoli, Maurizio Zollo, three anonymous review-ers, senior editor Nicholas Argyres, and participants at semi-nars at Bocconi University, the Academy of Management 2008Annual Meeting, and at the Strategic Management Society2009 Annual Conference for comments and suggestions at var-ious stages of the research on which this paper is based. Thisresearch was funded by CROMA-Bocconi.

Endnotes1We refer to the complementarity literature stemming from theTopkis algebra (Topkis 1998), as applied to economics andmanagement by Milgrom and Roberts (1990) and developed instrategy and organization theory by “configurationists” (Rivkinand Siggelkow 2003, Porter and Siggelkow 2008) building onNK performance landscape literature (Kaufman 1993).2The DSM is a compact matrix representation of a system.It is a square matrix where the rows and columns are theconstituent subsystems (in our case, the product components)or nodes of the matrix, and the elements of the matrix are

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the edges or interfaces (information exchange protocols anddependency patterns) between them. The matrix layout is asfollows: the product components’ names are placed down theside of the matrix as row headings and across the top as col-umn headings in the same order. If there exists an interactionfrom node i to node j , then the value of element ij (column i,row j) is unity (or marked with an X). Otherwise, the value ofthe element is 0 (or left empty). In the binary matrix represen-tation of a system, the diagonal elements of the matrix do nothave any interpretation in describing the system, so they areusually either left empty or blacked out. Following Pimmlerand Eppinger (1994), we analyzed four types of interactionsbetween product components: (a) spatial (e.g., physical adja-cency, alignment, orientation), (b) energetic (e.g., heat, vibra-tion, electricity), (c) material (e.g., air, oil, fluids, flows), and(d) informative (e.g., signals, control transfers). Moreover, wealso identified which of these interfaces are open standard andwhich are closed. Thus, for each element of the matrix—i.e.,for each couple of components—we verified what interactionexists and gathered data on interactions’ degree of coupling,counting the number of closed interfaces.3The Fine et al. (2005) measure of component modularity ismi = �1/4Fi + �2Ini5, where mi is the modularity degree ofcomponent i, Fi is the number of functions the componentperforms, Ini is the number of interfaces it has with otherelements, and �1 > 0 and �2 > 0 are two normalization scalars(�1 is selected to bring the modularity values to a range of60117; �2 weighs the interfaces with respect to functions). Ourmeasure of component modularity (CM) adapts and modifiesmi by (a) setting �1 = 1 and �2 = 1, and (b) considering thenumber of closed interfaces instead of the total number ofinterfaces.4Additional variables were included in the questionnaire. Thevariable product and/or process innovation in vendor rating—used in the tests of endogeneity and in the TSLS modelsas an instrument for the variable supplier’s capabilities—ismeasured as the value of the interviewees’ responses to thefollowing item (question) of the questionnaire: “Product and/orprocess innovations are key factors in vendor rating and selec-tion of the analyzed component.” The variable productionflexibility in vendor rating—also used in the tests of endo-geneity and in the TSLS models as an instrument for the vari-able supplier’s capabilities—is measured as the value of theinterviewees’ responses to the following item of the question-naire: “Production flexibility in vendor rating is a key factorin vendor rating and selection of the analyzed component.”As for the others control variables, they are measured on afive-point Likert scale, where 5 equals “I completely agree”and 1 equals “I completely disagree.”5The Heckman procedure consists of a two-equation model.First, there is the regression model; second, there is the selec-tion model. Incorporating the selection equation allows us totest whether the relationships indicated by H1 are biased bythe OEMs’ strategies. The independent variables in the selec-tion equation include all the variables in the outcome equa-tion along with an additional variable that helps determineselection. When the cross-equation correlation of the errorterms (rho) is equal to 0, OLS provides unbiased results;when the error term is different from 0 and we can reject thenull hypothesis that it is equal to 0, the estimates are biased(Woolridge 2002).

6In unreported regressions, we checked whether H2B resultsare spurious by splitting the cross-product variable (IS × CM)into two subsidiary variables. The CM value used to split thecross-product variable is the value at which the level of 0015×

IS − 0024 × CM × IS becomes negative. H2B is supportedwhen 005 < CM ≤ 1, whereas when CM ≤ 005, both the inter-action effect (IS × CM) and CM are nonsignificant, and IS ispositive and significant. This suggests that H2B may be fullysupported only for the highest levels of modularity.

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Anna Cabigiosu is currently a research fellow and teachesmanagement in organizations at the University of Padova,where she received her Ph.D. in economics and manage-ment. Her research interests include service and product mod-ularity, innovation strategy and management, and knowledgemanagement.

Arnaldo Camuffo is a professor of business organization atthe Bocconi University. He received his Ph.D. in managementfrom the Ca’ Foscari University of Venice, Italy, and an MBAfrom the Sloan School of Management at the MassachusettsInstitute of Technology. His research interests include produc-tion systems, organizational structures, and strategic humanresource management.

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