Integrating Supply

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Integrating supply chaininfrastructure and process

to create joint valueTeresa M. McCarthy-Byrne

Marketing Department, Bryant University, Smithfield, Rhode Island, USA, and

John T. Mentzer Department of Marketing and Logistics,

The University of Tennessee, Knoxville, Tennessee, USA

Abstract

Purpose – The purpose of this paper is to explore the structural and process integration mechanisms

used by firms to form a hybrid mode of supply chain governance referred to as supply chain valueintegration (SCVI).

Design/methodology/approach – The authors employ a multi-method approach, conductingdepth interviews following the grand touring technique to develop the model, and survey methodologyto test the model using partial least squares approach for structural path estimation.

Findings – SCVI is a multi-dimensional concept comprised of two second-order constructs, SCVIinfrastructure and SCVI process, each comprised of three first-order constructs, that lead to joint valuecreation and improved supply chain performance.

Practical implications – Dependent suppliers striving to compete on value creation can adoptbonding behavior by establishing an integrative infrastructure comprised of people, technology, andknowledge components, which balances the level of dependence and motivates the customer to engagein integrative processes.

Originality/value – The authors expand on previous models of integration by developing and

testing a comprehensive multi-dimensional theory of SCVI that explains the relationships betweenresource dependency theory, resource-based view of the firm, and relational exchange theory.

Keywords Governance, Supply chain management, Resource management, Cross-functional integration

Paper type Research paper

1. IntroductionResearchers have recognized the competitive advantages to collaborative supply chainrelationships in which integrated efforts result in joint value creation that would not bepossible outside the relationship (Dyer and Singh, 1998; Ganeson, 1994; Joshi andCampbell, 2003). Many suggest the benefits of collaborative supply chain relationshipsonlyexist whenpower and dependence are balanced (Oliver, 1990; Mentzeretal.,2000).As

such, companies involved in a supply chain relationship characterized by asymmetricbalance of power and dependence face considerable challenges in developing integrativerelationships with their supply chain partners. For example, retail supply chains haveexperienced widespread consolidation and resultant shift of dependence (Whipple et al.,1999),withapproximately 80 percentof an average supplier’s revenues generated from theten largest retail customers, compared to 30 percent of revenues ten years before (Boyle,2003). The motivation behind pervasive retail consolidation and resultant increasedeconomies of scale is improved purchasing power andtheability of customers to negotiate

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0960-0035.htm

Supply chaininfrastructure

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Received August 2009Revised July 2010

Accepted August 2010

International Journal of PhysicalDistribution & Logistics Management

Vol. 41 No. 2, 2011pp. 135-161

q Emerald Group Publishing Limited0960-0035

DOI 10.1108/09600031111118530



lower prices from suppliers (USDA, 2000). However, for suppliers, a low-price approach isless likely to result in a sustainable competitiveadvantage than is developmentof complexrelational resources and capabilities (Barney, 1991). In such an environment, how can adependent supplier entice large customers to enter into collaborative supply chain

relationships with the goal of integrating activities for joint value creation and equitablerisk and reward, rather than compete solely on a low-price strategy?

Forcoordinated supply chain efforts to result in increased value for both the customerand supplier, several questions must be answered. Which processes should be integratedacross supply chain partners to create joint value? To facilitate integration of thoseprocesses, what actions and mechanisms should be coordinated? What outcomes shouldbe experienced by supply chain partners involved in the integrative relationship?Responding to calls by Flint etal. (2005) and Mentzer and Kahn (1995) to develop theoriesin the supply chain management discipline, we answer these questions by presenting abroad theory of supply chain value integration (SCVI) explaining the relationshipsbetween three existing theories widely referenced in supply chain integration research –

resource dependency theory (RDT), resource-based view (RBV), and relational exchangetheory (RET). We develop and empirically test a comprehensive, multi-dimensionalmodel of SCVI that emerged from depth interviews with supply chain managers as wellas a review of the literature identifying several one-dimensional structure and processconstructs of supply chain integration (Heide and John, 1990; Zaheer and Venkatraman,1995; Joshi and Stump, 1999; McNally and Griffin, 2005).

Our model of SCVI expands on previous supply chain integration researchby attempting to create a comprehensive model tapping into higherorder constructscomprised of many single-order constructs identified in previous research. Twoapproaches to supply chain integration that have been explored extensively in theliterature are joint action (JA) and efficient consumer response (ECR) has been measuredas a process construct representing the degree to which supply chain partners cooperate

on activities that are important for both parties (Heide and John, 1990; Zaheer andVenkatraman, 1995; Joshi and Stump, 1999; McNally and Griffin, 2005). In each of thesestudies, JA was the dependent variable conceptualized as a two- or three-item singleconstruct measuring customers’ perceptions of different integrative processes with anupstream supplier. Alternatively, ECR has been measured as a 37-item first-order processconstruct reflecting three dimensions: demand integration, supply integration, andenablers and integrators (Corsten and Kumar, 2005). While JA and ECR have both beenmeasured as first-order process constructs, our research suggests SCVI is a complexconcept comprised of second-order structure and process constructs.

Specifically, our study illustrates how supplier dependence (SD) can lead to supplychain bonding behavior and, subsequently, integrative relationships with customersleading to joint value creation. Zaheer and Venkatraman (1995) suggest integrativegovernance involves determination of the appropriate interorganizational structureand process. SCVI is a mode of hybrid governance defined as:

[. . .] the systemic, strategic coordination of business structures and processes across firms ina supply chain, for the purposes of improving the efficiency and effectiveness of theindividual companies and the supply chain as a whole.

Drawing on the relevant literature, we develop our conceptual framework (Figure 1)and hypotheses. Subsequently, we draw on insights from qualitative research to refine

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the constructs in the model and develop a measurement instrument, which is thenempirically tested.

2. Conceptual framework of SCVISupplychain relationships exist in many forms, andarearrayedon a continuum anchoredby market (discrete) and hierarchy (equity vertical integration) governance structureswith hybrid relationships on the continuum (Williamson, 1991). Hybrid structures arecharacterized by interpenetrated organizational boundaries and coordinated integrationof activities between buyer and seller (Heide and John, 1990). Hybrid governancestructures are preferred when “specialized governance mechanisms” exist that areexpected to “minimize the sum of production and transaction costs” (Joshi and Stump,1999, p. 3) while increasing customer value delivery when compared to the costs of spotmarket transactions or vertical integration.

2.1 Dependence

In relationships characterized by supply chain integration, supply chain partners areselected based on their strategic resources. Resource-based theories such as RDTand theRBV explain behaviors among supply chain partners in these hybrid modes of governance. Resource-based theories explain the impetus for interdepartmentalintegration among business units within firms (Barney, 1991; Ruekert and Walker,1987), and more recently, integration across firms in a supply chain (Corsten and Kumar,2005; Heide, 1994; Kim et al., 2006; Wu et al., 2006). RDT suggests that firms aredependent on other firms to obtain critical resources (Emerson, 1962).

One strategy for managing supply chain dependence is bilateral hybrid relationshipsin which coordinated efforts enhance the effectiveness of both firms (Heide, 1994). Oliver(1990) suggests that asymmetric dependence could undermine efforts to develop hybridrelationships, and symmetrical dependence is necessary to promote hybrid modes of

governance that benefit both parties (Cooper and Gardner, 1999; Mentzer et al., 2000). Tobalanceasymmetricallydependentrelationships toward moresymmetric, interdependentrelationships, the more dependent firm often engages in dependence balancing such as“bondingbehavior”(Heide andJohn, 1988, p. 24). Forexample,in anticipation of increasedfuture relational exchange (Macneil, 1980), a supplier might invest in technologyinfrastructure to facilitate timely and accurate sharing of information on supply visibilityand demand forecasting, thereby adding value to the exchange relationship by reducingredundant workload for their customer (Hsu et al., 2008). Bonding behavior in the form

Figure 1.Conceptual framework

Supply chain value integration

InfrastructureDependence ProcessSupply

chain

performance

Effective

supply

chain

relations

SCVI outcomesSupply chain

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of relationship-specific asset investment provides a means for the supplier to compete onfactors other than price. The above example of bonding behavior is referred to by Koganand Tapiero (2007, p. 265) as supply chain infrastructure, and is defined as “the means for[. . .] [supply chain] firms sharing a common interest to participate in mutual exchange.”

An investment in supply chain infrastructure is the means by which a supplierdemonstrates to a customer their interest in enabling a more efficient and effectiveexchange relationship. Dwyer et al. (1987, p. 14) suggest, “The buyer’s perception of theeffectiveness of the exchange relation is a [. . .] potential competitive advantage for theseller that insulates [itself] from price competition.”

The RBV (Barney, 1991) contends that achieving and sustaining competitiveadvantage is a function of a firm’s core competencies that are built around resources thatare valuable, rare, difficult to imitate, and not easily substitutable. Investing in a supplychain infrastructure that enables joint value creation through integration of processeswithcustomers provides suppliers witha valuableresource thatis difficultfor competitorsto imitate. To evolve from a dependent relationship with buyers characterized byarms-length transactions based on price negotiation to a hybrid, integrative relationship,

dependent suppliers will invest in relationship-specific SCVI infrastructure (SCVIINF):

H1. As the level of SD increases, the level of supplier investment in SCVIINFincreases.

2.2 Integration infrastructure and processThe transaction cost perspective suggests that supplier idiosyncratic investment leavesa supplier open to opportunism by their customer without the presence of contracts orhostage exchanges. RET suggests opportunism may be suppressed through relationalnorms,the expectation of relational continuity ( Joshi andCampbell, 2003),and joint valuecreation (Dyer and Singh, 1998). With potential for joint value creation, the negativeimpact of opportunism on expected future exchanges often dominates the inclination to

act opportunistically. RET asserts that integrative exchange entails emphasis onstructure and process planning to facilitate joint value creation in anticipation of futureexchange (Macneil, 1980). The structure and process approach to measuring integrationis widely supported in the literature, highlighting that interaction (structure) antecedeseffective and efficient collaboration (process) (Urban and Hauser, 1993; Wu et al., 2006).

Twostudiesexploring integrationacross supply chains focus on the role of informationtechnologyon integration (Kim etal.,2006;Wu etal.,2006).Inbothstudies,integrationwasconceptualized as a two-dimensional model in which the first dimension – integratedtechnology infrastructure – facilitates the second dimension – integrated supply chainactivities. Corsten and Kumar (2005) follow the structure-conduct-performance approachto integration, in which supplier investment in relationship-specific infrastructure resultsin increased integration of supply chain processes. Similarly, we adopt the

conceptualization of SCVI as an infrastructure dimension that facilitates the integrativeprocess dimension:

H2. The higher the level of supplier investment in SCVIINF, the higher the level of SCVI process (SCVIPROC).

As discussed below under model development, our research expands on previousstudies that model integration of infrastructure as a single construct (Wu et al., 2006;Kim et al., 2006) as well as studies that model integration of processes as a single

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construct such as JA (Heide and John, 1990; Joshi and Stump, 1999; Zaheer andVenkatraman, 1995) and ECR (Corsten and Kumar, 2005) by exploring additionalSCVIINF and SCVIPROC.

2.3 Outcomes of integrationRET – associated with social embeddedness (Granovetter, 1985) and increased socialinteraction resulting in aggregation of a tacit and disperse knowledge base (Dyer andSingh, 1998; Macneil, 1980) – has contributed to our understanding of the motivationsfor and outcomes of integrative supply chain exchange relationships. Tacit knowledge iscreated within a partnership as a result of engaging in socially complex routines andprocesses. As such, tacit knowledge transferred among supply chain partners is difficultto codify, transfer, and imitate, and creates resources for supply chain partners to moreefficiently and effectively outperform their competitors (Dyer and Sing, 1998). Improvedsupply chain performance (SCP) outcomes in an integrative relationship can derive fromboth reduced costs and increased revenues. Among the costs reduced are those

associated with writing, monitoring, and enforcing formal contracts (Dyer and Singh,1998), which are replaced with self-enforcing relational governance. In addition,improved performance can result from increased revenues from joint value creation. Forexample, McCarthy and Golicic (2001) describe a situation where collaborativeforecasting (CF) efforts between a customer and supplier allowed early and expeditiousresponsiveness by both firms to changes in customer demand for a product with a shortlife cycle, creating joint value in the form of increased revenues for both firms as well assatisfied end customers for whom inventory was available in the retail stores. Firmsworking together in a supply chain pool their talents, skills, and resources to achievehigher levels of market and financial performance than possible without such unity intheir actions (Kim et al., 2006; Wu et al., 2006):

H3. The higher the level of SCVIPROC, the higher the level of SCP.

Much research on integration has focused on perceived interdepartmental effectivenessor supply chain relations (Ellinger et al., 2000; Kahn and Mentzer, 1998; Kim et al., 2006;Peterson et al., 2005; Ruekert and Walker, 1987). Effective interdepartmental relationsare perceptions of individuals who interact with people in other functions that therelationship is worthwhile, equitable, productive, and satisfying (Ruekert and Walker,1987). Supply chain relationship effectiveness has been described as the extent to whicheach partner carries out its responsibilities andcommitments, the time and effort to buildand maintain the relationship, and relationship satisfaction (Ruekert and Walker, 1987).These studies have empirically shown effective relations are an outcome of integrativeactivities such as collaboration and interaction:

H4. The higher the level of SCVIPROC, the higher the level of effective supplychain relations (ESCR).

RET proposes the nature of exchange relationships between entities is directed by thelevel of expectation of economic and non-economic reward derived from cooperative or

joint efforts (Dwyer et al., 1987; Macneil, 1980; Thibaut and Kelley, 1959). Paramount in arelational exchange is the potential outcome of joint improved performance for both thebuyer and seller resulting from effective relations (Dwyer et al., 1987). Hsu et al.’s (2008)survey of supply chain managers in the USA, New Zealand, and Europe suggests supply


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chain relationships mediate the impact of integration on firm and SCP. Accordingly,effective supply chain relationships produce improved financial performance:

H5. The higher the level of ESCR, the higher the level of SCP.

Althoughprevious studieshaveadoptedthe structure andprocessapproach to integrationand recognized integration as a multi-dimensional concept (Corsten and Kumar, 2005;Heide and John, 1990; Joshi and Stump, 1999; Kim et al., 2006; McNally and Griffin,2005; Wu et al., 2006), conceptualization and measurement of integration varied widely,thereby confounding our understanding of the concept. Therefore, to more thoroughlyexplore the complex nature of supply chain integration and test the hypotheses, we useda multiple-method approach of both quantitative and qualitative techniques. For thefirst study, a qualitative approach was used to gain a comprehensive and explicitunderstanding of the hybrid mechanisms involved in integrating infrastructure andprocesses across firms in a supply chain. Depth interviews were conducted with23 executives representing several different industries at different tiers in the supplychain. Qualitative data analysis resulted in development of multi-dimensional latentconstructs for SCVI and corresponding items to subsequently quantitatively measure theconstructs and relationships in the second study. For the second study, a surveymethodology was used for data collection, and the partial least squares (PLS)measurement technique was used for data analysis.

3. Qualitative studyAlthough many studies have examined various forms of supply chain integration, mosthave focused on a particular area of integration and attempted to develop acomprehensive model. For example, for the structural component of integration, severalstudies have focused on the information aspect of integration infrastructure (Kim et al.,2006; Wu et al., 2006; Hsu et al., 2008). To build on this research, a more comprehensive

model should include multiple infrastructure elements. Furthermore, JA has been usedto measure the process component of integration, and has been conceptualized as athree-item construct measuring the degree of JAs between firms in decisions related tothree business processes – product development, planning, and forecasting (Heide and

John, 1990); a two-item construct measuring dimensions of integrative processes – product and strategic planning (McNally and Griffin, 2005; Joshi and Stump, 1999); and athree-item construct of integrative activities that influence downstream customerdemand (Zaheer and Venkatraman, 1995). Each study has contributed to knowledge byexploring a specific dimension of supply chain integration.

However, because the multi-dimensional nature of SCVIINF and SCVIPROC has notbeen extensively explored in the literature, we employed inductive field research – assuggested for relatively undeveloped areas of knowledge (Morgan etal., 2005) – to gain a

richer understanding of the constructs and their relationships with other constructs.Drawingon insights from the literature to establish our domain of inquiry, we conducteddepth interviews following the grand touring technique (McCracken, 1988). Depthinterviews enable interpretive analyses to capture the complexity and richness of SCVI,and facilitate development and refinement of multi-dimensional constructs andmeasures (Morgan et al., 2005). We interviewed 23 executives from eight US companiesin seven different industries, with each interview lasting from 1 to 3 hours. Informantswere executives and senior managers with extensive knowledge of their firms’

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relationships with supply chain partners. A summary of the informants, industries andlocation in the supply chain is presented in Table I. Six of the eight firms were supplychain partners. These companies were chosen based on the likelihood that thephenomenon would manifest itself in different ways, evidencing varying degrees of

supply chain integration.The primary data source was interviews (taped and transcribed verbatim) containing

verbal reports of perceptions of the firm’s involvement in SCVI. In total, 21 interviewswere conducted on-site in the informant’s office, and two were conducted by telephone.A semi-structured interview protocol was developed to guide data collection. To achievetheoretical sampling, whereby initiallybroad-based interview questions are restructuredas data collection progresses to focus on concepts evolving from previously collecteddata (Strauss and Corbin, 1998), analysis occurred throughout the data collectionprocess, immediately following transcription of the interviews from each site.

Industry Supply chain position

Informant

designation Informant title Relationships

Textile Raw materialmanufacturer (RMM)

RMMaRMMbRMMcRMMd

Business supplychain specialistBusiness logisticsdirectorDirector of salesPurchasing manager

Supplier toFGM1 and FGM2

Apparel Finished goodsmanufacturer (FGM1)

FGM1a Director/supply chainmanagement

Customer of RMMSupplier to R1

Apparel Finished goodsmanufacturer (FGM2)

FGM2aFGM2b

PresidentVP/operations

Customer of RMMSupplier to R1 and R2

Hometextiles

Finished goodsmanufacturer (FGM3)

FGM3aFGM3b

FGM3cFGM3dFGM3e

Chief executiveofficer

Chief operatingofficerVP/purchasingDirector/logisticsProduction planner

Customer of RMMSupplier to R1

Officesupplies

Finished goodsmanufacturer (FGM4)

FGM4aFGM4bFGM4c

Director/supply chainmanagementMarketing managerSales manager

CPF Finished goodsmanufacturer (FGM5)

FGM5aFGM5bFGM5c

Marketing managerDivisional salesmanagerDirector/logistics

Departmentstores Retailer (R1) R1aR1bR1cR1d

BuyerMarketing managerDirector/replenishmentprocessInventory manager

Customer of FGM1,FGM2, and FGM3

Specialtystores

Retailer (R2) R2a Divisionalmerchandisemanager

Customer of FGM2Table I.

Informant profiles


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Interviews began with establishing rapport between the interviewer and informant bymaking introductions and asking questions to ascertain demographic data (Strauss andCorbin, 1998). The initial question was, “Can you tell me about some of the differenttypes of relationships you have with your supply chain partners?” Additional questions

were intended to elicit which (if any) activities were integrated with supply chainpartners to create joint value, the reasons for engaging (or not engaging) in integrationactivities, how the activities were accomplished, and the expected outcomes, as well asother questions guided by the interview content. Interviews were supplemented withfacility tours and documents relevant to the interview content. Documents provided byinformants, such as supplier scorecards, organizational charts, and collaborativeforecasts, enriched the data analysis by tangibly illustrating the interview data content.Interviews were conducted until conceptual redundancy emerged from the data therebyachieving theoretical saturation, i.e. no new properties, dimensions, or variationcontributing to the theory emerged from the data analysis. Specifically, in the context of this study, multiple integration concepts related to infrastructure and process emergedrepeatedly, and incremental interviews provided no additional information.

Following Strauss and Corbin (1998) guidelines, we used NVivo 4.0 software tomethodologically code (open, axial, and selective coding) over 500 pages of transcribedinterviews as well as other research documents and to systematically identify, name,and categorize ideas and concepts as they emerged from the data. Emergent themes werecompared to the related literature to help understand relationships among the themesand to enable a comprehensive understanding of the phenomena. We assessed thetrustworthiness of our findings by applying the techniques of triangulation of findingsacross multiple data sources, replication of themes and concepts across multipleinformants, informant feedback on our interpretation of the interviews, and independentreview of coding and analysis by colleagues familiar with the technique (Strauss andCorbin, 1998). For content validity, we presented the findings to an additional 34 supply

chain executives, who provided a judgmental confirmation of the findings and thetheoretical model of SCVI.

4. Model developmentFigure 2 shows the SCVI model that emerged from analysis of the qualitative data andintegration of the qualitative findings with existing literature. Analysis of the responsesto questions intended to tap the domain of SCVI delineated the concept as embodied bysupply chain relationship infrastructure and process, with both infrastructure andprocess as multi-dimensional concepts. The following discussion presents quotes frominformants along with supporting literature reflective of the infrastructure and processconstructs in the SCVI model.

4.1 Supply chain value integration4.1.1 SCVI process. Informants offered many examples of processes that arecoordinated with their supply chain partners to create joint value while resulting inprofitable outcomes for both firms. Based on analysis of the informants’ descriptions,these integrative processes can be grouped into three distinct categories: demandmanagement (DM), CF, and demand planning (DP).

4.1.1.1 Demand management. Several of the processes described by informants thatwere coordinated between supply chain partners focused on different approaches

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to “simulate,” “alter,” or “modify” demand to produce a mutually beneficial outcome. Thistakes many forms, such as new product development, exclusive packaging, andco-branded or trade brand promotions. For example, the vice president (VP) of operationsfor an apparel manufacturer (FGM2b, Table I) described how their firm often is intimatelyinvolved in their customers’ product development, explaining that his company’s design

Figure 2.Structural model of SCVI

Technologyinfrastructure

(TINF)

Supplier

dependence

(SD)

H1 0.54

H2 0.73

H4 0.46 H3

H5

0.35

0.32

SCVI

infrastructure

(SCVIINF)

R2 = 0.29

SCVI Process

(SCVIPROC)

R2

= 0.53

Effective supply

chain relations

(ESCR) R2 = 0.21

Supply chain

performance

(SCP) R2 = 0.33

People

infrastructure

(PINF)

λ = 0.771

ω = 0.403

λ = 0.874

ω = 0.425

λ = 0.772ω = 0.412

λ = 0.832

ω = 0.429

λ = 0.790ω = 0.238

λ =0.883

ω =0.516

Knowledge

infrastructure

(KINF)

Demand

management

(DM)

Collaborative

forecasting

(CF)

Demand

planning

(DP)

Notes: λ First-order LV loading on second-order LV; all are significant at: p < 0.005 ω = First-order LV

weight on second-order LV; all are significant at: p < 0.005


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experts frequently recommend changes to patterns and design specifications that wouldimprove the appearance or construction of the garment after reviewing samples receivedfrom the vendor.

As an example of working closely with supply chain partners to develop exclusive

packaging, the sales manager for an office supply manufacturer (FGM4c) describedworking collaboratively with key accounts to customize the packaging of selectedproducts to suit the needs of the different channel customers – including mass market,super store, and commercial customers – resulting in the same product having multiplepackaging variations. In addition to product and packaging, promotional activities arealso often designed together by supply chain partners with the intent of creating mutualbenefit. The marketing manager from an office supply manufacturer (FGM4b),described how they regularly “brainstormed” with one of their large retail customers todesign new promotions to “stimulate category sales,” suggesting, “Those [ideas] wereextremely successful and they were very, very well executed.” Similarly, a retaildivisional merchandise manager (R2a) described working closely with a key supplier on

new promotions “to stimulate extra demand [. . .

] that will add X-amount of profit to ourmutual organizations.” This informant specifically suggested that they are better able to“alter regular demand patterns” through their integrative efforts.

These examples illustrate the parallels between a firm’s traditional marketingactivities, and benefits gained by coordinating those marketing efforts across firms in asupply chain. Essentially, informants recognized that the process of brainstormingwith partners who were not constrained by each other’s corporate culture fostered a“morphing” of ideas leading to creation of innovative marketing programs that wouldnot have been developed by either company working independently. Based on theseresearch results, DM was defined as:

[. . .] the mutual modification or creation of demand across firms in the supply chain for the

purposes of improving the long-term performance of the individual companies and the supplychain as a whole.

4.1.1.2 Collaborative forecasting. The second category of coordinated businessprocesses described by informants relates to efforts of supply chain partners to developa consensus estimation of demand. Four elements characterizing the collaborativeprocess emerged repeatedly across firms, including information sharing, regularlyscheduled forecasting meetings, focus on forecast accuracy, and a consensus forecastthat is adopted by both supply chain partners. For R1 and FGM3 (Table I), the regularlyscheduled meetings are formalized and resource intensive, involving teams from bothcompanies that meet in person at the retailer’s location each month to review theforecasts. As described by the retailer (R1c), “This [meeting] is not a sales call [. . .]Thisis

truly a collaborative process in my mind.” For others, the meetings are weekly, monthly,or quarterly phone calls between trading partners solely scheduled for the purpose of discussing and estimating demand. Examples of the types of information shared duringthis process include historical performance, point-of-sale data, new store openings, andmarketing and promotional calendars. Forecast accuracy was tracked closely, and forinformants involved in the CF process, the accuracy level was readily recalled andemphatically discussed. The director of supply chain management at an apparelmanufacturer (FGM1) designated forecast accuracy as, “the most important thing.

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If you can’t help forecast your customer’s [demand] accurately, then you know you’reboth in trouble.”

Oneinformant (RMMa)suggested the ultimatereasonforcollaborating on the forecastwith trading partners is, “ [. . .] so that together you can make better decisions.” Although

the level of involvement of each partner varies, the end result is a forecast of demand thatboth partners agree to and trust. Based on these results, we adopt the McCarthy andGolicic (2001) definition of CF as a purposive exchange of specific and timely informationbetween supply chain partners to develop a single shared projection of demand.

4.1.1.3 Demand planning. The third category of integrated processes that emergedfrom the interview data is similar to traditional sales and operations planning processesthat occur within firms. However, informants described DP as coordination of the flow of demand across companies in the supply chain. When responding to requests for specificexamples, many informants described critical events, but all characterized the process asan ongoing, routine process necessary to fulfillend-customerdemand. On a routine basis,this process between trading partners involved coordination of production schedules

based on suppliers’ manufacturing andshipping lead times to meet customers’ inventoryrequirements. In many cases, it included adopting just in time and vendor managedinventory strategies. All informants recounted this process as being far more productiveand beneficial because it was collaborative rather than peremptory. This sentiment wasmanifest in the discussion with a director of supply chain (FGM4a) who described theprocess with his retail customer (R1), suggesting, “It doesn’t help them in any way toinsist on schedules that we can’t meet, they just have disappointed customers and stockouts,” and later stated that “collaboration” is “best for both companies as opposed to bestfor just the retailer.”

These examples illustrate that planning the flow of dependent demand in a supplychain can be much more efficient and effective for both partners with integrativeefforts. Based on the qualitative data analysis presented above, we define DP as:

[. . .

] coordination of the flow of dependent demand through companies in the supply chain forthe purposes of improving the long-term performance of the individual companies and thesupply chain as a whole.

As described by the informants, SCVIPROC is comprised of three dimensions: DM, CF,and DP. Therefore, SCVIPROC is conceptualized as a second-order constructcomprised of three first-order constructs.

4.1.2 SCVI infrastructure. When discussing the types of relationships in which theywere engaged, informants consistently mentioned different structures that facilitate theability to coordinate processes between the firms. As described by the chief operatingofficer of a textiles manufacturer (FMG3b), “You need to have a certain infrastructure inplace to truly accomplish collaboration and the desired outcomes.” Based on input from

informants, we adapt Kogan and Tepiero’s (2007, p. 265) definition of supply chaininfrastructure to focus on joint value creation by defining SCVIINF as “the means forsupply chain firms sharing an interest in joint value creation to participate in mutualexchange.” Analysis indicated three dimensions of SCVIINF, or means by which firmsparticipate in mutual exchange: technology infrastructure, people infrastructure,and knowledge infrastructure (KINF).

4.1.2.1 Technology infrastructure. The most frequently cited facilitator of integrativeprocesses was technology. Informants realize the value of timely and ready access


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to information between firms that is enabled by technology. A relatively simple technologysolution wasdescribedby a textile manufacturer (RMM) whoused “LotusNotes as a vehicleto speed the communication which will be more immediate” as a “collaborative effort” withtheir customer to respond quicker to changing needs. Alternatively, a home textiles firm

(FGM3) sent their own computer team to their key customers to help establish moreelaborate “systems of information transfer [. . .] to help our customers grow their business.”As described, theinvestmentintechnology infrastructureranged widely, butthegoal was toenable timely electronic information exchange to improve collaborative decision making.

4.1.2.2 People infrastructure. Although technology was frequently mentioned, manystill recognized the value in personal contact. A retail buyer (R1a) stated that, of all theforms of communication available to her, the one she would least like to abandon isregular personal contact, because:

You’d be surprised at what comes out in face-to-face meetings versus just sending an e-mail[. . .] There’s a lot more information [. . .] [that] they would never put it into writing and theyprobably wouldn’t give it to you over the phone.

This perspective was echoed by many informants, indicating routine personal contact isintegral to facilitating integrative partner relationships. Regularly, scheduled meetingsare valuable to build strong relationships whether in person or remotely. Someinformant firms established dedicated teams of people to regularly communicate withstrategic customers. Teams create multiple touch points with supply chain partners,increasing the ability to communicate efficiently. Many companies feel so strongly aboutthe value of personal contact that they have dedicated staff to their strategic accountsand locate them near the supply chain partner’s headquarters. Essentially, informantfirms established a people infrastructure – including regularly scheduled meetings,dedicated staff, interfunctional teams, and co-located personnel – which we define as anembedded social context for fostering bonding behavior and facilitating joint value

creation with supply chain partners.4.1.2.3 Knowledge infrastructure. In order to capitalize on multiple points of contact,some companies provide training to their employees to improve theirinformation-gathering skills. One company implemented a formal educationalstructure, requiring all employees to take 40 hours per year of training to “learn theproper way to communicate with customers.” Other companies recognized the value insending their employees who possess an expertise in a particular skill to their supplychain partner to train the partner in more efficient and effective methods. Another toolfrequently mentioned to improve the quality of integration activity was feedback loopsto supply chain partners that advise them on their level of performance. Thesefeedback loops were referred to as report cards or performance scorecards.

Suppliers appreciate these scorecards as they provide immediate feedback on their

ability to deliver value to the customer, and what they need to improve to strengthen therelationship. While most of the scorecards were developed by customers for theirsuppliers, one consumer electronics manufacturer also supplied scorecards to theircustomers. Whether in the form of educational classes, interfirm training, or feedbackloops, the KINF represents a resource for supply chain partners to codify, criticallyevaluate, and respond to the effectiveness of their integrative actions.

Analysis of the interview dataillustratesthatSCVIINF and SCVIPROCare both higherorder multi-dimensional constructs, each comprised of three first-order constructs.

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The rich understanding of the complexity of the SCVINF and process constructs enableddevelopment of the measures and indices used to test the model and hypotheses shown inFigure 2. The interviews also provided triangulation for the conceptual frameworkdeveloped from the literature review, including dependence as an antecedent variable and

ESCR, and SCP outcomes.

5. Quantitative study: test of hypothesesA survey research design was appropriate for this research due to the advantage itprovides to test newly developed theories, measure untested constructs that emergedfrom the qualitative study, collect perceptual data from large populations, and quantifyand apply survey data to complex path models with latent variables (LVs) (Kerlingerand Lee, 2000). The sample selected was limited to one business segment (consumerpackaged foods (CPF)) and one point in the supply chain (manufacturer-retailerrelationship) to maximize systematic variance and control for random variance(Kerlingerand Lee, 2000). Survey items asked questions related to the CPF manufacturer

and its relationship with a specific retail customer. To maximize variation of theoreticalrelationships, respondents were randomly instructed to consider a retail customer of either high- or low-strategic importance. Using a commercially purchased mailing list,we selected all companies in the national database representing CPF manufacturersidentified by six-digit standardized industrial codes. Targeted respondents were seniorlevel executives (e.g. VP, SVP, or CEO). The mailing list identified more than 3,000 suchprofessionals representing a wide variety of CPF manufacturers.

5.1 Index development Measures, standardized loadings, and weights are in the Appendix. Ganesan’s (1994)dependence scale was adapted, and two additional items were added (DEP3 and DEP8)based on interview informant descriptions. Ellinger etal.’s (2000) effective relations scale

was adapted to ESCR by rewording the items to reflect effectiveness for both firms in thepartnership rather than for just the respondent firm. Similarly, Matsuno et al.’s (2005)firm performance measures were modified to SCP by adding measures reflectingrespondents’ perceptions of their retail customers’ performance in addition to thosemeasuring respondent firm performance. New measures were developed for thefirst-order constructs of SCVIINF (technology TINF, people PINF, and KINF), andSCVIPROC (DM, CF, and DP).

Based on analysis of the interviews and theoretical considerations (Diamantopoulosand Winklhofer, 2001), it was determined that formative measures were appropriate foreach of the first-order constructs (TINF, PINF, KINF, DP, CF, and DM) comprisingSCVIINF and SCVIPROC because linear combinations of the items define the constructs(Hulland, 1999). We therefore itemized the domain of each construct into a set of relevant

activities to capture multiple but potentially unrelated facets of integration (see theAppendix). Following the decision rules of Jarvis et al. (2003) for determining formativeor reflective specification, the constructs are measured formatively as weighted linearcombinations of their respective items.

We implemented several procedural methods during questionnaire design to controlfor potential common method bias. First, we assured respondent anonymity whichreduces respondents’ evaluative apprehension, and thus, reduces method bias duringresponse editing and reporting stages (Podsakoff et al., 2003). Another procedural


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remedy to reduce method bias included methodological separation of measurementbetween predictor and dependent variables. To achieve this, we used different stem andresponse formats as well as different instructions for the dependent and independentvariables to “reduce[s] the respondent’s ability and/or motivation to use previous

answersto fillin gaps in what is recalled and/or to infer missingdetails,” (Podsakoff etal.,2003). Finally, the fact that:

. we derived many of our survey items based on analysis of interviews;

. use of the scales adapted from prior research were triangulated through theinterview data analysis; and

. a cognitive pretest was conducted with industry executives to confirm itemreadability, clarity, and comprehension for content validity, the probability of context effects is minimized (Lindell and Whitney, 2001).

In addition to these procedural remedies, statistical tests were conducted to assess thepresence of common method bias and are presented below in the discussion of the

measurement model.We administered a pretest survey to ensure integrity of the data collection

instrument and purify the indices. In total, 69 completed questionnaires were returnedfrom 124 mailed, yielding a 56 percent response rate. Item analysis was used to identifyitems with low-standardized regression loadings ( r , 0.70) for reflective variables andinsignificant weights for formative variables, which were removed unless the item wasdetermined to be critical to the theoretical domain of the construct. This is aparticularly important concept for formative indices as a census of indicators isnecessary to capture the entire domain of the focal construct, and removal of anindicator could change the composition of the LV (Diamantopoulos and Winklhofer,2001). One reflective item was removed (DEP8) due to low and insignificant loading inthe pretest and one formative item was removed (DP7) due to insignificant weighting

and determination that removal would not alter the composition of the focal construct.

5.2 Data collectionOf the 2,823 remaining companies from the sampling frame, pre-qualification callswere placed to a random sample of 1,500 companies to confirm mailing address andappropriateness for the study (e.g. the CPF manufacturers sell products directly toretail customers). Among the 1,500 companies randomly selected, 792 agreed toparticipate, 167 could not be reached after four attempts, 61 declined to participate, and480 were not qualified (e.g. did not sell directly to retail customers). To incentivizeparticipation, the names of those who returned completed questionnaires were enteredinto a drawing for a $500 donation to the charity of their choice.

Of 792 questionnaires mailed, 374 were returned completed, resulting in an effectiveresponse rate of 47 percent. Responses to questions assessing respondents’ position,experience, and knowledge level supported the suitability of the respondents as keyinformants. Almost half the respondents (46.9 percent) indicated their position wasowner/CEO, president, or S/VP. The remaining 53.1 percent were general managers,directors, or middle managers. Years of experience was heavily skewed to the high end,with 70.1 percent reporting “more than 15 years” of industry experience. Respondents’self-assessment of knowledge of the supply chain relationship was also heavily skewedto the high end with a mean and median response of 4 on a five-point scale, indicating

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a “High” level of knowledge. Most of the firms (72.8 percent) had been conductingbusiness with their customer for more than five years, with 35.2 percent reporting morethan 15 years.

Two tests for nonresponse bias were conducted. Surveys were classified as early or

late depending on the time between the initial mailing and the return postmark date.An independent samples t -test revealed were no significant differences ( a , 0.05) onany item for the two groups. In addition, 30 nonrespondents were contacted and askedto verbally complete five substantive survey items. The nonsignificant differencesbetween nonrespondents and respondents ( p , 0.05) indicated that nonresponse biaswas not a problem (Mentzer and Flint, 1997).

5.3 AnalysisThe statistical technique used for estimation of the structural path model in Figure 2 wasPLS, which is a component-based prediction-oriented method for estimating linearrelationships between LVs (Chin, 1998; Lohmoller, 1989). PLS is a more suitable

technique than covariance-based methods (e.g. LISREL, EQS, and AMOS) whenestimating models combining both formative and reflective measures, and when thegoal is prediction of dependent endogenous variables (Chin, 1998; Diamantopoulos andWinklhofer, 2001). Unlike covariance-based techniques that attempt to estimateparameters by minimizing the difference between the sample covariances and thoseimplied by the theoretical model, PLS attempts to estimate parameters that minimize theresidual variance of the dependent variable (Chin, 1998). Therefore, traditional measuresof significance testing for goodness of fit (e.g. x2 ) do not apply to PLS as the technique isnot attempting to “fit” the specified model to sample data. We tested for stability andsignificance of the model using 1,000 bootstrapping runs to calculate t -values forparameter estimates (Chin, 2006).

PLS uses a three-step estimation algorithm to compute:

(1) sets of weights linking manifest variables (MV) to their respective LVs in themeasurement model;

(2) LV case values with simple or multiple regressions using weight relations of MVs as input; and

(3) loadings, path coefficients, and mean scores of the LVs and MVs using ordinaryleast squares regressions (Chin, 1998).

5.4 Measurement model Each of the item factor loadings was inspected to ensure individual item reliability(see the Appendix). Over 81 percent of the loadings were above 0.70, and all loadingswere above the 0.50 threshold recommended by Hulland (1999). As an additional

indicator of item reliability, variable weights were also examined. Three items (PINF4,DP5, and DM5) had low weighting in relation to the other weights comprising theirrespective constructs, empirically suggesting these items contribute minimally to theirLV scores. To assess convergent validity of the measurement model, Cronbach’s alphaand average variance extracted (AVE) were examined (Table II). All Cronbach’s alphavalues exceeded the recommended value of 0.70 (Hair et al., 1998), and the AVE foreach construct exceeded the acceptable level of 0.50, indicating that greater than50 percent of the variance is captured by the construct (Larker and Larcker, 1981).


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Three approaches to assessing discriminant validity were employed. First, we usedLarker and Larcker’s (1981) criterion, which demonstrates discriminant validity when thesquare root of the AVE for each construct is greater than the correlations between thatconstruct and all other constructs in the model. The results (Table II) illustrate asatisfactory level of discriminant validity. Second, following Bagozzi and Warshaw’s(1990) criterion, discriminant validity was further evidenced when calculations indicatedthat twice the standard error for each correlation among the constructs is greater than1 minus the respective correlation. Finally, examination of the cross loadings (range of 0.032-0.558) indicate the specified loadings between MV and their associated constructsare all higher than the non-specified cross loadings, further providing support fordiscriminant validity. After successfully establishing convergent and discriminantvalidity,we used the LV scorescomputed by SmartPLS 2.0.M3(Ringle etal., 2005) for each

of the first-order constructs (TINF, PINF, KINF, DM, CF, and DP) to serve as formativeindicators of the second-orderconstructs (SCVIINFand SCVIPROC) as shown in Figure 2.

In addition to the previously discussed battery of procedural controls executedduring questionnaire design to mitigate common method bias, we also conducted twostatistical tests. Results from the Harmon unrotated factor-analytic test (Podsakoff andOrgan, 1986) suggest the presence of ten factors with eigenvalues greater than 1,accounting for a cumulative 73 percent variance, none of which accounted for themajority of variance thereby suggesting common method is not present. Further, atheoretically unrelated marker variable measuring the degree to which respondentscould predict industry sales volume was added to the model to test for common methodvariance. Parameter estimates indicate none of the LVs in the model are significantlyrelated to the marker variable, indicating an absence of instrumentation bias due to

single source reporting (Lindell and Whitney, 2001). These results offer support thatcommon method bias did not exist in the study.

5.5 Higher order LVsTo assess our operationalization of SCVIINF and SCVIPROC as higher order constructs,we first examined the loadings and weights of the first-order constructs as they relate tothe second-order constructs, and the path coefficients leading to and from thesecond-order constructs. As shown in Figure 2, all of the first-order loadings are strong

Correlations among constructsa AVE SD TINF PINF KINF DM CF DP ER SCP TR

SD 0.933 0.766 0.875

TINF 0.942 0.828 0.404 0.910 PINF 0.784 0.539 0.494 0.555 0.734KINF 0.861 0.635 0.412 0.319 0.544 0.797 DM 0.793 0.507 0.402 0.470 0.592 0.404 0.712 CF 0.908 0.775 0.365 0.547 0.544 0.452 0.558 0.881DP 0.866 0.507 0.461 0.449 0.444 0.555 0.519 0.590 0.712 ESCR 0.945 0.863 0.462 0.251 0.408 0.437 0.389 0.320 0.440 0.929SCP 0.900 0.590 0.354 0.233 0.374 0.390 0.391 0.338 0.475 0.495 0.768 TR 0.985 0.873 0.223 0.066 0.177 0.279 0.253 0.171 0.223 0.579 0.391 0.934

Note: Italic numbers in the diagonal are the square root of the AVE

Table II.Convergent anddiscriminant validity

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and significant (range 0.771-0.883 and p , 0.005). The TINF, PINF, and KINF constructsare fairly equally weighted and significant ( p , 0.005), suggesting these itemscontribute equally to SCVIINF. The weights for DM, CF, and DP indicate that DM andDP contribute somewhat more heavily to the SCVIPROC score than does CF, but all

weights are significant at p , 0.005. All path coefficients leading to and from SCVIINFand SCVIPROC (range 0.725-0.320) were also significant.

We compared the Figure 2 model to an alternative model omitting the second-orderLVs (i.e. paths were specified from SD directly to TINF, PINF, and KINF; from TINF,PINF, and KINF directly to DM, CF, and DP; and from DM, CF, and DP directly to ESCRand SCP). The findings (Table III) comparing the path coefficients in the second-ordermodel to those in the first-order model demonstrate path coefficients and t -statistics areconsistently stronger for the second-order operationalizations than for the first-orderrelationships. In addition, three of the path coefficients were not significant whenoperationalized as first-order constructs. These findings triangulate the results from thequalitative study and support operationalization of SCVIINF and SCVIPROC assecond-order constructs.

5.6 Structural model Significance levels for all parameters estimated by PLS were determined by examiningt -values calculated on the basis of 1,000 bootstrapping runs (Figure 2). The significantpath coefficients are all well above the 0.20 suggested threshold (Chin, 1998), supportingall five hypotheses. As expected, SCVIINF is positively related to SCVIPROC, indicatingthat as supply chain partners increasingly integrate their technology, relational,and KINF, integration of processes such as DM, CF, and DP also increase. In addition,the R 2-values associated with each endogenous LV in Figure 2 measures the variance

With second-order LV Without second-order LVPath coefficient t -statistic Path coefficient t -statistic

SD ! SCVIINF 0.542 22.20 SD ! TINF 0.404 12.706SD ! PINF 0.494 20.763SD ! KINF 0.414 13.700

SCVIINF ! SCVIPROC 0.780 45.04 TINF ! CF 0.352 8.913TINF ! DM 0.210 6.231TINF ! DP 0.285 8.043PINF ! CF 0.232 5.964PINF ! DM 0.410 10.815PINF ! DP 0.044 1.263a

KINF ! CF 0.198 6.394KINF ! DM 0.072 2.213

KINF ! DP 0.420 11.403SCVIPROC ! ESCR 0.486 17.39 CF ! ESCR 0.009 0.254a

DM ! ESCR 0.217 6.073DP ! ESCR 0.322 7.503

SCVIPROC ! SCP 0.330 10.50 CF ! SCP 0.011 0.291a

DM ! SCP 0.121 3.430DP ! SCP 0.260 7.795

Note: aNot significant

Table III.Comparison of

second-order modelto first-order model


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explained by the predictor variable. For example, SCVIINF explains 53 percent of the variance in SCVIPROC. Each R 2 in the model (range 0.53-0.21) is within acceptablevalues reported in similar studies (Hulland, 1999)[1].

6. Discussion and implicationsDwyer and Oh (1987, p. 348) proposed that research should focus on “the structure andprocesses of channel dyads, because obviously, the eventual goal of research in thistradition must be to suggest more effective ways to manage channel relationships.”Similarly, Weitz and Jap (1995) called for development and testing of models that lead tobetterunderstanding of what firms aredoing to effectivelymanage interfirm relationships.Ourresearch fulfills both these goals of supply chain research. Our research illustrates thatin dependent relationships, supply chain partners develop a multi-dimensional integrativeinfrastructure to facilitate integration of multi-dimensional processes, resulting inimproved supply chain relations andperformance. Supply chain integration between firmsis a complex procedure, and the qualitative phase of this research was designed to tap into

and make evident the multi-dimensional aspects of integration that create joint value. Thisresearch expands on previous studies by conceptualizing and testing both infrastructureand process integration as separate second-order constructs constituting SCVI, with eachsecond-order construct comprised of multiple first-order constructs. An empirical test of the theoretical model reveals SCVIINF explains 53 percent of the variance in SCVIPROC.

In a dependent relationship, firms with an integrative mindset establish a complexinfrastructure comprised of technology, people, and knowledge dimensions. Resultsindicate the presence of all three elements is significantly more effective than any oneelement. An interesting finding suggests that PINF is weighted more heavily than TINFand KINF, thereby contributing more heavily to SCVIINF. In today’s businessenvironment characterized by speed, connectivity, and information visibility, personalinteraction between supply chain members remains a valuable resource. This was borne

out in an interview where an informant stated she often became privy to importantinformation in face-to-face meetings that would never have been shared in emails.Similar to SCVIINF, SCVIPROC is a complex second-order construct consisting of threefirst-order constructs. Both qualitative and quantitative results indicate that firmsshould treat these as separate but related activities. Integrative capabilities in any one of these processes have less impact than integrative capabilities in all three together.

6.1 Theoretical implicationsFrom a theoretical perspective, we demonstrate the relationship between RDT and theRBV in supply chain theory. Specifically, we establish the use of bonding behavior bysuppliers to evolve from dependence on a customer for critical resources to where thesupplier possesses valuable and inimitable resources for the customer. Bonding behavior

results in dependence balancing (Heide and John, 1988) thatengenders advancement of thesupply chain relationship from transactional toward one that is more integrative. We alsodemonstrate the relationship between these two dependency theories that consider theindividual firm as the unit of analysis, and RET that distinguishes a dyad or supply chainnetwork as the unit of analysis (Dyer and Singh, 1998). SCVI is an encompassing conceptillustrating that dependency and relational theories are not mutually exclusive.

Following Dubin’s (1969) requirement that theory building in an applied field haspractical application that can affect and improve conditions in practice, SCVI provides

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a prescriptive approach to supply chain governance explaining how supply chainpartners leverage their combined resources in a hybrid relationship to create joint value.Specifically, our research demonstrates that, even in dependent relationships, suppliersengaging in bonding behavior can foster integrative value-creating relationships with

customers. Supplier bonding behavior in the form of customer-specific investment intechnology, people, and KINFs illustrates anticipation of future relational exchange andprovides a source of competitive advantage reducing the need to compete solely on price.SCVIINF facilitates supply chain integration of joint-value-creating processes, resultingin improved performance for both firms involved in the integrative activities and thesupply chain as a whole.

Our results support the Wu et al. (2006, p. 502) suggestion that conceptualization of integration processes as a higher order construct comprised of multiple “well-balancedsupply chain activities rather than fragmented and imbalanced activities” contributes tomarket and financial performance. Further, our approach to developing the SCVI modelanswers Fabbe-Costes and Jahre’s (2007) call for using qualitative research to improveupon extant measures of supply chain integration. Our comprehensive model and strongempirical results may explain some of the weak or insignificant results of previousstudies. For example, Zaheer and Venkatraman (1995) tested an integration model withquasi-integration as the antecedent structural construct to the JA process construct andfound a positive relationship but with a very low R 2 (0.03). Quasi-integration wasoperationalized as a single-item construct measuring the volume of business accountedfor by the focal firm, which is more closely aligned with the concept of dependence in ourstudy and others (Heide and John, 1988). Our results support SCVIINF as a mediatingvariable between dependence and SCVIPROC, which could explain the low R 2 in theZaheer and Venkatraman study.

Our results may also explain the lack of significant findings to support informationexchange as antecedent to effective relations (Ellinger et al., 2000; Kahn and Mentzer,

1998). In prior studies, information exchange was more closely aligned with our conceptof SCVIINF, and our study illustrates the impact of SCVIINF on effective relations ismediated by SCVIPROC. It is not the exchange of information between supply chainpartners that results in more effective relations, but the integration of processesfacilitated by infrastructure (including information exchange). The multiple-methodapproach used in this study allowed deeper, richer understanding of supply chainintegration and its antecedents and outcomes than previously studied, providing theempirical basis on which to develop and test our path model.

6.2 Managerial implicationsFor suppliers wishing to compete on joint-value creation, implementing SCVIINF canestablish a competitive advantage by creating an environment in which the customer

perceives ease of business exchange compared to competing suppliers. This isparticularly important in relationships where the supplier is asymmetrically dependenton the customer. In addition, the multi-dimensional nature of SCVIPROC should berecognized and managed in the supply chain relationship.Supply and demand processesare often separated organizationally across firms in a supply chain. ConceptualizingSCVIPROC as both supply and demand processes can help supply chain partners fosterthe idea that they are separate yet closely related business elements to be managedconcurrently and cooperatively. For example, DM is more closely aligned with


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demand activities where supply chain partners work together to create or modifydemand through collaborative product development, packaging, promotional activity,and pricing. CF involves estimating the demand stimulated through the DM process. Anincreased understanding of demand-related activities provides input to the CF process.

Finally, DP deals more with the supply side of integrative activities by coordinating theflow of dependent demand through companies in the supply chain. Ultimately,managing the three elements of SCVIPROC together can provide synergies to helpbridge the gap that exists between supply and demand activities, and increase thepotential for performance improvements.

A common concern in supply chain relationships is that one firm might experiencesubstantial financial benefits at the expense of the other firms in the relationship. Thisresearch indicates that, if SCVIINF and SCVIPROC are implemented in the mannerprescribed in the study, the outcome is more ESCR and improved SCP for both partnersand the supply chain as a whole. By integrating DM, CF, and DP processes, supply chainpartners should perceive the relationship as equitable, productive, and satisfying.Because SCVI is developing supply chain capabilities that result in competitiveadvantage, it is logical that the supply chain with the competitive advantage willincrease its market share, and all supply chain partners will reap the benefits.

6.3 Limitations and future researchA limitation of this study is the generalizibility of single research study results. Thisexploratory study was purposively limited to one industry and one link in the supplychain to minimize extraneous variation. Future research can capture a deeperunderstanding of the complex phenomenon of SCVI, which requires capturing theperspectives of multiple trading partners involved in the integrative activities. Toexpand on the current findings, several contingencies of supply chain integrationperformance can be explored by testing the SCVI model in different industries, across

different tiers in the supply chain, and in international settings. For example, barriers tointegration that did not emerge in the current study may exist across different cultures,or in intensely cost-competitive industries such as apparel and consumer electronics thatoutsource production offshore to low-cost country providers. Extending the research toexplore the phenomenon across multiple tiers in a supply chain can also help to identifyadditional exogenous or moderating variables such as trust and uncertainty that canimpact supply chain integration. Furthermore, lower level managers and workforce mayperceive integration efforts and outcomes differently than the primarily senior-levelmanagement comprising our sample.

Many of the measurement items and scales were developed from the qualitativeinterviews, and some were shown to contribute minimally to the SCVI model (i.e. PINF4,DP4, and DM5), presenting an opportunity for future research to modify existing and

identify new items representing the domain of the constructs. Qualitative researchgathered in different industries and across different supply chain tiers may tap into newdimensions comprising the constructs, thereby contributing to a more robust index.An additional limitation stemming from the structural equation modeling methodologythat provides an opportunity for future research is the assumption of causality, which isonly as valid as the theoretical justification guiding the research. To most effectively testcausal links, longitudinal research can be conducted, including testing for feedbackloops in the model. For example, do increased levels of SCVIPROC, ESCR,and SCP result

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in increased levels of dependence and SCVIINF? Longitudinal research could also helpto determine the level at which investments in SCVI results in diminishing returns.

Note

1. As a test of robustness, the structural model was also tested in AMOS with all measuresoperationalized as reflective scales. The model exhibited good overall fit (RMSEA ¼ 0.049,TLI ¼ 0.890, CFI ¼ 0.885, PNFI ¼ 0.753 and CMIN/DF ¼ 1.894), and all path weightswere significant (range 0.943-0.532).

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Further reading

Fabbe-Costes, N. and Larcker, D.F. (1981), “Evaluating structural equation models withunobservable variables and measurement error”, Journal of Marketing Research, Vol. 18,February, pp. 39-50.

About the authorsTeresa M. McCarthy-Byrne is an Associate Professor of Marketing at Bryant University.Her current research projects explore the degree to which firms integrate activities with otherfirms in their supply chain, such as CF, DM, and DP. Her research has been published in The

Journal of Forecasting, International Journal of Physical Distribution & Logistics Management,Transportation Journal, Foresight, Journal of Marketing Education, and Journal of Business

Forecasting. Prior to earning her PhD from the University of Tennessee, Knoxville,Dr Teresa M. McCarthy-Byrne worked in the retail industry for 14 years in forecasting andinventory planning and control. Teresa M. McCarthy-Byrne is the corresponding author and canbe contacted at: [email protected]

John T. Mentzer (1951-2010) was a Chancellor’s Professor and the Harry J. and

Vivienne R. Bruce Excellence Chair of Business in the Department of Marketing and Logisticsat the University of Tennessee. He published nine books and more than 200 articles and papers inthe Harvard Business Review, Industrial Marketing Management , Journal of Business Logistics,

Journal of Marketing , International Journal of Physical Distribution & Logistics Management , Journal of Business Research, Transportation and Logistics Review, Transportation Journal , Journal of the Academy of Marketing Science, Columbia Journal of World Business, Research in Marketing , Business Horizons, and other journals. He was past president of the Academy of Marketing Science and the Council of Supply Chain Management Professionals (then the Councilof Logistics Management). He was the 2004 recipient of the Council of Logistics ManagementDistinguished Service Award, the 2007 recipient of the International Society of LogisticsArmitage Medal, and the 2008 recipient of the Academy of Management Science DistinguishedService Award.

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Appendix

C o n s t r u c t a n d i t e m s

S t d f a c t o r l o a d i n g s a

W e i g h t s b

S u p p l i e r d e p e n d e n c e ( S D )

A n c h o r s : s t r o n g l y d i s a g r e e ( 1 ) – s t r o n g l y a g r e e ( 7 )

D E P 1 : t h i s c u s t o m e r i s c r u c i a l t o o u r f u t u r e p e r f o r m a n c e

0 . 8 9 8

0 . 1 6 8

D E P 2 : t h i s c u s t o m e r w o

u l d b e d i f fi c u l t t o r e p l a c e i f t h e r e l a t i o n s h i p w a s d i s c o n t i n u e d

0 . 8 9 0

0 . 1 7 2

D E P 3 : t h i s c u s t o m e r i s a n i m p o r t a n t s o u r c e o f r e v e n u e f o r a v

a r i e t y o f p r o d u c t s f o r m y

fi r m

0 . 8 2 9

0 . 1 6 7

D E P 4 : m y fi r m n e e d s t h

i s c u s t o m e r t o a c c o m p l i s h o u r g o a l s

0 . 8 9 2

0 . 1 6 7

D E P 5 : m y fi r m w o u l d h

a v e d i f fi c u l t y m a k i n g u p t h e s a l e s v o

l u m e g e n e r a t e d b y t h i s

c u s t o m e r I f t h e r e l a t i o n s h i p w a s d i s c o n t i n u e d

0 . 9 1 6

0 . 1 6 4

D E P 6 : m y fi r m d o e s n o t h a v e a g o o d a l t e r n a t i v e t o t h i s c u s t o

m e r

0 . 8 2 2

0 . 1 4 5

D E P 7 : m y fi r m i s h i g h l y d e p e n d e n t o n t h i s c u s t o m e r

0 . 7 7 5

0 . 1 5 9

D E P 8 : t h i s c u s t o m e r p u r c h a s e s p r o d u c t f r o m m y fi r m b e c a u s

e t h e i r c u s t o m e r e x p e c t s

t h e m t o c a r r y i t

D e l e t e d i n p r e t e s t

T e c h n o

l o g y i n f r a s t r u c t u r e ( T I N F ) c


I n o u r r e l a t i o n s h i p w i t h

t h i s c u s t o m e r , m y fi r m [ .

.

.

]

T I N F 1 : [ .

.

.

] c o m m u n i c a

t e s b y e m a i l o r o t h e r e l e c t r o n i c m e t h o

d s

0 . 8 7 9

0 . 3 1 7

T I N F 2 : [ .

.

.

] a c c e s s e s t h i s c u s t o m e r ’ s p r o d u c t i n f o r m a t i o n e l e c t r o n i c a l l y ( f o r e x a m p l e ,

i n t e r n e t , v a l u e - a

d d e d n e t w o r k , a n d E D I )

0 . 8 7 8

0 . 2 6 5

T I N F 3 : [ .

.

.

] t r a n s m i t s i n f o r m a t i o n t o t h i s c u s t o m e r e l e c t r o n i c

a l l y

0 . 9 4 4

0 . 2 9 3

T I N F 4 : [ .

.

.

] r e c e i v e s i n f

o r m a t i o n f r o m t h i s c u s t o m e r e l e c t r o n i c a l l y

0 . 9 3 7

0 . 2 2 6

P e o p

l e i n f r a s t r u c t u r e ( P I N F ) c


I n o u r r e l a t i o n s h i p w i t h

t h i s c u s t o m e r , m y fi r m [ .

.

.

]

P I N F 1 : [ .

.

.

] h a s r e g u l a r l y s c h e d u l e m e e t i n g s

0 . 8 6 7

0 . 4 8 1

P I N F 2 : [ .

.

.

] h a s i n t e r f u n

c t i o n a l t e a m s t h a t w o r k w i t h t h i s c u s t o m e r

0 . 8 6 9

0 . 4 7 5

P I N F 3 : [ .

.

.

] h a s p e r s o n n

e l t h a t a r e s p e c i fi c a l l y d e d i c a t e d t o w o r k i n g w i t h t h i s c u s t o m e r

0 . 6 2 3

0 . 2 2 2

P I N F 4 : [ .

.

.

] l o c a t e s p e r s

o n n e l n e a r t h i s c u s t o m e r ’ s b u s i n e s s f a c i l i t i e s

0 . 5 1 4

0 . 0 6 3

K n o w l e d g e i n f r a s t r u c t u r e ( K I N F ) c

( c o n t

i n u e d )

Table AI.Measures


and process

159





W e i g h t s b

A n c h o r s : s t r o n g l y

d i s a g r e e ( 1 ) – s t r o n g l y a g r e e ( 7 )

I n o u r r e l a t i o n s h i p w i t h t h i s c u s t o m e r , m y fi r m [ .

.

.

]

K I N F 1 : [ .

.

.

] t r a i n s o u r e m p l o y e e s o n i s s u e s t h a t w i l l s t r e n g t h e n o u r r e l a t i o n s h i p

0 . 7 1 5

0 . 3 1 0

K I N F 2 : [ .

.

.

] t r a i n s t h e i r e m p l o y e e s o n i s s u e s t h a t a r e i m

p o r t a n t t o b o t h c o m p a n i e s

0 . 8 4 1

0 . 3 6 2

K I N F 3 : [ .

.

.

] i s c o n c e r n e d a b o u t h o w t h i s c u s t o m e r e v a l u a t e s o u r p e r f o r m a n c e

0 . 8 1 7

0 . 2 1 6

K I N F 4 : [ .

.

.

] i s i n t

e r e s t e d i n u n d e r s t a n d i n g t h e i r e v a l u a t i o n o f o u r p e r f o r m a n c e

0 . 8 1 5

0 . 3 6 6

D e m a n d p l a n n i n g ( D P ) c



M y fi r m [ .

.

.

]

D P 1 : [ .

.

.

] a d j u s t s

p r o d u c t i o n s c h e d u l e s t o a c c o m m o d a t e t h i s c u s t o m e r ’ s n e e d s

0 . 7 9 3

0 . 3 8 6

D P 2 : [ .

.

.

] h a s a d j u

s t e d o u r p r o d u c t i o n s y s t e m t o m e e t t h

e r e q u i r e m e n t s o f t h i s c u s t o m e r

0 . 7 9 1

0 . 1 6 5

D P 3 : [ .

.

.

] w o r k s w

i t h t h i s c u s t o m e r t o d e t e r m i n e t h e d e l i v e r y s c h e d u l e s t h a t w i l l b e s t

m e e t t h e i r n e e d s

0 . 6 6 6

0 . 1 6 8

D P 4 : [ .

.

.

] h a s s t a r t e d a c o - m a n a g e d i n v e n t o r y p r o g r a m w

i t h t h i s c u s t o m e r s u c h a s q u i c k

r e s p o n s e ( Q R

) , v e n d o r m a n a g e d i n v e n t o r y ( V M I ) , j u s t i n t i m e ( J I T ) , c o n t i n u o u s

r e p l e n i s h m e n t p r o g r a m ( C R P ) , o r e l e c t r o n i c K a n b a n

0 . 6 4 5

0 . 3 3 7

D P 5 : [ .

.

.

] k e e p s t h i s c u s t o m e r i n f o r m e d o f m a n u f a c t u r i n g a n d s h i p p i n g l e a d t i m e s

0 . 6 8 5

0 . 0 5 3

D P 6 : [ .

.

.

] w o r k s w

i t h t h i s c u s t o m e r t o d e t e r m i n e t h e b e s t m e t h o d o f p r o d u c t d e l i v e r y

0 . 6 7 7

0 . 2 9 3

D P 7 : [ .

.

.

] r u n s w a r e h o u s e s o r d i s t r i b u t i o n c e n t e r s s p e c i fi c a l l y f o r s e r v i c i n g t h i s

c u s t o m e r ’ s n e e d s

D e l e t e d i n p r e t e s t

C o l l a b o r a t i v e f o r e c a s t i n g ( C F ) c

A n c h o r s : n e v e r ( 1

) – a l w a y s ( 7 )

C F 1 : w h e n w o r k i n g o n o u r f o r e c a s t , w e r e g u l a r l y ( a t l e a s t o n c e a q u a r t e r ) r e q u e s t

i n f o r m a t i o n f r o m t h i s c u s t o m e r t h a t m i g h t a f f e c t t h e f o r e c a s t

0 . 9 1 0

0 . 3 7 6

C F 2 : w e t r y t o i n c

o r p o r a t e t h i s c u s t o m e r ’ s f o r e c a s t i n t o

o u r o w n f o r e c a s t

0 . 8 6 3

0 . 2 3 0

C F 3 : w e t r y t o w o

r k t o g e t h e r w i t h t h i s c u s t o m e r i n o r d e r t o c r e a t e o n e m u t u a l f o r e c a s t

t h a t i s a g r e e

d u p o n b y b o t h fi r m s ?

0 . 8 6 3

0 . 1 7 9

C F 4 : w e w o r k w i t h t h i s c u s t o m e r t o h e l p t h e m i m p r o v e

t h e i r f o r e c a s t a c c u r a c y ?

0 . 8 8 5

0 . 3 4 5

D e m a n d m a n a g e m e n t ( D M ) c

A n c h o r s : n e v e r ( 1

) – a l w a y s ( 7 )

H o w o f t e n d o e s y o u r fi r m [ .

.

.

]

D M 1 : [ .

.

.

] p a r t i c i p a t e i n t h i s c u s t o m e r ’ s d e c i s i o n s r e g a r d i n g r e t a i l p r i c i n g ?

0 . 6 7 4

0 . 3 2 9

( c o n t i n u e d )

Table AI.

IJPDLM41,2

160





W e i g h t s b

D M 2 : [ .

.

.

] c o n s u l t w i t h t h i s c u s t o m e r t o h e l p d e s i g n p r o m o t i o n a l a c t i v i t i e s ( s u c h a s

a d v e r t i s i n g , i n - s t o r e s a l e s o r d i s p l a y s , c o u p o n s , e t c . ) t h a t a r e e x c l u s i v e t o t h i s

r e l a t i o n s h i p

?

0 . 7 9 8

0 . 3 7 5

D M 3 : [ .

.

.

] w o r k w

i t h t h i s c u s t o m e r t o h e l p m a k e d e c i s i o n s r e g a r d i n g p r o d u c t

p a c k a g i n g ?

0 . 7 1 4

0 . 1 0 1

D M 4 : [ .

.

.

] g e t i n v

o l v e d i n t h i s c u s t o m e r ’ s d e c i s i o n s t o p u r s u e n e w m a r k e t s e g m e n t s ?

0 . 6 2 7

0 . 1 1 3

D M 5 : [ .

.

.

] e n c o u r a g e t h i s c u s t o m e r t o b e i n v o l v e d i n y o u r p r o d u c t d e v e l o p m e n t

d e c i s i o n s ?

0 . 6 9 3

0 . 0 6 8

D M 6 : [ .

.

.

] c o n s u l t

w i t h t h i s c u s t o m e r t o d e s i g n n e w p r o d u c t s t h a t a r e e x c l u s i v e t o t h i s

r e l a t i o n s h i p

?

0 . 7 4 2

0 . 4 0 0

E f f e c t i v e s u p p l y c h a i n r e l a t i o n s ( E S C R )



E S C R 1 : t h e r e l a t i o

n s h i p w i t h t h i s c u s t o m e r h a s b e e n p r o d u c t i v e f o r b o t h o f u s

0 . 9 2 6

0 . 2 7 7

E S C R 2 : t h e t i m e a

n d e f f o r t s p e n t i n d e v e l o p i n g a n d m a

i n t a i n i n g t h i s r e l a t i o n s h i p h a s

b e e n w o r t h w h i l e f o r b o t h o f u s

0 . 9 4 7

0 . 2 8 7

E S C R 3 : t h e r e l a t i o

n s h i p w i t h t h i s c u s t o m e r h a s b e e n r e

w a r d i n g f o r b o t h o f u s

0 . 9 5 4

0 . 2 7 0

E S C R 4 : b o t h c o m p a n i e s h a v e b e n e fi t e d f r o m t h i s r e l a t i o n s h i p

0 . 8 8 8

0 . 2 4 2

S u p p l y c h a i n p e r f o r m a n c e ( S C P )

A n c h o r s : g r e a t l y d

e c l i n e d ( 1 ) – g r e a t l y i m p r o v e d ( 7 )

S C P 1 : r e s p o n s i v e n

e s s t o c u s t o m e r e m e r g e n c i e s ( f o r e x a m

p l e , s t o c k o u t s , s u d d e n d e m a n d

i n c r e a s e s , a

n d s u d d e n c h a n g e s i n r e q u i r e m e n t s )

0 . 6 2 8

0 . 1 1 5

S C P 2 : c o n s i s t e n c y

o f o r d e r - t o - d e l i v e r y c y c l e t i m e

0 . 9 5 1

0 . 1 4 5

S C P 3 : m a r k e t s h a r e f o r m y fi r m

0 . 7 4 6

0 . 1 2 1

S C P 4 : m a r k e t s h a r e f o r t h i s c u s t o m e r

0 . 8 1 7

0 . 1 2 3

S C P 5 : s a l e s f o r m y fi r m

0 . 7 7 1

0 . 1 5 4

S C P 6 : s a l e s f o r t h i s c u s t o m e r

0 . 8 3 6

0 . 1 1 8

S C P 7 : r e t u r n o n s a l e s f o r m y fi r m

0 . 7 4 4

0 . 1 5 3

S C P 8 : r e t u r n o n s a l e s f o r t h i s c u s t o m e r

0 . 8 2 3

0 . 1 0 9

S C P 9 : r e t u r n o n i n v e s t m e n t f o r m y fi r m

0 . 7 8 0

0 . 1 4 5

S C P 1 0 : r e t u r n o n i n v e s t m e n t f o r t h i s c u s t o m e r

0 . 8 5 2

0 . 1 1 5

N o t e s : a A l l s t a n d

a r d i z e d l o a d i n g s s i g n i fi c a n t a t : , 0 . 0 0 5 ;

b a l l w e i g h t s s i g n i fi c a n t a t : , 0 . 0 5 ;

c f o r m a t i v e i n d i c e s

Table AI.


and process

161